Social Constructivist Approach of Learning Motivation

Social Constructivist approach of Motivation
Recommendation of diverse peer messages on Social Networking Services
Sébastien Louvigné
Ueno laboratory
Graduate School of Information Systems
The University of Electro-Communications
April 22, 2016
Sébastien Louvigné (Ueno lab. UEC) Doctorate Course 1 / 59

Outline
1 Introduction
Research Objective
Social Constructivism
Goal & Purpose for Motivation
Proposed Research
2 Goal-based data from SNS
SNS Data
Systemic Functional Linguistics
Transitivity Model
Goal-based messages from
peers
Summary
3 Recommending peers messages
LDA model
Topic distribution
Goal & Purpose
Recommendation
Experimentation
Self-evaluation results
4 Learning communities
Learning Activity reports
Evaluations
5 Conclusion
Discussion
Future works

Introduction
Outline
1 Introduction
Research Objective
Proposed Research
SNS Data
Transitivity Model
peers
Summary
LDA model
Topic distribution
Goal & Purpose
Recommendation
Experimentation
Evaluations
5 Conclusion
Discussion
Future works

Introduction Research Objective
Motivation for Learning
Internal force generating behaviors to achieve goals
Central part of educational psychology (Weiner, 1985).
Why do I want to learn? (reason, purpose)
What do I want to achieve? (outcome, goal)
Lack of Motivation
Largest cause of education failure (Samuelson, 2010).

Introduction Research Objective
Learning in social environments
Collaborative Learning
People interact to learn together (Dillenbourg, 1999).
Contemporary pedagogical approaches
Increasingly integrate collaboration for learning
Make learning more meaningful
Need to include psychological functions
Research Objective
Enhance learning motivation using social learning environments

Introduction Social Constructivism
Social Constructivist approach
Vygotsky’s Social Developmental theory
People actively and cognitively construct knowledge (Piaget, 1937).
People learn from others (Vygotsky, 1978; Vygotsky, 1986)
Key characteristics
Expand “Zone of Proximal Development”
Support from “More Knowledgeable Others”
Development of Higher psychological functions

Social Constructivism in Learning
Contemporary learning
Increasingly 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 approaches
How 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 research
1 Collaborative learning environment to Enhance / Generate new
intrinsic motivation
2 More diverse social environment -> Social Network Services (SNS)

Introduction Goal & Purpose for Motivation
Motivation for Learning
Diﬀerent types of motivation
Self-Determination Theory (Ryan & Deci, 2000)
Towards an internalization of motivation
Intrinsic 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 formal
education.
Goal Orientation should be set properly
Risk of conﬂict / discouragement / harm intrinsic motivation.

Goal & Purpose
Definitions
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 -> efficient when linked with learner’s needs (purpose for
learning).
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 not
mastering task (not being
wrong)
Performance
orientation
Being superior, the
smartest, best at task in
comparison to others
(normative standards)
Avoiding inferiority, not
looking stupid or dumb in
comparison to others
(normative standards)
High inﬂuence of self-set goals on intrinsic motivation (Locke &
Latham, 1990).
Adopt new purposes / perceptions -> more intrapersonal goal
orientation.

Introduction Proposed Research
Research purpose
Needs
Incorporation of Psychological aspects
Learning Motivation enhancement
Diversity in collaborative learning environments
Hypothesis
1 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 diversity
Massive 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 SNS
1 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 from
Social Media
II. Recommending peers
messages to enhance learning
motivation
1. Data Collection 3. Topic Distribution
4. Goal Expression
2. Data Analysis 5. Recommendation System
6. Observation
7. Evaluation
8. Learning communities

Goal-based data from SNS
Outline
1 Introduction
Research Objective
Proposed Research
SNS Data
Transitivity Model
peers
Summary
LDA model
Topic distribution
Goal & Purpose
Recommendation
Experimentation
Evaluations
5 Conclusion
Discussion
Future works

Goal-based data from SNS SNS Data
Social Networking Services
Internet + SNS
Essential part of personal life / communication
Many research works on education
Largest SNS: Facebook & Twitter (Tess, 2013)
Various results -> 2 opinions
Positive impact on learning behavior
Only 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
Twitter
Short text messages
Metadata (e.g. user proﬁle, social network)
Large amount of data publicly available
Research works on Twitter
Access for informational purposes (Hughes et al. 2012).
Correlation with cognition stimulation / conscientiousness.
Small amount of information generates reaction (Sysomos, 2010).
Data containing Learning concepts
Filter 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 interpersonal
relations."

Goal-based data from SNS Systemic Functional Linguistics
Systemic Functional Grammar (SFG)
Functional semantic perspective
Linking 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).

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 elements
Material: 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 - Verbiage
Behavioral: 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”) in
goal-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
Findings
1 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 meaning
Giving 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
Limitations
Focus on ideational dimension, Transitivity model

Recommending peers messages
Outline
1 Introduction
Research Objective
Proposed Research
SNS Data
Transitivity Model
peers
Summary
LDA model
Topic distribution
Goal & Purpose
Recommendation
Experimentation
Evaluations
5 Conclusion
Discussion
Future works

Context
Needs
Learning motivation enhancement
Integration in collaborative learning environments
more diverse social presence,
intrinsic motivational contents from other peers.
Objective
1 Recommendation system
Goal-based messages from other peers.
Diverse purposes (reasons) for a shared goal (desired outcome).
2 Motivation evaluation
Inﬂuence of observing peers’ messages.

Recommender Systems
Technology Enhanced Learning systems (Manouselis et al. 2012)
Recommending personalized contents
Similarity 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 +W b
(n
(di )
i,j +a)
Dirichlet: ˆq
(d)
k =
n
(d)
k +a
n
(.)
k +Ka
; ˆf
(w)
j =
n
(w)
j +b
n
(.)
j +W b
(Griﬃths & Steyvers. 2004)

Recommending peers messages Topic distribution
LDA results
Finding diverse “topics” -> diverse purposes
Diverse 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 comparison
Jensen-Shannon Divergence
TJSD(qdi
,qdj
) =
1
2
DKL(qdi
km)+
1
2
DKL(qdj
km)
based on Kullback-Leibler divergence DKL(qdi
km) = Âk qdi ,k ln
qdi ,k
m with
m = 1
2 (qdi
+qdj
).
Advantages
Symmetric method.
Measuring the similarity between 2 probability distributions.
Complementary -> dissimilarity = diversity.

Goal-based Recommendation System
Algorithm
1 Input:
qG : LDA Topic Distribution for each document for a speciﬁc goal G
X: 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 access
1 Login using Twitter account
2 Write & Evaluate learning goals
Create “Learning Goal Proﬁle”
3 Observe diverse messages from peers
Further accesses
1 Login using Twitter account
2 Observe diverse messages from peers
Based on previously created learning goal
3 Update learning goals
New expression and evaluations

Learning Goal Proﬁle

Recommending peers messages Self-evaluation results
Evaluating Motivation
Measurement methods
Self-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 success
factors in learning.

Questionnaire

Experiment
Participants
77 Undergraduate students in University of Electro-Communications
(Tokyo)
English classes
Scenario
1 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, and
Confidence.

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 and
goal-setting.

Causal relationships between goal attributes
Similarity

Learning communities
Outline
1 Introduction
Research Objective
Proposed Research
SNS Data
Transitivity Model
peers
Summary
LDA model
Topic distribution
Goal & Purpose
Recommendation
Experimentation
Evaluations
5 Conclusion
Discussion
Future works

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 Communities
Need 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 messages
Diversity: learning purposes + learning activities.

Learning communities Evaluations
T-test (pre-observation / post-observation)
Variations (P[T  t])
Attributes Learning activity
messages
Only learning
purposes
Commitment 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/A
Hours 1.10 (0.10) 0.10 (0.50)
Significant impact on Commitment, and Motivation (extrinsic /
intrinsic).

Conclusion
Outline
1 Introduction
Research Objective
Proposed Research
SNS Data
Transitivity Model
peers
Summary
LDA model
Topic distribution
Goal & Purpose
Recommendation
Experimentation
Evaluations
5 Conclusion
Discussion
Future works

Conclusion
Conclusion
Using Social Context to enhance Learning Motivation
1 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.
Results
Observing diverse SNS messages from peers
Positive impact on Motivation.
Diversity: positive impact on Attainability, Specificity, and
Confidence.
Confidence and Commitment appear as measure of success in
goal-setting.
Motivation and Commitment enhancement with Learning
Communities implementation.

Conclusion Discussion
Contributions
Enhancing motivation with a more diverse social environment
Integrated Motivational contents in:
Collaborative learning environment
Recommendation System
Importance of Diversity
Observing diverse purposes from peers enhanced self-perceptions
Recommendation factor
LDA
3-level distinction: document-topic-word
Recommendation based on topic dissimilarity
Learning Communities
More dynamic implementation of motivation
Better enhancement of motivation

Conclusion Future works
Future works
Learning Environment
Integration of motivation in Learning Management Systems.
Motivation evaluation
Develop “Free choice” method
Time of study
Behavior and decision making (e.g. joining optional class)
Other learning subjects
LDA
e.g. Short text analysis, Including grammatical features

List of Publications
S. Louvigné, and N. Rubens (2016), “Meaning-Making Analysis and Topic Classiﬁcation of
SNS Goal-based messages”. Behaviormetrika 43(1).
S. Louvigné, Y. Kato, N. Rubens, and M. Ueno (2015), “SNS messages Recommendation
for Learning Motivation”. Artiﬁcial Intelligence in Education (International Conference).
S. Louvigné, Y. Kato, N. Rubens, and M. Ueno (2015), “Goal-based messages
Recommendation utilizing Latent Dirichlet Allocation”. The 14th IEEE International
Conference on Advanced Learning Technologies (ICALT).
J. Shi, and S. Louvigné (2014), “Goal-Setting and Meaning-Making in Mined Dataset of
Tweets Using SFG Approach”. Journal of Electrical Engineering.
S. Louvigné, N. Rubens, F. Anma, and T. Okamoto (2012), “Utilizing Social Media for
Goal Setting based on Observational Learning”. 2012 IEEE 12th International Conference
on Advanced Learning Technologies (ICALT).
S. Louvigné, N. Rubens, F. Anma, and T. Okamoto (2012), “Utilizing Social Media for
Observational Goal Setting”. Computers and Advanced Technology in Education
(International Conference).

Bibliography
L. Vygotsky (1978), “Mind in Society: The Development of Higher Psychological
Processes”. Harvard University Press.
J. S. Eccles, and A. Wigfield (2002), “Motivational Beliefs, Values, and Goals”. Annual
review 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 Student
Motivation in Learning and Teaching Contexts”. Journal of Educational Psychology.
C. Ames (1992), “Classrooms: Goals, Structures, and Student Motivation”. Journal of
Educational Psychology.
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. Griffiths, and M. Steyvers (2004), “Finding scientific topics”. National academy of
Sciences of the United States of America.

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Social Constructivist Approach of Learning Motivation

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