1) The study analyzed discussion posts from an online cooperative learning environment to identify predictive features of learning outcomes. 2) Word frequencies in discussion posts were found to correlate with test scores, while access frequencies did not. The correlations varied depending on the discussion phase and test question type. 3) Students who provided personal experiences to exemplify course concepts in their posts ("experiential episodes") had higher recall test scores, indicating exemplification supported learning.

1. Predictive Discursive features for learning outcome in online cooperative learning TOMIDA, Eiji Ehime University, Japan OKIBAYASHI, Yohei Yamaguchi University, Japan TAMURA, Yasuhisa Sophia University, Japan 1 SITE 2011: Society for Information Technology & Teacher Education International Conference Nashville, Tennessee, USA, March 7-11, 2011 PM2:45 March 8, 2011

2. Background & Objectives Research tasks in online cooperative learning Enhancing the quality of discussion is key for learning in Face-to-Face interaction. ex. Explorative Talk (Mercer, 1996) Are there any domain-general actions for facilitating actions in online discussion? Objectives Discovering the discursive actionswhich lead to positivelearning outcomes. Teachers can facilitate such actions to enhance learning. Such actions are also useful for process evaluation. 2

3. AnalysisPlan Identifying words whose frequency is correlated with test scores. Constructing a working hypothesis about the relationship between specific words and test scores. Examining the constructed working hypothesis. 3

4. Analysis Procedure Counting the words appeared in threads. Calculate frequency of each word for person. Calculating correlation coefficients between word frequencies and test scores. Categorization by Named Entity extraction technique Named Entityis a kind of ontological categories. Categories: person, organization, plant, animal, artifact, time, location, natural entity, place, color, abstract entity, quantity, shape Introduced to obtain domain-general indices. 4

5. Research Target and Tools Classes: An educational psychology course in a teacher training program. Participants: 61 sophomores Discussion Topic: Theories of Vygotsky and Piaget Phase 1: Discussion over basic understanding Phase 2: Thematically free discussion Class activities: Reading > FtFdiscussion > onlinediscussion > FtF discussion LMS: Moodle 1.47 Measures for learning outcome: 8 recall test items, 8 recognition test items

6. TextData Preprocessing Extracting all posted entries from a backup file ofMoodle system. Dividing sentences into morphological units. お待ちしております。 お待ち し て おり ます 。 Calculating frequencies of each word for person. Categorizing by Named Entity technique. JUMAN6.0（Kurohashi & Kawahara, 2009） 6

7. Results Analysis 1: Correlations between access frequencies and test scores Analysis 2: Correlations between word frequencies and test scores Analysis 3:Correlations between ontologically categorized word frequencies and test scores Analysis 4: Relationship between the types of exemplification and test scores. 7

8. Analysis 1: Access andTest Scores

9. 9 Analysis 2: Word Frequencies and Test Scores

10. Analysis 2: Word Frequencies and Test Scores Correlational trends vary with phases Phase 1: more significant rs in recognition Phase 2: more significant r s in recall Total frequencies shows the similar trend Total (phase 1) is only correlated with recognition score. Total (phase 2) is only correlated with recall score. Which word is more important? Domain-specific words vs. Domain-general words 10

11. 11 Phase 1: Domain-general Words

12. 12 Phase 2: Domain-general Words

13. Analysis 3: Categorization by Named Entity 13 Phase 1 Phase 2

14. Analysis 4: Hypothesis &Procedure Working Hypothesis Coordinating academic concepts with personal experience will enhance learning. Constructing coding categories for analysis Coding reliability Degree of concordance between two independent coders Phase 1 (N = 54) Kappa = .766 (81.5%) Phase 2 (N = 76) Kappa = .744 (80.3%) 14

15. Analysis 4: Categories for Exemplification Types 15

17. Thematically-freediscussion may enhance coordination between academic concepts and personal experiences. 16

18. Relationships between Exemplification and Test Scores Students who produced Experiential Episodes in phase 2 scoredhigher recall performance. F (1, 50) = 10.51, p = .002 N Mean SD Experiential Episodes 25 7.16 .31 Others 27 5.78 .30 17

19. Summary and Implication Word frequencies predicted test scores, but access frequencies did not. Task structure might affect quality of learning. Exemplification was important in online cooperative learning Studentsproduced experiential episodes marked higher recall scores. Teachers can show exemplification models to facilitate students’ active online discussion. 18

20. Acknowledgement 19 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.