Practice exams make perfect: Incorporating course resource use into an early warning system

Richard Joseph Waddington, Sung Jin Nam

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

Early Warning Systems (EWSs) are being developed and used more frequently to aggregate multiple sources of data and provide timely information to stakeholders about students in need of academic support. As these systems grow more complex, there is an increasing need to incorporate relevant and real-time course-related information that could be predictors of a student's success or failure. This paper presents an investigation of how to incorporate students' use of course resources from a Learning Management System (LMS) into an existing EWS. Specifically, we focus our efforts on understanding the relationship between course resource use and a student's final course grade. Using ten semesters of LMS data from a requisite Chemistry course, we categorized course resources into four categories. We used a multinomial logistic regression model with semester fixed-effects to estimate the relationship between course resource use and the likelihood that a student receives an "A" or "B" in the course versus a "C." Results suggest that students who use Exam Preparation or Lecture resources to a greater degree than their peers are more likely to receive an "A" or "B" as a final grade. We discuss the implications of our results for the further development of this EWS and EWSs in general.

Original languageEnglish
Title of host publicationLAK 2014
Subtitle of host publication4th International Conference on Learning Analytics and Knowledge
Pages188-192
Number of pages5
DOIs
StatePublished - 2014
Event4th International Conference on Learning Analytics and Knowledge, LAK 2014 - Indianapolis, IN, United States
Duration: Mar 24 2014Mar 28 2014

Publication series

NameACM International Conference Proceeding Series

Conference

Conference4th International Conference on Learning Analytics and Knowledge, LAK 2014
Country/TerritoryUnited States
CityIndianapolis, IN
Period3/24/143/28/14

Keywords

  • Data analysis
  • Data integration
  • Data mining
  • Early warning systems
  • Learning analytics
  • Learning management systems
  • Modeling
  • Multinomial logistic regression

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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