Computer Supported Collaborative Learning (CSCL) in Virtual Communities (VCs): Linguistic Analysis and Inner Speech

1

IONIAN UNIVERSITY

DEPARTMENT OF INFORMATICS

MSc in Informatics

Subject area: Informatics and Humanistic Studies

MASTER THESIS

Computer Supported Collaborative Learning (CSCL) in

Virtual Communities (VCs):

Linguistic Analysis and Inner Speech

Stefanos Nikiforos (ΠΜ201205)

Supervisor: Katia - Lida Kermanidis

Corfu 2014

2

Supervisor: Lect. Katia - Lida Kermanidis

Comittee: Ass. Prof. Panagiotis Vlamos

Lect. Konstantinos Chorianopoulos

http://di.ionio.gr/en/department/staff/faculty/155-konstantinos-chorianopoulos.html

3

Abstract

Virtual Learning Communities constitute an aspect of particular importance for

Computer Supported Collaborative Learning offering new opportunities in education

and setting new challenges. Strong sense of community improves learning outcomes,

provides satisfaction to the students and reduces the feeling of isolation.

Furthermore, feeling of membership in a community increases the collaboration

among the students, their commitment to the common goals and motivates them to

learn.

Language has a prominent role in learning communities being a communicative

tool that serves information exchange and knowledge construction and sharing.

Collaborative learning, occurring by the active engagement of the individuals, is

strongly associated with communities. Collaborative efforts (interaction through

language) among the members of the community leads to learning.

Inner speech is an esoteric mental language, usually not outerly expressed. In communities, under certain conditions, it is externalized having a completely idiosyncratic syntax and specific structure. It consists of apparent lack of cohesion and it is also fragmented in nature and abbreviated in comparison to the formal language used in most everyday interactions. This type of language is expected to lead to misunderstanding, unless the thoughts of the individuals are in accordance and mean the same (community).

As Virtual Classes are frequently embodied in the learning procedure, a

challenge of detecting whether they were transformed into communities or not, is

arising. In this study, a detailed linguistic analysis in the discourse among the class

members is proposed in order to detect whether a Virtual Class is a community or

not. This analysis is focused on two axes: inner speech and collaborative learning as

they both are basic features of a community.

4

Contents

Introduction ......................................................................................................... 6

1. Communities

1.1 Learning communities ............................................................................... 8

1.2 Virtual communities .................................................................................. 9

2. Collaborative learning and Inner speech

2.1 Collaborative learning .............................................................................. 11

2.2 Computer Supported Collaborative Learning (CSCL) .............................. 12

2.3 Inner speech ............................................................................................ 14

3. Virtual Learning Communities Linguistic Analysis Model

3.1 Model analysis of inner speech ............................................................... 16

3.1.1. Inner Speech Analysis summary .............................................. 25

3.2 Model analysis of collaborative learning ................................................ 28

3.2.1 Collaborative Learning Analysis summary ................................ 32

4. case studies .................................................................................................... 33

5. Results

5.1 Inner speech analysis ............................................................................... 36

5.1.1 Inner speech analysis in VC 1.1 ................................................. 36

5.1.2 Inner speech analysis in VC 1.2 ................................................. 39

5.1.3 Inner speech analysis in VC 2 .................................................... 43

5.1.4 Inner speech analysis in VC 3 .................................................... 48

5.1.5 Inner speech analysis in students' essay texts (ST) ................... 51

5.1.6 Comparative results - Discussion .............................................. 56

5.2 Collaborative learning analysis ................................................................ 68

5.2.1 Collaboration analysis in VC 1.1 ............................................... 68

5

5.2.2 Collaboration analysis in VC 1.2 ............................................... 69

5.2.3 Collaboration analysis in VC 2 .................................................. 71

5.2.4 Collaboration analysis in VC 3 ................................................... 72

5.2.5 Collaboration analysis in Students' Texts (ST) ........................... 74

5.2.6 Comparative results - Discussion .............................................. 75

5.3 Statistical analysis .................................................................................... 81

6. Conclusion ...................................................................................................... 82

Appendix ............................................................................................................ 84

References ........................................................................................................ 139

6

Introduction

Virtual Learning Communities offer new opportunities in education and set new

challenges in Computer Supported Collaborative Learning. Creation of virtual classes

has become widespread. Nevertheless there are questions arising: Is every VC always

a community as well? How can we detect the existence of a VC community? What

are its idiosyncratic properties?

In this study, a detailed linguistic analysis in the discourse among the class members is proposed in order to detect whether a Virtual Class is a community or not. This analysis is focused on two axes: inner speech and collaborative learning as they both are basic features of a community.

Various works using discourse analysis have been presented in the CSCL field.

Spanger et al. (2009) analyzed a corpus of referring expressions targeting to develop algorithms for generating expressions in a situated collaboration.

Wells (2002), examined the role of dialogue in activity and interaction using analysis of discourse between two students engaged in a joint activity.

Other studies use machine learning techniques in order to build automated classifiers of affect in chat logs (Brooks, 2013).

Blau et al. (1998) analyzed the language in tutorial conversations attempting to detect collaboration and the power relationships reflected in actual conversations between tutors and clients.

Veermans and Cesareni (2005) examined four case studies from different countries through analyzing and categorizing postings in virtual environments. Their target was to examine the content of students' and teachers' work in each project.

Zhang et al. (2007) analyzed the online knowledge building discourse in a community targeting to investigate the ability of students to take responsibility for their own knowledge advancement. They focused on socio-cognitive dynamics of knowledge building reflected in the online discourse.

Maness (2008) analyzed students' chat reference conversations and created a linguistic profile of them, focusing on the formality of the language.

Rovai (2002), examined the relationship between the sense of community and cognitive learning in an online educational environment.

Kollar et al. (2005), investigated how external collaboration scripts interact with learner's internal scripts aiming at facilitating collaborative argumentation in web-based learning environments.

Innes (2007) examined the quality of discourse in problem-based learning groups focusing on the type and quality of dialogue.

7

Daniel et al. (2003) explored how the notions of social capital and trust can be extended in virtual communities.

Unlike these studies, the proposed approach takes into account the correlation between community properties and both inner speech and collaborative learning features (Bielaczyc and Collins, 1999) by applying linguistic analysis to the discourse among class members as a means for community detection. To this end, the discourse of four different types of VCs is analyzed and compared against non-conversational language use by the same students, in order to detect differences.

This approach is considered as important because since now the distinction between the Virtual Classes and the communities has not been emphasized. This distinction is essential, as the differences between them are great. In Virtual Classes, the students, having stable and predefined roles, might work individually (action) leading to competition among them or cooperatively in the best case (individual action for a common target). In contrast, in communities the students interact and collaborate each other, while their roles are dynamic and interchangeable. This interaction results in socially constructed knowledge and learning (Bielaczyc and Collins, 1999; Veermans and Cesareni ,2005; Warschauer, 1997; Stahl et al, 2006; Stahl, 2000; Dillenbourg and Fischer, 2007; Innes, 2007; Rovai, 2002; Wells, 2002; Koschmann, 1999; Knipfer et al, 2009; Daniel et al, 2003).

For this reason, two detailed linguistic analysis models focused on the features of both collaborative learning and inner speech are proposed. The combination of these models can reveal the existence of a community or not. Since now very few work has been done concerning inner speech and collaborative learning in VCs in CSCL field. When this happens, it is focused on specific features. In contrast, the proposed models for community detection are comprehensive and are based on a set of features. Furthermore, these two models are applied in non-conversational language aiming to better weighted results.

This study is organized as follows: in Section 1, communities (in general), learning communities and virtual communities are presented. In Section 2, collaborative learning and inner speech and their correlation to the communities are referred. The proposed linguistic analysis model is presented in Section 3, while in section 4 case studies are described. In section 5 the results of the two models of analysis and their statistical analysis are presented, finishing in Section 6 with the conclusion.

8

1. Communities

Community is a complex, rich and diverse term. It is usually referred as a small

social unit that shares common values. A number of conditions existing in a

community affect the identity of the participants and their degree of cohesiveness

(Mc Millan and Chavis, 1986; Daniel et al, 2003).

There are various types of communities. Three types seem to be the most

common: 1) geographic communities, where location is the basic feature, 2)

communities of culture, consisting of people sharing a common culture and 3)

community organizations, which can be either informal (family) or formal

(professional association).

The most essential elements of the sense of belonging in a community are: 1)

mutual interdependence among members, 2) connectedness, 3) trust, 4)

interactivity and 5) shared values and goals (Rovai, 2002; Daniel et al, 2003). People

communicate, interact and influence one another, within a community (Engeström,

2001). Members establish their own identity and learn to function in the community.

Interaction among members affect the community structure either negatively or

positively (Mc Millan and Chavis, 1986).

One way of interaction among community members is mutualism. It is

considered as the most beneficial interaction as it involves collaboration which has

positive effect in all members, in contrast to competition that has positive effects for

some members and negative for others (Patterson, 2005).

1.1 Learning communities

Building of a community goes through various stages targeting to the creation of

the "true" community. In this "true" community members give deep respect to their

colleagues and care for the needs of others. However, a challenge for a community is

how to incorporate individuality and differences among its members. This challenge

is crucial for learning communities (Wells, 2002; Bielaczyc and Collins, 1999).

There is a significant relationship between classroom community and learning

(Innes, 2007). The stronger is the sense of community, the greater is learning

perceived, resulting also in less isolation and greater satisfaction (Rovai, 2002; Daniel

et al, 2003). Strong feelings of community provide benefits to students by increasing

1) the commitment to group goals, 2) collaboration among them, 3) satisfaction with

their efforts and 4) motivation to learn. In addition, students having a high sense of

community "feel burned out less often at school" (Rovai, 2002).

9

1.2 Virtual Communities

A social network of individuals who interact through the internet is defined as a

virtual community. Virtual communities consist of persons having mutual interests or

goals and cross geographical, political or cultural borders (Daniel et al, 2003).

Virtual communities have been developed in the recent years, especially after

the growth and the wide spread of the internet. They resemble real life communities

and they can serve multiple purposes. Members of a virtual community are able to

interact through social networking sites, chat rooms, forums and even virtual worlds.

Sharing similar interests allow members to bond each other and possibly form

friendship (Mc Millan and Chavis, 1986). Virtual communities offer the advantage of

instant information exchange and give users a feeling of membership and belonging.

Members can also give and receive support (Warschauer, 1997).

The most common type of virtual communities is social networking services. In

recent years, virtual communities are also used for educational purposes (Virtual

Learning Communities - VLCs). A VLC should be tolerate to differences and

individualism. Virtuality offers this opportunity as the members are judged

exclusively by their effort and work, no matter of their appearance, colour, gender or

disability (Warschauer, 1997; Daniel et al, 2003).

A classroom community consists of two (2) components: 1) feelings of

connectedness among students and 2) commonality of learning expectations and

goals (Rovai, 2002).

A learning community is strong under certain circumstances: students 1) feel

connected to each other and to the teacher, 2) use immediate communication in

order to reduce any existing social or psychological distance among them, 3) share

common interests and values, 4) help and trust each other, 5) actively engage in

two-way communications and 6) pursue common learning objectives (Rovai, 2002).

If students' satisfaction is achieved virtual (online) classes can be inviting and

successful learning environments. Specifically, students standing in front of their

computer screen should feel as if they are working together with a group of peers

(Rovai, 2002; Mc Millan and Chavis, 1986; Daniel et al, 2003; Dillenbourg and Fischer,

2007; Warschauer, 1997).

Learning is the feeling that knowledge is actively constructed within the

community. The acquisition of knowledge and understanding is enhanced within the

community and the learning needs of the students are satisfied. Students in a

classroom community internalize, at least partially group's values and goals. Learning

is the goal for a learning community, it is therefore an indispensable component

(Rovai, 2002).

10

Sense of community is a powerful force in our culture. There is therefore a

challenge of building learning communities based on faith, hope and tolerance and

using sense of community as a tool for collaboration (Mc Millan and Chavis, 1986).

11

2. Collaborative Learning and Inner Speech

2.1 Collaborative Learning

In Collaborative Learning (CL) knowledge can be created within a population

where members actively interact by sharing experiences and taking asymmetry

roles. Learners in CL are engaged in a common task, they capitalize on one another's

resources and skills and each one is both depended on and accountable to each

other. CL includes both face to face conversations and computer discussions.

Conversation analysis and statistical discourse analysis are methods used for

examining CL (Daniel et al, 2003).

In CL groups of students work together to search for understanding, meaning or

solutions as well as to create artifacts or products of their learning. Collaborative

writing, joint problem solving, study teams and group projects are common activities

in CL. This kind of work and the referred activities exist also in the cooperative

learning. CL, in contrast, occurs when individuals are actively engaged in a

community in which learning takes place through collaborative efforts (Stahl et al,

2006). Learning is not just the accumulation of new knowledge to those that already

exist, but the transformation and the reorganization of the previous knowledge.

When the students are confronted with data opposed to something they already

know, they experience cognitive collision that might lead them to revision and

reorganize their own ideas (Vekyri, 2007; Daniel et al, 2003; Stahl, 2000; Knipfer et

al, 2009; Bereiter, 1994). Public discussion is a central way for a learning community

to expand its knowledge (Bielaczyc and Collins, 1999).

Students in CL learn how to be able to solve problems as a group. Groups of

three (3) to five (5) students are allocated by the instructor who assigns a problem to

be solved. Ideas and solutions of each team should be presented to entire class,

allowing groups to come together as a whole. Discussion follows, targeting to

evaluate and not to judge the students' work. The goal is to remove focus of the

instructor's authority (Bielaczyc and Collins, 1999). Collaborative scripts should

target to create roles and mediating interactions, while allowing for flexibility in

dialogue and activities (Kollar et al, 2005).

Students' motivation to learn, their interpersonal relationships and the

expectations for personal success are improved by CL methods (Stahl, 2000). It is

shown that students working in small groups achieved significantly more than

students who worked individually. Discussion-based practices improved text

comprehension and critical-thinking skill for students across ethnic and

socioeconomic backgrounds. CL can also lead to students' success by deepening the

understanding of a given topic (Stahl et al, 2006).

12

Learning through CL methods seems not to be commonly used. Teachers' role

should be differentiated (Bereiter, 1994; Bielaczyc and Collins, 1999). In CL teachers

act as guides. During the learning process, students lead discussions and work

independently with teacher oversight and help when asked, rather than explicit

direction. Participation of learners is a key element to CL as it functions as the

method by which the learning process occurs. CL occurs when children and adults in

communities switch between "knowledge performers" and "observing helpers".

Horizontal structure allows for flexible leadership, which is one of the key aspects of

CL (Jameson et al, 2006).

Different types of CL have been implemented:

- Collaborative Network Learning (via electronic dialogue among co-learners,

learners and experts)

- Learning Management Systems (collection of tools used by learners to assist or

to be assisted by others)

- Collaborative Learning Network (learning systems working as a network, e-

learning)

- CL in Virtual Worlds

- Computer Supported Collaborative Learning (CSCL) (use of technology in a

learning environment to help mediate and support group interactions in a CL

context. Controlling and monitoring interactions, regulating tasks, rules and roles

and mediating the acquisition of new knowledge can be achieved with the use of

technology as well) (Stahl et al, 2006; Dillenbourg and Fischer, 2007).

2.2 Computer Supported Collaborative Learning (CSCL)

Computer-supported collaborative learning (CSCL) is defined as

a pedagogical approach wherein learning takes place via social interaction using a

computer or through the Internet. It is characterized by the sharing and construction

of knowledge among participants using technology as their primary means of

communication or as a common resource (Koschmann, 1999). CSCL can be

implemented both in online and classroom learning environments and can take

place synchronously or asynchronously (Bielaczyc and Collins, 1999). It can be

applied in all levels of education. As a consequence it poses the challenge of

increasing students' access to computers and the Internet.

Virtual Learning Communities (VLCs) constitute an aspect of particular

importance for Computer Supported Collaborative Learning (CSCL). Encouraging

students to learn together in small groups has been increasingly emphasized in the

broader learning sciences. Combining these two ideas (computer support and

13

collaborative learning) is an educational challenge that CSCL is designed to address it

(Stahl et al, 2006).

CSCL emerged as a strategy rich with research implications for the growing

philosophies of constructivism and social cognitivism (Daniel et al, 2003). The rapid

development of social media technologies and the increasing need of individuals to

understand and use those technologies has brought researchers from many

disciplines to the field of CSCL. CSCL is used today in traditional and online schools

and knowledge-building communities. The field of CSCL is based on learning theories

that emphasize that knowledge is the result of learners interacting with each other,

sharing knowledge, and building knowledge as a group (Stahl et al, 2006). Since the

field focuses on collaborative activity and collaborative learning, it inherently takes

much from constructivist and social cognitivist learning theories (Daniel et al, 2003).

Roots of collaborative epistemology related to CSCL are found in Vygotsky's

Social Learning Theory. Theory's notion of internalization and the idea that

knowledge is developed by one's interaction with his or her surrounding culture and

society are very important to CSCL. Another key element is the "zone of proximal

development". A range of tasks that might be too difficult for learners to master by

themselves can be made possible with the assistance of a more skilled individual or

teacher. Individual learners have different developmental capabilities in

collaborative situations than when they are working alone (Dillenbourg and Fischer,

2007; Knipfer et al, 2009; Innes, 2007). The measure of the difference between these

two capabilities is defined as the "zone of proximal development" (Stahl et al, 2006;

Engeström, 2001; Wells, 2002). These ideas are central to CSCL as knowledge

building is achieved through interaction with others.

An essential element to the zone of proximal development is the acquisition of

language. According to Vygotsky (2008), language is fundamental to children's

cognitive growth because language provides purpose and intention so that behaviors

can be better understood. Children communicate and learn from others through

dialogue, an important tool in the zone of proximal development (Warschauer,

1997).

Learning theories related to CSCL focus on the social aspect of learning and

knowledge building, recognizing that learning and knowledge building involve inter-

personal activities including conversation, argument, and negotiation (Stahl, 2000).

Collaboration theory, suggested by Stahl (2004) as a system of analysis for CSCL,

postulates that knowledge is constructed in social interactions such as discourse. The

theory suggests that learning is not a matter of accepting fixed facts, but is the

dynamic, on-going, and evolving result of complex interactions primarily taking place

within communities of people. The goal of collaboration theory is to develop an

14

understanding of how meaning is collaboratively constructed, preserved, and re-

learned through the media of language and artifacts in group interaction (Daniel et

al, 2003; Wells, 2002; Warschauer, 1997; Koschmann, 1999) .

CSCL software environments provide various forms of pedagogical support for

collaborative learning. But in most cases their role is secondary to the interpersonal

collaboration process among the students and possibly the teacher. Human group

processes cannot be replaced by the software, which has a supportive role (Stahl et

al, 2006).

Wikis are a common approach to CSCL providing a way to encourage discussion

among learners. Learners can also experience and resolve sociocognitive conflicts

during knowledge building in wikis (Knipfer et al, 2009). Technology-mediated

discourse allows participants that may be separated by time and distance to engage

in conversations and build knowledge together. Other approaches commonly used in

CSCL are the problem-based learning and the project-based learning where need for

collaboration is essential.

In CSCL teachers still have a vital role as they facilitate learning. Teacher is

responsible for the proper design of a project and for discussion monitoring

(Dillenbourg and Fischer, 2007).

2.3 Inner Speech

Vygotsky (2008) emphasizes the inter-relationship of language development and

thought. This relationship establishes the connection between speech (inner speech

or oral language) and the development of mental concepts and cognitive awareness.

Inner speech is described by Vygotsky (2008) as being qualitatively different from

external speech. Inner speech is developed from external speech via a gradual

process of internalization (Emerson, 1983).

Language is an external tool for the child at the beginning of its life, used for

social interaction. Child's behavior is guided by using this tool in a kind of a self-talk

or "thinking out loud". Gradually, self-talk is used a tool for shelf-directed and shelf

regulating behavior (Emerson, 1983). As speaking has been appropriated and

internalized, self-talk is "evaporated in thoughts" at the age of the child's school

age. Egocentric language is not dying at the threshold of the school age, but it is

transformed in inner speech (mute language) (Ehrich, 2006). Vygotsky (1987),

suggests that self-talk "develops along a rising not declining curve; it goes through an

evolution, not an involution. In the end, it becomes inner speech". Speaking is being

developed along two lines: the line of social communication and the line of inner

speech. Inner speech mediates and regulates child's activity through its thoughts.

The thoughts are, in turn, mediated by the semiotics of inner speech. Despite

15

thinking can take place even without language, it is mediated by language and is

developed in a higher level of sophistication (Emerson, 1983).

Inner speech is a language having its own specific features. Firstly, it has a

completely specific syntax consisting of apparent lack of cohesion, fragmented

nature and the abbreviation in comparison to the outer language. Clauses are

abbreviated by keeping the predicate and its accompanying words and omitting the

subject and the parts of sentence related to it. This abbreviation does not lead in

misunderstandings if the thoughts of the individuals are in accordance and mean the

same. In a different case, misunderstanding is inevitable. The more identical the

thoughts of the individuals are, the less linguistic cues are used. In an extreme level

of common understanding, entire discourse can be served by one single word

(Vygotsky, 2008; Socolov, 1972;).

In contrast, as in written language interlocutors are absent, it is fully developed

and its syntax structure reach to its higher level. In this language, abbreviations do

not serve communication needs. As a consequence written language, in comparison

to oral language, is fully developed and has a complicated syntax structure. Oral

language can become even more abbreviated when the mental proximity of the

interlocutors creates a community perception, allowing communication with

intimation and ellipticity. This exception of the oral language is the rule for inner

speech. Word is much more meaningful in inner speech than in the outer speech

(Vygotsky, 2008).

Inner speech is an efficient cognitive resource, because the abbreviations and

short-cuts speed it up. This speed makes inner speech in some ways more useful

than outer speech (Wiley, 2006).

According to Vygotsky, higher psychological functions appear first

interpsychologically, in collaborative action, and later intrapsychologically,

internalized by the individual (Engeström, 2011).

16

3. Virtual Learning Communities Linguistic Analysis Model

Sharing of knowledge within a community is achieved through shared codes and

language (Daniel et al, 2003; Stahl, 2000; Innes, 2007). Language is not only a tool for

communication as it also serves knowledge and information exchange (Dillenbourg

and Fischer, 2007; Knipfer et al, 2009). Furthermore, it "determines how individuals

perceive information they receive from individuals within their community or

outside the community" (Daniel et al, 2003). Communication and dialogue are in a

privileged position in the learning process, due to the assumption that knowledge is

socially constructed (Innes, 2007).

As a consequence, important results might come from applying linguistic

analysis in the language expressed among the class members.

3.1 Model analysis of inner speech

Socolov (1972) suggests that, in a community, under certain conditions, the

specific features of inner speech takes characteristics and appear in outer (surface)

speech. The more inner speech appears, the stronger the indication it gives us of the

existence of a community. Taking into account Vygotsky's (2008) argument that the

stronger the specific mental action of inner speech is, the clearer the peculiarities of

its syntax structure appear and attempting to identify the existence of inner speech,

it is argued in this study that we can apply linguistic analysis on the discourse that

has been developed among the team members. Subsequently, it could be defined

whether there are any inner speech features appeared in outer speech (Wiley,

2006).

Focusing on this attempt, a linguistic analysis based on the following features is

proposed.

IS-1 Omission of Subjects

According to Vygotsky (2008), one basic characteristic of inner speech is the

absence of the subject. So, it is proposed to investigate how many verbs are used in

the discourse without having a subject.

1) Verbs without having any subject or No subject

total number of verbs Verb count

2) Verbs with one or more subjects or With subject


Applying these measures, a strong indicator of inner speech could be offered to

our study.

17

Clause types

IS-2 Omission of Conjunction

Omission of the conjunctions reveals that the language is desultory, elliptical

and informal. For this reason, the count of the clauses that are not joined by a

conjunction is suggested.

number of clauses without conjunction between them or no conjunction

total number of clauses clause count

IS-3 Informal clauses

In inner speech there is a common code for communication between the

communicating parties. This common code transforms the language genre and style,

and makes it more specific (Emerson, 1983). According to Vygotsky (2008), the main

feature of inner speech is ellipticity, i.e. the clauses consist of only the predicate. The

informal clauses (IS-3) (Maness, 2008; Pérez-Sabater, 2012), the clauses having no

verb (IS-4) and the semantically abbreviated clauses being elliptical in meaning (IS-5)

are therefore counted.

number of clauses having informal features or informal


IS-4 omission of verbs

number of clauses having no verbs or no verb


IS-5 Elliptical clauses

number of clauses with elliptical meaning or elliptical


Another feature of ellipticity in the language is the number of words used. Wiley

(2006) suggests that in inner speech "the stock of vocabulary is much smaller than in

outer language". It is then suggested to count the total of the words used per period.

This provides us with an indicator of the predicative speech.

IS-6 Words per clause

total number of words or word count


18

Alternatively it could be used:

IS-7 Words per period

total number of words or word count

total number of periods period count

IS-8 Punctuation

Another qualitative feature of inner speech is the reduced use of punctuation.

As inner speech has the characteristics of informal language, punctuation is likely to

be sparse as well (Brooks et al., 2013; Mannes, 2008; Pérez-Sabater, 2012). For this

reason, focusing on the punctuation used in the discourse is proposed.

More specific, the following punctuation marks should be counted:

a) parenthesis

total number of parentheses or parentheses


b) commas

total number of commas or commas


c) question marks

total number of question marks or question marks


d) dots

total number of dots or dots


e) exclamation marks

total number of exclamation marks or exclamation marks

total number of clauses clause

count

f) full stops

total number of full stops or full stops


19

g) total of the punctuation marks

total number of punctuation marks or punctuation marks


IS-9 Word types

Investigating the word types used by the team members, we get access to

another indicator of inner speech. The counting of the following word types is

proposed:

a) adverbs

Using adverbs is essential in a discourse when the speaker/writer wants to

specify his/her meaning and make it more clear to the receiver/reader. In the case of

inner speech, use of adverbs does not seem to be so essential, due to the

common/mutual understanding among the members of a team/class (Emerson,

1983).

It should then be counted:

total number of adverbs or adverbs

total number of words word count

But focusing only at the total number of the adverbs used might be misleading,

due to the variety of the adverb types. For this reason, the counting of the adverb

types as well is proposed. There are adverb types essential for a discourse, while

some other types can be omitted. In inner speech, adverbs of place, adverbs of

manner or relative adverbs are eliminated, while viewpoint and commenting

adverbs seem to be more essential.

So, in addition to adverb counting, the counting of adverb types (IS-10) is also

proposed:

a) adverbs of place

total number of adverbs of place or adverbs of place

total number of adverbs total adverbs

b) adverbs of time

total number of adverbs of time or adverbs of time


20

c) adverbs of manner

total number of adverbs of manner or adverbs of manner


d) adverbs of certainty

total number of adverbs of certainty or adverbs of certainty


e) quantitative adverbs

total number of quantitative adverbs or quantitative adverbs


f) interrogative adverbs

total number of interrogative adverbs or interrogative adverbs


g) relative adverbs

total number of relative adverbs or relative adverbs


h) viewpoint and commenting adverbs

total number of viewpoint and commenting advs or viewpoint & commenting


IS-9.b adjectives

Adjectives are used to enrich the language and make it more specific. They are

needed when one wants to define something and to reduce the ambiguity. Absence

of the adjectives makes the language elliptical, ambiguous and general. As Vygotsky

(2008) claimed, inner speech contains only the predicate, i.e. only the terms that are

related to the verb. Wiley (2006), suggests that in inner speech "adjectives and other

modifiers can usually be dispensed with". As a result, it is expected that adjectives

will not be so commonly used in inner speech. For this reason, calculating the

average of the adjectives used in the discourse is proposed.

total number of adjectives or adjectives


IS-9.c greeklish words

Greeklish is a type of informal written language used by Greeks. Specifically, it is

used when someone types Greek words with Latin letters. This type of language is

21

used only in the case of an informal communication and it is commonly used in

chatting, email and sms (Short Message Service). Therefore, if greeklish words are

used in a discourse it could be argued that the expressed language is informal.

total number of greeklish words or greeklish


IS-9.d informal words

Similarly to the greeklish words, the use of informal words (shortened and

simplified word forms, idioms, diminutives) indicates informal communication which

is common only in the cases of team members feeling familiarity with each other.

Counting the informal words could offer an indication of inner speech.

total number of informal words or informal


IS-9.e Emoticons

Another language feature found mainly in computing is the use of emoticons. It

offers an indication of informal communication (Brooks et al., 2013; Mannes, 2008;

Pérez-Sabater, 2012), a basic feature of inner speech.

total number of Emoticons or Emoticons


IS-11 Syntax

Another aspect of the ellipticity of inner speech is expected to be the reduced

use of subordination and prepositional phrases. Use of subordinate clauses and

prepositional phrases enriches language and makes it more understandable.

According to Wiley (2006), inner speech is "simpler in both semantics and syntax,

using fewer words and fewer parts of speech".

It is argued that inner speech can be detected by counting these syntax terms.

total number of subordinate clauses or subordinate clauses


Special attention must be given to subordinate clauses that are used as an

object of the verb. In this case, subordinate clauses should not be taken into

account, because they constitute a predicate and are therefore essential for the

meaning of the sentence, and their existence is a basic feature of inner speech.

22

Similar to the subordinate clauses is the use of prepositional phrases. Elliptical

language contains as little linguistic terms as possible. Therefore, prepositional

phrases are not likely to be commonly used in inner speech.

total number of prepositional phrases or prepositional phrases


Additional terms

It is considered that using additional terms in a sentence makes the language

more explanatory and precise. This seems to be essential for achieving

communication. But does the same hold in the case of inner speech? Its elliptical

form and the mutual understanding should be kept in mind. So, it is expected that

these additional terms will not be commonly used in inner speech. Such terms as

apposition (IS-13), epexegesis (IS-14) and terms in the possessive (IS-15) and in the

accusative case (IS-16) should be included in the model of inner speech analysis.

Terms in the possessive or accusative case may reveal a lot of things (owner,

subject, property, time, value, object, content or relation). These terms should be

counted as a whole, despite the big variety, because counting them by category is

not likely to add something to the analysis.

IS-13 total number of appositions or appositions


IS-14 total number of epexegesis or epexegesis


IS-15 total number of terms in possessive case or terms in possessive case


IS-16 total number of terms in accusative case or terms in accusative case


It is expected that the less these terms are used, the greater is the indication of

the existence of inner speech.

IS-12 Article types

The two types of articles (definite/indefinite) give different meaning to a

discourse. Indefinite articles are used when someone wants to specify and describe

the meaning of his message. In inner speech, where common/mutual understanding

exists, the message is definite and clear to the receiver (Emerson, 1983; Mairesse et

al, 2007). For this reason, it is expected that the use of indefinite articles will be

limited in inner speech. Additionally, definite articles are likely to constitute the

majority of the articles in inner speech. The following metrics are then proposed:

23

a) total number of definite articles or definite articles

total number of articles total articles

b) total number of indefinite articles or indefinite articles

total number of articles total articles

In addition to these metrics, counting of the average number of articles to the

total words and to the total periods used, is also suggested, because it is likely to

offer an indication of the ellipticity of language.

c) total number of articles or articles


d) total number of articles or articles

total number of periods period count

IS-17 Abbreviations

Abbreviations is a core feature of inner speech (Vygotsky, 2008; Socolov, 1972;

Wiley, 1996). Using an abbreviation in a communication means that one knows that

the receiver of the message is able to understand it. This requires a common

understanding between the sender and the receiver of the message which leads to

the result that the communication has inner speech features. Counting the

abbreviations might give an indicator of the existence of inner speech.

The use of the following metric is proposed:

total number of abbreviations or abbreviation


IS-18 Metaphors

Similar to the use of abbreviations, it is considered that someone uses a

metaphor only in the case that the receiver is expected to understand it.

Additionally, metaphors are powerful for creating and exchanging rich sets of

meaning (Daniel et al, 2003). Accepting this, it is argued that metaphors are an index

mark of mutual understanding and therefore an index mark of inner speech.

total number of metaphors or metaphors


IS-19 Similes

In contrast to metaphors, similes are used in order to give more

details/explanations. Subsequently, their presence indicates a necessity for

24

additional information for achieving the common understanding. So, the absence of

similes can be an indicator for inner speech.

total number of similes or similes


IS-20 Word variety

According to Vygotsky (2008), the number of distinct words used in the

discourse within a community is usually restricted, due to the mutual understanding.

As a result, it is expected that, in the case of communities, the variety of the words

used is likely to be poor. "Vocabulary can be quite small and the same words can be

used over and over again" (Wiley, 2006). In contrast, when someone is addressing a

general audience, he/she is expected to use a richer variety of words (Mairesse et al,

2007). Targeting to examine whether a discourse indicates the existence of a

community or not, it is proposed to use the metric of word variety as follows:

variety of words or variety


Calculating the word variety, we count how many different words are used in a

discourse. Different forms of words (i.e. verbs in present or past tense, nouns in

single or plural) are counted as one single word. Articles are excluded in the

aforementioned counts due to their frequency.

Within a community this metric is likely to be small, indicating a robust and

condensed communication.

25

3.1.1. Inner Speech Analysis summary

IS-1 Omission of Subjects No subject

Verb count

With subject

Verb count

IS-2 Omission of Conjunction No conjunction

clause count

IS-3 Informal clauses Informal clauses

clause count

IS-4 Omission of verbs No verb

clause count

IS-5 Elliptical clauses Elliptical clauses

clause count

IS-6 Words per clause word count

clause count

IS-7 Words per period word count

period count

IS-8 Punctuation marks a) parenthesis

clause count

b) commas

clause count

c) question marks

clause count

d) dots

clause count


clause count

f) full stops

clause count

g) punctuation (total)

clause count

IS-9 Word types a) adverbs

word count

b) adjectives

word count

c) greeklish

word count

d) informal

word count

26

e) emoticons

word count

IS-10 Adverb types a) Adverbs of place

Total adverbs

b) Adverbs of time

Total adverbs

c) Adverbs of manner

Total adverbs

d) Adverbs of certainty

Total adverbs


Total adverbs


Total adverbs

g) relative adverbs

Total adverbs

h) Viewpoint and commenting advs

Total adverbs

IS-11 Syntax a) Subordinate clause

clause count

b) prepositional phrases

clause count

IS-12 Article types a) Definite

Total articles

b) indefinite

Total articles

c) articles

word count

d) articles

periods

IS-13 Apposition Apposition

clause count

IS-14 Epexegesis Epexegesis

Clause count

IS-15 Additional terms in

possessive case

terms in possessive case

word count


accusative case

terms in accusative case

word count

27

IS-17 Abbreviations Abbreviations

word count

IS-18 Metaphors Metaphors

clause count

IS-19 Similes Similes

clause count

IS-20 Word variety Variety

word count

28

3.2 Model analysis of collaborative learning

Collaboration is considered to be the most important shared characteristic in

Virtual Learning Communities (Daniel et al, 2003). Focusing on the discourse among

the members of a team, a group, a class or a community, it is suggested that we

might be able to investigate index marks of collaborative learning. Linguistic analysis

could then be applied on the discourse, focusing on specific characteristics.

CA-1 Verbs in first plural person

Use of verbs in the first person plural form (we) can constitute an indicator of

team action, work or knowledge that has been produced collaboratively. According

to Mc Millan and Chavis (1986) "personal investment is an important contributor to

a person's feeling of group membership and to his or her sense of community". This

could be applied by calculating the average of the verbs in the first plural person to

the total number of the verbs used.

total number of verbs in 1st person plural or Verbs (we)


Clause types

The types of clauses used in the discourse could be another indicator of

collaboration. Specifically, the clauses expressing emotion and the ones that express

rewarding could be searched.

CA-2 Emotional clauses

As it has already been mentioned (Mc Millan and Chavis, 1986), emotion is a

unique characteristic of the discourse within a community. Additionally, emotion is

directly related to inner speech as well. Wiley (2006) suggests that "inner language is

so pervaded with emotion". As a result, it is suggested to focus on the clauses

expressing emotions in order to investigate whether there were developed

emotional relationships among the members of a group (Brooks et al., 2013). These

clauses (emotional) can be counted and calculate their average to the total number

of the clauses used.

total number of clauses showing emotion or emotional clauses


CA-3 Rewarding clauses

Members of a group that have developed strong relationships and strong ties

feel the need to reward their partners for their effort. According to Mc Millan and

Chavis (1986), "it is obvious that for any group to maintain a positive sense of

29

togetherness, the individual-group association must be rewarding for its members".

As a result, it is probable for rewarding clauses to be used in the discourse within

the group members. Focusing on the rewarding clauses it can be examined whether

there were strong ties in a class (Bielaczyc and Collins, 1999; Mairesse et al, 2007). If

one reaches at this result, it can then be argued that the class members have

collaborated enough.

total clauses expressing reward or rewarding clauses


Word types

In addition to the clause types metrics, it is considered that the type of the

words used must also be examined. Subsequently, the use of the following metrics is

proposed.

CA-4 Clauses of negation

Clauses of negation can offer an index mark of the attitude of the

team/community members (Mairesse et al, 2007). This kind of clauses is likely to be

less as the collaboration increases. Community members tend to offer constructive

proposals/suggestions to each other, rather than disagreements. That is why it is

argued that we should count the clauses of negation of a discourse. Clauses

containing negative words ("no", "not", "don't") are considered as negative. It is

proposed to count them in percentage to the total clauses.

It is argued that a large number of clauses of negation might indicate lack of

collaboration.

total number of clauses of negation or clauses of negation


CA-5 Clauses of reason

Clauses of reason are used in a discourse in order to argue, express an opinion

and explain proposals. When this happens, it shows that a member of a

team/community respects his team members and that he is proposing something,

without giving orders. Using clauses of reason might offer us an index of

collaboration. Subsequently, they include in the model of analysis.

total number of clauses of reason or clauses of reason


30

CA-6 Familiarity words

Use of familiarity words indicates the familiarity intimacy that has been

developed among the members of a team which has been transformed into a

community (Mairesse et al, 2007). Especially in the cases of a virtual class that the

students did not know each other before the creation of the class, this metric can be

considered as a strong indication of the existence of a community.

total number of words showing familiarity or familiarity words


CA-7 Inclusive words

The existence of inclusive words in the discourse might offer an index of a

feeling of membership. Words such as "together", "team", "company", "community"

etc. can be considered as inclusive words (Mairesse et al, 2007).

total number of inclusive words or inclusive words


CA-8 Social words

Social words (like "friend", "colleague", "mate") can provide an index mark for

the existence of a community (Mairesse et al, 2007). The more social words are used

in the discourse, the more likely it is for strong social relationships among the

members of a class to exist.

total number of social words or social words


CA-9 Emotional words

In addition to the evaluation of the emotional sentences used in the discourse,

the evaluation of the emotional words is considered as useful as well (Brooks et al.,

2013).

total number of emotional words or emotional words


This metric should be enriched by another one in order to investigate whether

the emotional words express positive (CA-10) or negative emotion (CA-11). So, the

use of the following is proposed:

total number of positive emotion words or positive emotion

total number of emotional words emotional words

31

total number of negative emotion words or negative emotion

total number of emotional words emotional words

It is expected that in the case of collaborative learning, the majority of the

emotional words will express positive emotion, as there is strong correlation

between the members' positive experience and the community bond (Mc Millan and

Chavis, 1986; Brooks et al., 2013; Mairesse et al, 2007).

CA-12, CA-13 Use of first person plural pronouns

Using possessive or personal pronouns in the first person plural form (we, ours)

indicates the sense of belonging in a team (Mc Millan and Chavis, 1986). It also

indicates the sense of the common/mutual belonging, the co-construction of

knowledge and the feeling of sharing items with others. Pronouns are also directly

related to inner speech. Wiley (2006) refers that " this pronominal scheme I will treat

as a niche or circuitry or set of channels within which inner speech goes on. The

pronouns are the saddle, and if you want to ride through this linguistic land you have

to get on that saddle and inhabit the pronouns".

CA-12 total number of pronouns in the 1st plural person or (we, ours)

total number of pronouns pronouns

For a more precise specification the following could be used:

CA-13 total number of pronouns in the 1st plural person or (we, ours)_

total number of possessive + personal pronouns possessive + personal

32

3.2.1 Collaborative Learning Analysis summary

CA-1 Verbs in first plural person Verbs (we)

Verb count

CA-2 Emotional clauses emotional clauses

clause count

CA-3 Rewarding clauses rewarding clauses

clause count

CA-4 Clauses of negation Clauses of negation

clause count

CA-5 Clauses of reason Clauses of reason

clause count

CA-6 Familiarity words familiarity words

word count

CA-7 Inclusive words inclusive words

word count

CA-8 Social words social words

word count

CA-9 Emotional words emotional words

word count

CA-10 Positive emotion positive emotion words

emotional words

CA-11 Negative emotion negative emotion words

emotional words

CA-12 Use of first person plural

pronouns

Us, ours

Pronouns

CA-13 Use of first person plural

pronouns

Us, ours

Possessive + personal pronouns

33

4. Case studies

The five different learning communities used as case studies in this work are

described in this section.

Virtual class 1 (VC 1)

Virtual Class 1 (VC 1) was created during the spring semester of the school year

between two (2) schools, an elementary school (ES) and a high school (HS). These

schools were located in two different towns in Greece. The elementary school

students (20) who participated (ages 11-12) were attending the 6th grade of the

elementary education. The high school students (20) (ages 12-13) were attending

the 1st grade class of the secondary education. The main target of that project was

the collaboration between the two classes in order for the students to create a wiki

and exchange information about the town/location they live in.

Students of each school were divided into working groups, each one consisting

of two or three students. Seventeen (17) groups were created. Each group took over

a topic, collected the relevant material and uploaded it. Both the topic and the

material were selected by the members of the team. The teachers who were

involved in the project had a supporting and inspirational role and tried to minimize

their involvement.

Wikispaces was the collaborative platform used in this project.

The students uploaded artifacts (links, photos, text) associated with their own

town on topics of their choice. Consequently, students of the partner school used

these artifacts to present that town. So, the students of ES used the material posted

by students of HS to present their (HS's) town. HS students worked reversely.

Firstly, a web page was created to be used by the students for introducing each

other. Besides that, students of the two schools introduced themselves through a

video conference that took place at the beginning of the project. During the

implementation of the project students were exchanging communication messages

in a special web page (discussion posts). So they had the ability to communicate, to

post their comments, to express their proposals or arguments. Each group created

its own page and every member (not only the members of the team) was able to

post his comments. The discourse among the members of the groups was recorded

at this web page. Furthermore, the log files of the actions (insertions, corrections,

deletions) could be accessed through the revision history provided by the selected

tool (wikispaces).

34

Subgroups:

VC 1.1

Discourse in VC 1 has been divided into two subgroups for the needs of the

analysis. The first subgroup (VC 1.1) contains the discourse among the team

members after having completed their task. Students expressed their impressions,

opinions and feelings for the already completed project. In this case, there was no

problem to be solved and the students chatted in a more free frame. This

communication took place on a special web page, which functioned as a forum for all

the students of both schools.

VC 1.2

The second subgroup (VC 1.2) contains the discourse among the team members

during the project, i.e. during the effort to solve the given problem (construction of a

wiki that presents each other's place). In this case, communication is focused on the

task and serves students' needs of each team for collaboration.


Virtual Class 2 (VC 2) was created during the winter semester of the school year

between two (2) elementary schools (ES1 and ES2) located in different towns in

Greece. Each class had twenty (20) students (ages 11-12).

The elementary students (20) from ES1 who participated in this VC were the

same ones described in VC 1.

The design of this project’s teams was the same as the previous one described in

VC 1. The groups created in this VC were eighteen (18).

The two main differences that have to be mentioned are

i) the difference between the educative level of the students in VC 1, which does

not exist in this VC, and

ii) the previous experience for the elementary students gained through their

participation in VC 1.


In this case study a real class was transformed into a virtual one through running

an one month project using online collaborative tools. The target of the project was

the creation of presentations for a national holiday.

The students who participated in the Virtual Class 3 (VC 3) were the same

students of the elementary school ES1 that joined the two aforementioned virtual

35

classes (VC 1 and VC 2). During this project the students were divided into nine (9)

groups. Each group consisted of two (2) or three (3) students. Creation of the groups

was made according to the students' choice.

Each group took the responsibility of creating a presentation related to the

general topic of the project. Topics were selected by the members of the group in

collaboration with the other group members. Nine (9) presentations were therefore

produced at the end of the project.

Teachers had an active instructive role. The selected environment for the

project was Google Drive due to its capability to allow for collaborative work from a

distance.

Students were guided to create two (2) files in order to create a collaborative

platform: one (1) presentation file and one (1) document file for the necessities of

the communication among the group members. In these files both the members of

each group and the teachers were defined as common users.

Log files of the activities were kept through the functionality provided by the

tool used (historical revision). The communication messages among the participants

(students and teachers) were also recorded at the aforementioned document file.

Student's essay texts (ST)

Having in mind that chat communication is generally informal (Brooks, 2013;

Bielaczyc and Collins, 1999) it had to be examined whether the results of the analysis

appeared exclusively under these conditions (case studies) or whether they were

constant attributes of the students' language use. An index mark of the students'

formal language was also required. For this reason, students' essay texts (ST) were

also used in the analysis in comparison to the language used in VCs.

These essay texts were written by the same elementary students of ES1 that

took part in the case studies (VC 1, VC 2 and VC 3). They were written throughout

the same school year when the case studies took place. Students wrote them within

the linguistics course in their school.

These texts are narrative essays written by 7 different students (4 boys and 3

girls) out of a total of 20 in the class. They contain a total of 3.577 words and 666

clauses, while VC 1.1 had 210 and 52, VC 1.2 had 453 and 106, VC 2 had 471 and 102

and VC 3 had 704 and 147 respectively.

36

5. Results

5.1 Inner speech analysis

Having completed the model of inner speech analysis, it was applied in the case

studies in order to evaluate its performance.

5.1.1 Inner speech analysis in VC 1.1

Omission of subjects

In VC 1.1 88% of the verbs used had not any subject. Only 12% of the verbs had

a subject.


Verb count

44/50 0,88

With subject

Verb count

6/50 0,12

Clause types

Performing the model for clause types, 12 informal clauses were found out of a

total of 52. Two (2) clauses had no verb and six (6) were elliptical.

The total words used were 210, when the total of the clauses were 52 and the

total of periods were 17. So, students used 4,04 words per clause or 12,35 words per

period.


clause count

0/52 0


clause count

12/52 0,23


clause count

2/52 0,04


clause count

6/52 0,12


clause count

210/52 4,04


period count

210/17 12,35

37

Punctuation

The total number of punctuation marks used in this discourse was 37 (27

exclamation marks, 7 full stops, 1 comma, 1 question mark and 1 dots).

It is remarkable that exclamation marks constitute the majority, while full stops

are only 7. Furthermore, total of the "ending" marks for a clause are 37, while the

clauses are 52.

IS-8 Punctuation

marks

a) parentheses

clause count

0/52 0

b) commas

clause count

1/52 0,02

c) question marks

clause count

1/52 0,02

d) dots

clause count

1/52 0,02


clause count

27/52 0,52

f) full stops

clause count

7/52 0,13


clause count

37/52 0,71

Word types

Adverbs were the most common type of the words used in the discourse. They

were 25, adjectives were 3, informal words 4 and 1 emoticon, when total words

were 210.


word count

25/210 0,12

b) adjectives

word count

3/210 0,01

c) greeklish

word count

0/210 0

d) informal

word count

4/210 0,02

e) emoticons

word count

1/210 0

38

Adverb types

The adverbs used in this discourse were counted according to their types. So, 4

adverbs of place, 7 adverbs of manner and 13 quantitative adverbs were counted.

Quantitative adverbs constitute the majority. No other kind of adverbs was used.

Syntax types

In this discourse there were 15 subordinate clauses and 13 prepositional

phrases, when the total clauses were 52.

IS-11 Syntax a) Subordinate clauses

clause count

15/52 0,29


clause count

13/52 0,25

Article types

The definite articles were 19 while indefinite articles were not used at all.


Total adverbs

4/25 0,16

b) Adverbs of time

Total adverbs

0/25 0


Total adverbs

7/25 0,28


Total adverbs

0/25 0


Total adverbs

14/25 0,56


Total adverbs

0/25 0

g) relative adverbs

Total adverbs

0/25 0


Total adverbs

0/25 0

39


Total articles

19/19 1,00

b) indefinite

Total articles

0/19 0

c) articles

word count

19/210 0,09

d) articles

periods

19/17 1,12

Additional terms

Additional terms were 2 (1 epexegesis and 1 term in possessive case).


Clause count

0/52 0


Clause count

1/52 0,02

IS-15 Terms in possessive case possessive case

word count

1/210 0

IS-16 Terms in accusative case accusative case

word count

0/210 0

Abbreviations-Metaphors-Similes

No abbreviations were used. Students also did not use any metaphors or similes.

Word variety

The variety of the words used in the discourse was examined. There were 65

different words, when the total words were 191.


word count

65/191 0,34

5.1.2 Inner speech analysis in VC 1.2

The discourse in VC 1.2 took place during the effort of the students to solve the

problem of creating their project.


Nearly all the verbs used in this discourse had no subject. In a great percentage,

77 verbs out of a total of 84, had no subject.

40


Verb count

77/84 0,92

With subject

Verb count

7/84 0,08

Clause types

The clauses used were at mainly informal (80) or elliptical (60). Also, 22 clauses

had no verb and 2 had no conjunction.


clause count

2/106 0,02


clause count

80/106 0,75


clause count

22/106 0,21


clause count

60/106 0,57


clause count

453/106 4,27


period count

453/64 7,08

Punctuation

Students in this VC did not seem to be very typical in use of punctuation. In a

total of 106 clauses, they used 28 full stops, 30 exclamation marks, 8 commas, 3

question marks and 3 dots. It should be noticed that the majority of them are the

exclamation marks which indicates an informal type of communication (Brooks et al.,

2013; Pérez-Sabater, 2012).

41

IS-8 Punctuation

marks

a) parentheses

clause count

0/106 0

b) commas

clause count

8/106 0,08

c) question marks

clause count

3/106 0,03

d) dots

clause count

3/106 0,03


clause count

30/106 0,28

f) full stops

clause count

28/106 0,26


clause count

72/106 0,68

Word types

Students used 453 words. 33 of them were adverbs, 30 adjectives, 29 informal

and 2 greeklish.


word count

33/453 0,07

b) adjectives

word count

30/453 0,07

c) greeklish

word count

2/453 0

d) informal

word count

29/453 0,06

e) emoticons

word count

0/453 0

Adverb types

The total number of adverbs were 33. The majority of them (13) were

quantitative. 8 were adverbs of manner, 6 adverbs of place, 4 adverbs of time, 1

relative and 1 viewpoint and commenting adverb.

42


Total adverbs

6/33 0,18

b) Adverbs of time

Total adverbs

4/33 0,12


Total adverbs

8/33 0,24


Total adverbs

0/33 0


Total adverbs

13/33 0,39


Total adverbs

0/33 0

g) relative adverbs

Total adverbs

1/33 0,03


Total adverbs

1/33 0,03

Syntax types

Prepositional phrases were 22 and the subordinate clauses 13 out of a total of

106 clauses.


clause count

13/106 0,12


clause count

22/106 0,21

Article types

In a total of 453 words, there were 53 definite articles, while the indefinites

were just 1.


Total articles

53/54 0,98

b) indefinite

Total articles

1/54 0,02

c) articles

word count

54/453 0,12

d) articles

periods

54/64 0,84

43

Additional terms

In a total of 106 clauses neither epexegesis nor apposition were used. Attributes

in possessive or accusative case were nearly null (3 and 1 respectively) out of a total

of 453 words.


Clause count

0/106 0

IS-14 Epexegesis epexegesis

Clause count

0/106 0


word count

3/453 0,01


word count

1/453 0


Similar to the above mentioned terms, abbreviations were just a few (2) and

metaphors and similes were null.


word count

2/453 0


clause count

0/453 0


clause count

0/453 0

Word variety

Students used 157 different words out of a total of 399.


word count

157/399 0,39

5.1.3 Inner speech analysis in VC 2


In VC 2 85% of the verbs had no subject.

44


Verb count

66/78 0,85

With subject

Verb count

12/78 0,15

Clause types

In this VC, informal clauses were the majority (59). A significant number (35)

were elliptical, while in 26 clauses the verb was omitted and 12 clauses had no

conjunction.

Counting of the words per clause gave the average of 4,62, while counting them

per period the average was 7,03.


clause count

12/102 0,12


clause count

59/102 0,58


clause count

26/102 0,25


clause count

35/102 0,34


clause count

471/102 4,62


period count

471/67 7,03

Punctuation

In VC 2 the majority of the punctuation marks were the full stops (24), while a

significant number of question marks (20) and exclamation marks (15) appeared.

Total punctuation marks were 66 in 102 clauses.

45

IS-8 Punctuation marks a) parentheses

clause count

3/102 0,03

b) commas

clause count

2/102 0,02

c) question marks

clause count

20/102 0,20

d) dots

clause count

2/102 0,02


clause count

15/102 0,15

f) full stops

clause count

24/102 0,24


clause count

66/102 0,65

Word types

Extended use of greeklish words (61) appeared in this discourse. Informal words

were 35, adverbs 27 and adjectives 21 out of a total of 471 words.


word count

27/471 0,06

b) adjectives

word count

21/471 0,04

c) greeklish

word count

61/471 0,13

d) informal

word count

35/471 0,07

e) emoticons

word count

2/471 0

Adverb types

Four types of adverbs were used in this discourse. Adverbs of place, quantitative

and viewpoint and commenting adverbs were in almost equal frequency.

46


Total adverbs

9/27 0,33

b) Adverbs of time

Total adverbs

0/27 0


Total adverbs

2/27 0,07


Total adverbs

0/27 0


Total adverbs

8/27 0,30


Total adverbs

0/27 0

g) relative adverbs

Total adverbs

0/27 0


Total adverbs

8/27 0,30

Syntax types

Total clauses were 102. 14 of them were subordinate clauses. The prepositional

phrases were 26.


clause count

14/102 0,14


clause count

26/102 0,25

Article types

Similar to the previous analyzed case studies (VC 1.1, VC 1.2), VC 2 also used,

almost exclusively, definite articles.

47

Additional terms

Four additional terms (1 epexegesis, 2 possessive case and 1 accusative case

attributes) were counted.


Clause count

0/102 0

IS-14 Epexegesis epexegesis

Clause count

1/102 0,01


word count

2/471 0


word count

1/471 0


This VC did use abbreviations (8) and metaphors (4). No similes were used.


word count

8/471 0,02


clause count

4/102 0,04


clause count

0/102 0

Word variety

Students used 147 different words. Total words were 413, giving a score of 0,36.


word count

147/413 0,36


Total articles

56/58 0,97

b) indefinite

Total articles

2/58 0,03

c) articles

word count

58/471 0,12

d) articles

periods

58/67 0,87

48

5.1.4 Inner speech analysis in VC 3


In VC 3 the two categories, "with subject" and "no subject", were almost equal.

Verbs without having any subject were slightly more.


Verb count

64/125 0,51

With subject

Verb count

61/125 0,49

Clause types

Informal (101) and elliptical (89) clauses outmatched in this discourse. There

were also 21 clauses without having a verb and 20 without having conjunctions. It is

remarkable that students used 704 words in this discourse. Words per clause were

4,79 and words per period 8,09.


clause count

20/147 0,14


clause count

101/147 0,69


clause count

21/147 0,14


clause count

89/147 0,61


clause count

704/147 4,79


period count

704/87 8,09

Punctuation

The majority of the punctuation marks in VC 3 were full stops (37) and commas

(20). Question marks were 10 and exclamation marks were 9.

49


clause count

1/147 0,01

b) commas

clause count

20/147 0,14

c) question marks

clause count

10/147 0,07

d) dots

clause count

3/147 0,02


clause count

9/147 0,06

f) full stops

clause count

37/147 0,25


clause count

80/147 0,54

Word types

Adjectives (55) and informal words (53) were almost equal, while adverbs were

43.


word count

43/704 0,06

b) adjectives

word count

55/704 0,08

c) greeklish

word count

5/704 0,01

d) informal

word count

53/704 0,08

e) emoticons

word count

0/704 0

Adverb types

Quantitative adverbs, adverbs of manner and adverbs of place were the most

common types of the adverbs used in this discourse.

50


Total adverbs

11/43 0,26

b) Adverbs of time

Total adverbs

4/43 0,09


Total adverbs

11/43 0,26


Total adverbs

0/43 0


Total adverbs

15/43 0,33


Total adverbs

0/43 0

g) relative adverbs

Total adverbs

0/43 0


Total adverbs

2/43 0,05

Syntax types

Prepositional phrases were the majority (42). Subordinate clauses were just 11.


clause count

11/147 0,07


clause count

42/147 0,29

Article types

It is remarkable that, also in VC 3, articles were almost exclusively definite.


Total articles

103/110 0,94

b) indefinite

Total articles

7/110 0,06

c) articles

word count

110/704 0,16

d) articles

periods

110/87 1,26

51

Additional terms

Additional terms were nearly null, with the exception of attributes in possessive

case (11).


Clause count

0/147 0


Clause count

1/147 0,01


word count

11/704 0,02


word count

0/704 0


Abbreviations were 30 in a total of 704 words. No similes were used in the

discourse. Metaphors were 18.


word count

30/704 0,04


clause count

18/147 0,12


clause count

0/147 0

Word variety

223 different words were found in this discourse, giving a score of 0,38.


word count

223/594 0,38

5.1.5 Inner speech analysis in students' essay texts (ST)


The majority (67%) of the verbs used in the students' texts did not have any

subject.

52


Verb count

447/663 0,67

With subject

Verb count

216/663 0,33

Clause types

Informal and no verb clauses were nearly null. Neither omission of conjunction

nor elliptical clauses were commonly used.

It is remarkable that the words per period were nearly up to 13.


clause count

17/666 0,03


clause count

3/666 0


clause count

6/666 0,01


clause count

26/666 0,04


clause count

3577/666 5,37


period count

3577/279 12,82

Punctuation

Use of commas and full stops is increased and indicates a rather formal language

(Brooks et al., 2013; Mannes, 2008). Question marks were null. Exclamation marks

were just 13 in 666 clauses.

53


clause count

0/666 0

b) commas

clause count

120/666 0,18

c) question marks

clause count

0/666 0

d) dots

clause count

3/666 0


clause count

13/666 0,02

f) full stops

clause count

264/666 0,40


clause count

403/666 0,61

Word types

Adverbs and adjectives were the most common word types used in students'

texts. There was only 1 informal word, which strongly indicates formal language.


word count

205/3577 0,06

b) adjectives

word count

315/3577 0,09

c) greeklish

word count

3/3577 0

d) informal

word count

1/3577 0

e) emoticons

word count

0/3577 0

Adverb types

Adverbs of time were the most common type (69). Quantitative adverbs were

55, adverbs of manner 46 and adverbs of place 32.

54


Total adverbs

32/205 0,16

b) Adverbs of time

Total adverbs

69/205 0,34


Total adverbs

46/205 0,23


Total adverbs

1/205 0


Total adverbs

55/205 0,27


Total adverbs

0/205 0

g) relative adverbs

Total adverbs

2/205 0,01


Total adverbs

0/205 0

Syntax types

Prepositional phrases (238) and subordinate clauses (153) seem to be very

common syntax types in the students' texts.


clause count

153/666 0,23


clause count

238/666 0,36

Article types

Definite articles were the majority (467), but use of indefinites is increased. The

total use of articles seems increased as well.

55


Total articles

467/531 0,88

b) indefinite

Total articles

64/531 0,12

c) articles

word count

531/3577 0,15

d) articles

periods

531/279 1,90

Additional terms

Additional terms were not commonly used by students.


Clause count

0/666 0


Clause count

5/666 0,01


word count

108/3577 0,03


word count

33/3577 0,01


Abbreviations were very few in students' texts (16 in a total of 3577 words).

Metaphors were also few (24) in a total of 666 clauses. Similes were just 10.


word count

6/3577 0


clause count

24/666 0,04


clause count

10/666 0,02

Word variety

Word variety is calculated for each one student individually, in order to identify

any possible existing differences between them.

Student 1 got a score of 0,47 by using 323 different words in a total of 688.

Student 2 scored 0,50 (250/503). The lowest score was for student 3: 0,41 (271/660).

56

Student 4 wrote short texts (232 words) with a variety score of 0,47. Student 5 got

0,55 (184/335). Student 6 used 387 words and scored 0,48. Student 6 had the

highest score of 0,65, having written the shortest texts (241 words). In conclusion,

students achieved an average score of 0,50.

IS-20 Word

variety

Student

1

Student

2

Student

3

Student

4

Student

5

Student

6

Student

7

Variety/

word count

323/688 250/503 271/660 109/232 184/335 187/387 156/241

Score 0,47 0,50 0,41 0,47 0,55 0,48 0,65

Average

score

0,50

5.1.6 Comparative results - Discussion

Feature Metric VC 1.1 VC 1.2 VC 2 VC 3 ST

IS-1 Omission

of

Subjects

No subject

Verb count

0,88 0,92 0,85 0,51 0,67

With subject

Verb count

0,12 0,08 0,15 0,49 0,33

In VC 1.1, VC 1.2 and VC 2 omission of subjects is very frequent providing a

strong indicator of inner speech existence (Vygotsky, 2008). Especially in VC 1.2 this

indication is the strongest one.

In contrast, this feature is rather sparse in VC 3. This is possibly the case because

of the active instructive role of the teachers, which affects language and makes it

more formal (Bielaczyc and Collins, 1999; Maness, 2008). It is expected that language

in VC 3 is influenced by the discrimination of roles between the teachers and the

students. As they do not have the same role (teachers are the instructors), members

of the VC have to specify their identity i.e. use of subjects (Pérez-Sabater, 2012).

Despite the fact that in Students' texts omission seems to be commonly used, it

is significantly lower than in VC 1.1, VC 1.2 and VC 2 (tables IS-1/1-IS-1/3, page 84).

This feature (omission of subjects) is a basic feature of inner speech (Vygotsky,

2008) providing a strong indicator of inner speech existence.

57


IS-2 Omission of

Conjunction

No conjunction

clause count

0 0,02 0,12 0,14 0,03

IS-3 Informal

clauses

Informal clauses

clause count

0,23 0,75 0,58 0,69 0

IS-4 Omission of

verbs

No verb

clause count

0,04 0,21 0,25 0,14 0,01

IS-5 Elliptical

clauses

Elliptical clauses

clause count

0,12 0,57 0,34 0,61 0,04

IS-6 Words per

clause

word count

clause count

4,04 4,27 4,62 4,79 5,37

IS-7 Words per

period

word count

period count

12,35 7,08 7,03 8,09 12,82

In IS-2 feature (omission of conjunction) significant differences are recorded

between both VC 2 and VC 3 and the other cases (tables IS-2/1-IS-2/10, pages 86-

87). VC 1.1 has zero clauses that are not linked together, being thus in accordance

with VC 2, where these clauses are rare (0,02).

Feature IS-3 strongly indicates that the language in all VCs was informal.

Informal clauses might be expected in VC 3, which had already been a community,

but they are also greatly used in all other VCs. This indicates the familiarity among

the team members and therefore the existence of the community.

Two thirds of the total clauses in VC 1.2 were informal. This is undoubtedly an

impressive feature of this discourse. Also in VC 2 and VC 3 informal clauses were very

common, indicating an informal language. This feature gives a very strong indication

of inner speech, as in ST informal clauses are null. Even in VC 1.1, where the

proportion of informal clauses reaches 0,23, there is a great difference with ST (0)

(table IS-3/1, page 88).

IS-4 and IS-5 are much higher in VCs than in ST, confirming the existence of

informal language (Santaholma, 2005). Specifically in IS-4 one fourth of the total

clauses in VC 2 had no verb. It was approximately the same in VC 1.2. In all VCs this

feature is higher than in ST. Even in VC 1.1 which has the lowest score, it is four

times greater than in ST. Omission of verbs appears rarely in ST, confirming that this

a feature of informal language (Vygotsky, 2008; Santaholma, 2005).

58

IS-5 (elliptical clauses) have the lowest percentage in ST (0,04). In VC 1.2 and in

VC 3 ellipticity reaches up to 60%. But also in VC 2 a considerable percentage (34%)

of the total clauses are elliptical. VC 1.1 has the lowest score (0,12), but still remains

three times higher than in ST. As ellipticity is a basic feature of inner speech

(Vygotsky, 2008), this feature provides us with a strong indication of inner speech

existence. VC 3, which presented the highest percentage, was a priori a community

confirming what Vygotsky (2008) has argued. IS-5 shows that VC 1.2 is using inner

speech at the same level with VC 3, which is considered as an index mark for the

ellipticity, revealing the similarity between these two VCs. We should therefore

accept that VC 1.2 is also a community.

It is also obvious from IS-6 and mainly from IS-7, that the number of the words

used in VCs is restricted, which indicates a strong correlation with inner speech.

Exception to the restricted use of words is VC 1.1, possibly affected of the absence of

a problem-based project. It should be kept in mind that in this VC students had

completed their task and, in that discourse, they expressed their impressions,

thoughts and feelings in a free frame.

VC 1.1 used 4,04 words per clause, when the words used in ST are 5,37. If this

feature is examined in comparison with IS-7, we can see that IS-7 gives a more clear

indication of ellipticity. ST, constituting the formal language of students, contain

12,82 words per period, while in VC this number reached merely 7,03. We can notice

that in VC 1.1 students' language for a non-problem based task contains almost the

same amount of words as in ST. In contrast, in all other VCs, where the discourse is

about a problem-based task, the words per period are significantly lower. In VC 3,

this feature is increased, but it might be influenced by the teachers' language who

had an instructive role in the procedure (Bielaczyc and Collins, 1999).

59


IS-8 Punctuation

marks

a) parentheses

clause count

0 0 0,03 0,01 0

b) commas

clause count

0,02 0,08 0,02 0,14 0,18

c) question marks

clause count

0,02 0,03 0,20 0,07 0

d) dots

clause count

0,02 0,03 0,02 0,02 0


clause count

0,52 0,28 0,15 0,06 0,02

f) full stops

clause count

0,13 0,26 0,24 0,25 0,40


clause count

0,71 0,68 0,65 0,54 0,61

Parenthesis

Parentheses are rarely used. In VC 1.1 and VC 1.2 they are null. But in VC 2 and

VC 3 they are 0,03 and 0,01 respectively. In ST parentheses are null, giving an index

point of students' formal language (Brooks et al., 2013).

Commas

Use of commas is clearly higher in ST than in VCs. Taking this into account we

note that use of commas in VCs is restricted, indicating informal language (Brooks et

al., 2013).

Question marks

Students did not use question marks in their essays. In contrast, students in VC

2 used them a lot (0,20). In VC 1.1 lower use was expected as students expressed

their impressions, i.e. they made statements. Low use is observed also in VC 1.2. It

seems that the students in this VC did not have the same need to ask a lot of

questions. The score in VC 3 was 0,07.

Once again, the increased use of question marks in VCs indicates informal

language (Brooks et al., 2013).

60

Dots

Similarly to the use of question marks, dots were not used in ST. In VCs, dots had

a score of 0,02 to 0,03.

Exclamation marks

In formal language exclamation marks are used in a low level basis (Brooks et al.,

2013; Pérez-Sabater, 2012). Their use can therefore give an indication of informal

language.

According to the results, score in ST was 0,02 when the lowest score in VCs was

three times higher (VC 3: 0,06). In VC 2 the score was increased (0,15) and in VC 1.2

reached up to 0,28. In VC 1.1, where the students were asked to express their

impressions of the project that they had already completed, it was expected that the

use of the exclamation marks would be increased. Nevertheless, the score was

extremely higher (0,52).

Full stops

Full stops are a basic feature of formal language (Mannes, 2008). It is essential

to use at least one full stop per sentence. Alternatively, someone could use a

question mark or an exclamation mark or dots.

Use of full stops seems to be very common in ST (0,40), while in VCs this feature

varies from 0,24 to 0,26. VC 1.1 is an exception because the use of full stops is much

lower (0,13). This may be attributed to the increased use of exclamation marks. Once

again, low use of full stops indicates that VCs used informal language (Brooks et al.,

2013).

Punctuation (total)

Examining the total use of the punctuation marks might offer a general view of

the students' language. But because there is a big variety of punctuation marks and

each one has a different use, i.e. a different meaning, examining only the total use of

punctuation marks cannot analyze the language in depth. For this reason the total

use of punctuation marks should be examined in combination with the use of each

type of mark.

VC 1.1 used the punctuation marks at the highest rate (0,71). Having in mind

that they needed to express their impressions, it seems that punctuation marks are

used as an additional tool of expression. This is confirmed by the increased use of

the exclamation marks.

61

VC 1.2, which presented also a high percentage in this feature (0,68), appears to

show a more balanced use of the different punctuation marks. In VC 3 the

percentage was the lowest one.

In ST the percentage is 0,61, with use of only three types of punctuation marks.

One of them is used rarely (exclamation marks: 0,02). So the percentage in ST (0,61)

results, formed by the use of only two types of punctuation marks (full stops and

commas), indicates formal language (Brooks et al., 2013).

Overall, in VCs the use of punctuation marks is more complicated as it results

from various types of punctuation marks, bringing out the need to use richer

expressive means.


IS-9 Word

types

a) adverbs

word count

0,12 0,07 0,06 0,06 0,06

b) adjectives

word count

0,01 0,07 0,04 0,08 0,09

c) greeklish

word count

0 0 0,13 0,01 0

d) informal

word count

0,02 0,06 0,07 0,08 0

e) emoticons

word count

0 0 0 0 0

Adverbs

The highest percentage is observed in VC 1.1. Use of adverbs is approximately

the same in all other cases. It is then necessary to analyze their use by the various

adverb types (which will follow below).

Adjectives

Adjectives are commonly used in ST (0,09), in VC 3 (0,08) and in VC 1.2 (0,07).

But in VC 2 the result is 0,04 and in VC 1.1 it is 0,01 indicating a more precise and

restricted language, as adjectives offer additional information to the meaning, i.e.

modification (Wiley, 2006; Vygotsky, 2008).

Greeklish

Use of greeklish in VC 2 (0,13) is quite extensive, which indicates strongly an

informal language. In all the other cases greeklish is not substantially used.

62

Informal words

Informal words in ST are null. In VC 1.2, VC 2 and VC 3 they are 0,06, 0,07 and

0,08 respectively, adding one more indication of the informal language used by the

students in the VCs.

Emoticons

Emoticons cannot offer anything in the analysis as they were not used in any

case.


IS-10 Adverb

types

a) Adverbs of place

Total adverbs

0,16 0,18 0,33 0,26 0,16

b) Adverbs of time

Total adverbs

0 0,12 0 0,09 0,34


Total adverbs

0,28 0,24 0,07 0,26 0,23


Total adverbs

0 0 0 0 0


Total adverbs

0,56 0,39 0,30 0,33 0,27


Total adverbs

0 0 0 0 0

g) relative adverbs

Total adverbs

0 0,03 0 0 0,01

h) Viewpoint and commenting

Total adverbs

0 0,03 0,30 0,05 0

Adverb types

Average use of adverbs of place in ST is approximately equal to VC 1.1 and VC

1.2. In VC 2 and VC 3 it is higher than in the other cases, i.e. 0,33 and 0,26

respectively. It is observed that in these two VCs (VC 2 and VC 3), use of adverbs of

place is higher than in the other cases.

Adverbs of time seem to play an important role in ST (0,34), because of their

narrative nature. This is not observed in VCs, where the highest score is 0,12 (VC

1.2). In VC 3 the score is 0,09, while in VC 1.1 and VC 2 use of adverbs of time is null.

Specifically in VC 1.1, time was expected to have a minor importance because the

students had already completed their project.

63

In VC 2 adverbs of manner were used very infrequently (0,07), in contrast to the

other VCs which used them in an approximately equal percentage (VC 1.2: 0,24 - VC

3: 0,26 - VC 1.1: 0,28). In ST the score is lower: 0,23.

Adverbs of certainty are so rarely used that they cannot offer any index.

Quantitative adverbs are commonly used in VC 1.1, where students needed to

describe their impressions. In this case, quantitative adverbs helped students to

express the degree of their impressions, feelings, emotions. It is therefore expected

that in VC 1.1, where the use of emotional clauses was increased, the use of

quantitative adverbs would be increased as well. In VC 1.2 use of quantitative

adverbs had the score of 0,39, in VC 2 0,30 and in VC 3 0,33. The lowest percentage

is observed in ST (0,27).

Interrogative adverbs are not used in any case, neither in VCs nor in ST.

A rather low use of relative adverbs was also observed. In ST the score is 0,01

(lowest) and in VC 1.2 it is 0,03 (highest). In all other cases (VC 1.1, VC 2 and VC 3)

they are null.

Viewpoint and commenting adverbs are not used in ST. The same holds in VC

1.1. In VC 1.2 and in VC 3 the use of this type of adverbs is increased (0,03 and 0,05

respectively). The most impressive point is the high percentage in VC 2 (0,30).

It is therefore clear that students of VC 2 used this type of adverbs in order to

express their comments and views about their project. This happens, even to a

smaller extent, in all VCs where a problem-based project existed. In the non

problem-based project (VC 1.1) and also in ST, this type of adverbs is not used.

Case study Percentage of adverbs Number of adverb types Most frequent

VC1.1 0,12 3 quantitative

VC1.2 0,07 6 quantitative

VC2 0,06 4 Adverbs of place

VC3 0,06 5 quantitative

ST 0,06 5 Adverbs of time

In VC 1.1 adverbs had the most frequent use (0,12). This increased use results

from only three types of adverbs (adverbs of place, adverbs of manner and

quantitative adverbs). Quantitative adverbs were the most frequent (0,52), adverbs

64

of manner were used in a percentage of 0,28 and adverbs of place in a percentage of

0,16.

Far lower, VC 1.2 used adverbs in a percentage of 0,07. Despite this fact, the

types of the adverbs used were six. Students in this VC did not use adverbs of

certainty and interrogative adverbs (these types are not also used in any other case).

Once again, the most frequent type are the quantitative adverbs.

In VC 2, use of adverbs is approximately equal to this of VC 1.2. But in this case

the result comes by using four types. In this VC quantitative and viewpoint and

commenting adverbs are equally used (0,30). Adverbs of place reached

approximately the same percentage (0,27), while the proportion of adverbs of

manner was 0,07 (the lowest use of this type in all cases).

In VC 3, use of adverbs is equal to VC 2 (0,06). The students in this case used five

types of adverbs. It is clear that in one more case, quantitative adverbs were the

majority (0,35). Adverbs of manner and adverbs of place were also commonly used

(0,26 for both types).

Despite the fact that in ST use of adverbs is approximately the same as in the

most VCs (with the exception of VC 1.1), it is observed that the types were five. The

majority of them, in this case, are not the quantitative adverbs, but the adverbs of

time (0,34). It seems that time was not so important in VCs as it was in ST. Adverbs of

time play an important role in ST, because of their narrative nature. The difference in

this type is significantly high (0,34 in ST, 0,12 in VC 1.2, 0,09 in VC 3 - tables IS-10/1-

IS-10/4, pages 105-106). Additionally, it should be noted that viewpoint and

commenting adverbs are not used in ST, while in VCs the percentage vary from 0,03

to 0,30.



clause count

0,29 0,12 0,14 0,07 0,23


clause count

0,25 0,21 0,25 0,29 0,36

Use of subordinate clauses makes the language more precise, providing details

and additional information to the communication. In the case that these clauses are

missed, the language becomes elliptical, harsh and difficult to understand. Having in

mind that the members of a community develop a common code for mutual

understanding (Emerson, 1983), this additional information could be omitted.

65

It is clear that in both VC 1.1 and ST, subordinate clauses are used frequently (VC

1.1: 0,29 and ST: 0,23). It seems that VC 1.1 is influenced by the absence of the

"problem" in their task and the students felt more free to express their feelings and

impressions. The students, prompted by intention to express their emotions, used a

lot of subordinate clauses. High use of subordinate clauses is also observed in ST.

This might be considered as expected because the students in their essays are

addressing a general readership. Therefore their language has to be more formal and

more detailed in order to avoid misunderstandings (Mairesse et al, 2007). In

contrast, use of subordinate clauses in VCs is quite lower (VC 1.2: 0,12 - VC 2: 0,14).

The lowest use is in VC 3 (0,07) confirming the argument for the ellipticity of

language in communities (VC 3 was a priori a community). As VC 1.2 and VC 2 tend

to have similar low use of subordinate clauses, one more index of the community

existence in these VCs is added.

In addition to use of subordinate clauses, investigating the prepositional phrases

used shows clearly that there is lower use in VCs than in ST. In ST the score was 0,36,

while in VCs it reached 0,25 in VC 1.1, 0,21 in VC 1.2, 0,25 in VC 2 and 0,29 in VC 3. It

is then obvious that language in ST contains more details and it is richer in additional

terms, i.e. additional information, showing the ellipticity of the language in VCs.


IS-12 Article

types

a) Definite

Total articles

1,00 0,98 0,97 0,94 0,88

b) indefinite

Total articles

0 0,02 0,03 0,06 0,12

c) articles

word count

0,09 0,12 0,12 0,16 0,15

d) articles

periods

1,12 0,84 0,87 1,26 1,90

Definite articles constitute the majority in every case. Applying a more detailed

analysis, a difference between ST and VCs is revealed. The lowest score in VCs is

0,94, while the score in ST is 0,88. The difference increases if we compare ST to VC 2

(0,97), VC 1.2 (0,98) or VC 1.1 (1,00).

In VC 1.1 students were at the end of their project. The community was already

constructed then, while in the other VCs the discourse contains communication also

from the beginning of the project, when it would be impossible for a community to

exist. It is then expected that in VC 1.1 inner speech was developed at a higher

degree. Definite articles are referred to something already known indicating that the

66

language was more accurate and served the ability of the developed mutual

understanding among the class members (Emerson, 1983).

Counting the total articles per period is showing once more that there is a

difference between ST and VCs (ST: 1,90 - VC 3: 1,26 - VC 1.1: 1,12 - VC 2: 0,87 - VC

1.2: 0,84 - tables IS-12/1-IS-12/4, pages 110-111).



Clause count

0 0 0 0 0


Clause count

0,02 0 0,01 0,01 0,01

IS-15 Terms in

possessive case

possessive case

word count

0 0,01 0 0,02 0,03

IS-16 Terms in

accusative case

accusative case

word count

0 0 0 0 0,01

Apposition is rarely used and cannot add anything in the analysis. The same

happens with IS-14 (epexegesis).

Examining the additional terms in the possessive or the accusative case shows

that they are used more in ST, despite the fact that the difference with VCs is small.

Absence of these additional terms, in addition to the absence of apposition and

epexegesis, is confirming the elliptical language of the students in VCs (tables IS-

15/1-IS-15/4, page 113) and tables IS-16/1-IS-16/4, pages 113-114).



word count

0 0 0,02 0,04 0


clause count

0 0 0,04 0,12 0,04


clause count

0 0 0 0 0,02

Abbreviations indicate informal language (Mannes, 2008) and inner speech

(Vygotsky, 2008). It was therefore expected that they were used in VCs (VC 2 and VC

3) and they were not used in ST.

67

Metaphors could be considered as an indication of a common code for mutual

understanding (Daniel et al, 2003), as they do not always give a clear message (the

receiver has to "decode" it). This is confirmed by the score in VC 3 (0,12). Students of

this VC, which were a priori a community, do not hesitate to use metaphors, in

contrast to the other cases.

Similes, unlike metaphors, are used in order to add details and make the

meaning of the language more clear. Null use of similes in VCs indicates the mutual

understanding among the team members, i.e. it indicates lack of additional

information that could be considered as ellipticity by the non class members.

Feature Metric VC 1.1 VC 1.2 VC 2 VC 3 Average

IS-20 Word

variety

Variety

word count

0,34 0,39 0,36 0,38 0,37

Feature Metric ST 1 ST 2 ST 3 ST 4 ST 5 ST 6 ST 7 Average

IS-20 Word

variety

Variety

word count

0,47 0,50 0,41 0,47 0,55 0,48 0,65 0,50

Word variety in VCs varies from 0,34 to 0,39, with an average of 0,37. The

lowest score was in VC 1.1, and can be explained by the restricted frame of the

discourse (expressing their impressions). The highest score is in VC 1.2 (0,39), not

much higher than VC 3 (0,38) and VC 2 (0,36).

Examining the word variety in ST gives scores between 0,41 and 0,65, with an

average of 0,50.

Comparison of the two average scores confirms that the variety of the words

used in VCs is significantly lower than in ST (table IS-20/1, page 116). It is therefore

clear that the students' language in VCs appears to be restricted (using a narrower

vocabulary).

68

5.2 Collaborative learning analysis

5.2.1 Collaboration analysis in VC 1.1

Verbs in first person plural form

Students used fifty (50) verbs in this discourse. Thirty eight (38) of them (76%)

were in the first person plural form (we), giving a strong indication that the students

felt as members of a team.

CA-1 Verbs in first

plural person

Verbs (we)

Verb count

38/50 0,76

Clause types

The total number of clauses used in this discourse were fifty two (52). Seventeen

(17) of them were expressing emotions and one (1) was giving reward. No clauses of

negation existed in VC 1.1. In contrast, a considerable number of clauses of reason

appeared (15).

CA-2 Emotional clauses Emotional clauses

clause count

17/52 0,33

CA-3 Rewarding clauses Rewarding clauses

clause count

1/52 0,02


clause count

0/52 0


clause count

15/52 0,29

Word types

There were 210 words used in VC 1.1. The majority of them were familiarity

words (70), while the emotional words were thirty seven (37). Twelve (12) social

words were also counted. It should be stressed that all emotional words were

expressing positive emotion.

69

CA-6 Familiarity words Familiarity words

word count

70/210 0,33

CA-7 Inclusive words Inclusive words

word count

2/210 0,01

CA-8 Social words Social words

word count

12/210 0,06

CA-9 Emotional words Emotional words

word count

37/210 0,18

CA-10 Positive emotion Positive emotion words

emotional words

37/37 1,00

CA-11 Negative emotion Negative emotion words

emotional words

0/37 0

Pronouns in first person plural

The total number of pronouns used in this discourse were thirty (30). Eleven (11)

of them were possessive and eighteen (18) were personal pronouns. Ten (10) of

these twenty nine (29) (personal + possessive) pronouns were in the first person

plural form.

CA-12 Use of first plural

person pronouns

Us, ours

pronouns

10/30 0,33

CA-13 Us, ours


10/29 0,34

5.2.2 Collaboration analysis in VC 1.2

Verbs in first person plural form

In VC 1.2 a significant percentage (43%) of the verbs used were in the first

person plural form (we).

CA-1 Verbs in first

plural person

Verbs (we)

Verb count

36/84 0,43

Clause types

A remarkable number of emotional clauses was used in VC 1.2. Rewarding

sentences were at a rather high percentage as well. Clauses of negation were three

70

(3) out of a total of 105 clauses, giving an index of positive attitude. Clauses of

reason were nearly null (2/105).


clause count

22/105 0,21


clause count

17/105 0,16


clause count

3/105 0,03


clause count

2/105 0,02

Word types

Emotional words (47) constitute the majority of the word types used in this

discourse. Familiarity words were 24. Despite the fact that emotional words were

divided approximately in half (positive - negative), positive emotional words were

the majority (55%).


word count

24/453 0,05


word count

0/453 0


word count

1/453 0


word count

47/453 0,10


emotional words

26/47 0,55


emotional words

21/47 0,45

Pronouns in first plural person

The total number of pronouns used were sixteen (16). Eleven (11) of them were

possessive and four (4) were personal pronouns. Seven (7) out of the latter fifteen

(15) (personal and possessive) were in the first person plural, indicating the feeling

of belonging to a team.

71


person pronouns

Us, ours

pronouns

7/16 0,44

CA-13 Us, ours


7/15 0,47

5.2.3 Collaboration analysis in VC 2

Verbs in first plural person form

In VC 2, almost one third (1/3) of the total verbs were used in the first person

plural form.

CA-1 Verbs in first

plural person

Verbs (we)

Verb count

24/78 0,31

Clause types

Emotional and rewarding clauses were the most commonly used types of

clauses in VC 2. Specifically, emotional clauses constituted almost one fourth of the

total clauses.


clause count

22/101 0,22


clause count

16/101 0,16


clause count

7/101 0,07


clause count

1/101 0,01

Word types

Emotional words were the most frequent (34). Almost all of these words (32/34)

expressed positive emotions.

72


word count

10/466 0,02


word count

0/466 0


word count

3/466 0,01


word count

34/466 0,07


emotional words

32/34 0,94


emotional words

2/34 0,06


It is undeniable that the majority of the pronouns used were in the first person

plural form.


person pronouns

Us, ours

pronouns

28/41 0,68

CA-13 Us, ours


28/40 0,70

5.2.4 Collaboration analysis in VC 3

Verbs in first plural person

It is impressive that in VC 3 the use of verbs in the first plural person is very low

(3%).

CA-1 Verbs in first

plural person

Verbs (we)

Verb count

4/125 0,03

Clause types

In a total of 147 clauses, emotional clauses were fourteen (14). Clauses of

negation and rewarding clauses were equally used (9), while there was one (1)

clause of reason.

73


clause count

14/147 0,10


clause count

9/147 0,06


clause count

9/147 0,06


clause count

1/147 0,01

Word types

Familiarity (29) and emotional words (28) were the most common word types. It

is obvious that, in VC 3 the positive emotional words constitute the majority of the

emotional words, which indicates a positive attitude to the team/class (Mairesse et

al, 2007; Mc Millan and Chavis, 1986).


word count

29/704 0,04


word count

1/704 0


word count

15/704 0,02


word count

28/704 0,04


emotional words

19/28 0,68


emotional words

9/25 0,32


No pronouns in the first person plural were used. As we can note, this feature

appears to correlate with CA-1 (Verbs in first person plural).


person pronouns

Us, ours

pronouns

0/32 0

CA-13 Us, ours


0/12 0

74

5.2.5 Collaboration analysis in Students' Texts (ST)

Verbs in first plural person form

Use of verbs in the first person plural form was rather low in ST (only 103 verbs

in a total of 663 used).

CA-1 Verbs in first

plural person

Verbs (we)

Verb count

103/663 0,16

Clause types

Emotional clauses in ST were very few (33 in a total of 666 clauses). Rewarding

clauses were null. Clauses of negation and clauses of reason were minimally used.


clause count

33/666 0,05


clause count

0/666 0


clause count

36/666 0,05


clause count

11/666 0,02

Word types

Familiarity, inclusive and social words were null. Emotional words were rarely

used. It is clear that the positive emotional words outweigh the negative.


word count

0/3577 0


word count

5/3577 0


word count

12/3577 0


word count

82/3577 0,02


emotional words

53/82 0,65


emotional words

29/82 0,35

75


Use of pronouns in the first person plural was low. There were just eighteen (18)

out of a total of 179 (personal and possessive pronouns).


person pronouns

Us, ours

pronouns

18/237 0,08

CA-13 Us, ours


18/179 0,10

5.2.6 Comparative results - Discussion

Verbs in first person plural


CA-1 Verbs in

first plural

person

Verbs (we)

Verb count

0,76 0,43 0,31 0,03 0,16

There is no doubt that VC 1.1 presents the strongest indication of team working.

This can be explained if one takes into account that students were at the end of their

project, when the ties among them were stronger than in the beginning when they

did not even know each other.

This feature is considerably higher in VC 1.2 than in VC 2, possibly due to the

different level of education of the two collaborated subgroups. According to

Vygotsky (1978), zone of the proximal development offers students a chance of

learning. In VC 1.2, where one of the two collaborated subgroups was composed of

elementary school students and the second one of high school students, it would be

expected that there was a challenge for the elementary students for increased

participation and collaboration, i.e. a challenge for learning.

But even in the case of subgroups being in the same level of education (VC 2),

feature CA-1 is almost the double compared to the ST.

An impressive point is the very low index in VC 3. Two points should be

reminded:

a) In this case a physical class was transformed into a virtual one and students

collaborated distantly through the internet and

b) teaching was directed by the teachers who had an active instructive role.

76

Τhe active role of the teachers might influence students and prevent them from

using the first person plural (we), due to the existing distance between them and the

teachers. Another explanation could be offered if we consider that when the

teachers were giving instructions, they did it usually in the second person plural

(you), as they did not participate in the completion of the problem solving task.

These instructions given by the teachers were very common, according to their

instructive role.

Nevertheless, it is undoubted that, if we take into account the results in VC 1.1

and VC 1.2, which substantially constitute one class (VC 1), students appear to

behave as a community.

Clause types



clause count

0,33 0,21 0,22 0,10 0,05


clause count

0,02 0,16 0,16 0,06 0


clause count

0 0,03 0,07 0,06 0,05


clause count

0,29 0,02 0,01 0,01 0,02

Emotional clauses were much higher in VC 1.1 than in the other schemata. This

is not strange, because, in this case students are requested to express their feelings

about the already completed project. But, as we can see, emotional clauses reach a

high percentage in VC 1.2 and VC 2 as well, showing that the students felt

comfortable to express their feelings even during their effort to "solve" the problem

based task. Considering that the subgroups were unfamiliar with each other in the

beginning of the project and it was therefore difficult for them to express their

emotions, this result is more significant than it seems.

It is also remarkable that VC 1.1 and VC 1.2 have almost the same result (0,21-

0,22).

Attempting to explain the low percentage in VC 3, one should focus on the

formality of the procedure given by the active role of the teachers (Bielaczyc and

Collins, 1999; Maness, 2008). Another factor might be the existing close relations

among the students, which had already been working as a team for seven years

(from kindergarten till the 6th grade). VC 3 was also influenced by the existing daily

communication among the students in their physical class. It is then expected that a

77

student under these circumstances, does not greatly feel the need of expressing his

emotions. But whenever he feels that need, he is able to express them in person.

This was impossible in the other VCs where face-to-face communication was

impossible.

In each case, emotional clauses are clearly higher in VCs than in ST. Even in VC 3,

they are double in number.

Rewarding clauses reached the same percentage in VC 1.2 and VC 2 indicating

that the students felt the need to reward their partners for their effort.

Subsequently, this fact reveals the existence of strong relations among them, i.e. the

existence of a community.

In VC 1.1 rewarding clauses were expected to be low, as the discourse was

developed after the completion of the project. But even in this case, rewarding

clauses were also used.

In VC 3 rewarding clauses were low, possibly due to the possibility of students to

express their rewards during their daily interaction in the physical classroom.

Nevertheless, all VCs used rewarding clauses at an obvious higher level than in ST

(tables CA-3/1-CA-3/4, pages 120-121).

Clauses of negation had a rather similar use in VC 2 and VC 3. In ST they were

approximately the same in number, while in VC 1.2 they were about half as many.

It is impressive that in VC 1.1 Clauses of negation were not used, clearly

indicating positive emotion, a basic feature within a community.

Clauses of reason were extremely high in VC 1.1, where the students felt the

need to give reasons for their impressions and feelings expressed in the discourse. In

all other cases (VCs and ST), this type of clauses were almost equally used.

78

Word types


CA-6 Familiarity

words

Familiarity words

word count

0,33 0,05 0,02 0,04 0

CA-7 Inclusive

words

Inclusive words

word count

0,01 0 0 0 0

CA-8 Social

words

Social words

word count

0,06 0 0,01 0,02 0

CA-9 Emotional

words

Emotional words

word count

0,18 0,10 0,07 0,04 0,02

CA-10 Positive

emotion

Positive emotion words

emotional words

1,00 0,55 0,94 0,68 0,65

CA-11 Negative

emotion

Negative emotion words

emotional words

0 0,45 0,06 0,32 0,35

There was a great difference in the use of familiarity words. One third of the

total clauses in VC 1.1 were familiarity ones, giving a strong indication for community

existence. This indication is more significant because it appears at the end of the

project.

In VC 1.2 the percentage of familiarity words was about the same as in VC 3. But

one should keep in mind that VC 3 was already a community, as students have been

collaborating for seven years (from kindergarten till the 6th grade). Taking this into

account, index in VC 1.2 seems greater. VC 2 gives a lower index in this feature.

In every case, analyzing ST shows that use of familiarity words should not be

considered as a typical feature of students' language.

It is difficult to consider inclusive words as a criterion for collaboration, because

of their sparse use.

Social words are not commonly used. Despite this, VC 1.1 once again uses them

in the highest percentage, empowering the indication of the community existence.

Even in VC 1.2 and VC 3, where social words are rarely used, their average is higher

than in ST.

Use of emotional words emphasizes the results given by the previous features

and confirms the evidence of the existence of a community. Almost one in every five

79

words used by the students in VC 1.1 was expressing emotions. This could be

considered as an expected feature of their language, as they were asked to express

their impressions on the completed project. But we should notice that the second

most frequent use of emotional words is counted in VC 1.2 (the other subgroup of

VC 1). That means that these students had the need to express their emotions both

during their effort to solve the problem based project, and also after its completion .

Use of emotional words was rather high also in VC 2. In VC 3, these words were

more rarely used. But one should keep in mind that the students in VC 3 had the

chance to express their emotions in person during their everyday school life. But

even in this case, emotional words were double compared to ST.

Examining whether the emotions that appeared in the discourse were positive

or negative, we can see that 100% of the emotions in VC 1.1 were positive, while in

VC 1.2 they were almost equal (nevertheless positive were the majority). In all VCs

and also in ST positive emotions are always the majority. An impressive point is the

percentage of positive emotion in VC 3 (94%), confirming the existence of the

community.

Pronouns in first person plural


CA-12 Use of first

person

plural

pronouns

Us, ours

pronouns

0,33 0,44 0,68 0 0,08

CA-13 Us, ours

Possessive + personal

pronouns

0,34 0,47 0,70 0 0,10

In all VCs the pronouns in the first person plural form are widely used. In VC 2

there is a remarkable percentage of 70%. Also in VC 1.2 these pronouns are about

half of the total pronouns used, consisting a significant percentage, while in VC 1.1

one third of the pronouns are in the first person plural form. As we can see, there is

a great difference between VCs and ST, where these pronouns, that indicate the

feeling of belonging in a team, reach only 10% of the total number of pronouns

(tables CA-12/1-CA-12/3, pages 132-133).

An exception to these results is VC 3, where these pronouns are null. Having in

mind that VC 3 was a priori a community, it was expected that these students would

be focused on their task, concentrated on their teachers' directions, without being

80

concerned to build relationships among them, as these already existed (table CA-

12/4, page 133).

81

5.3 Statistical analysis

Attempting to investigate whether there were statistically significant differences between the results of the analysis, a two-tailed independent samples t-test was applied. This test is considered to be appropriate for this study, as the samples in every case study were not the same and the population of them was unequal. The choice of a two-tailed test was made, because no attempt was made for any prediction about the result of the comparison (Roussos and Tsaousis, 2006).

This test was applied for each feature of the analysis, except of those that

presented no difference between the case studies. In every case it was checked whether there were significant differences both between the VCs and the ST and also between the VCs. The null hypothesis was the same in every comparison:

Ho: the metrics of the feature are not different H1: the metrics of the feature are different

Whenever the result of the t-test is greater than the critical value the null

hypothesis is rejected and the H1 hypothesis is accepted. If the result is less than the

critical value, the null hypothesis is accepted (Roussos and Tsaousis, 2006).

82

6. Conclusion

Results of the analysis show that the VCs examined in this study were transformed into communities, providing students with the benefits of the community membership. In VC3, which was a priori a community, community existence was confirmed.

Comparison between VCs and ST revealed statistical significant differences in

the language used. Conversations in VCs was mainly informal, elliptical in meaning

and abbreviated, i.e. it had the basic features of inner speech.

Collaborative learning analysis revealed as well that students of these VCs

collaborated enough and had the membership feeling.

Active instructive role of the teachers affects the language and makes it more

formal.

There are differences in the language use between problem-based and non-

problem based projects.

The existence of a common code and the mutual understanding in communities

was confirmed.

Existence of emotion among community members and their positive attitude

was confirmed as well.

Combining the results of the two models of analysis lead to the result that the

VCs, being analyzed in this study, were transformed into communities.

It is therefore suggested that the proposed model for linguistic analysis in the

discourse among the members of a VC can provide useful results. These results can

be used in both ways:

1) evaluate a VC by detecting whether it was transformed into a community or

not, and

2) improve the design of VCs

Each VC, during its transformation into a community, will have to "pass" through two axes: inner speech and collaborative learning. These two axes are not always

distinct from each other, but they are complementary and have common features, i.e. there is a common ground between them. As a result, a clear distinction of these axes is not always possible. Nevertheless, a VC has to immerse within inner speech and collaborative learning in order to transform into a community (Figure 1).

83

Figure 1. Transformation of a VC into a community

Applying linguistic analysis in the discourse among the members of a VC can provide us with useful results. Combining the result of the two categories (inner speech and collaboration) we can get strong indications for the community existence. Furthermore, results of the analysis can help us to improve the design of the VCs.

However there is a field for future research, e.g. applying this model and evaluating it on a larger corpus and different case studies.

Virtual Class

Community

Collaborative

learning Inner Speech

84

Appendix

Statistical analysis (Inner Speech)

IS-1 Omission of subjects

Comparison of VCs vs ST

Feature Metric VC 1.1 ST

IS-1 Omission of

Subjects

No subject

Verb count

0,88 0,67

t(18)=2,35, p<,05

Table IS-1/1


IS-1 Omission of

Subjects

No subject

Verb count

0,92 0,67

t(16)=4,92, p<,01

Table IS-1/2

Feature Metric VC 2 ST

IS-1 Omission of

Subjects

No subject

Verb count

0,85 0,67

t(20)=2,42, p<,05

Table IS-1/3


IS-1 Omission of

Subjects

No subject

Verb count

0,51 0,67

t(10)=-0,96, n.s.

Table IS-1/4

Comparison between VCs

Feature Metric VC 1.1 VC 1.2

IS-1 Omission of

Subjects

No subject

Verb count

0,88 0,92

t(19)=-0,49, n.s.

Table IS-1/5

85

Feature Metric VC 1.1 VC 2

IS-1 Omission of

Subjects

No subject

Verb count

0,88 0,85

t(28)=0,12, n.s.

Table IS-1/6


IS-1 Omission of

Subjects

No subject

Verb count

0,88 0,51

t(17)=2,41, p<,05

Table IS-1/7


IS-1 Omission of

Subjects

No subject

Verb count

0,92 0,85

t(22)=0,70, n.s.

Table IS-1/8


IS-1 Omission of

Subjects

No subject

Verb count

0,92 0,51

t(10)=3,31, p<,01

Table IS-1/9

Feature Metric VC 2 VC 3

IS-1 Omission of

Subjects

No subject

Verb count

0,85 0,51

t(16)=2,40, p<,05

Table IS-1/10

86

IS-2 Omission of Conjunction



IS-2 Omission of

Conjunction

No conjunction

clause count

0

0,03

t(6)=-2,69, p<,05

Table IS-2/1


IS-2 Omission of

Conjunction

No conjunction

clause count

0,02

0,03

t(15)=-1,03, n.s.

Table IS-2/2


IS-2 Omission of

Conjunction

No conjunction

clause count

0,12

0,03

t(19)=2,06, n.s.

Table IS-2/3


IS-2 Omission of

Conjunction

No conjunction

clause count

0,14 0,03

t(11)=1,57, n.s.

Table IS-2/4



IS-2 Omission of

Conjunction

No conjunction

clause count

0 0,02

t(13)=-1,47, n.s.

Table IS-2/5

87


IS-2 Omission of

Conjunction

No conjunction

clause count

0 0,12

t(16)=-3,08, p<,01

Table IS-2/6


IS-2 Omission of

Conjunction

No conjunction

clause count

0 0,14

t(8)=-2,97, p<,02

Table IS-2/7


IS-2 Omission of

Conjunction

No conjunction

clause count

0,02

0,12

t(19)=-2,53, p<,05

Table IS-2/8


IS-2 Omission of

Conjunction

No conjunction

clause count

0,02

0,14

t(11)=-2,19, n.s.

Table IS-2/9


IS-2 Omission of

Conjunction

No conjunction

clause count

0,12

0,14

t(24)=0,75, n.s.

Table IS-2/10

88

IS-3 Informal clauses



IS-3 Informal

clauses

Informal clauses

clause count

0,23 0

t(14)=-2,78, p<,02

Table IS-3/1


IS-3 Informal

clauses

Informal clauses

clause count

0,75 0

t(13)=7,26, p<,01

Table IS-3/2


IS-3 Informal

clauses

Informal clauses

clause count

0,58 0

t(16)=5,24, p<,01

Table IS-3/3


IS-3 Informal

clauses

Informal clauses

clause count

0,69 0

t(8)=7,16, p<,01

Table IS-3/4



IS-3 Informal

clauses

Informal clauses

clause count

0,23 0,75

t(27)=-2,55, p<,02

Table IS-3/5

89


IS-3 Informal

clauses

Informal clauses

clause count

0,23 0,58

t(27)=-1,05, n.s.

Table IS-3/6


IS-3 Informal

clauses

Informal clauses

clause count

0,23 0,69

t(22)=-2,25, p<,05

Table IS-3/7


IS-3 Informal

clauses

Informal clauses

clause count

0,75 0,58

t(28)=1,74, n.s.

Table IS-3/8


IS-3 Informal

clauses

Informal clauses

clause count

0,75 0,69

t(20)=0,41, n.s.

Table IS-3/9


IS-3 Informal

clauses

Informal clauses

clause count

0,58 0,69

t(21)=-1,38, n.s.

Table IS-3/10

90

IS-4 Omission of verbs



IS-4 Omission of

verbs

No verb

clause count

0,04 0,01

t(16)=0,96, n.s.

Table IS-4/1


IS-4 Omission of

verbs

No verb

clause count

0,21 0,01

t(14)=4,01, p<,01

Table IS-4/2


IS-4 Omission of

verbs

No verb

clause count

0,25 0,01

t(17)=3,39, p<,051

Table IS-4/3


IS-4 Omission of

verbs

No verb

clause count

0,14 0,01

t(8)=2,60, p<,05

Table IS-4/4



IS-4 Omission of

verbs

No verb

clause count

0,04 0,21

t(24)=-2,71, p<,02

Table IS-4/5

91


IS-4 Omission of

verbs

No verb

clause count

0,04 0,25

t(24)=-2,56, p<,02

Table IS-4/6


IS-4 Omission of

verbs

No verb

clause count

0,04 0,14

t(14)=-1,70, n.s.

Table IS-4/7


IS-4 Omission of

verbs

No verb

clause count

0,21 0,25

t(28)=-0,38, n.s.

Table IS-4/8


IS-4 Omission of

verbs

No verb

clause count

0,21 0,14

t(18)=0,68, n.s.

Table IS-4/9


IS-4 Omission of

verbs

No verb

clause count

0,25 0,14

t(23)=0,93, n.s.

Table IS-4/10

92

IS-5 Elliptical clauses



IS-5 Elliptical

clauses

Elliptical clauses

clause count

0,12 0,04

t(15)=1,25, n.s.

Table IS-5/1


IS-5 Elliptical

clauses

Elliptical clauses

clause count

0,57 0,04

t(14)=6,77, p<,01

Table IS-5/2


IS-5 Elliptical

clauses

Elliptical clauses

clause count

0,34 0,04

t(17)=3,46, p<,01

Table IS-5/3


IS-5 Elliptical

clauses

Elliptical clauses

clause count

0,61 0,04

t(9)=10,70, p<,01

Table IS-5/4



IS-5 Elliptical

clauses

Elliptical clauses

clause count

0,12 0,57

t(27)=-3,86, p<,01

Table IS-5/5

93


IS-5 Elliptical

clauses

Elliptical clauses

clause count

0,12 0,34

t(30)=-1,66, n.s.

Table IS-5/6


IS-5 Elliptical

clauses

Elliptical clauses

clause count

0,12 0,61

t(22)=-5,08, p<,01

Table IS-5/7


IS-5 Elliptical

clauses

Elliptical clauses

clause count

0,57 0,34

t(29)=2,08, p<,05

Table IS-5/8


IS-5 Elliptical

clauses

Elliptical clauses

clause count

0,57 0,61

t(21)=-0,63,n.s.

Table IS-5/9


IS-5 Elliptical

clauses

Elliptical clauses

clause count

0,34 0,61

t(24)=-2,98, p<,01

Table IS-5/10

94

IS-6 Words per clause



IS-6 Words per

clause

word count

clause count

4,04 5,37

t(20)=-4,16, p<,01

Table IS-6/1


IS-6 Words per

clause

word count

clause count

4,27 5,37

t(18)=-3,46, p<,01

Table IS-6/2


IS-6 Words per

clause

word count

clause count

4,62 5,37

t(17)=-3,22, p<,01

Table IS-6/3


IS-6 Words per

clause

word count

clause count

4,79 5,37

t(13)=-1,49, n.s.

Table IS-6/4



IS-6 Words per

clause

word count

clause count

4,04 4,27

t(27)=-0,82, n.s.

Table IS-6/5

95


IS-6 Words per

clause

word count

clause count

4,04 4,62

t(27)=-1,40, n.s.

Table IS-6/6


IS-6 Words per

clause

word count

clause count

4,04 4,79

t(19)=-2,14, p<,05

Table IS-6/7


IS-6 Words per

clause

word count

clause count

4,27 4,62

t(27)=-0,55, n.s.

Table IS-6/8


IS-6 Words per

clause

word count

clause count

4,27 4,79

t(27)=-1,45, n.s.

Table IS-6/9


IS-6 Words per

clause

word count

clause count

4,62 4,79

t(15)=-1,07, n.s.

Table IS-6/10

96

IS-7 Words per period



IS-7 Words per

period

word count

period count

12,35 12,82

t(17)=-0,14, n.s.

Table IS-7/1


IS-7 Words per

period

word count

period count

7,08 12,82

t(19)=-3,05, p<,01

Table IS-7/2


IS-7 Words per

period

word count

period count

7,03 12,82

t(13)=-6,19, p<,01

Table IS-7/3


IS-7 Words per

period

word count

period count

8,09 12,82

t(14)=-4,71, p<,01

Table IS-7/4



IS-7 Words per

period

word count

period count

12,35 7,08

t(23)=1,73, n.s.

Table IS-7/5

97


IS-7 Words per

period

word count

period count

12,35 7,03

t(16)=2,69, p<,02

Table IS-7/6


IS-7 Words per

period

word count

period count

12,35 8,09

t(17)=2,19, p<,05

Table IS-7/7


IS-7 Words per

period

word count

period count

7,08 7,03

t(19)=1,13, n.s.

Table IS-7/8


IS-7 Words per

period

word count

period count

7,08 8,09

t(20)=0,41, n.s.

Table IS-7/9


IS-7 Words per

period

word count

period count

7,03 8,09

t(18)=-1,00, n.s.

Table IS-7/10

98

IS-8 Punctuation marks



IS-8 Punctuation

marks

commas

clause count

0,02 0,18

t(8)=-5,87, p<,01

question marks

clause count

0,02 0

t(14)=1, n.s.

dots

clause count

0,02 0

t(15)=0,63, n.s.

exclamation marks

clause count

0,52 0,02

t(14)=1,33, n.s.

full stops

clause count

0,13 0,40

t(13)=-3,61, p<,01

punctuation (total)

clause count

0,71 0,61

t(14)=0,69, n.s.

Table IS-8/1


IS-8 Punctuation

marks

commas

clause count

0,08 0,18

t(13)=-3,47, p<,01

exclamation marks

clause count

0,28 0,02

t(13)=-3,97, p<,01

full stops

clause count

0,26 0,40

t(17)=-2,21, p<,05

punctuation (total)

clause count

0,68 0,60

t(16)=0,37, n.s.

Table IS-8/2

99


IS-8 Punctuation

marks

parenthesis

clause count

0,03 0

t(18)=0,33, n.s.

commas

clause count

0,02 0,18

t(7)=-6,31, p<,01

question marks

clause count

0,20 0

t(15)=2,02, n.s.

exclamation marks

clause count

0,15 0,02

t(16)=1,72, n.s.

full stops

clause count

0,24 0,40

t(15)=-3,56, p<,01

punctuation (total)

clause count

0,65 0,61

t(21)=0,62, n.s.

Table IS-8/3


IS-8 Punctuation

marks

commas

clause count

0,14 0,18

t(14)=-2,59, p<,05

question marks

clause count

0,07 0

t(8)=2,75, p<,05

exclamation marks

clause count

0,06 0,02

t(8)=1,81, n.s.

full stops

clause count

0,25 0,40

t(14)=-2,10, n.s.

punctuation (total)

clause count

0,54 0,61

t(12)=-0,77, n.s.

Table IS-8/4

100



IS-8 Punctuation

marks

commas

clause count

0,02 0,08

t(20)=-2,40, p<,05

exclamation marks

clause count

0,52 0,28

t(14)=0,87, n.s.

full stops

clause count

0,13 0,26

t(25)=-1,57, n.s.

punctuation (total)

clause count

0,71 0,68

t(14)=0,57, n.s.

Table IS-8/5


IS-8 Punctuation

marks

question marks

clause count

0,02 0,20

t(25)=-1,26, n.s.

exclamation marks

clause count

0,52 0,15

t(16)=0,87, n.s.

full stops

clause count

0,13 0,24

t(28)=-0,77, n.s.

punctuation (total)

clause count

0,71 0,65

t(16)=0,48, n.s.

Table IS-8/6

101


IS-8 Punctuation

marks

commas

clause count

0,02 0,14

t(10)=-2,72, p<,05

question marks

clause count

0,02 0,07

t(18)=-0,10, n.s.

exclamation marks

clause count

0,52 0,06

t(14)=1,14, n.s.

full stops

clause count

0,13 0,25

t(20)=-1,69, n.s.

punctuation (total)

clause count

0,71 0,54

t(14)=0,82, n.s.

Table IS-8/7


IS-8 Punctuation

marks

commas

clause count

0,08 0,02

t(15)=2,81, p<,02

question marks

clause count

0,03 0,20

t(17)=-1,72, n.s.

exclamation marks

clause count

0,28 0,15

t(22)=0,06, n.s.

Table IS-8/8

102


IS-8 Punctuation

marks

commas

clause count

0,08 0,14

t(15)=-0,43, n.s.

question marks

clause count

0,03 0,07

t(15)=-1,28, n.s.

exclamation marks

clause count

0,28 0,06

t(15)=1,80, n.s.

Table IS-8/9


IS-8 Punctuation

marks

commas

clause count

0,02 0,14

t(9)=-2,53, p<,05

question marks

clause count

0,20 0,07

t(18)=1,35, n.s.

exclamation marks

clause count

0,15 0,06

t(20)=0,94, n.s.

Table IS-8/10

IS-9 Word types



IS-9 Word types adverbs

word count

0,12 0,06

t(16)=2,10, n.s.

adjectives

word count

0,01 0,09

t(7)=-4,20, p<,01

informal

word count

0,02 0

t(14)=0,99, n.s.

Table IS-9/1

103



word count

0,07 0,06

t(16)=1,50, n.s.

adjectives

word count

0,07 0,09

t(14)=-1,23, n.s.

informal

word count

0,06 0

t(13)=4,34, p<,01

Table IS-9/2


IS-9 Word types adjectives

word count

0,04 0,09

t(10)=-2,83, p<,02

informal

word count

0,07 0

t(16)=2,98, p<,01

Table IS-9/3


IS-9 Word types informal

word count

0,08 0

t(8)=3,02, p<,02

Table IS-9/4




word count

0,12 0,07

t(24)=0,89, n.s.

adjectives

word count

0,01 0,07

t(20)=-3,93, p<,01

informal

word count

0,02 0,06

t(15)=0,70, n.s.

Table IS-9/5

104



word count

0,12 0,06

t(22)=1,52, n.s.

adjectives

word count

0,01 0,04

t(29)=-2,82, p<,01

greeklish

word count

0 0,13

t(16)=-2,45, p<,05

informal

word count

0,02 0,07

t(18)=-0,11, n.s.

Table IS-9/6



word count

0,12 0,06

t(20)=1,44, n.s.

adjectives

word count

0,01 0,08

t(12)=-3,15, p<,01

informal

word count

0,02 0,08

t(18)=-0,23, n.s.

Table IS-9/7



word count

0,07 0,04

t(24)=1,62, n.s.

greeklish

word count

0 0,13

t(16)=-2,39, p<,05

Table IS-9/8

105



word count

0,04 0,08

t(14)=-1,13, n.s.

greeklish

word count

0,13 0,01

t(16)=2,41, p<,05

Table IS-9/9

IS-10 Adverb types



IS-10 Word

types

Adverbs of time

Total adverbs

0 0,34

t(6)=-6,67, p<,01

Adverbs of manner

Total adverbs

0,28 0,23

t(12)=-0,10, n.s.

quantitative adverbs

Total adverbs

0,56 0,27

t(11)=3,05, p<,02

Table IS-10/1


IS-10 Word

types

Adverbs of time

Total adverbs

0,12 0,34

t(15)=-0,89, n.s.


Total adverbs

0,39 0,27

t(12)=0,91, n.s.

relative adverbs

Total adverbs

0,03 0,01

t(11)=0,81, n.s.

Viewpoint and commenting adverbs

Total adverbs

0,03 0

t(11)=1,00, n.s.

Table IS-10/2

106


IS-10 Word

types

Adverbs of place

Total adverbs

0,33 0,16

t(13)=2,00, n.s.

Adverbs of time

Total adverbs

0 0,34

t(6)=-6,67, p<,01

Adverbs of manner

Total adverbs

0,07 0,23

t(16)=-4,34, p<,01


Total adverbs

0,30 0,27

t(12)=0,45, n.s.


Total adverbs

0,30 0

t(11)=3,07, p<,02

Table IS-10/3


IS-10 Word

types

Adverbs of place

Total adverbs

0,26 0,16

t(9)=0,18, n.s.

Adverbs of time

Total adverbs

0,09 0,34

t(11)=-4,40, p<,01


Total adverbs

0,33 0,27

t(5)=1,46, n.s.


Total adverbs

0,05 0

t(7)=1,00, n.s.

Table IS-10/4

107



IS-10 Word

types

Adverbs of time

Total adverbs

0 0,12

t(11)=-1,90, n.s.


Total adverbs

0,56 0,39

t(21)=1,51, n.s.

Table IS-10/5


IS-10 Word

types

Adverbs of manner

Total adverbs

0,28 0,07

t(12)=1,75, n.s.


Total adverbs

0,56 0,30

t(20)=1,56, n.s.


Total adverbs

0 0,30

t(11)=-3,06, p<,02

Table IS-10/6


IS-10 Word

types

Adverbs of time

Total adverbs

0 0,09

t(7)=-1,96, n.s.


Total adverbs

0,56 0,33

t(10)=0,64, n.s.


Total adverbs

0 0,05

t(7)=-1,00 n.s.

Table IS-10/7

108


IS-10 Word

types

Adverbs of place

Total adverbs

0,18 0,33

t(18)=-1,89, n.s.

Adverbs of time

Total adverbs

0,12 0

t(11)=1,91, n.s.

Adverbs of manner

Total adverbs

0,24 0,07

t(17)=2,53, p<,05


Total adverbs

0,03 0,30

t(22)=-1,30, n.s.

Table IS-10/8


IS-10 Word

types

Adverbs of time

Total adverbs

0 0,09

t(7)=-1,96, n.s.

Adverbs of manner

Total adverbs

0,07 0,26

t(11)=-2,91, p<,02


Total adverbs

0,30 0,05

t(14)=2,52, p<,05

Table IS-10/9

IS-11 Syntax



IS-11 Syntax Subordinate clauses

clause count

0,29 0,23

t(19)=1,07, n.s.

prepositional phrases

clause count

0,25 0,36

t(20)=-0,95, n.s.

Table IS-11/1

109



clause count

0,12 0,23

t(18)=-2,11, p<,05


clause count

0,21 0,36

t(11)=-2,83, p<,02

Table IS-11/2



clause count

0,14 0,23

t(22)=-1,11, n.s.


clause count

0,25 0,36

t(19)=-1,93, n.s.

Table IS-11/3



clause count

0,07 0,23

t(12)=-5,05, p<,01


clause count

0,29 0,36

t(13)=-1,32, n.s.

Table IS-11/4




clause count

0,29 0,12

t(21)=2,37, p<,05

Table IS-11/5


IS-11 Syntax Subordinate clause

clause count

0,29 0,14

t(26)=1,74, n.s.

Table IS-11/6

110



clause count

0,29 0,07

t(17)=3,93, p<,01

Table IS-11/7



clause count

0,12 0,07

t(22)=2,45, p<,05

Table IS-11/8



clause count

0,14 0,07

t(22)=2,62, p<,02

Table IS-11/9

IS-12 Article types



IS-12 Article

types

Definite

Total articles

1,00 0,88

t(6)=4,74, p<,01

articles

word count

0,09 0,15

t(16)=-2,83, p<,02

articles

periods

1,12 1,90

t(17)=-2,18, p<,05

Table IS-12/1

111


IS-12 Article

types

Definite

Total articles

0,98 0,88

t(18)=2,24, p<,05

articles

word count

0,12 0,15

t(18)=-2,74, p<,02

articles

periods

0,84 1,90

t(18)=-3,66, p<,01

Table IS-12/2


IS-12 Article

types

Definite

Total articles

0,97 0,88

t(7)=4,10, p<,01

articles

word count

0,12 0,15

t(22)=-1,76, n.s.

articles

periods

0,87 1,90

t(12)=-8,18, p<,01

Table IS-12/3


IS-12 Article

types

Definite

Total articles

0,94 0,88

t(11)=0,34, n.s.

articles

word count

0,16 0,15

t(9)=1,62, n.s.

articles

periods

1,26 1,90

t(14)=-3,77, p<,01

Table IS-12/4

112



IS-12 Article

types

articles

periods

1,12 0,84

t(24)=0,31, n.s.

Table IS-12/5


IS-12 Article

types

articles

periods

1,12 0,87

t(15)=0,89, n.s.

Table IS-12/6


IS-12 articles

word count

0,09 0,16

t(21)=-3,72, p<,01

Article

types

articles

periods

1,12 1,26

t(17)=-0,47, n.s.

Table IS-12/7


IS-12 Article

types

articles

periods

0,84 1,26

t(19)=-1,19, n.s.

Table IS-12/8


IS-12 Article

types

articles

periods

0,87 1,26

t(15)=-3,45, p<,01

Table IS-12/9

113

IS-15 Additional terms in genitive case



genitive case

genitive case

word count

0 0,03

t(7)=-4,44, p<,01

Table IS-15/1



genitive case

genitive case

word count

0,01 0,03

t(10)=-3,54, p<,01

Table IS-15/2



genitive case

genitive case

word count

0 0,03

t(9)=-3,78, p<,01

Table IS-15/3



genitive case

genitive case

word count

0,02 0,03

t(11)=-2,02, n.s.

Table IS-15/4

IS-16 Additional terms in accusative case



accusative case

accusative case

word count

0 0,01

t(6)=-5,58, p<,01

Table IS-16/1

114



accusative case

accusative case

word count

0 0,01

t(19)=-2,28, p<,05

Table IS-16/2



accusative case

accusative case

word count

0 0,01

t(7)=-5,04, p<,01

Table IS-16/3



accusative case

accusative case

word count

0 0,01

t(7)=-6,38, p<,01

Table IS-16/4

IS-17 Abbreviations




word count

0,02 0

t(18)=1,49, n.s.

Table IS-17/1



word count

0,04 0

t(8)=1,22, n.s.

Table IS-17/2

115




word count

0,02 0,04

t(8)=-1,33, n.s.

Table IS-17/3

IS-18 Metaphors




clause count

0 0,04

t(6)=-6,39, p<,01

Table IS-18/1



clause count

0 0,04

t(6)=-6,39, p<,01

Table IS-18/2



clause count

0,12 0,04

t(9)=1,24, n.s.

Table IS-18/3




clause count

0,04 0,12

t(14)=-1,10, n.s.

Table IS-18/4

116

IS-19 Similes



clause count

0 0,02

t(6)=-3,41, p<,02

Table IS-19/1



clause count

0 0,02

t(6)=-3,41, p<,02

Table IS-19/2



clause count

0 0,02

t(6)=-3,41, p<,02

Table IS-19/3



clause count

0 0,02

t(6)=-3,41, p<,02

Table IS-19/4

IS-20 Word variety

Feature Metric VCs (average) ST


word count

0,37 0,50

t(8)=-4,41, p<,01

Table IS-20/1

117

Statistical analysis (Collaborative Learning)

CA-1 Verbs in first plural person



CA-1 Verbs in first

plural person

Verbs (we)

Verb count

0,76 0,16

t(20)=7,10, p<,01

Table CA-1/1


CA-1 Verbs in first

plural person

Verbs (we)

Verb count

0,43 0,16

t(18)=4,31, p<,01

Table CA-1/2


CA-1 Verbs in first

plural person

Verbs (we)

Verb count

0,31 0,16

t(20)=3,37, p<,01

Table CA-1/3


CA-1 Verbs in first

plural person

Verbs (we)

Verb count

0,03 0,16

t(7)=-2,91, p<,05

Table CA-1/4



CA-1 Verbs in first

plural person

Verbs (we)

Verb count

0,76 0,43

t(24)=3,73, p<,01

Table CA-1/5

118


CA-1 Verbs in first

plural person

Verbs (we)

Verb count

0,76 0,31

t(29)=2,50, p<,02

Table CA-1/6


CA-1 Verbs in first

plural person

Verbs (we)

Verb count

0,76 0,03

t(14)=9,63, p<,01

Table CA-1/7


CA-1 Verbs in first

plural person

Verbs (we)

Verb count

0,43 0,31

t(23)=-0,48, n.s.

Table CA-1/8


CA-1 Verbs in first

plural person

Verbs (we)

Verb count

0,43 0,03

t(14)=8,00, p<,01

Table CA-1/9


CA-1 Verbs in first

plural person

Verbs (we)

Verb count

0,31 0,03

t(15)=5,11, p<,01

Table CA-1/10

119

CA-2 Emotional clauses



CA-2 Emotional

clauses

emotional clauses

clause count

0,33 0,05

t(14)=5,02, p<,01

Table CA-2/1


CA-2 Emotional

clauses

emotional clauses

clause count

0,21 0,05

t(13)=2,98, p<,02

Table CA-2/2


CA-2 Emotional

clauses

emotional clauses

clause count

0,22 0,05

t(16)=2,81, p<,02

Table CA-2/3


CA-2 Emotional

clauses

emotional clauses

clause count

0,10 0,05

t(16)=0,82, n.s.

Table CA-2/4



CA-2 Emotional

clauses

emotional clauses

clause count

0,33 0,21

t(26)=2,05, n.s.

Table CA-2/5

120


CA-2 Emotional

clauses

emotional clauses

clause count

0,33 0,22

t(29)=1,72, n.s.

Table CA-2/6


CA-2 Emotional

clauses

emotional clauses

clause count

0,33 0,10

t(22)=3,39, p<,01

Table CA-2/7


CA-2 Emotional

clauses

emotional clauses

clause count

0,21 0,10

t(20)=1,52, n.s.

Table CA-2/8


CA-2 Emotional

clauses

emotional clauses

clause count

0,22 0,10

t(22)=1,61, n.s.

Table CA-2/9

CA-3 Rewarding clauses



CA-3 Rewarding

clauses

rewarding clauses

clause count

0,02 0

t(14)=1,00, n.s.

Table CA-3/1

121


CA-3 Rewarding

clauses

rewarding clauses

clause count

0,16 0

t(13)=3,64, p<,01

Table CA-3/2


CA-3 Rewarding

clauses

rewarding clauses

clause count

0,16 0

t(15)=2,81, p<,02

Table CA-3/3


CA-3 Rewarding

clauses

rewarding clauses

clause count

0,06 0

t(8)=1,80,n.s.

Table CA-3/4



CA-3 Rewarding

clauses

rewarding clauses

clause count

0,02 0,16

t(25)=-2,23, p<,05

Table CA-3/5


CA-3 Rewarding

clauses

rewarding clauses

clause count

0,02 0,16

t(25)=1,88, n.s.

Table CA-3/6

122


CA-3 Rewarding

clauses

rewarding clauses

clause count

0,02 0,06

t(22)=-0,38, n.s.

Table CA-3/7


CA-3 Rewarding

clauses

rewarding clauses

clause count

0,16 0,06

t(20)=2,05, n.s.

Table CA-3/8


CA-3 Rewarding

clauses

rewarding clauses

clause count

0,16 0,06

t(21)=1,69, n.s.

Table CA-3/9

CA-4 Clauses of negation



CA-4 Clauses of

negation

Clauses of negation

clause count

0 0,05

t(6)=-4,20, p<,01

Table CA-4/1


CA-4 Clauses of

negation

Clauses of negation

clause count

0,03 0,05

t(19)=-1,22, n.s.

Table CA-4/2

123


CA-4 Clauses of

negation

Clauses of negation

clause count

0,07 0,05

t(21)=-0,62, n.s.

Table CA-4/3


CA-4 Clauses of

negation

Clauses of negation

clause count

0,06 0,05

t(14)=-0,53, n.s.

Table CA-4/4



CA-4 Clauses of

negation

Clauses of negation

clause count

0 0,03

t(13)=-1,41, n.s.

Table CA-4/5


CA-4 Clauses of

negation

Clauses of negation

clause count

0 0,07

t(15)=-1,77, n.s.

Table CA-4/6


CA-4 Clauses of

negation

Clauses of negation

clause count

0 0,06

t(8)=-2,91, p<,02

Table CA-4/7

124


CA-4 Clauses of

negation

Clauses of negation

clause count

0,03 0,07

t(28)=-0,42, n.s.

Table CA-4/8


CA-4 Clauses of

negation

Clauses of negation

clause count

0,03 0,06

t(21)=-0,72, n.s.

Table CA-4/9

CA-5 Clauses of reason



CA-5 Clauses of

reason

Clauses of reason

clause count

0,29 0,02

t(15)=4,55, p<,01

Table CA-5/1


CA-5 Clauses of

reason

Clauses of reason

clause count

0,01 0,02

t(8)=-1,07, n.s.

Table CA-5/2


CA-5 Clauses of

reason

Clauses of reason

clause count

0,01 0,02

t(7)=-1,25, n.s.

Table CA-5/3

125



CA-5 Clauses of

reason

Clauses of reason

clause count

0,29 0,02

t(15)=4,38, p<,01

Table CA-5/4


CA-5 Clauses of

reason

Clauses of reason

clause count

0,29 0,01

t(14)=4,75, p<,01

Table CA-5/5


CA-5 Clauses of

reason

Clauses of reason

clause count

0,29 0,01

t(14)=4,78, p<,01

Table CA-5/6

CA-6 Familiarity words



CA-6 Familiarity

words

familiarity words

word count

0,33 0

t(14)=4,70, p<,01

Table CA-6/1


CA-6 Familiarity

words

familiarity words

word count

0,05 0

t(12)=4,42, p<,01

Table CA-6/2

126


CA-6 Familiarity

words

familiarity words

word count

0,02 0

t(16)=2,32, p<,05

Table CA-6/3


CA-6 Familiarity

words

familiarity words

word count

0,04 0

t(8)=2,35, p<,05

Table CA-6/4



CA-6 Familiarity

words

familiarity words

word count

0,33 0,05

t(15)=4,18, p<,01

Table CA-6/5


CA-6 Familiarity

words

familiarity words

word count

0,33 0,02

t(14)=4,64, p<,01

Table CA-6/6


CA-6 Familiarity

words

familiarity words

word count

0,33 0,04

t(17)=3,90, p<,01

Table CA-6/7

127

CA-7 Inclusive words


CA-7 Inclusive

words

inclusive words

word count

0,01 0

t(15)=1,21, n.s.

Table CA-7/1

CA-8 Social words



CA-8 Social

words

social words

word count

0,06 0

t(14)=2,56, p<,05

Table CA-8/1


CA-8 Social

words

social words

word count

0,01 0

t(22)=-1,30, n.s.

Table CA-8/2


CA-8 Social

words

social words

word count

0,02 0

t(8)=1,68, n.s.

Table CA-8/3



CA-8 Social

words

social words

word count

0,06 0

t(15)=2,37, p<,05

Table CA-8/4

128


CA-8 Social

words

social words

word count

0,06 0,01

t(14)=2,41, p<,05

Table CA-8/5


CA-8 Social

words

social words

word count

0,06 0,02

t(20)=0,34, n.s.

Table CA-8/6

CA-9 Emotional words



CA-9 Emotional

words

emotional words

word count

0,18 0,02

t(14)=4,97, p<,01

Table CA-9/1


CA-9 Emotional

words

emotional words

word count

0,10 0,02

t(14)=3,30, p<,01

Table CA-9/2


CA-9 Emotional

words

emotional words

word count

0,07 0,02

t(16)=2,16, p<,05

Table CA-9/3

129


CA-9 Emotional

words

emotional words

word count

0,04 0,02

t(9)=0,58, n.s.

Table CA-9/4



CA-9 Emotional

words

emotional words

word count

0,18 0,10

t(26)=1,80, n.s.

Table CA-9/5


CA-9 Emotional

words

emotional words

word count

0,18 0,07

t(27)=2,57, p<,02

Table CA-9/6


CA-9 Emotional

words

emotional words

word count

0,18 0,04

t(22)=3,88, p<,01

Table CA-9/7


CA-9 Emotional

words

emotional words

word count

0,10 0,04

t(21)=2,23, p<,05

Table CA-9/8

130


CA-9 Emotional

words

emotional words

word count

0,10 0,07

t(28)=0,83, n.s.

Table CA-9/9


CA-9 Emotional

words

emotional words

word count

0,07 0,04

t(23)=1,37, n.s.

Table CA-9/10

CA-10 Positive emotion



CA-10 Positive

emotion

positive emotion words

emotional words

1,00 0,65

t(6)=3,93, p<,01

Table CA-10/1


CA-10 Positive

emotion


emotional words

0,55 0,65

t(13)=-0,44, n.s.

Table CA-10/2


CA-10 Positive

emotion


emotional words

0,94 0,65

t(9)=2,79, p<,05

Table CA-10/3

131


CA-10 Positive

emotion


emotional words

0,68 0,65

t(4)=-0,30, n.s.

Table CA-10/4



CA-10 Positive

emotion


emotional words

1,00 0,55

t(9)=2,47, p<,05

Table CA-10/5


CA-10 Positive

emotion


emotional words

1,00 0,55

t(9)=2,47, p<,05

Table CA-10/6


CA-10 Positive

emotion


emotional words

1,00 0,94

t(8)=1,51, n.s.

Table CA-10/7


CA-10 Positive

emotion


emotional words

1,00 0,68

t(3)=1,50, n.s.

Table CA-10/8

132


CA-10 Positive

emotion


emotional words

0,55 0,94

t(10)=-2,01, n.s.

Table CA-10/9


CA-10 Positive

emotion


emotional words

0,55 0,68

t(5)=0,02, n.s.

Table CA-10/10


CA-10 Positive

emotion


emotional words

0,94 0,68

t(3)=1,25, n.s.

Table CA-10/11

CA-12 Use of first person plural pronouns



CA-12 Use of first

person plural

pronouns

Us, ours

Pronouns

0,33 0,08

t(16)=2,31, p<,05

Us, ours


pronouns

0,34 0,10

t(18)=1,96, n.s.

Table CA-12/1

133


CA-12 Use of first

person plural

pronouns

Us, ours

Pronouns

0,44 0,08

t(9)=2,51, n.s.

Us, ours


pronouns

0,47 0,10

t(10)=2,38, p<,05

Table CA-12/2


CA-12 Use of first

person plural

pronouns

Us, ours

Pronouns

0,68 0,08

t(19)=6,91, p<,01

Us, ours


pronouns

0,70 0,10

t(21)=6,27, p<,01

Table CA-12/3


CA-12 Use of first

person plural

pronouns

Us, ours

Pronouns

0 0,08

t(6)=-1,97, n.s.

Us, ours


pronouns

0 0,10

t(6)=-1,90, n.s.

Table CA-12/4

134



CA-12 Use of first

person plural

pronouns

Us, ours

Pronouns

0,33 0,44

t(16)=-0,69, n.s.

Us, ours


pronouns

0,34 0,47

t(15)=-0,89, n.s.

Table CA-12/5


CA-12 Use of first

person plural

pronouns

Us, ours

Pronouns

0,33 0,68

t(27)=-3,24, p<,01

Us, ours


pronouns

0,34 0,70

t(27)=-3,18, p<,01

Table CA-12/6


CA-12 Use of first

person plural

pronouns

Us, ours

Pronouns

0,33 0

t(13)=3,17, p<,01

Us, ours


pronouns

0,34 0

t(13)=3,18, p<,01

Table CA-12/7

135


CA-12 Use of first

person plural

pronouns

Us, ours

Pronouns

0,44 0,68

t(15)=-1,92, n.s.

Us, ours


pronouns

0,47 0,70

t(14)=-1,50, n.s.

Table CA-12/8


CA-12 Use of first

person plural

pronouns

Us, ours

Pronouns

0,44 0

t(8)=3,10, p<,02

Us, ours


pronouns

0,47 0

t(8)=3,15, p<,02

Table CA-12/9


CA-12 Use of first

person plural

pronouns

Us, ours

Pronouns

0,68 0

t(15)=8,18, p<,01

Us, ours


pronouns

0,70 0

t(15)=8,31, p<,01

Table CA-12/10

136

References

Bereiter Carl, (1994), Implications of postmodernism for science, or, science as

progressive discourse, Educational Psychologist, Vol. 29, Iss. 1, pp. 3-12. Available:

http://www.tandfonline.com/doi/abs/10.1207/s15326985ep2901_1#.UzNKJvl_ty0

(last accessed October 2013)

Bielaczyc, K., and Collins, A., (1999), Learning communities in classrooms: A reconceptualization of educational practice, In C. Reigeluth (Ed.)Instructional design theories and models (pp. 169–292) Mahwah, HJ: Erlbaum. Available: http://isites.harvard.edu/fs/docs/icb.topic541040.files/Bielaczyc%20and%20Collins-Learning%20Communities%20in%20Classrooms.pdf (last accessed: December 2013)

Blau R. Susan, Hall John, and Strauss Tracy, (1998), Exploring the Tutor/ClientConversation: A Linguistic Analysis. The Writing Center Journal, Volume 19, Number 1, Fall/Winter 1998. Available: http://casebuilder.rhet.ualr.edu/wcrp/publications/wcj/wcj19.1/wcj19.1_blau.pdf (last accessed October 2013)

Brooks Michael, Kuksenok Katie, Torkildson Megan K., Perry Daniel, Robinson John J.,

Scott Taylor J., Anicello Ona , Zukowski Ariana, Harris Paul and Aragon Cecilia R.,

(2013), Statistical Affect Detection in Collaborative Chat, CSCW '13 Proceedings of

the 2013 conference on Computer supported cooperative work, pp. 317-328.

Available: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.296.9852&r

ep=rep1&type=pdf (last accessed: January 2014)

Daniel Ben, Schwier A. Richard, McCalla Gordon (2003), Social Capital in Virtual

Learning Communities and Distributed Communities of Practice, Canadian Journal of

Learning and Technology/La revue canadienne de l’apprentissage et de la

technologie,[S.l.],Oct.2003.

Available:http://cjlt.csj.ualberta.ca/index.php/cjlt/article/view/85/79 (last accessed:

February 2014)

Dillenbourg P., Fischer F. (2007), Basics of Computer-Supported Collaborative Learning, Zeitschrift für Berufs- und Wirtschaftspädagogik, 21, pp. 111-130. Available: http://pileas.csd.auth.gr/file.php/47/MA8HMA_2013/DillenbourgFischer-CSCL-Basics-ARTICLE.pdf (last accessed: August 2013)

Ehrich F. J., (2006), Vygotskian Inner Speech and the Reading Process, Australian

Journal of Educational & Developmental Psychology Vol. 6, 2006, pp 12-25.

Available: http://files.eric.ed.gov/fulltext/EJ815614.pdf (last accessed: February

2014)

Emerson, Caryl, (1983), The Outer Word and Inner Speech: Bakhtin, Vygotsky, and

the Internalization of Language, Critical Inquiry, 10 (2):245, pp. 245-264. Available:

http://www.tandfonline.com/doi/abs/10.1207/s15326985ep2901_1%23.UzNKJvl_ty0

http://isites.harvard.edu/fs/docs/icb.topic541040.files/Bielaczyc%20and%20Collins-Learning%20Communities%20in%20Classrooms.pdf

http://isites.harvard.edu/fs/docs/icb.topic541040.files/Bielaczyc%20and%20Collins-Learning%20Communities%20in%20Classrooms.pdf

http://casebuilder.rhet.ualr.edu/wcrp/publications/wcj/wcj19.1/wcj19.1_blau.pdf

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.296.9852&rep=rep1&type=pdf


http://cjlt.csj.ualberta.ca/index.php/cjlt/article/view/85/79

http://pileas.csd.auth.gr/file.php/47/MA8HMA_2013/DillenbourgFischer-CSCL-Basics-ARTICLE.pdf

http://pileas.csd.auth.gr/file.php/47/MA8HMA_2013/DillenbourgFischer-CSCL-Basics-ARTICLE.pdf

http://files.eric.ed.gov/fulltext/EJ815614.pdf

137

http://www.jstor.org/discover/10.2307/1343349?uid=3738128&uid=2&uid=4&sid=2

1103643743873 (last accessed: September 2013)

Engeström Yrjö, (2011) From design experiments to formative interventions, in Theory Psychology, October 2011, vol. 21, no. 5, 598-628. Available: http://tap.sagepub.com/content/21/5/598.abstract (last accessed: September 2013)

Engeström Yrjö, (2001), Expansive Learning at Work: toward an activity theoretical

reconceptualization, Journal of Education and Work, Vol. 14, No. 1. Available:

http://www.handover.eu/upload/library/jimaw6szeyuluh4tho6oq.pdf (last accessed:

August 2013)

Innes B. Robert, (2007), Dialogic Communication in Collaborative Problem Solving Groups, International Journal for the Scholarship of Teaching and Learning, 1(1). Available:http://digitalcommons.georgiasouthern.edu/cgi/viewcontent.cgi?article=1178&context=int_jtl&seiredir=1&referer=http%3A%2F%2Fscholar.google.gr%2Fscholar%3Fhl%3Del%26q%3DDialogic%2BCommunication%2Bin%2BCollaborative%2BProblem%2BSolving%2BGroups%26btnG%3D#search=%22Dialogic%20Communication%20Collaborative%20Problem%20Solving%20Groups%22 (last accessed: August 2013)

Jameson, J., Ferrell, G., Kelly, J., Walker, S. and Ryan, M., (2006), Building trust and

shared knowledge in communities of e-learning practice: collaborative leadership in

the JISC eLISA and CAMEL lifelong learning projects. British Journal of Educational

Technology,37: 949–967. Available:

http://onlinelibrary.wiley.com/enhanced/doi/10.1111/j.1467-8535.2006.00669.x/

(last accessed: February 2014)

Knipfer Kristin, Mayr Eva, Zahn Carmen, Schwan Stephan, Hesse Friedrich W., (2009), Computer support for knowledge communication in science exhibitions: Novel perspectives from research on collaborative learning, Educational Research Review, Volume 4, Issue 3, Pages 196-209. Available:http://www.sciencedirect.com/science/article/pii/S1747938X09000311 (last accessed: August 2013)

Kollar Ingo, Fischer Frank, Slotta James D., (2005), Internal and external collaboration

scripts in web-based science learning at schools, In Proceedings of th 2005

conference on Computer support for collaborative learning: learning 2005: the next

10 years! (CSCL '05), International Society of the Learning Sciences 331-340.

Available: http://telearn.archives-ouvertes.fr/docs/00/19/06/41/PDF/Kollar-Ingo-

2005.pdf (last accessed: February 2014)



http://tap.sagepub.com/content/21/5/598.abstract

http://www.handover.eu/upload/library/jimaw6szeyuluh4tho6oq.pdf

http://digitalcommons.georgiasouthern.edu/cgi/viewcontent.cgi?article=1178&context=int_jtl&seiredir=1&referer=http%3A%2F%2Fscholar.google.gr%2Fscholar%3Fhl%3Del%26q%3DDialogic%2BCommunication%2Bin%2BCollaborative%2BProblem%2BSolving%2BGroups%26btnG%3D%23search=%22Dialogic%20Communication%20Collaborative%20Problem%20Solving%20Groups%22





http://onlinelibrary.wiley.com/enhanced/doi/10.1111/j.1467-8535.2006.00669.x/

http://www.sciencedirect.com/science/article/pii/S1747938X09000311

http://telearn.archives-ouvertes.fr/docs/00/19/06/41/PDF/Kollar-Ingo-2005.pdf

http://telearn.archives-ouvertes.fr/docs/00/19/06/41/PDF/Kollar-Ingo-2005.pdf

138

Koschmann Timothy (1999), Toward a dialogic theory of learning: Bakhtin's

contribution to understanding learning in settings of collaboration, CSCL '99

Proceedings of the 1999 conference on Computer support for collaborative learning,

Article No. 38.

Available:http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.22.5920&rep=r

ep1&type=pdf (last accessed: August 2013)

Mairesse Francois, Walker A. Marilyn, Mehl R. Matthias, and Moore K. Roger, (2007),

Using Linguistic Cues for the Automatic Recognition of Personality in Conversation

and Text. Journal of Artificial Intelligence Research, 30, 2007, pp. 457-500. Available:

http://people.csail.mit.edu/francois/research/papers/personality-jair07.pdf (last

accessed: January 2014)

Maness M. Jack, (2008), A Linguistic Analysis of Chat Reference Conversations with

18–24 Year-Old College Students. The Journal of Academic Librarianship, Vol. 34, Iss.

1, January 2008, pp. 31-38. Available:

http://ucblibraries.colorado.edu/facultyprofiles/files/publications/ADmanessj/Mane

ss-Lingustic%20Analysis%20of%20Chat%20Reference.pdf (last accessed: January

2014)

McMillan W. David, Chavis George M. David (1986), Sense of Community: A

Definition and Theory, Journal of Community Psychology, Volume 14. Available:

https://communities.autodesk.com/india/sites/default/files/secure/docs/McMillanC

havis---psychological-Sense-of-community.pdf (last accessed February 2014)

Patterson Glenys, (2005), Collaboration/Competition Crossroads: National/

Supranational Tertiary Education Policies on a Collision Course, Tertiary Education

and Management, December 2005, Volume 11, Issue 4, pp 355-368. Available:

http://link.springer.com/article/10.1007/s11233-005-0980-7# (last accessed:

February 2014)

Pérez-Sabater Carmen, (2012), The Linguistics of Social Networking: A Study of

Writing Conventions on Facebook. Linguistik online, 56, 6/2012. Available:

http://www.linguistik-online.de/56_12/perez-sabater.html (last accessed: February

2014)

Rovai P. Alfred (2002), Sense of community, perceived cognitive learning, and persistence in asynchronous learning networks, The Internet and Higher Education, Volume 5, Issue 4, Pages 319-332. Available: http://dx.doi.org/10.1016/S1096-7516(02)00130-6 (last accessed: February 2014).

Roussos L. Petros, Tsaousis Giannis (2006), Applied Statistics in Social Sciences,

Ellinika grammata, Athens. (In Greek)



http://people.csail.mit.edu/francois/research/papers/personality-jair07.pdf

http://ucblibraries.colorado.edu/facultyprofiles/files/publications/ADmanessj/Maness-Lingustic%20Analysis%20of%20Chat%20Reference.pdf

http://ucblibraries.colorado.edu/facultyprofiles/files/publications/ADmanessj/Maness-Lingustic%20Analysis%20of%20Chat%20Reference.pdf

https://communities.autodesk.com/india/sites/default/files/secure/docs/McMillanChavis---psychological-Sense-of-community.pdf

https://communities.autodesk.com/india/sites/default/files/secure/docs/McMillanChavis---psychological-Sense-of-community.pdf

http://link.springer.com/article/10.1007/s11233-005-0980-7%23

http://www.linguistik-online.de/56_12/perez-sabater.html

http://dx.doi.org/10.1016/S1096-7516(02)00130-6

http://dx.doi.org/10.1016/S1096-7516(02)00130-6

139

Santaholma Marianne Elina, (2005), Linguistic representation of Finnish in a limited domain speech-to-speech translation system. In: Proceedings of the 10th Conference of the European Association for Machine Translation (EAMT), pp. 226-234. Available: http://archive-ouverte.unige.ch/download/unige:3434/ATTACHMENT01 (last accessed: February 2014).

Socolov N. A., (1972), Inner Speech and Thought, Plenum, New York, pp. 46-122.

Spanger Phillip, Masaaki Yasuhara, Ryu Iida and Takenobu Tokunaga, (2009), A

Japanese corpus of referring expressions used in a situated collaboration task.

Proceedings of the 12th European Workshop on Natural Language Generation, pp.

110-113. Available: http://delivery.acm.org/10.1145/1620000/1610213/p110-

spanger.pdf?ip=46.103.197.48&id=1610213&acc=OPEN&key=4D4702B0C3E38B35%

2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E6D218144511F3437&CFID=3101

44203&CFTOKEN=44019478&__acm__=1396213753_20a6e0a4545ac35f73204e2ab

ea994f0 (last accessed: December 2013)

Stahl Gerry (2000), A Model of Collaborative Knowledge-Building, In B. Fishman & S.

O'Connor-Divelbiss (Eds.), Fourth International Conference of the Learning Sciences,

(pp. 70-77), Mahwah, NJ: Erlbaum. Available:

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.97.8816&rep=rep1&type

=pdf (last accessed: July 2013)

Stahl Gerry (2004), Building Collaborative Knowing, What We Know About CSCL, Computer-Supported Collaborative Learning Series, Volume 3, 2004, pp 53-85. Available: http://link.springer.com/chapter/10.1007/1-4020-7921-4_3 (last accessed: February 2014)

Stahl G., Koschmann T., Suthers D., (2006), Computer-supported collaborative learning: An historical perspective, In R. K. Sawyer (Ed.), Cambridge handbook of the learning sciences (pp. 409-426), Cambridge, UK: Cambridge University Press. Available: http://gerrystahl.net/cscl/CSCL_English.htm (last accessed: August 2013)

Veermans Marjaana and Cesareni Donatella, (2005), The nature of the discourse in

web-based Collaborative Learning Environments: Case studies from four different

countries. Computers & Education, Vol.45, Iss.3, November 2005, pp. 316–336.

Available:https://www.tlu.ee/~kpata/haridustehnoloogiaTLU/discoursecontentcomp

arison.pdf (last accessed: December 2013)

Vekyri Ioanna (2007), Learning as knowledge building, in Panteliadou Souzana,

Botsas Giorgos, Learning disabilities, basic concepts and features, University of

Thessaly , Department of Special Education, Volos. (In Greek) Available:

http://www.specialeducation.gr/files4users/files/pdf/teyxos_a.pdf (last accessed

February 2014)

Vygotsky S. Lev, (1978), Mind in society: The development of higher psychological processes, Cambridge, Mass.: Harvard University Press.

http://archive-ouverte.unige.ch/download/unige:3434/ATTACHMENT01

http://delivery.acm.org/10.1145/1620000/1610213/p110-spanger.pdf?ip=46.103.197.48&id=1610213&acc=OPEN&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E6D218144511F3437&CFID=310144203&CFTOKEN=44019478&__acm__=1396213753_20a6e0a4545ac35f73204e2abea994f0%20







http://link.springer.com/chapter/10.1007/1-4020-7921-4_3

http://gerrystahl.net/cscl/CSCL_English.htm

https://www.tlu.ee/~kpata/haridustehnoloogiaTLU/discoursecontentcomparison.pdf

https://www.tlu.ee/~kpata/haridustehnoloogiaTLU/discoursecontentcomparison.pdf

http://www.specialeducation.gr/files4users/files/pdf/teyxos_a.pdf

140

Vygotsky S. Lev, (2008), Thought and Language, Gnosi, Athens, pp. 378-436. (In Greek)

Warschauer Mark, (1997), Computer-Mediated Collaborative Learning: Theory and

Practice, The Modern Language Journal, Volume 81, Issue 4, pp. 470–481, Winter

1997.

Available:http://moodle.bracu.ac.bd/pluginfile.php/2542/mod_resource/content/1/

Warschauer-M_Computer-mediated%20collaborative%20learning-

theory%20and%20Practice.pdf (last accessed: December 2013)

Wells Gordon, (2002), The Role of Dialogue in Activity Theory, Mind, Culture, and

Activity ,Vol. 9,Iss. 1,pp.43-66. Available:

http://www.tandfonline.com/doi/abs/10.1207/S15327884MCA0901_04#.UzNOnfl_t

y0 (last accessed: August 2013)

Wiley Norbert, (2006), Inner Speech as a Language: A Saussurean Inquiry, Journal for

the Theory of Social Behaviour, Volume 36, Issue 3, pp. 319–341, September 2006.

Available: http://cdclv.unlv.edu//pragmatism/wiley_speech.pdf (last accessed:

November 2013)

Zhang Jianwei, Scardamalia Marlene, Lamon Mary, Messina Richard and Reeve

Richard, (2007), Socio-cognitive dynamics of knowledge building in the work of 9-

and 10-year-olds. Educational Technology Research and Development, April

2007, Vol. 55, Iss. 2, pp. 117-145. Available:

http://medicina.iztacala.unam.mx/medicina/Socio-

cognitive%20dynamics%20of%20knowledge%20building.pdf (last accessed:

November 2013)

http://moodle.bracu.ac.bd/pluginfile.php/2542/mod_resource/content/1/Warschauer-M_Computer-mediated%20collaborative%20learning-theory%20and%20Practice.pdf



http://www.tandfonline.com/doi/abs/10.1207/S15327884MCA0901_04%23.UzNOnfl_ty0

http://www.tandfonline.com/doi/abs/10.1207/S15327884MCA0901_04%23.UzNOnfl_ty0

http://cdclv.unlv.edu/pragmatism/wiley_speech.pdf

http://medicina.iztacala.unam.mx/medicina/Socio-cognitive%20dynamics%20of%20knowledge%20building.pdf

http://medicina.iztacala.unam.mx/medicina/Socio-cognitive%20dynamics%20of%20knowledge%20building.pdf

Computer Supported Collaborative Learning (CSCL) in Virtual Communities (VCs): Linguistic Analysis and Inner Speech

Documents