Social Constructivist Approach of Learning Motivation

Social Constructivist approach of MotivationRecommendation of diverse peer messages on Social Networking Services

Sébastien Louvigné

Ueno laboratoryGraduate School of Information Systems

The University of Electro-Communications

April 22, 2016

Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 1 / 59

Outline

1 IntroductionResearch ObjectiveSocial ConstructivismGoal & Purpose for MotivationProposed Research

2 Goal-based data from SNSSNS DataSystemic Functional LinguisticsTransitivity ModelGoal-based messages frompeersSummary

3 Recommending peers messagesLDA modelTopic distributionGoal & PurposeRecommendationExperimentationSelf-evaluation results

4 Learning communitiesLearning Activity reportsEvaluations

5 ConclusionDiscussionFuture works


Introduction

Outline







Introduction Research Objective

Motivation for Learning

Internal force generating behaviors to achieve goalsCentral part of educational psychology (Weiner, 1985).

Why do I want to learn? (reason, purpose)What do I want to achieve? (outcome, goal)

Lack of MotivationLargest cause of education failure (Samuelson, 2010).


Introduction Research Objective

Learning in social environments

Collaborative LearningPeople interact to learn together (Dillenbourg, 1999).Contemporary pedagogical approaches

Increasingly integrate collaboration for learningMake learning more meaningfulNeed to include psychological functions

Research ObjectiveEnhance learning motivation using social learning environments


Introduction Social Constructivism

Social Constructivist approach

Vygotsky’s Social Developmental theoryPeople actively and cognitively construct knowledge (Piaget, 1937).People learn from others (Vygotsky, 1978; Vygotsky, 1986)

Key characteristicsExpand “Zone of Proximal Development”Support from “More Knowledgeable Others”Development of Higher psychological functions



Social Constructivism in Learning

Contemporary learningIncreasingly integrates social constructivism

Promote & Facilitate the construction of knowledge

Pedagogical approaches: “Scaffolding” (Wood et al, 1976)

Cognitive apprenticeship (Collins et al, 1991)Communities of Practice (Lave & Wenger, 1991)Learning Communities (Scardamalia & Bereiter, 1994)Computer-Supported Collaborative Learning (Scardamalia &Bereiter, 1989)



Need for more psychological aspects

Contemporary collaborative approachesHow to learn psychological functions from others?

Important role of intrinsic motivation in CSCL (Rientes et al, 2009)

Limited diversity (learners with similar characteristics)Increasing social presence

Proposed research1 Collaborative learning environment to Enhance / Generate new

intrinsic motivation2 More diverse social environment -> Social Network Services (SNS)


Introduction Goal & Purpose for Motivation

Motivation for Learning

Different types of motivationSelf-Determination Theory (Ryan & Deci, 2000)

Towards an internalization of motivationIntrinsic motivation -> positive effects on learning.Focus on expectancy, value, and goals.



Goal & Purpose for Learning Motivation

Goal enhances Learning: “What to achieve”

Critical factor of motivation (personal emotions, beliefs) (Schunk et al.2002)

Purpose for learning: “Why to learn”

Strong connection goal-purpose -> intrinsic motivation (Eccles et al.1998)Makes learning more meaningful (Ames, 1992)



Problem Statement

”Why learning?”Highly structured education -> Syllabus states objectives.Learners have their own conceptions -> Often unrelated with formaleducation.

Goal Orientation should be set properlyRisk of conflict / discouragement / harm intrinsic motivation.



Goal & Purpose

Definitions1 Goal: terminal point towards which action is directed (e.g. “master a

language”).2 Purpose: provides the psychological force to attain a goal (i.e.

reasons for learning).

Goals -> efficient when linked with learner’s needs (purpose forlearning).Learners have different purposes (conceptual perceptions).

Goal orientations have different effects on intrinsic motivation.



Goal Orientations

Distinctions

Approach state Avoidance state

Mastery

orientation

Mastering task, learning,understanding(self-improvement)

Avoiding misunderstanding,avoiding not learning or notmastering task (not beingwrong)

Performance

orientation

Being superior, thesmartest, best at task incomparison to others(normative standards)

Avoiding inferiority, notlooking stupid or dumb incomparison to others(normative standards)

High influence of self-set goals on intrinsic motivation (Locke &Latham, 1990).

Adopt new purposes / perceptions -> more intrapersonal goal

orientation.Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 13 / 59

Introduction Proposed Research

Research purpose

NeedsIncorporation of Psychological aspectsLearning Motivation enhancementDiversity in collaborative learning environments

Hypothesis1 Learners enhance motivation by observing goal/purposes from other

peers (SNS).2 Diversity of goal purposes positively affects learners’ motivation and

self-perception.



Social Networking Services

SNS for diversityMassive resource of diverse information.

Media, content publishing, sharing, collaboration, etc.Including motivational and goal-based messages.

Essential and influential media.Including for learning (Bandura, 2001).

How to use motivation on SNS1 Collecting motivational and goal-based data from Social Media.2 Analyzing the diversity of contents (i.e. purposes for a same goal).3 Recommending diverse purposes for learning.



Proposed recommendation system

Diversity in Learning Communities → Learning purposes

1) Expression -> 2) Observation -> 3) Evaluation



Proposed Research

Features

I. Goal-based data fromSocial Media

II. Recommending peersmessages to enhance learning

motivation

1. Data Collection 3. Topic Distribution4. Goal Expression

2. Data Analysis 5. Recommendation System6. Observation7. Evaluation8. Learning communities


Goal-based data from SNS

Outline







Goal-based data from SNS SNS Data

Social Networking Services

Internet + SNSEssential part of personal life / communicationMany research works on education

Largest SNS: Facebook & Twitter (Tess, 2013)

Various results -> 2 opinionsPositive impact on learning behaviorOnly communicative tool for socializing (Madge et al. 2009)

Research works agree on:Necessity to consider SNS in academic life“Backstage” role in development of student identity (Selwyn, 2009)


Goal-based data from SNS SNS Data

Large-Scale Dataset

TwitterShort text messagesMetadata (e.g. user profile, social network)Large amount of data publicly available

Research works on TwitterAccess for informational purposes (Hughes et al. 2012).

Correlation with cognition stimulation / conscientiousness.

Small amount of information generates reaction (Sysomos, 2010).

Data containing Learning conceptsFilter stream data (“learn”, “study”).

Learning DB: 270 millions messages (May 2011 - March 2013).


Goal-based data from SNS Systemic Functional Linguistics

Systemic Functional Grammar (SFG)

Form of language description (Halliday, 1994)1 “Systemic” -> Language: network of systems, interrelated sets of

options for making meaning.2 “Functional” -> Language: multidimensional architecture reflecting

“the multidimensional nature of human experience and interpersonalrelations."


Goal-based data from SNS Systemic Functional Linguistics

Systemic Functional Grammar (SFG)

Functional semantic perspectiveLinking linguistic elements and functions to create meaning.Metafunctions of language:

Ideational (creating meaning),Interpersonal (interactivity, mood),Textual (internal organization).

Multidimensional architecture of language (Halliday, 2003).Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 22 / 59

Goal-based data from SNS Transitivity Model

Transitivity Model

Analyzing meaning-creating of learning goals (Ideational)Model of organization of meaning creating systems (Matthiessen, 2010).

Processes & Definitions Key elementsMaterial: Processes of doing in the

physical world

Actor - Goal - Process -Circumstance

Relational: Concerned with the process of

being in the world of abstract relations

Actor - Goal - Process (be) -Attributes - Carrier - Token - Value

Mental: Encodes the meanings of feeling

and thinking

Senser - Phenomenon -Circumstance

Verbal: Process of saying Sayer - Target - VerbiageBehavioral: Processes of physiological and

psychological behavior

Behaver

Existential: Processes of existing and

happening

Existent – Circumstance


Goal-based data from SNS Transitivity Model

Learning data vs Goal data

Data analysis results

Higher usage of mental processes (e.g. “need”, “ like”, “want”) ingoal-based messages.

Goals: strong relation with expression of needs and feelings.


Goal-based data from SNS Goal-based messages from peers

Largescale goal-based Dataset

Goal Database creation process

Filtering (learning data)Segmenting (subjects)Labeling (goal-based messages)Analyzing (patterns)


Goal-based data from SNS Summary

Discussion

Findings1 Construction of Goal-based dataset of peers messages

Analysis of ideational metafunction of Twitter messages (SFG,Transitivity model).

2 Mental processes to create goal-based meaningGiving social and personal meaning (physiological and psychological;feelings and emotions).

3 Top Actor lexicon having mainly “I”Personal experiences, Individual meaning.

4 Large variety of Circumstances

LimitationsFocus on ideational dimension, Transitivity model


Recommending peers messages

Outline








Context

NeedsLearning motivation enhancementIntegration in collaborative learning environments

more diverse social presence,intrinsic motivational contents from other peers.

Objective1 Recommendation system

Goal-based messages from other peers.Diverse purposes (reasons) for a shared goal (desired outcome).

2 Motivation evaluationInfluence of observing peers’ messages.



Recommender Systems

Technology Enhanced Learning systems (Manouselis et al. 2012)

Recommending personalized contentsSimilarity of item contents / user profiles / other info

Need to consider diversity (Erdt et al. 2015)

Recommend outcomes different from learners’ expectations


Recommending peers messages LDA model

Latent Dirichlet Allocation (LDA)

Probabilistic model for collections of discrete data (Blei et al. 2003)

d : Document

Z : Topic

W : Word

Documents: Mixture of topics -> purposes for learning

Full conditional: P(zi = j |z�i ,w) µ n(wi )�i ,j +b

n(.)j +Wb(n(di )�i ,j +a)

Dirichlet: q̂ (d)k =

n(d)k +an(.)k +Ka

; f̂ (w)j =

n(w)j +b

n(.)j +Wb

(Griffiths & Steyvers. 2004)


Recommending peers messages Topic distribution

LDA results

Finding diverse “topics” -> diverse purposesDiverse topics within dataset of goal-based Twitter messages


Recommending peers messages Topic distribution

Perplexity

Finding optimal number of topics

Different optimal number of topics for each learning subject.Not related with number of messages.


Recommending peers messages Goal & Purpose Recommendation

Goal-based Recommendation

Process

Recommending Learning Purpose messages based on:Similarity: similar goal.Diversity: various purposes.



Dissimilarity

Topic distribution comparisonJensen-Shannon Divergence

TJSD(qdi ,qdj ) =12DKL(qdikm)+

12DKL(qdjkm)

based on Kullback-Leibler divergence DKL(qdikm) = Âk qdi ,k ln qdi ,km with

m = 12(qdi +qdj ).

AdvantagesSymmetric method.Measuring the similarity between 2 probability distributions.Complementary -> dissimilarity = diversity.



Goal-based Recommendation System

Algorithm1 Input:

qG : LDA Topic Distribution for each document for a specific goal GX : user’s Twitter message expressing purpose for goal G

2 Apply LDA Topic distribution to X

X ! qX where qX =�

qX ,k=1, . . . ,qX ,k=K

3 Calculate Jensen-Shannon divergence between qX and {qd | 8d 2 G}4 Output: recommend the N most dissimilar documents from G


Recommending peers messages Experimentation

User Interface



Scenarios

First access1 Login using Twitter account2 Write & Evaluate learning goals

Create “Learning Goal Profile”

3 Observe diverse messages from peers

Further accesses1 Login using Twitter account2 Observe diverse messages from peers

Based on previously created learning goal

3 Update learning goalsNew expression and evaluations



Learning Goal Profile


Recommending peers messages Self-evaluation results

Evaluating Motivation

Measurement methodsSelf-Report (auto-evaluation questionnaire)

Precise analysis / Personal characteristics / Learner profile

Subjectivity / Non-synchronism / Learning sequencing

Free Choice (Time spent on activities / continuing tasks)Appropriate for Intrinsic motivation

Difficult to measure in open environment

Peer-review (rating by others)More objective / Behaviors

Difficult to judge



Goal attributes for Motivation Evaluation

Goal-Setting: Attributes influencing learning and performance (Locke,

1990; Zimmerman et al. 1992; Bekele, 2010).

Goal attributes

Leading eventually to personal satisfaction (Fulfillment).Fulfillment and achievement motivation: important successfactors in learning.



Questionnaire



Experiment

Participants77 Undergraduate students in University of Electro-Communications(Tokyo)English classes

Scenario1 Create a “Learning Goal Profile”2 Observe messages from peers (similar / diverse)3 Repeat previous steps over time



Goal attributes evaluation / Recommendation method

Average difference before / after observation



Goal attributes evaluation / Recommendation + Class type

Average difference before / after observation

Diversity: Significant impact on Attainability, Specificity, andConfidence.



T-test (pre-observation / post-observation)

Average difference (P[T<=t] one-tail)Mandatory classes Optional classes

Attributes Similar Diverse Similar Diverse

Importance -3.63 (0.28) -8.00 (0.13) 0.00 (0.00) -4.00 (0.27)

Attainability 1.81 (0.38) 14.00 (0.04) 5.00 (0.34) 4.00 (0.38)

Easiness 0.00 (0.50) 2.00 (0.42) -2.50 (0.45) 0.00 (0.50)

Specificity -3.63 (0.30) 14.00 (0.04) 2.50 (0.36) 4.00 (0.33)

Commitment 9.09 (0.13) 4.00 (0.32) -5.00 (0.27) 8.00 (0.27)

Confidence 0.00 (0.50) 16.00 (0.05) 0.00 (0.50) 12.00 (0.23)

Achievement 3.63 (0.32) 4.00 (0.33) -2.50 (0.40) 4.00 (0.27)

Satisfaction 18.18 (0.01) 4.00 (0.37) 0.00 (0.20) 12.00 (0.14)

Motivation 1.81 (0.39) 4.00 (0.32) 7.50 (0.15) 12.00 (0.15)



Causal relationships

DirectLiNGAM (Shimizu et al, 2011)



Causal relationships between goal attributes

Diversity

Confidence and Commitment: success factors in learning andgoal-setting.



Causal relationships between goal attributes

Similarity


Learning communities

Outline







Learning communities Learning Activity reports

Learning Communities

Key characteristics (Bielaczyc et al. 1999)1 Diversity of expertise.2 Shared objective.3 Focus on learning “how to learn”.4 Mechanisms to share what has been learned.

Implementing Learning CommunitiesNeed for more diverse message types.

“Learning Activity” reports: detailing “what” students learned, and“how” they learned.


Learning communities Learning Activity reports

Learning Community messages Recommendation

Process

Recommending Learning Community messagesDiversity: learning purposes + learning activities.


Learning communities Evaluations

T-test (pre-observation / post-observation)

Variations (P[T t])Attributes Learning activity

messagesOnly learning

purposesCommitment 14.29 (0.03) 5.33 (0.09)Confidence 8.57 (0.11) 14.67 (0.24)Achievement 12.86 (0.06) 4.00 (0.18)Fulfillment 7.14 (0.22) 6.67 (0.03)Motivation 12.86 (0.08) 6.67 (0.40)- Extrinsic 12.86 (0.06) N/A- Intrinsic 14.29 (0.02) N/AHours 1.10 (0.10) 0.10 (0.50)

Significant impact on Commitment, and Motivation (extrinsic /intrinsic).


Conclusion

Outline







Conclusion

Conclusion

Using Social Context to enhance Learning Motivation1 Focusing on psychological functions.2 Diversity of messages from peers for learning.3 Recommendation of diverse purposes from peers.4 Implementation of learning communities characteristics.

ResultsObserving diverse SNS messages from peers

Positive impact on Motivation.Diversity: positive impact on Attainability, Specificity, andConfidence.Confidence and Commitment appear as measure of success ingoal-setting.Motivation and Commitment enhancement with Learning

Communities implementation.


Conclusion Discussion

Contributions

Enhancing motivation with a more diverse social environmentIntegrated Motivational contents in:

Collaborative learning environmentRecommendation System

Importance of DiversityObserving diverse purposes from peers enhanced self-perceptionsRecommendation factor

LDA3-level distinction: document-topic-wordRecommendation based on topic dissimilarity

Learning CommunitiesMore dynamic implementation of motivationBetter enhancement of motivation


Conclusion Future works

Future works

Learning EnvironmentIntegration of motivation in Learning Management Systems.

Motivation evaluationDevelop “Free choice” method

Time of study

Behavior and decision making (e.g. joining optional class)

Other learning subjects

LDAe.g. Short text analysis, Including grammatical features



List of Publications

S. Louvigné, and N. Rubens (2016), “Meaning-Making Analysis and Topic Classification ofSNS Goal-based messages” . Behaviormetrika 43(1).

S. Louvigné, Y. Kato, N. Rubens, and M. Ueno (2015), “SNS messages Recommendationfor Learning Motivation”. Artificial Intelligence in Education (International Conference).

S. Louvigné, Y. Kato, N. Rubens, and M. Ueno (2015), “Goal-based messagesRecommendation utilizing Latent Dirichlet Allocation”. The 14th IEEE InternationalConference on Advanced Learning Technologies (ICALT).

J. Shi, and S. Louvigné (2014), “Goal-Setting and Meaning-Making in Mined Dataset ofTweets Using SFG Approach” . Journal of Electrical Engineering.

S. Louvigné, N. Rubens, F. Anma, and T. Okamoto (2012), “Utilizing Social Media forGoal Setting based on Observational Learning”. 2012 IEEE 12th International Conferenceon Advanced Learning Technologies (ICALT).

S. Louvigné, N. Rubens, F. Anma, and T. Okamoto (2012), “Utilizing Social Media forObservational Goal Setting”. Computers and Advanced Technology in Education(International Conference).



Bibliography

L. Vygotsky (1978), “Mind in Society: The Development of Higher PsychologicalProcesses” . Harvard University Press.

J. S. Eccles, and A. Wigfield (2002), “Motivational Beliefs, Values, and Goals” . Annualreview of psychology.

D. H. Schunk, J. L. Meece, and P. R. Pintrich (2002), “Goals and Goal Orientations” .Motivation in Education: Theory, Research, and Applications.

P. R. Pintrich (2003), “A Motivational Science Perspective on the Role of StudentMotivation in Learning and Teaching Contexts” . Journal of Educational Psychology.

C. Ames (1992), “Classrooms: Goals, Structures, and Student Motivation” . Journal ofEducational Psychology.

E. A. Locke, and G. P. Latham (2002), “Building a practically useful theory of goal settingand task motivation: A 35-year odyssey”. American Psychologist.

D. M. Blei, A. Y. Ng, and M. I. Jordan (2003), “Latent Dirichlet Allocation”. Journal ofMachine Learning Research.

T. L. Griffiths, and M. Steyvers (2004), “Finding scientific topics” . National academy ofSciences of the United States of America.



Thank you
