Social Constructivist Approach of Learning Motivation

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  1. 1. Social Constructivist approach of Motivation Recommendation of diverse peer messages on Social Networking Services Sbastien Louvign Ueno laboratory Graduate School of Information Systems The University of Electro-Communications April 22, 2016 Sbastien Louvign (Ueno lab. UEC) Doctorate Course 1 / 59
  2. 2. 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 Sbastien Louvign (Ueno lab. UEC) Doctorate Course 2 / 59
  3. 3. Introduction 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 Sbastien Louvign (Ueno lab. UEC) Doctorate Course 3 / 59
  4. 4. 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). Sbastien Louvign (Ueno lab. UEC) Doctorate Course 4 / 59
  5. 5. 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 Sbastien Louvign (Ueno lab. UEC) Doctorate Course 5 / 59
  6. 6. Introduction Social Constructivism Social Constructivist approach Vygotskys 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 Sbastien Louvign (Ueno lab. UEC) Doctorate Course 6 / 59
  7. 7. Introduction Social Constructivism Social Constructivism in Learning Contemporary learning Increasingly integrates social constructivism Promote & Facilitate the construction of knowledge Pedagogical approaches: Scaolding (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) Sbastien Louvign (Ueno lab. UEC) Doctorate Course 7 / 59
  8. 8. Introduction Social Constructivism 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) Sbastien Louvign (Ueno lab. UEC) Doctorate Course 8 / 59
  9. 9. Introduction Goal & Purpose for Motivation Motivation for Learning Dierent types of motivation Self-Determination Theory (Ryan & Deci, 2000) Towards an internalization of motivation Intrinsic motivation -> positive eects on learning. Focus on expectancy, value, and goals. Sbastien Louvign (Ueno lab. UEC) Doctorate Course 9 / 59
  10. 10. 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) Sbastien Louvign (Ueno lab. UEC) Doctorate Course 10 / 59
  11. 11. Introduction Goal & Purpose for Motivation 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 conict / discouragement / harm intrinsic motivation. Sbastien Louvign (Ueno lab. UEC) Doctorate Course 11 / 59
  12. 12. Introduction Goal & Purpose for Motivation Goal & Purpose Denitions 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 -> ecient when linked with learners needs (purpose for learning). Learners have dierent purposes (conceptual perceptions). Goal orientations have dierent eects on intrinsic motivation. Sbastien Louvign (Ueno lab. UEC) Doctorate Course 12 / 59
  13. 13. 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 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 inuence of self-set goals on intrinsic motivation (Locke & Latham, 1990). Adopt new purposes / perceptions -> more intrapersonal goal orientation. Sbastien Louvign (Ueno lab. UEC) Doctorate Course 13 / 59
  14. 14. 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 aects learners motivation and self-perception. Sbastien Louvign (Ueno lab. UEC) Doctorate Course 14 / 59
  15. 15. Introduction Proposed Research 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 inuential 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. Sbastien Louvign (Ueno lab. UEC) Doctorate Course 15 / 59
  16. 16. Introduction Proposed Research Proposed recommendation system Diversity in Learning Communities Learning purposes 1) Expression -> 2) Observation -> 3) Evaluation Sbastien Louvign (Ueno lab. UEC) Doctorate Course 16 / 59
  17. 17. Introduction Proposed Research 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 Sbastien Louvign (Ueno lab. UEC) Doctorate Course 17 / 59
  18. 18. Goal-based data from SNS 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 Sbastien Louvign (Ueno lab. UEC) Doctorate Course 18 / 59
  19. 19. 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) Sbastien Louvign (Ueno lab. UEC) Doctorate Course 19 / 59
  20. 20. Goal-based data from SNS SNS Data Large-Scale Dataset Twitter Short text messages Metadata (e.g. user prole, 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). Sbastien Louvign (Ueno lab. UEC) Doctorate Course 20 / 59
  21. 21. 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 reecting the multidimensional nature of human experience and interpersonal relations." Sbastien Louvign (Ueno lab. UEC) Doctorate Course 21 / 59
  22. 22. 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). Sbastien Louvign (Ueno lab. UEC) Doctorate Course 22 / 59
  23. 23. 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 & Denitions 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 Sbastien Louvign (Ueno lab. UEC) Doctorate Course 23 / 59
  24. 24. 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. Sbastien Louvign (Ueno lab. UEC) Doctorate Course 24 / 59
  25. 25. 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) Sbastien Louvign (Ueno lab. UEC) Doctorate Course 25 / 59
  26. 26. 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 Sbastien Louvign (Ueno lab. UEC) Doctorate Course 26 / 59
  27. 27. Recommending peers messages 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 Sbastien Louvign (Ueno lab. UEC) Doctorate Course 27 / 59
  28. 28. Recommending peers messages 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 Inuence of observing peers messages. Sbastien Louvign (Ueno lab. UEC) Doctorate Course 28 / 59
  29. 29. Recommending peers messages Recommender Systems Technology Enhanced Learning systems (Manouselis et al. 2012) Recommending personalized contents Similarity of item contents / user proles / other info Need to consider diversity (Erdt et al. 2015) Recommend outcomes dierent from learners expectations Sbastien Louvign (Ueno lab. UEC) Doctorate Course 29 / 59
  30. 30. 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) n (wi ) i,j +b n (.) j +W b (n (di ) i,j +a) Dirichlet: q (d) k = n (d) k +a n (.) k +Ka ; f (w) j = n (w) j +b n (.) j +W b (Griths & Steyvers. 2004) Sbastien Louvign (Ueno lab. UEC) Doctorate Course 30 / 59
  31. 31. Recommending peers messages Topic distribution LDA results Finding diverse topics -> diverse purposes Diverse topics within dataset of goal-based Twitter messages Sbastien Louvign (Ueno lab. UEC) Doctorate Course 31 / 59
  32. 32. Recommending peers messages Topic distribution Perplexity Finding optimal number of topics Dierent optimal number of topics for each learning subject. Not related with number of messages. Sbastien Louvign (Ueno lab. UEC) Doctorate Course 32 / 59
  33. 33. Recommending peers messages Goal & Purpose Recommendation Goal-based Recommendation Process Recommending Learning Purpose messages based on: Similarity: similar goal. Diversity: various purposes. Sbastien Louvign (Ueno lab. UEC) Doctorate Course 33 / 59
  34. 34. Recommending peers messages Goal & Purpose Recommendation Dissimilarity Topic distribution comparison Jensen-Shannon Divergence TJSD(qdi ,qdj ) = 1 2 DKL(qdi km)+ 1 2 DKL(qdj km) based on Kullback...

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