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IntroductionMethodology
Results & Findings
Goal-based messages Recommendationutilizing Latent Dirichlet Allocation
Sébastien Louvigné, Yoshihiro Kato, Neil Rubens, andMaomi Ueno
Graduate School of Information SystemsThe University of Electro-Communications
Tokyo, Japan
Jul 8, 2014
Sébastien Louvigné ([email protected]) - UEC Tokyo IEEE - ICALT 2014. Athens, Greece.
IntroductionMethodology
Results & Findings
Outline
1 IntroductionLearning Goal & PurposeProblem StatementProposed research
2 MethodologyGoal-based DataGoal & Purpose RecommendationImplementation
3 Results & FindingsLDA resultsPerplexityLearners EvaluationDiscussion
Sébastien Louvigné ([email protected]) - UEC Tokyo IEEE - ICALT 2014. Athens, Greece.
IntroductionMethodology
Results & Findings
Learning Goal & PurposeProblem StatementProposed research
Goals for Learning
Goal enhances Learning
Providing a sense of direction to attain speci�c standards.
Critical motivator (personal emotions, beliefs).
(Schunk et al. 2002)
Goal orientations
Refer to purposes for engaging in achievement tasks.
(Pintrich, 2003)
Sébastien Louvigné ([email protected]) - UEC Tokyo IEEE - ICALT 2014. Athens, Greece.
IntroductionMethodology
Results & Findings
Learning Goal & PurposeProblem StatementProposed research
Goal & Purpose
De�nitions
1 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 � e�cient when linked with learner's needs (purpose forlearning).
Learners have di�erent purposes (conceptual perceptions).
Goal orientations have di�erent e�ects on intrinsicmotivation.
Sébastien Louvigné ([email protected]) - UEC Tokyo IEEE - ICALT 2014. Athens, Greece.
IntroductionMethodology
Results & Findings
Learning Goal & PurposeProblem StatementProposed research
Problem Statement
�Why learning?�
Highly structured education � Syllabus states objectives.
Learners have their own conceptions � Often unrelated withformal education.
Goal Orientation should be set properly
Risk of con�ict / discouragement / harm intrinsic motivation.
Sébastien Louvigné ([email protected]) - UEC Tokyo IEEE - ICALT 2014. Athens, Greece.
IntroductionMethodology
Results & Findings
Learning Goal & PurposeProblem StatementProposed research
Goal Theory
Goal Setting
Goal properties in�uencing learning performance and intrinsicmotivation (Locke & Latham, 1990; Zimmerman et al. 1992; Bekele, 2010).
Self-set goals often motivate better than assigned goals.
Goal orientations
Mastery goals (internal) vs. Performance goals (external)(Ames, 1992).
Task involvement vs. Ego involvement (Nicholls, 1979).
Approach / Avoidance distinction (Elliot, 1997).
Personal goals
Focusing on Personal conceptions (Self theories).
Sébastien Louvigné ([email protected]) - UEC Tokyo IEEE - ICALT 2014. Athens, Greece.
IntroductionMethodology
Results & Findings
Learning Goal & PurposeProblem StatementProposed research
Proposed approach
This research: Using Social Context for motivation
Sharing goal orientation (goal content + purpose) with others.
Adopting new purposes for learning.
Sébastien Louvigné ([email protected]) - UEC Tokyo IEEE - ICALT 2014. Athens, Greece.
IntroductionMethodology
Results & Findings
Learning Goal & PurposeProblem StatementProposed research
Research purpose
Research Question
How to use Social Networks (i.e. peers) to improvelearning motivation?
Hypothesis
Learners enhance motivation by observing goal purposes fromother peers.
Diversity of goal purposes a�ects learners' motivation.
Sébastien Louvigné ([email protected]) - UEC Tokyo IEEE - ICALT 2014. Athens, Greece.
IntroductionMethodology
Results & Findings
Learning Goal & PurposeProblem StatementProposed research
Proposed System
Recommendation System
Diversity of Learning Purposes from peers.
Sébastien Louvigné ([email protected]) - UEC Tokyo IEEE - ICALT 2014. Athens, Greece.
IntroductionMethodology
Results & Findings
Learning Goal & PurposeProblem StatementProposed research
Learning in Social Settings
Previous works
Social Cognitive Theory
Knowledge acquisition by observing others (Bandura, 1988).
Social constructivism
Groups of learners construct knowledge collaboratively(Vygotsky, 1978).
Cognitive apprenticeship
Modelling, Sca�olding, Re�ecting knowledge (Collins, 2006).
This research: enhance motivation
Using peers' motivational contents to enhance purpose forlearning.
Sébastien Louvigné ([email protected]) - UEC Tokyo IEEE - ICALT 2014. Athens, Greece.
IntroductionMethodology
Results & Findings
Goal-based DataGoal & Purpose RecommendationImplementation
Largescale Dataset
Social Media: Twitter
Short text messages
Metadata (e.g. user pro�le, social network)
Large amount of data publicly available
(Louvigné et al. 2012; Shi & Louvigné, 2014)
Sébastien Louvigné ([email protected]) - UEC Tokyo IEEE - ICALT 2014. Athens, Greece.
IntroductionMethodology
Results & Findings
Goal-based DataGoal & Purpose RecommendationImplementation
Latent Dirichlet Allocation (LDA)
Probabilistic model for collections of discrete data (Blei et al. 2003)
α : Dirichlet parameter prior on
per-document topic distribution
β : Dirichlet parameter prior on
per-topic word distribution
(Asuncion et al. 2009)
Documents: Mixture of topics
Full conditional: P(zi = j |z−i ,w) ∝n(wi )−i ,j +β
n(.)j +Wβ
(n(di )−i ,j +α)
Dirichlet: φ̂(w)j =
n(w)j +β
n(.)j +Wβ
(Gri�ths & Steyvers. 2004)
Sébastien Louvigné ([email protected]) - UEC Tokyo IEEE - ICALT 2014. Athens, Greece.
IntroductionMethodology
Results & Findings
Goal-based DataGoal & Purpose RecommendationImplementation
Data Organization
How to use the database (goal + purpose)
LDA: Find various goals & purposes within a same learningsubject.
Probability distribution: message belonging to a �topic�.
Sébastien Louvigné ([email protected]) - UEC Tokyo IEEE - ICALT 2014. Athens, Greece.
IntroductionMethodology
Results & Findings
Goal-based DataGoal & Purpose RecommendationImplementation
Goal-based Recommendation
Process
Recommending Learning Purpose messages based on:
Similarity: similar goal.Diversity: various purposes.
Sébastien Louvigné ([email protected]) - UEC Tokyo IEEE - ICALT 2014. Athens, Greece.
IntroductionMethodology
Results & Findings
Goal-based DataGoal & Purpose RecommendationImplementation
User Interface
Sébastien Louvigné ([email protected]) - UEC Tokyo IEEE - ICALT 2014. Athens, Greece.
IntroductionMethodology
Results & Findings
Goal-based DataGoal & Purpose RecommendationImplementation
Goal Pro�le
Sébastien Louvigné ([email protected]) - UEC Tokyo IEEE - ICALT 2014. Athens, Greece.
IntroductionMethodology
Results & Findings
LDA resultsPerplexityLearners EvaluationDiscussion
LDA results
Finding various �topics�
Diverse topics within dataset of goal-based Twitter messages
Sébastien Louvigné ([email protected]) - UEC Tokyo IEEE - ICALT 2014. Athens, Greece.
IntroductionMethodology
Results & Findings
LDA resultsPerplexityLearners EvaluationDiscussion
Perplexity
Finding optimal number of topics
Di�erent optimal number of topics for each learning subject.
Not related with number of messages.
Sébastien Louvigné ([email protected]) - UEC Tokyo IEEE - ICALT 2014. Athens, Greece.
IntroductionMethodology
Results & Findings
LDA resultsPerplexityLearners EvaluationDiscussion
Goal attributes for Motivation Evaluation
Goal-Setting: Attributes in�uencing learning andperformance (Locke, 1990; Zimmerman et al. 1992; Bekele, 2010).
Goal attributes
Leading eventually to personal satisfaction (Ful�llment).
Ful�llment and achievement motivation: importantsuccess factors in learning.
Sébastien Louvigné ([email protected]) - UEC Tokyo IEEE - ICALT 2014. Athens, Greece.
IntroductionMethodology
Results & Findings
LDA resultsPerplexityLearners EvaluationDiscussion
Goal attributes evaluation
Before peers' messages observation
High perception: Importance, attainability, di�culty.
Low perception: Commitment, performance, ful�llment,con�dence.
Sébastien Louvigné ([email protected]) - UEC Tokyo IEEE - ICALT 2014. Athens, Greece.
IntroductionMethodology
Results & Findings
LDA resultsPerplexityLearners EvaluationDiscussion
Goal attributes evaluation
After observation
Similarity: slight increase in learner's perception of goalattributes.
Diversity: higher increase in speci�city and commitment,decrease for di�culty.
Sébastien Louvigné ([email protected]) - UEC Tokyo IEEE - ICALT 2014. Athens, Greece.
IntroductionMethodology
Results & Findings
LDA resultsPerplexityLearners EvaluationDiscussion
Conclusion
Using Social Context to enhance learning motivation
1 Observing goal purposes from peers.
Adopt new purposes.
2 Diversity of goal purposes.
A�ect intrinsic motivation.
Results
LDA for learning purposes recommendation
Various topics (i.e. purposes) for a same learning subject (i.e.mastery goal).
Observing goal purposes from peers
Similarity: con�rms learner perception on goal,Diversity: improve speci�city, commitment.
Sébastien Louvigné ([email protected]) - UEC Tokyo IEEE - ICALT 2014. Athens, Greece.
IntroductionMethodology
Results & Findings
LDA resultsPerplexityLearners EvaluationDiscussion
Future works
LDA
e.g. Short text analysis, Including grammatical features
Motivation evaluation
Long term experiment�Free choice�Evaluation from peers
Sébastien Louvigné ([email protected]) - UEC Tokyo IEEE - ICALT 2014. Athens, Greece.
IntroductionMethodology
Results & Findings
LDA resultsPerplexityLearners EvaluationDiscussion
Bibliography
E. A. Locke (1996), �Motivation through conscious goal setting�. Applied &Preventive Psychology.
D. H. Schunk, J. L. Meece, and P. R. Pintrich (2002), �Goals and GoalOrientations�. Motivation in Education: Theory, Research, and Applications.
P. R. Pintrich (2003), �A Motivational Science Perspective on the Role of
Student Motivation in Learning and Teaching Contexts� . Journal of EducationalPsychology.
E. A. Locke, and G. P. Latham (2002), �Building a practically useful theory of
goal setting and task motivation: A 35-year odyssey�. American Psychologist.
D. M. Blei, A. Y. Ng, and M. I. Jordan (2003), �Latent Dirichlet Allocation� .Journal of Machine Learning Research.
T. L. Gri�ths, and M. Steyvers (2004), �Finding scienti�c topics�. Nationalacademy of Sciences of the United States of America.
S. Louvigné, N. Rubens, F. Anma, and T. Okamoto (2012), �Utilizing Social
Media for Goal Setting based on Observational Learning� . IEEE Icalt 2012.
J. Shi, and S. Louvigné (2014), �Goal-Setting and Meaning-Making in Mined
Dataset of Tweets Using SFG Approach�. Journal of Electrical Engineering.Sébastien Louvigné ([email protected]) - UEC Tokyo IEEE - ICALT 2014. Athens, Greece.
IntroductionMethodology
Results & Findings
LDA resultsPerplexityLearners EvaluationDiscussion
Thank you
Sébastien Louvigné ([email protected]) - UEC Tokyo IEEE - ICALT 2014. Athens, Greece.