EduPal leaves no professor behind: Supporting faculty via a peer-powered recommender system

TL;DR

This paper introduces EduPal, a peer-powered recommender system that supports faculty by crowdsourcing pedagogical practices and providing personalized, theory-based recommendations to improve teaching experiences, especially for underresourced educators.

Contribution

It presents a novel knowledge-based chatbot that crowdsources and filters pedagogical advice, enhancing faculty support through a collaborative, feedback-driven platform.

Findings

Prototype received favorable feedback from STEM faculty

System effectively crowdsources useful pedagogical practices

Supports underresourced faculty with personalized recommendations

Abstract

The swift transitions in higher education after the COVID-19 outbreak identified a gap in the pedagogical support available to faculty. We propose a smart, knowledge-based chatbot that addresses issues of knowledge distillation and provides faculty with personalized recommendations. Our collaborative system crowdsources useful pedagogical practices and continuously filters recommendations based on theory and user feedback, thus enhancing the experiences of subsequent peers. We build a prototype for our local STEM faculty as a proof concept and receive favorable feedback that encourages us to extend our development and outreach, especially to underresourced faculty.