Group Formation, Data Mining

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GROUP FORMATION INCOLLABORATIVE LEARNI

 The formation of eective groups to performin a collaborative manner to produce bresults.

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CONTENTS

o

 TERMINOLOIE! roup

"ollaborative Learning

"ooperative Learning

oO#$E"TI%E

o

!&R%E'o"L&!TERIN

oRE(&IREMENT! )OR "L&!TERIN

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 WHAT IS A GROUP ?

o

* group b+ de,nition is people -ho come together foron voluntar+ basis

oas a spirit of co/operation to -ork together

oas mutual0 social and economic bene,t

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 WHY A GROUP IS NEEDED ?

o

* roup is needed To organi1e and guide an action

)or training members in necessar+ skills

 To provide a channel for information sharing

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COLLABORATIVE LEARNING

o

"ollaborative learning is a situation in -hich t-o or molearn or attempt to learn something together.

o"ollaborative learning capitali1e on one another3s resouskills  4 *sking one another for information0 evaluaanother3s ideas0 monitoring -ork etc.5

o"ollaborative learning refers to methodologienvironments in -hich learners engage in a common taeach individual depends on and is accountable to each

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COOPERATIVE LEARNING

o

"ooperative learning de,ned as a teaching arrangementsmall0 heterogeneous groups of students -ork togachieve a common goal.

o!tudents assume responsibilit+ for their o-n.

o The #asic elements are9 positive interdependencopportunities0 and individual accountabilit+.

o;ositive Interdependence is an element of cooperative-here members of group perceive that -orking toindividuall+ and collectivel+ bene,cial0 and success dethe participation of all the members. 6<8

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COLLABORATIVE vs. COOPERATIV

o

"ollaboration• Mutual engagement of participants

• * coordinated eort to solve the problem

• "ontinuous shared conception of the problem.

o"ooperation• =ivision of labour

• Individual responsibilit+ for sections

• "oordination -hen assembling partial results

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OBJECTIVE

o

Our ob>ective is formation of groups?teams to im"ollaborative Learning among students

oOur aim is to make the team heterogeneouso The unfairness of forming a group -ith onl+ -eak students is o

groups -ith onl+ strong students are equally undesirable.

o In heterogeneous groups0 the -eaker students gain from seei

better students approach the problems and the stronger studdeeper understanding of the sub>ect b+ teaching it to the other

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 WHAT THIS PROJECT DOES ?

•

 The pro>ect implements the collaborative process of ensure that all students -ho ma+ not be at the saacademicall+ can cooperate  -ith each other and lemutual sharing of kno-ledge.

• This pro>ect implements this ver+ techni:ue -ith theformation of groups and clustering the data and then

them.

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SURVEY

o

Computers in Human Behavior   b+ =e-i+anti0 #rand!.$ochems @ #roers 4ABBC5 emphasi1es on collaborativein as+nchronous computer/supported collaborative environments. 6C8

o* stud+ -as carried out to gain response from studeneDperiences -ith collaborative learning in s+n

computer supported collaborative learning environments.

o The ,ndings revealed positive results and students3 sa-ith collaborative learning

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CATEGORY OF THE PROJECT

• The pro>ect belongs to the categor+ of *rti,cial Intelligand "omputational Intelligence.

o*rti,cial intelligence 4*I5 is the intelligence eDhimachines or soft-are. It is the stud+ and design of iagents0 in -hich an intelligent agent is a s+stem that pits environment and takes actions that maDimi1e its ch

success. 6F8o"omputational Intelligence is a set of nature

computational methodologies and approaches tocompleD real/-orld problems 6F8

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CLUSTERING

o"luster *nal+sis or simple clustering is the process of part

set of data ob>ects into subsets.

oEach subset is a cluster such that ob>ects in a cluster areone another0 +et dissimilar to ob>ects on other clusters.

o#asic "lustering Methods9

• ;artitioning Methods.

• ierarchical Methods.• =ensit+ #ased Methods.

• rid #ased Methods.

oere a speci,c kind of ;artitioning Method of clustering kGK-MeansH -hich is a centroid based techni:ue is used.

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REQUIREMENTS FOR CLUSTERIN

o!calabilit+

o*bilit+ to deal -ith dierent t+pes of attributes

o=iscover+ of clusters -ith arbitrar+ shape

oRe:uirements for domain kno-ledge to determine inputparameters

o*bilit+ to deal -ith nois+ datao Incremental clustering and insensitivit+ to input order

o"apabilit+ of clustering high/dimensional data6F8

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REFERENCES

. "lustering and !e:uential ;attern Mining of Online "ollaborative

b+ =ilhan ;erera0 $ud+ Ja+0 Irena Joprinska0 members of IEEE "om

*nd b+ Jalina 'acef0 and Osmar Kaiane.A. "ooperative Learning !tructures "an Increase !tudent *chievem

=otson "ulminating ;ro>ect

4Jagan Online Maga1ine0 inter ABB5

7. roup )ormation *lgorithms in "ollaborative Learning "onteDts9Mapping of the Literature #+ ilmaD Marreiro "ru1 and !ei>i Isotani

<. =ata Mining and *lgorithms b+ Ra>an "hattanvelli.

F. =ata Mining "oncepts and Techni:ues b+ $ia-ei an0 Micheline J

;ei.

. "omputers in uman #ehavior0 ;age A70 ;age </F< b+ =e

ru-el0 !.$ochems @ #roers 4ABBC5