social constructivist approach of learning motivation
TRANSCRIPT
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
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 2 / 59
Introduction
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
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 3 / 59
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).
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 4 / 59
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
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 5 / 59
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
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 6 / 59
Introduction Social Constructivism
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)
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 7 / 59
Introduction Social Constructivism
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)
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 8 / 59
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.
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 9 / 59
Introduction Goal & Purpose for Motivation
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)
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 10 / 59
Introduction Goal & Purpose for Motivation
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.
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 11 / 59
Introduction Goal & Purpose for 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.
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 12 / 59
Introduction Goal & Purpose for 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.
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 14 / 59
Introduction Proposed Research
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.
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 15 / 59
Introduction Proposed Research
Proposed recommendation system
Diversity in Learning Communities → Learning purposes
1) Expression -> 2) Observation -> 3) Evaluation
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 16 / 59
Introduction Proposed Research
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
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 17 / 59
Goal-based data from SNS
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
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 18 / 59
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)
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 19 / 59
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).
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 20 / 59
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."
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 21 / 59
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
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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.
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 24 / 59
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)
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 25 / 59
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
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 26 / 59
Recommending peers messages
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
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 27 / 59
Recommending peers messages
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.
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 28 / 59
Recommending 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
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 29 / 59
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)
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Recommending peers messages Topic distribution
LDA results
Finding diverse “topics” -> diverse purposesDiverse topics within dataset of goal-based Twitter messages
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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.
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 32 / 59
Recommending peers messages Goal & Purpose Recommendation
Goal-based Recommendation
Process
Recommending Learning Purpose messages based on:Similarity: similar goal.Diversity: various purposes.
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Recommending peers messages Goal & Purpose Recommendation
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.
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 34 / 59
Recommending peers messages Goal & Purpose Recommendation
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
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Recommending peers messages Experimentation
User Interface
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Recommending peers messages Experimentation
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
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Recommending peers messages Experimentation
Learning Goal Profile
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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
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 39 / 59
Recommending peers messages Self-evaluation results
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.
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Recommending peers messages Self-evaluation results
Questionnaire
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Recommending peers messages Self-evaluation results
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
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Recommending peers messages Self-evaluation results
Goal attributes evaluation / Recommendation method
Average difference before / after observation
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Recommending peers messages Self-evaluation results
Goal attributes evaluation / Recommendation + Class type
Average difference before / after observation
Diversity: Significant impact on Attainability, Specificity, andConfidence.
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Recommending peers messages Self-evaluation results
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)
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Recommending peers messages Self-evaluation results
Causal relationships
DirectLiNGAM (Shimizu et al, 2011)
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Recommending peers messages Self-evaluation results
Causal relationships between goal attributes
Diversity
Confidence and Commitment: success factors in learning andgoal-setting.
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 47 / 59
Recommending peers messages Self-evaluation results
Causal relationships between goal attributes
Similarity
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 48 / 59
Learning communities
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
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 49 / 59
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.
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Learning communities Learning Activity reports
Learning Community messages Recommendation
Process
Recommending Learning Community messagesDiversity: learning purposes + learning activities.
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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).
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 52 / 59
Conclusion
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
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 53 / 59
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.
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 54 / 59
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
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 55 / 59
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
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 56 / 59
Conclusion Future works
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).
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 57 / 59
Conclusion Future works
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.
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Conclusion Future works
Thank you
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