Site2011 tomidaokibayashitamura

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Site2011 tomidaokibayashitamura

  1. 1. Predictive Discursive features for learning outcome in online cooperative learning<br />TOMIDA, Eiji Ehime University, Japan<br />OKIBAYASHI, Yohei Yamaguchi University, Japan<br />TAMURA, Yasuhisa Sophia University, Japan<br />1<br />SITE 2011: Society for Information Technology & Teacher Education International Conference<br />Nashville, Tennessee, USA, March 7-11, 2011<br />PM2:45 March 8, 2011<br />
  2. 2. Background & Objectives<br />Research tasks in online cooperative learning<br />Enhancing the quality of discussion is key for learning in Face-to-Face interaction.<br />ex. Explorative Talk (Mercer, 1996)<br />Are there any domain-general actions for facilitating actions in online discussion?<br />Objectives<br />Discovering the discursive actionswhich lead to positivelearning outcomes.<br />Teachers can facilitate such actions to enhance learning.<br />Such actions are also useful for process evaluation.<br />2<br />
  3. 3. AnalysisPlan<br />Identifying words whose frequency is correlated with test scores.<br />Constructing a working hypothesis about the relationship between specific words and test scores.<br />Examining the constructed working hypothesis.<br />3<br />
  4. 4. Analysis Procedure<br />Counting the words appeared in threads. Calculate frequency of each word for person.<br />Calculating correlation coefficients between word frequencies and test scores.<br />Categorization by Named Entity extraction technique<br />Named Entityis a kind of ontological categories.<br />Categories: person, organization, plant, animal, artifact, time, location, natural entity, place, color, abstract entity, quantity, shape<br />Introduced to obtain domain-general indices.<br />4<br />
  5. 5. Research Target and Tools<br />Classes: An educational psychology course in a teacher training program.<br />Participants: 61 sophomores<br />Discussion Topic: Theories of Vygotsky and Piaget<br />Phase 1: Discussion over basic understanding<br />Phase 2: Thematically free discussion<br />Class activities: <br />Reading > FtFdiscussion > onlinediscussion > FtF discussion<br />LMS: Moodle 1.47<br />Measures for learning outcome: 8 recall test items, 8 recognition test items<br />
  6. 6. TextData Preprocessing<br />Extracting all posted entries from a backup file ofMoodle system.<br />Dividing sentences into morphological units.<br />お待ちしております。<br />お待ち し て おり ます 。<br />Calculating frequencies of each word for person.<br />Categorizing by Named Entity technique.<br />JUMAN6.0(Kurohashi & Kawahara, 2009)<br />6<br />
  7. 7. Results<br />Analysis 1: Correlations between access frequencies and test scores<br />Analysis 2: Correlations between word frequencies and test scores<br />Analysis 3:Correlations between ontologically categorized word frequencies and test scores<br />Analysis 4: Relationship between the types of exemplification and test scores.<br />7<br />
  8. 8. Analysis 1: Access andTest Scores<br />
  9. 9. 9<br />Analysis 2: Word Frequencies and Test Scores<br />
  10. 10. Analysis 2: Word Frequencies and Test Scores<br />Correlational trends vary with phases<br />Phase 1: more significant rs in recognition<br />Phase 2: more significant r s in recall<br />Total frequencies shows the similar trend<br />Total (phase 1) is only correlated with recognition score.<br />Total (phase 2) is only correlated with recall score.<br />Which word is more important?<br />Domain-specific words vs. Domain-general words<br />10<br />
  11. 11. 11<br />Phase 1: Domain-general Words<br />
  12. 12. 12<br />Phase 2: Domain-general Words<br />
  13. 13. Analysis 3: Categorization by Named Entity<br />13<br />Phase 1<br />Phase 2<br />
  14. 14. Analysis 4: Hypothesis &Procedure<br />Working Hypothesis<br />Coordinating academic concepts with personal experience will enhance learning. <br />Constructing coding categories for analysis<br />Coding reliability<br />Degree of concordance between two independent coders<br />Phase 1 (N = 54) Kappa = .766 (81.5%)<br />Phase 2 (N = 76) Kappa = .744 (80.3%)<br />14<br />
  15. 15. Analysis 4: Categories for Exemplification Types<br />15<br />
  16. 16. Distribution of Exemplification Types<br /><ul><li>Experiential Episodes increased in phase 2.
  17. 17. Thematically-freediscussion may enhance coordination between academic concepts and personal experiences. </li></ul>16<br />
  18. 18. Relationships between Exemplification and Test Scores<br />Students who produced Experiential Episodes in phase 2 scoredhigher recall performance.<br />F (1, 50) = 10.51, p = .002<br /> N Mean SD<br />Experiential Episodes 25 7.16 .31<br />Others 27 5.78 .30<br />17<br />
  19. 19. Summary and Implication<br />Word frequencies predicted test scores, but access frequencies did not.<br />Task structure might affect quality of learning.<br />Exemplification was important in online cooperative learning<br />Studentsproduced experiential episodes marked higher recall scores.<br />Teachers can show exemplification models to facilitate students’ active online discussion.<br />18<br />
  20. 20. Acknowledgement<br />19<br />The present study is supported by KAKEN-HI (Type B, #19300284, Representative: TAMURA Yasuhisa, Sophia University), a grant-in-aid for scientific research by MEXT, Japan.<br />

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