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Learning Analytics Overview
Jun 6, 2024
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Lecture on Learning Analytics
Introduction
Former high school physics teacher.
Focus: Educational research and learning analytics.
Aim: Provide a high-level overview of learning analytics in the next 10-15 minutes.
Emphasis on actionable data for immediate educational improvement.
What is Learning Analytics?
Definition (2011):
Collection and analysis of data traces related to learning to inform and improve the process and/or outcomes.
Key Feature:
Closing the loop – data should be actionable in real-time - actionable for teachers, students, advisors.
Data in Learning Analytics
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New Developments:
Increased ability to collect diverse data.
Higher frequency and granularity of data collection.
Opportunities to connect different sources of data.
Types of Data (3 As)
Activity:
Actions taken by students (log files, physical traces, self-reports).
Artifacts:
Things created by students (answers, steps in problem-solving).
Association:
Connections made by students (interactions, temporal linkages).
Caveats
Big Data Myths:
Not neutral – influenced by the creation process.
Not natural – requires purposeful processing and planning.
Not easy to get good data – design and tracking are crucial.
Design in Learning Analytics
Data comes second to design.
Example Study:
Wise inquiry learning environment – revisiting steps with dynamic visualizations predicted learning.
Recent Study Example:
Dashboard in inquiry environment – hypothesizing alert for real-time teacher intervention.
Computation in Learning Analytics
Prediction:
Focus: Identifying individuals or features that matter (early alert systems).
Structure Discovery:
Clustering, network analysis, topic modeling (identify patterns).
Temporality:
Lag sequential analysis, hidden markov analysis (understand learning transitions).
Visual Analytics:
Visualize information to discern patterns (heat maps, sand key diagrams).
Natural Language Processing (NLP):
Extract information from student's language (rhetorical moves, argumentation).
Insight and Action
Model:
Learning analytics as a socio-technical system – human decision-making is crucial.
Process:
Questions from instructors, data interpretation, relative and absolute reference points.
Triangulation and Contextualization:
Attributing data accurately; actions based on insight.
Reflection on Pedagogy:
Changing thinking about learning and teaching based on data.
Ethical Considerations
Ethics & Privacy:
Important to handle data responsibly.
Equity:
Addressing algorithmic bias and ensuring fair outcomes.
Transparency:
Ensuring the actionable insights are understood and used correctly.
Conclusion
New Resource:
Learning Analytics Research Network - LA 101 – curated readings, videos, and tutorials.
Final Thoughts
Potential of learning analytics to change teaching and learning. Importance of informing but not dictating pedagogical decisions.
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