Goal-based Recommendation utilizing Latent Dirichlet Allocation

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.


Results & Findings

Outline

1 IntroductionLearning Goal & PurposeProblem StatementProposed research

2 MethodologyGoal-based DataGoal & Purpose RecommendationImplementation

3 Results & FindingsLDA resultsPerplexityLearners EvaluationDiscussion



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)



Results & Findings


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.



Results & Findings


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.



Results & Findings


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).



Results & Findings


Proposed approach

This research: Using Social Context for motivation

Sharing goal orientation (goal content + purpose) with others.

Adopting new purposes for learning.



Results & Findings


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.



Results & Findings


Proposed System

Recommendation System

Diversity of Learning Purposes from peers.



Results & Findings


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.



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)



Results & Findings


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)



Results & Findings


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�.



Results & Findings


Goal-based Recommendation

Process

Recommending Learning Purpose messages based on:

Similarity: similar goal.Diversity: various purposes.



Results & Findings


User Interface



Results & Findings


Goal Pro�le



Results & Findings

LDA resultsPerplexityLearners EvaluationDiscussion

LDA results

Finding various �topics�

Diverse topics within dataset of goal-based Twitter messages



Results & Findings


Perplexity

Finding optimal number of topics

Di�erent optimal number of topics for each learning subject.

Not related with number of messages.



Results & Findings


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.



Results & Findings


Goal attributes evaluation

Before peers' messages observation

High perception: Importance, attainability, di�culty.

Low perception: Commitment, performance, ful�llment,con�dence.



Results & Findings


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.



Results & Findings


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.



Results & Findings


Future works

LDA

e.g. Short text analysis, Including grammatical features

Motivation evaluation

Long term experiment�Free choice�Evaluation from peers



Results & Findings


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.


Results & Findings


Thank you


Goal-based Recommendation utilizing Latent Dirichlet Allocation

Education

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