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Understanding, predicting and optimizing learning with Learning Analytics

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Author: Jingyan Lu, The University of Hong Kong …

Author: Jingyan Lu, The University of Hong Kong
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http://www.cite.hku.hk/news.php?id=501&category=cite

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  • 1. Understanding, predicting and optimizing learning with Learning Analytics Jingyan Lu, The University of Hong Kong July 5th, LASI-Hong Kong 7/6/2013Jingyan Lu@hku 1
  • 2. Assessment in the Education Triangle Instruction Assessment Curriculum 7/6/2013Jingyan Lu@hku 2
  • 3. Assessment triangle Observation Interpretation Cognition Pellegrino, J. W., Chudowsky, N., & Glaser, R. (Eds.). (2002). Knowing what students know: The science and design of educational assessment. Washington, DC: National Academy Press. 7/6/2013Jingyan Lu@hku 3
  • 4. Conceptual Integration of Learning Analytics and Assessment Instruction Assessment Curriculum Observation Interpretation Cognition 7/6/2013Jingyan Lu@hku 4
  • 5. Contribution of measurement and statistical modeling in Assessment 7/6/2013Jingyan Lu@hku 5
  • 6. Assessment models X = T + E Argumentative Representation Structure Justification Position Conceptual Clarification Conclusion Multiple Perspectives Identify Stakeholder Number of Stakeholder Types of Stakeholder Matching Citing Case Information Outside Information Explanation 7/6/2013Jingyan Lu@hku 6
  • 7. Conceptual Assessment Framework: Evidence Center Assessment Design (ECD) Mislevey, R. J., Steinberg, L. S., Almond, R. G., Haertel, G. D., & Penuel, W. R. (2001). Leverage points for improving education assessment. Princeton: Educational testing Service. 7/6/2013Jingyan Lu@hku 7
  • 8. Assessment in the 21st Century Classroom  “This evidence-based approach (Mislevy et al, 2003) is particularly relevant in the 21st century technology rich classroom where student’s use of technology tools for learning create a multitude of data (e.g., artefacts, log data) which can be mined, assessed, and presented in ways that students and teachers can interpret it to support learning.”  Hansen & Wasson (forthcoming) Hansen, C. & Wasson, B. (forthcoming). Formaive e-assessment in the 21st century Classroom. NEAR, Iceland, March 7/6/2013Jingyan Lu@hku 8
  • 9. Applications--Where  Online Learning Systems - online courses or learning software or interactive environments that use intelligent tutoring systems, virtual labs, or simulations, such as Moodle.  Why: Big data 7/6/2013Jingyan Lu@hku 9
  • 10. Application of LA—Predictive Models in Instructional System  When are students ready to move onto the next topic?  When are students falling behind in a course?  When is a student at risk for not completing a course?  What grade is a student likely to get without intervention?  What is the best next course for a given student?  Should a student be referred to a counselor for help? 7/6/2013Jingyan Lu@hku 10
  • 11. Typical Adaptive Learning System Predictive nature of LA 7/6/2013Jingyan Lu@hku 11
  • 12. Why are we measuring (1): Modeling and theory building User knowledge modeling User behavior modeling User experience modeling User Profiling Domain Modeling 7/6/2013Jingyan Lu@hku 12
  • 13. Examples of interactive learning environments Khan Academy 7/6/2013Jingyan Lu@hku 13
  • 14. Stakeholders ADAPTIVE LEARNING SYSTEM 7/6/2013Jingyan Lu@hku 14
  • 15. Some Examples 7/6/2013Jingyan Lu@hku 15
  • 16. Examples (1): Online learning behavior 7/6/2013Jingyan Lu@hku 16
  • 17. What are we measuring on LMS: Student model  How peer assessment affect learning Lu, J., & Zhang, Z. (2012). Understanding the effectiveness of online peer assessment: A path model. Journal of Educational Computing Research, 46(3), 313-333. 7/6/2013Jingyan Lu@hku 17
  • 18. Where do we measure it: Task model  Peer assessment  Feedback  Grading 7/6/2013Jingyan Lu@hku 18
  • 19. How do we measure: Evidence model  Online behavior  Log data  Activities  Content 7/6/2013Jingyan Lu@hku 19
  • 20. Example 2: Modeling online critical reading 7/6/2013Jingyan Lu@hku 20
  • 21. Student Model  Critical reading behavior  Critical reading and writing argument 7/6/2013Jingyan Lu@hku 21
  • 22. Task Model DiigoOASIS 7/6/2013Jingyan Lu@hku 22
  • 23. Evidence Model  Understanding online reading behavior  Building up model from Reading to writing 7/6/2013Jingyan Lu@hku 23
  • 24. Thank You jingyan@hku.hk 7/6/2013Jingyan Lu@hku 24

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