pGPA: a personalized grade prediction tool to aid student success

Authors:
Mark Sheehan;Young Park
Affiliations:
Bradley University, Peoria, IL, USA;Bradley University, Peoria, IL, USA
Venue:
Proceedings of the sixth ACM conference on Recommender systems
Year:
2012

Citing 2
Cited 0

Recommender Systems: An Introduction

Recommender Systems: An Introduction
Predicting and preventing student failure – using the k-nearest neighbour method to predict student performance in an online course environment

International Journal of Learning Technology

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Abstract

Many educational institutions are starting to make use of their scholastic data to improve the academic experience for their students. To aid in this endeavor we have developed a research prototype implementation of a collaborative filtering-based tool called the personalized Grade Prediction Advisor (pGPA). The goal of this prototype tool is to demonstrate the potential of recommender technology by providing grade predictions for upcoming courses in a student's academic career to support decision-making for administrators, students, educators, and academic advisors. In this demonstration we briefly describe the underlying technology and potential applications of pGPA. We then present how a user can interact with pGPA to produce and interpret personalized grade predictions for an individual student or group of students.