The document discusses the integration of learning analytics with educational assessment to enhance instruction and support student learning in technology-rich classrooms. It emphasizes the use of predictive modeling to identify students' readiness for topics, detect at-risk students, and tailor educational experiences. Examples include adaptive learning systems and online platforms like Khan Academy that leverage data to inform teaching strategies.

1. Understanding, predicting and optimizing
learning with Learning Analytics
Jingyan Lu, The University of Hong Kong
July 5th, LASI-Hong Kong
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2. Assessment in the Education Triangle
Instruction Assessment
Curriculum
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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.
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4. Conceptual Integration of Learning
Analytics and Assessment
Instruction Assessment
Curriculum
Observation Interpretation
Cognition
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5. Contribution of measurement
and statistical modeling in
Assessment
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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
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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.
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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
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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
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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?
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11. Typical
Adaptive
Learning
System
Predictive
nature of
LA
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12. Why are we measuring (1):
Modeling and theory building
User knowledge modeling
User behavior modeling
User experience modeling
User Profiling
Domain Modeling
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13. Examples of interactive
learning environments
Khan Academy
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14. Stakeholders
ADAPTIVE
LEARNING
SYSTEM
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15. Some Examples
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16. Examples (1): Online
learning behavior
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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.
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18. Where do we measure it: Task
model
 Peer assessment
 Feedback
 Grading
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19. How do we measure: Evidence
model
 Online behavior
 Log data
 Activities
 Content
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20. Example 2: Modeling online
critical reading
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21. Student Model
 Critical reading behavior
 Critical reading and writing argument
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22. Task Model
DiigoOASIS
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23. Evidence Model
 Understanding online
reading behavior
 Building up model from
Reading to writing
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24. Thank You
jingyan@hku.hk
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