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 learningTOMIDA, Eiji Ehime University, JapanOKIBAYASHI, Yohei Yamaguchi University, JapanTAMURA, Yasuhisa Sophia University, Japan1SITE 2011: Society for Information Technology & Teacher Education International ConferenceNashville, Tennessee, USA, March 7-11, 2011PM2:45 March 8, 2011

2. Background & ObjectivesResearch tasks in online cooperative learningEnhancing 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?ObjectivesDiscovering 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. AnalysisPlanIdentifying 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 ProcedureCounting 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 techniqueNamed Entityis a kind of ontological categories.Categories: person, organization, plant, animal, artifact, time, location, natural entity, place, color, abstract entity, quantity, shapeIntroduced to obtain domain-general indices.4

5. Research Target and ToolsClasses: An educational psychology course in a teacher training program.Participants: 61 sophomoresDiscussion Topic: Theories of Vygotsky and PiagetPhase 1: Discussion over basic understandingPhase 2: Thematically free discussionClass activities: Reading > FtFdiscussion > onlinediscussion > FtF discussionLMS: Moodle 1.47Measures for learning outcome: 8 recall test items, 8 recognition test items

6. TextData PreprocessingExtracting 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. ResultsAnalysis 1: Correlations between access frequencies and test scoresAnalysis 2: Correlations between word frequencies and test scoresAnalysis 3:Correlations between ontologically categorized word frequencies and test scoresAnalysis 4: Relationship between the types of exemplification and test scores.7

8. Analysis 1: Access andTest Scores

9. 9Analysis 2: Word Frequencies and Test Scores

10. Analysis 2: Word Frequencies and Test ScoresCorrelational trends vary with phasesPhase 1: more significant rs in recognitionPhase 2: more significant r s in recallTotal frequencies shows the similar trendTotal (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 words10

11. 11Phase 1: Domain-general Words

12. 12Phase 2: Domain-general Words

13. Analysis 3: Categorization by Named Entity13Phase 1Phase 2

14. Analysis 4: Hypothesis &ProcedureWorking HypothesisCoordinating academic concepts with personal experience will enhance learning. Constructing coding categories for analysisCoding reliabilityDegree of concordance between two independent codersPhase 1 (N = 54) Kappa = .766 (81.5%)Phase 2 (N = 76) Kappa = .744 (80.3%)14

15. Analysis 4: Categories for Exemplification Types15

16. Distribution of Exemplification TypesExperiential Episodes increased in phase 2.

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

18. Relationships between Exemplification and Test ScoresStudents who produced Experiential Episodes in phase 2 scoredhigher recall performance.F (1, 50) = 10.51, p = .002 N Mean SDExperiential Episodes 25 7.16 .31Others 27 5.78 .3017

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