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Exploring affiliation network models
as a collaborative
filtering mechanism in e-learning

 

Miguel—Angel Sicilia
Salvado...
AOP i‘: -:| c-iilonships

     
   
     

   

People work with
such services,  for
learning about a

  
   
       

Lea...
Social Network Analysis (SNA)

General purposes in e-learning

I.  Hypothesis testing or exploratory studies aimed to find...
‘ . ".'. i TI! ’ ' . - .  ,. 
.'-‘. i I Illc I lOi‘l Ne Mo.  k

 

t y :3‘ '_, '_
rrltl :  ‘ t . 
TW° ‘“‘.1°. "‘*   j:7 :7...
Filtering participants

0 The affiliation network can be used to implement
different strategies for the definition of subgr...
i g ‘i'€Cl'li’i| C;UC-I

“lockmoctz-: |I

Processing Steps

V Remove tutors and nodes with

degree lower than two

V Rando...
Insiruc ‘oi'—| :-: c5 on—rn

Very active learners
that show low interest
in practical topics
(computer tools)

Vcombining ...
Changing course s'ii'uc: "i‘ure
O Re-organize structure joining or splitting topics. 

0 Topics that are connected with a ...
m-Slices

One-Mode valued The larger the

network 0 edge value
between two topics
the stronger or
more cohesive

     

th...
T6 is about IEEE
LOM and it is
closely related to
the rest. 

m-Slices

T4H2 and T4H4 are about
IMS LD and poorly related ...
Conclusons

/   / 
Use of affiliation models Development of mathematical, 
for exploring on-line ------------ '- quantitati...
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Affiliation networks as a collaborative filtering mechanism in elearning

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Affiliation networks as a collaborative filtering mechanism in elearning

  1. 1. Exploring affiliation network models as a collaborative filtering mechanism in e-learning Miguel—Angel Sicilia Salvador Sanchez-Alonso Leonardo Lezcano Information Engineering Research Unit Computer Science Depf. , University of Alcalé
  2. 2. AOP i‘: -:| c-iilonships People work with such services, for learning about a Learners interact through different kinds of services like newsgroups and chats. Nowadays learning experiences are setup around activities. particular objective (e. g. topic, competency). This kind of relationship activity-objective-people (AOP) is the basic material for the empirical analysis of social interaction through technology enhanced learning. /_ Analyzing Methods l - Intensive effort from the tutors to categorize and examine each of the interventions - Exposed to subjectivity of tutors Qualitative analysis J L Quantitative analysis - Computing of actual social interaction indicators - Help tutors in decision making - Processes large amounts of communication events «- Social Network Analysis
  3. 3. Social Network Analysis (SNA) General purposes in e-learning I. Hypothesis testing or exploratory studies aimed to finding correlations II. The summative assessment of learners I. Re-configuring the learning environment or undertaking other actions based on the analysis data Concretely, we approach AOP data in the form of an affiliation network, considering that learners’ participation in activities can be used to detect groups of common interest. Also, modeling data in that way, make it possible to devise different forms of “collaborative filtering" The usual interpretation of collaborative filtering is that of recommendations or ranking of information. Here we adopt a more general position, considering collaborative filtering as any course of action taken on the basis of the analysis of the social network structure.
  4. 4. ‘ . ".'. i TI! ’ ' . - . ,. .'-‘. i I Illc I lOi‘l Ne Mo. k t y :3‘ '_, '_ rrltl : ‘ t . TW° ‘“‘.1°. "‘* j:7 :7 ; , t 1 a 7?7"~~3* i Required sets defining , T w . ; r — _ - i r _; V : __ P d. t. a '~ ’. .«u<: > 7:’ V, ’ ' 7 if t " ' < :1, ' thith — ' >"« ~—1ZI~'- I network T -- . , . T t _ Threads planned »| EbiS: » A , » - rso. -. _ Discussion threads f ‘ T T ‘ . t'me, m.ust be (events) T l‘ i. 5'”‘''a' M ‘ ‘Q h ‘I . . Tutors or learners , _ 7 Participation should not 0' (actors) "‘~"*r: :,. -= . m_, W= : » be made mandatory 1 Undirected ties that afflliate actors Each thread m“5t have a dear t°pi° °' with events objective, distinguishable from the rest The above preconditions guarantee, to the extent possible, that participation in discussion is a function of interest, so the more the learner contributes to discussing a topic, the more she/ he shows an interest in the topic, thus allowing for a form of quantitative indicator
  5. 5. Filtering participants 0 The affiliation network can be used to implement different strategies for the definition of subgroups. 0 Identify groups that are close or distant in their interests. 0 Turn student groups into effective teams (Oakley, 2004) . / Compute the participation of actors in each of the topics, and then examine relationships with a hypergraph. . Test structural equivalence, (actors that have similar relations to the others) with block modelling technique, which provides a way of doing this with the help of automated algorithms.
  6. 6. i g ‘i'€Cl'li’i| C;UC-I “lockmoctz-: |I Processing Steps V Remove tutors and nodes with degree lower than two V Randomize learners’ and topics’ order of partitions V Set the number depending on the number of learners and topics (<6 .5>) V Apply Random Block Modeling Main Features Able to detect different meme. mm. _ " kind of structures (e. g. cohesion, centrality) " Allows exceptions or errors on input data (e. g. Empirical data) e S n e d N m S r 0 f W n 0 e S w M .0. w s cw t . l cl P. D. F. n 0 A. T L] F In. » . i. 2
  7. 7. Insiruc ‘oi'—| :-: c5 on—rn Very active learners that show low interest in practical topics (computer tools) Vcombining different interests to foster discussion or combining the same Interest to better focus those discussions. “Combining more active and more passive groups, or filter out the latter. This group shows attention only to Introductory issues on e-learning . . "rs 6' fly illlermg l rli 1‘. ‘i "’ll al ll Partitions of learners with no significant activity Active learners that show low interest in theoretical issues In general there is less interest from topics T5 onwards Introduce reinforcement activities TEMP
  8. 8. Changing course s'ii'uc: "i‘ure O Re-organize structure joining or splitting topics. 0 Topics that are connected with a high strength can be joined together, or even be separated in another course. Enhanced Modularityl 0 Concepts that are more peripheral might be removed, separated or re-arranged for future editions of the same learning experience. Therefore we need to identify highly related topics to a given intensity and we'll gel it with the help of m-slices.
  9. 9. m-Slices One-Mode valued The larger the network 0 edge value between two topics the stronger or more cohesive the common interest 4-slice . ‘ram . . . . An m-sIice 2; f 7’ ‘ is a maximal I ' subnetwork xx‘ containing the lines Colours show ‘- with a multiplicity the nesting of 9 equal to or greater the slices. Yellow 77 than m and the ones are also red vertices incident and red ones are m_s| ice are nested with these lines. also blue.
  10. 10. T6 is about IEEE LOM and it is closely related to the rest. m-Slices T4H2 and T4H4 are about IMS LD and poorly related to the rest, so it could be 33-slice is reasonable to separate LD cohesive group of contents to a second part of interest that the Course includes the three Introduction Topics, so they could be joined together
  11. 11. Conclusons / / Use of affiliation models Development of mathematical, for exploring on-line ------------ '- quantitative techniques for interaction in e-learning filtering the environment / _ _, J ' - - . . . . . - . o -- - . _ _ . - - " - . . _ _ c - " ' - o . . - I " o . . _ _ _ , , . -- ' ° - - . . . . . - I " - n . . _ . .- Because there aren't clear-cut thresholds for automated structure settings, tutor should take described techniques as an indicator to aid in decision making over the learning process / / _ »' Further Work V Evaluate indicators, regarding AOP data and their potential usages. V Gather evidence to turn them into standard facilities in e-learning platforms. '/ Provide an advanced tool for the analysis of social interaction. /v

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