Date Published: 2024

Leung, J. (2024). Insights into online educator professional development: Revealing resource recommendations with association rule mining. The Journal of Applied Instructional Design, 14(2). https://jaid.edtechbooks.org/jaid_14_2/rgusdrmvhy

Source

Since 2014, the EdHub Library has offered K-12 educators more than 500 self-paced learning activities focused on teaching practices, classroom strategies, and simulations. But with so many options, how can we make it easier for educators to find what they really need?

A recent study used a tool called Association Rule Mining (ARM) — think of it like finding patterns in how people use Netflix or shop online. By looking at how teachers interact with EdHub resources, the study helped uncover which topics are often explored together and how new and returning users find what they need.

What Did We Learn?

How teachers explore the library

  • New users often follow the suggested learning path for all activities, especially in the Dyslexia and Learning module.
  • Returning users tend to go straight to what they need using bookmarks, or they use the teacher indicator sitemaps.
  • Although the search engine provides a wide overview of PD across topics and indicators, most users prefer browsing through the topic categories and teacher indicator sitemaps.

What topics go well together

  • Teachers who start with the Beginning Teacher Assistance module often move on to topics like Cognitive Engagement and Problem-Solving Strategies under the Instructional Strategies topic.
  • Modules on Classroom Observations and Instructional Planning are also frequently used together.

Why Does This Matter?

By analyzing these patterns, EdHub can:

  • Recommend resources that fit educators’ professional goals at the module level.
  • Create smoother learning paths, especially for new teachers.
  • Help administrators find PD that matches school-wide priorities.

These insights suggest the potential of ARM for curating personalized learning paths, enhancing users’ experience, and informing instructional design improvements. For example, connecting beginning teacher modules with more advanced topics offers a roadmap for differentiated PD based on experience level and topic type. This study demonstrates how educational data mining can inform strategic PD recommendations and library structure — transforming how teachers access and benefit from digital learning platforms.