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This document discusses computational discourse science and its applications for emerging learning environments. It begins by outlining key areas of research like modeling knowledge construction in collaborative interactions and exploring emotions and language. It then describes theoretical frameworks and tools used for language and discourse analysis. Significant applications discussed are evaluating text difficulty, modeling emotions during learning, and analyzing cognitive, affective and social processes in computer-mediated collaborative learning environments like MOOCs. Main areas for future research proposed are taking a holistic view of learning processes and combining advanced natural language processing with social network analysis and behavioral data.
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- 1. Computational Discourse Science: Applications for Emerging Learning Environments Nia Dowell Cognitive Psychology Institute for Intelligent Systems University of Memphis https://sites.google.com/site/niadowell/
- 2. Psychology Linguistics Communication Education Computer Science International Relations Business Intelligence Natural Language Processing Text Analytics Computational Linguistics Text Mining Discourse processing Corpus Analysis Linguistic Analysis Computational Discourse Science
- 3. • Collaborative Interaction and Language • Modeling knowledge construction in small group collaborative interactions • Exploring social capital & learning in MOOCs • Modeling socioaffective responses in collaborative interactions • Modeling cognitive presence in collaborative interactions • Emotions, Learning and Language • Human perception of naturalistic affective states during human-computer interactions • Cohesion relationships in tutorial dialogue as predictors of affective states • Teachers Quality Training • Exploring growth of pedagogical and content knowledge through language • Political Language and Crisis • Modeling social disequilibrium in authoritarian regimes • Temporal dynamics of social cohesion and casualties during the Syrian revolution • Discourse and social dynamics in Arab Spring • Clinical Interactions • Quality of therapists and clients interpersonal communication • Psychological functioning Big Picture: Computational Discourse Science
- 4. Overview • Text analytics: Theory and Approaches • Significant applications in learning sciences • Moving forward • Open discussion
- 6. The Scope of Analytical Approaches Manual Content Analysis Automated Text Analysis • Theoretically motivated • Has been historically informative • Subjective • Time consuming; not scalable • Theoretically motivated • Psychologically validated • Objective, systematic, and quantitative • Fast; scalable • Enables real time adaptive, personalized feedback for students
- 7. Models of Discourse Processing in Psychology — Construction-integration model (Kintsch) — Structure building framework (Gernsbacher) — Causal structure (Trabasso, Van den Broek) — Landscape model (Van den Broek) — Constructionist theory (Graesser, Singer, Trabasso) — Event indexing model (Zwaan, Magliano, Graesser) — Memory-based resonance model (Myers, O’Brien) — Embodied cognition (Glenberg, Zwaan) Genre and rhetorical structure Situation model • Causal, intentional, temporal, spatial logical relationships • Connectives Textbase • Explicit ideas (propositions) • Referential cohesion Syntax Words Graesser & McNamara (2011). Topics in Cognitive Science. Multilevel Theoretical Framework
- 11. Overview • Text analytics: Theory and Approaches • Significant applications in learning sciences • Moving forward • Open discussion
- 13. Evaluating Texts • Objective measure text characteristics that can be used to • Evaluate text difficulty of student assignments • Make text modifications to facilitate learning…adding surface level indicators of relations between ideas in the texts (anaphoric referents, synonymous, connective ties, background information) • Reverse Cohesion Effect
- 19. Conversational Cues • Dialogue Features – Student Answer Quality – Tutor Feedback – Tutor Directness – Cohesion – Response Verbosity D’Mello, Dowell, & Graesser, 2009
- 20. Classification Accuracy • Posture – Boredom (73%) and Flow (82%) • Face – Confusion (76%) and Delight (90%) • Dialogue – Frustration (78%) D’Mello, Picard, & Graesser, 2007
- 27. Cognitive Affective Social Learner Holistic Learner Modeling Monitoring cognitive processes- to help understand their prior knowledge of domain Optimizing learners’ interactions with educational material – Scaling it for the appropriate level of difficulty Optimizing learners’ social interactions- personalized suggestions Monitoring students affect during learning – to facilitate adaptive feedback
- 28. Overview • Text analytics: Theory and Approaches • Significant applications in learning sciences • Moving forward • Open discussion