TY - GEN
T1 - Practice exams make perfect
T2 - 4th International Conference on Learning Analytics and Knowledge, LAK 2014
AU - Waddington, Richard Joseph
AU - Nam, Sung Jin
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Data analysis
KW - Data integration
KW - Data mining
KW - Early warning systems
KW - Learning analytics
KW - Learning management systems
KW - Modeling
KW - Multinomial logistic regression
UR - http://www.scopus.com/inward/record.url?scp=84898813289&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84898813289&partnerID=8YFLogxK
U2 - 10.1145/2567574.2567623
DO - 10.1145/2567574.2567623
M3 - Conference contribution
AN - SCOPUS:84898813289
SN - 1595930361
SN - 9781595930361
T3 - ACM International Conference Proceeding Series
SP - 188
EP - 192
BT - LAK 2014
Y2 - 24 March 2014 through 28 March 2014
ER -