Does Student- Teacher Interaction Matter in Distance Education? WAYNE FREEMAN DOUGLAS GLASS ST. ANDREWS UNIVERSITY A BRANCH OF WEBBER INTERNATIONAL UNIVERSITY.

Does Student-Teacher Interaction Matter in Distance Education?

WAYNE FREEMAN

DOUGLAS GLASSST. ANDREWS UNIVERSITY

A B RA N C H O F W E B B E R I N T E R N AT I O N A L U N I V E R S I T Y

Overview

ProblemLiterature Review

Research QuestionMethodsAnalysis

DiscussionConclusions

A previous version of

this research was

presented at the SoTL

Conference recently in

Savannah, Ga.

Previous Presentation

The Problem

How can student outcomes best be improved in distance education (DE)

and at what ‘costs’?

Literature Review• “Distance education (DE) can be much better and

also much worse than classroom instruction (CI) based on measured academic outcomes”

• Research methodologies in DE are “woefully inadequate and poorly reported”

• Research should focus on what makes DE effective or ineffective – not on comparing CI and DE.

(Bernard R. M., et al., 2009)

Literature Review• Student-Teacher interaction highly valued & course

was more satisfying (Nichols, 2011)

• Interaction an integral component of DE (Holden & Westfall, 2006)

• Asynchronous DE courses more positive versus synchronous DE courses compared to Classroom Instruction (Bernard, R.M., et.al., 2004)

Interaction is Important

Student <-> Content

(Anderson 2003)

Student <-> Teacher Student <-> Student

MODES OF INTERACTION

Interaction Equivalency Theorem

Any one of them?

Thesis 1 - Quality

Student-Content

Student-Student

Student-Teacher

Student-Content

Student-Content

Student-Content

Student-Teacher

Student-Teacher

Student-Student

Thesis 2 - Quantity

Increased interaction =

Higher learning quality?

But more costs and

time (Anderson 2003)(Miyazoe & Anderson, 2011)

Research Question

How does a low level of student-teacher Interaction impact student satisfaction and

achievement when student-content interaction is high?

The Pilot Study

MethodsResearch Design

The quality of the quantitative literature of distance education (DE) is poor!

• lack of experimental control• lack of procedures for randomly selecting participants • lack of random assignment to treatment group• poorly designed dependent measures • failure to account for a variety of variables related to

the attitudes of students and instructors(Bernard R., et. al. 2010)

MethodsResearch Design of Present Study

Quasi-Experimental

• Sample – Undergraduate and graduate students at a small liberal as college in the South

• Control Group – students enrolled in an asynchronous tutorial with no facilitator (n= 15)

• Treatment Group – students enrolled in an asynchronous tutorial with a facilitator (n=20)

MethodsInstrumentation

Pre-Tutorial

• Student Background Survey - Demographics and Self Efficacy for Online Learning (Artino)

• Test of APA Knowledge

Post-Tutorial

• Test of APA Knowledge

• Student Satisfaction Survey

MethodsData Collection/Preparation

Collection

• Invitation to Participate developed

• Outreach to potential participants (67 students agreed to participate)

Preparation

• Data consolidation from SurveyMonkey and Moodle

• Missing values/Multiple Imputation

AnalysisDescriptive Statistics

Variable Control Treatment

Gender 62% Female38% Male

49% Female51% Male

Race 70% White27% Black3% Other

67% White17% Black16% Other

GPA 3.1-3.5 3.1-3.5

Age 26 years old 24 years old

Online Self-Efficacy 4.7 out of 7 5.4 out of 7

Online Experience 1.4 courses 1.4 courses

AnalysisCorrelation

SATISFACTION POSTTEST

GROUP

ONLINEEXP Significant

ONLINESE Significant

GENDER Significant

AGE Significant

GPA

AnalysisRegression

Coefficientsa

Model (R Square = .346)

Unstandardized Coefficients

Standardized Coefficients

t Sig.B Std. Error Beta6 (Constant) 49.993 7.611 6.568 .000

GENDER -13.903 2.399 -.369 -5.795 .000SATIS 2.721 .569 .286 4.783 .000ONLINEEXP 2.446 .548 .281 4.466 .000PREQUIZ .349 .100 .214 3.492 .001GROUP 7.789 2.271 .206 3.430 .001RACE -5.502 1.709 -.199 -3.219 .002

a. Dependent Variable: POSTQUIZ

AnalysisAnalysis of Covariance (ANCOVA)

F Sig.GROUP 1.453 .230

No significant difference in post-test scores between the Control and Treatment Groups

Limitations• Small sample size/Low statistical power• Convenience sample• Self-reported data• Limited to tutorial, not full course• Measurement of satisfaction

Discussion/Conclusions• Statistical significance in regression

• Singular pedagogy tested

• Student Motivations/ Attitudes₋ Learning Styles - see-hear-do₋ ‘in’ vs ‘at’ college₋ Task Value

• Validity/ reliability across disciplines

ReferencesAndreson, T. (2003). Modes of interaction in distance education: Recent developments & research

questions (Vol. Handbook of Distance Education). (M. Moore, Ed.) Mahwah, NJ: Lawrence Eelbaum.Bernard, R. M., Abrami, P. C., Borokhovski, E., Wade, C. A., Tamim, R. M., Surkes, M. A., & Bethel, E.

(2009). A meta-analysis of three types of inrteraction treatments in distance education. Review of Educational Research, 1243-1289.

Bernard, R. M., Abrami, P. C., Lou, Y., Borokhovski, E., Wade, A., & Wozney, L. (2004). How does distance education compare with classroom instruction? A meta analysis of the empirical literature. Review of Educational Research 3(74), 260-277.

Holden, J. T., & Westfall, P. J.-L. (2006). An instructional media selection guide for distance learning. Boston: United States Distance Learning Association.

Mayer, R. (2001). Multi-Media Learning. Cambridge, UK: Cambridge University Press.Miyazoe, T., & Anderson, T. (2009). The Interactive Equivalency Theorem: Research Potential and Its

Application to Teaching. (pp. 1-6). Madison: 27th Annual Conference on Distance Teaching & Learning.Miyazoe, T., & Anderson, T. (2010 9(2)). The interactive equivalency theorem. Journal of Interactive

Online Learning, 94-104.Nichols, J. (2011). Comparing Educational Leadership Course and Professor Evaluations in on-line and

traditional instructional formats: What are the Students saying? College Student Journal 45(4) , 862-868.Russell, T. L. (1999). The No Significant Difference Phenomenon. Chapel Hill: Office of Instructional

telecommunications, NC State University.

Contact Information

Wayne Freeman - [email protected]

Douglas Glass – [email protected]