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P.A.R.Q.R

Related question recommendations for Piazza

PARQR is a Machine Learning driven recommendation system that improves the experience for online distance learning programs for both students and instructors. It reduces duplicate content and highlights important class activity to help users navigate the volume of content in typical class of 200-300 students. Currently, it works as an add-on for Piazza - a widely used platform for student collaboration and communication by many universities for both their in-class offerings as well as distance learning programs. It’s been actively used by Georgia Tech for it’s Online Master’s of Science (OMS) programs since 2018.

This work was published at ACM Conference on Learning@ Scale.

Link to the publication

PARQR is a Machine Learning driven recommendation system that improves the experience for online distance learning programs for both students and instructors. It reduces duplicate content and highlights important class activity to help users navigate the volume of content in typical class of 200-300 students. Currently, it works as an add-on for Piazza - a widely used platform for student collaboration and communication by many universities for both their in-class offerings as well as distance learning programs. It’s been actively used by Georgia Tech for it’s Online Master’s of Science (OMS) programs since 2018.

SUMMARY

PARQR is a Machine Learning driven recommendation system that improves the experience for online distance learning programs for both students and instructors. It reduces duplicate content and highlights important class activity to help users navigate the volume of content in typical class of 200-300 students. Currently, it works as an add-on for Piazza - a widely used platform for student collaboration and communication by many universities for both their in-class offerings as well as distance learning programs. It’s been actively used by Georgia Tech for it’s Online Master’s of Science (OMS) programs since 2018.

PROBLEM

How might we make online learning forums more efficient, less redundant, and better focused?

USER RESEARCH

The surveys we rolled out always have quantitative as well as a qualitative components. We asked certain questions to identify:
1. Current pain-points with Piazza
2. Current feedback on PARQR
3. Potential improvements for PARQR

SOLUTION DESIGN

Our team created an ML based post recommendation system, aimed specifically at reducing duplicate posts and focusing on making the process of consuming and managing large volumes of information efficient for students and instructors.

FOR STUDENTS: In-line recommendation when creating posts - PARQR suggests relevant content when users are typing the questions out. The recommendations update in real-time as students type. This prevents duplicate questions, and gets answers to students more quickly.

FOR STUDENTS: Student Recommendation dashboard - When students return to Piazza, PARQR shows them the important content that they may have missed out on, while they were away, through a recommendation dashboard.

FOR INSTRUCTORS: Recommending similar questions that have been answered - When Answering a question, PARQR suggests similar posts that have been already answered. The instructors can then simply add a reference to that answer to redirect students. This is really helpful when instructors have to answer large volumes of questions, many of which could be duplicate.

In order to handle duplicate questions, PARQR provides the capability of combining multiple unanswered questions together such that, the instructors can answer all the questions in one go. However, this functionality, as of now, does not allow modifying content for each linked question separately.

USER FEEDBACK (from MVP)