Learning Analytics Overview

Jun 6, 2024

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

  • *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)

  1. Activity: Actions taken by students (log files, physical traces, self-reports).
  2. Artifacts: Things created by students (answers, steps in problem-solving).
  3. 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

  1. Prediction:
  • Focus: Identifying individuals or features that matter (early alert systems).
  1. Structure Discovery:
  • Clustering, network analysis, topic modeling (identify patterns).
  1. Temporality:
  • Lag sequential analysis, hidden markov analysis (understand learning transitions).
  1. Visual Analytics:
  • Visualize information to discern patterns (heat maps, sand key diagrams).
  1. 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.