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Online learning processes - what users do and why it is important

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Keynote Young Researcher's Workshop at ITK07, Finland …

Keynote Young Researcher's Workshop at ITK07, Finland
Abstract:
Online settings provide non-reactive ways to observe learners' activities. While the learning itself is not directly visible for example in a logfile or within the history of a Wiki, the growing expressiveness in online learning environments takes us a step nearer to witness and analyse learning processes.

In this presentation four questions will be discussed:

(1) What is the relation between possible user activities in recent E-Learning environments and actual learning processes?

(2) What data is available to observe the users' activities?

(3) What data analysis methods are fitting to model these activities?

(4) What research designs do we need to get more insight into learning processes?

As an example, some ideas, experiences and results from an ongoing research project about online user participation and motivation will be presented and discussed, focussing on a multimethod approach.

Published in: Education, Technology

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  • 1. Online Learning Processes – What Users Do and Why it is Important Karsten D. Wolf Didactical Design of Interactive Learning Environments interaktiivinen tekniikka koulutuksessa 2007 Wednesday, 18.04.2007 cc by Karsten D. Wolf 2007 1
  • 2. Karsten D. Wolf Faculty educational science Didactical design of interactive learning environments 2
  • 3. www.everlearn.eu User Generated Content Didactical Prototyping Collaborative Learning cc by Karsten D. Wolf 2007 3
  • 4. 4
  • 5. roadmap 1. Why educational research is hard 2. How can we produce evidence in educational research? 3. Multimethod approaches in educational research 4. Alternative methods: Intra-individual analysis of micro process timeseries 5. Educational technology as a research frontier in learning science 6. Some applied results © Karsten D. Wolf 2007 5
  • 6. soft sciences are hard cc by Karsten D. Wolf 2007 6
  • 7. cc by Karsten D. Wolf 2007 7
  • 8. btw: this is not a real sim. rocks do not decelerate before hitting the ground! cc by Karsten D. Wolf 2007 8
  • 9. what if rocks would behave? cc by Karsten D. Wolf 2007 9
  • 10. cc by Karsten D. Wolf 2007 10
  • 11. covariates hell pic by Don Gato onWolf 2007 cc by Karsten D. Flickr 11
  • 12. 3 strategies control them live with them r&d approach Fischer/Waibel/Wecker (2006): Nutzenorientierte Grundlagenforschung im Bildungsbereich Argumente einer internationalen Diskussion cc by Karsten D. Wolf 2007 12
  • 13. experimental research design based research engineering approaches cc by Karsten D. Wolf 2007 13
  • 14. cc by Karsten D. Wolf 2007 14
  • 15. 75% cc by Karsten D. Wolf 2007 15
  • 16. Educational Researcher, Vol. 32, No. 9, 3–14 Educational Researcher, Vol. 33, No. 2, 3–11 cc by Karsten D. Wolf 2007 16
  • 17. Problems with quasi-experiments n o s p im ox Sd a r a p Steyer 2007 - www.causal-effects.de cc by Karsten D. Wolf 2007 17
  • 18. Problems with quasi-experiments Steyer 2007 - www.causal-effects.de cc by Karsten D. Wolf 2007 18
  • 19. Problems with quasi-experiments Steyer 2007 - www.causal-effects.de cc by Karsten D. Wolf 2007 19
  • 20. Random Assignment & Propensity Score Matching • Random Assignment • is the population large enough? • can the assignment truly random? • doesn‘t it feel like a lottery? • Propensity Score Matching • need to collect covariates • matching at an individual level between treatment and control © Karsten D. Wolf 2007 20
  • 21. Multimethod approaches: assessment methods • self-reports (behaviors, psychological contructs, forms of behaviors) • informant assessment • ecological momentary assessment • web based / computer based methods • ability tests • implicit methods (performance measures) • sequential observational methods © Karsten D. Wolf 2007 21
  • 22. Multimethod approaches:assessment methods • quantitative text analysis • multilevel analysis: physiological & biochemical measures • brain imaging • experimental methods • nonreactive methods © Karsten D. Wolf 2007 22
  • 23. Experiments vs. Assessments Why is Explanandum? If (A1 and A2) then Explanandum (general law, explanans) A1 (antecedent condition 1) A2 (antecedent condition 2) Therefore: E (explanandum) Experiments aim to develop and test general laws Assessments aim to show that the antecedent conditions are met Erdfelder & Musch 2006 © Karsten D. Wolf 2007 23
  • 24. Level of nonreactivity Low High Type 0 Type 1 Type2 Type 3 Type 4 Type 5 Setting initially designed yes yes yes yes yes no or selected for research Participants are aware likely likely likely likely no no of the research setting Participants are aware of the likely likely likely no no no research question Participants are aware of the likely likely no no no no research hypothesis Participants are aware of the likely no no no no no measures‘ manipulability Participative Bogus pipeline Personality Cover story Lost letter Analysis of Examples expert interview technique questionaire experiments technique archival data Fritsche and Linneweber 2006 © Karsten D. Wolf 2007 24
  • 25. Methods for analyzing multimethod data • Multitrait-Multimethod Matrix (Campbell and Fiske 1959) • categorical variables analysis for asessing multimethod association • multimethod item response theory • multilevel analysis • structural equation models • longitudinal methods © Karsten D. Wolf 2007 25
  • 26. cc by Karsten D. Wolf 2007 26
  • 27. 27
  • 28. 28
  • 29. 29
  • 30. 30
  • 31. 31
  • 32. 32
  • 33. I feel taken seriously (EG1 TN1 SoLe III) 33
  • 34. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 34
  • 35. Taken seriously Cognitive Load Participation I feel good I understand I am interested 35
  • 36. I am interested for19 students in EG1 SoLe III 36
  • 37. I feel taken seriously (EG1 TN1 SoLe III) 37
  • 38. Trend I feel taken seriously (EG1 TN1 SoLe III) Linear Trend 38
  • 39. Trend I feel taken seriously (EG1 TN1 SoLe III) 3rd Polynom 39
  • 40. Trend I feel taken seriously (EG1 TN1 SoLe III) Spline λ=10.000 40
  • 41. Trend I feel taken seriously (EG1 TN1 SoLe III) Spline λ=100.000 41
  • 42. Decomposition I feel taken seriously (EG1 TN1 SoLe III) Trend Microprocesses 42
  • 43. ARMA-Analysis 43
  • 44. Model parameters R2 Φ AIC BIC Φ1 = 0,307 AR(1) 1201,8 1208,0 0,094 Φ1 = 0,238 AR(2) 1190,2 1200,9 0,139 Φ2 = 0,223 Φ1 = 0,237 Φ2 = 0,222 AR(3) 1192,1 1206,5 0,139 Φ3 = 0,007 Φ1 = 0,236 Φ2 = 0,241 Φ4 = AR(5) -0,077 1194,2 1215,8 0,145 Φ5 = -0,018 Φ3 = 0,030 44
  • 45. AR(2)-Model 45
  • 46. Multivariate Analyis: Cross Correlations 0,120 I I 0,122 FG P -0.143 0.146 CL t-3 t-2 t-1 t 46
  • 47. Multivariate Models: GMDH I 0,097 FG P -0.15 0.13 CL t-2 t-1 t 47
  • 48. Fuzzy Modelling (Zadeh) “As the complexity of a system increases, our ability to make precise and significant statement about its behaviour diminishe until a threshold is reached … the closer one look at a real-world problem the fuzzier becomes its solution” (Kosko, 94) 48
  • 49. Fuzzification 1 0,5 0 Negative_X ZERO_X Positive_X 49
  • 50. Fuzzy Rule Induction IF TakenSeriously (t-1) NOT_Participation (t-1) & NOT_Participation (t-2) & TakenSeriously (t-2) OR OR NOT_Participation (t-1) NOT_Understand (t-1) & CognitiveLoad (t-2) & CognitiveLoad (t-2) THEN FeelGood TN1 (prewhitened) 50
  • 51. Advantages? Better communication? Different interrelationships at different levels? (Thresholds) non-linear models? basis for building hypotheses? 51
  • 52. disadvantages time consuming lot of rules no selection criteria no „test of significance“ 52
  • 53. Online learning as a research frontier? cc by Karsten D. Wolf 2007 53
  • 54. cc by Karsten D. Wolf 2007 54
  • 55. cc by Karsten D. Wolf 2007 55
  • 56. how to measure this? what is happening here? cc by Karsten D. Wolf 2007 56
  • 57. ??? cc by Karsten D. Wolf 2007 57
  • 58. ??? Learning accompanying actions Browse Search Write … cc by Karsten D. Wolf 2007 58
  • 59. ??? creation cc by Karsten D. Wolf 2007 59
  • 60. ??? construction cc by Karsten D. Wolf 2007 60
  • 61. ??? communication cc by Karsten D. Wolf 2007 61
  • 62. ??? cooperation cc by Karsten D. Wolf 2007 62
  • 63. ??? collaboration cc by Karsten D. Wolf 2007 63
  • 64. Some results from a prior online seminar ZfE Cover © Prof. Dr. Karsten D. Wolf 2007 64
  • 65. Activity # forum # created # chat msg entries words female 46.2 7.0 2301 n=23 (57.92) (7.44) (363.5) male 24.7 3.0 1919 n=13 (23.43) (3.08) (419.0) 1-tail significant test, n=36 © Prof. Dr. Karsten D. Wolf 2007 65
  • 66. Activity and Post-Interest # forum # created # chat msg entries words Post- .11 .19 .34* interest (.258) (.135) (.022) 1-tail significant test, n=36 © Prof. Dr. Karsten D. Wolf 2007 66
  • 67. Pre-Interest and Activity # forum # created # chat msg entries words .30* .28 .36* Pre-interest (.043) (.053) (.019) 1-tail significant test, n=36 © Prof. Dr. Karsten D. Wolf 2007 67
  • 68. Activity and Post-Interest # forum # created # chat msg fact entries words by p ors ou re-in .34* t t(.022) eres Post- .11 .19 t! interest (.258) (.135) 1-tail significant test, n=36 © Prof. Dr. Karsten D. Wolf 2007 68
  • 69. Activity and Social Relatedness # forum # created # chat msg entries words social -.08 .30* .31* relatedness (.319) (.036) (.035) 1-tail significant test, n=36 © Prof. Dr. Karsten D. Wolf 2007 69
  • 70. Pre-Knowledge and Activity # forum # created # chat msg entries words pre- .34* .47** .54*** problem (.021) (.002) (<.001) solving 1-tail significant test, n=36 © Prof. Dr. Karsten D. Wolf 2007 70
  • 71. Activity and Knowledge # forum # created # chat msg entries words post- .03 .28* .43** problem (.425) (.048) (.005) solving 1-tail significant test, n=36 © Prof. Dr. Karsten D. Wolf 2007 71
  • 72. Which means: constructivism is right after all! © Karsten D. Wolf 2007 72
  • 73. © Prof. Dr. Karsten D. Wolf 2005 http://www.karsten-d-wolf.de/ wolf@uni-bremen.de 73