https://github.com/openedx/recommenderxblock

edX: An XBlock to recommend resources to other students, written by Daniel Li, under my supervision
https://github.com/openedx/recommenderxblock

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edX: An XBlock to recommend resources to other students, written by Daniel Li, under my supervision

• Host: GitHub
• URL: https://github.com/openedx/recommenderxblock
• Owner: openedx
• License: agpl-3.0
• Created: 2017-07-07T15:37:29.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2024-09-24T14:22:00.000Z (3 months ago)
• Last Synced: 2024-09-24T14:52:32.082Z (3 months ago)
• Language: JavaScript
• Homepage:
• Size: 1.26 MB
• Stars: 5
• Watchers: 49
• Forks: 16
• Open Issues: 9
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE

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README

        
RecommenderXBlock

=================

This XBlock shows students a list of recommended resources for a given

problem. The resources are recommended, edited, and voted by students.

For each resource, we show its title, link, short summary, preview

screenshot, and votes:

.. image:: recommender_xblock.png

   :alt: Recommender screenshot

This is an module where students can share useful resources/hints and rate

them. This crowdsourcing mechanism allows a scalable solution to fulfill

students with varying learning needs.

* Staff Interface: manage problematic resourse easier, add comments, endorse,

  de-endorse resource

* Discussion around each resource

* Better interface for adding varying types of resource (e.g., specific timestamps

  in the video or specific elements in a learning sequence)

* Better user help/documentation

* Tag/categorize resources around specific misconceptions

In a randomized control trial in a computer science course, this XBlock led to 

similar learning outcomes in about 10% less time than without it (so efficiency of 

learning was about 10% better than without the XBlock -- students learned the same

in less time). Qualitative analysis as well as quantitative analysis of usage data 

showed it was helpful in contexts where there were complex, multiconcept problems. 

It was not helpful or used in contexts where there were simple, single-step problems. 

In an analysis comparing to other remediation systems within edX, it was more 

effective for deeper, more complex misconceptions, and less effective for simple 

errors.