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

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

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

Content Provider	ACM Digital Library
Author	Waddington, Richard Joseph Nam, SungJin
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.
Starting Page	188
Ending Page	192
Page Count	5
File Format	PDF
ISBN	9781450326643
DOI	10.1145/2567574.2567623
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-03-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Learning management systems Data analysis Learning analytics Data integration Multinomial logistic regression Data mining Early warning systems Modeling
Content Type	Text
Resource Type	Article

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