August 28, 2023
As part of various instructional approaches, adaptive learning platforms are becoming increasingly popular. In this article, we focus specifically on the use of adaptive learning in personalized, preclass learning for a flipped classroom. In the past, data on student engagement with course content generated by adaptive learning platforms was not easily accessible. However, this data is now proving to be invaluable in gaining a deeper understanding of the learning process and improving it. Our goal is to examine the connection between interactions on adaptive learning platforms and overall student success. We also aim to identify the variables that have the greatest impact on student success. To achieve this, we conducted a comprehensive analysis of adaptive learning platform data gathered from a Numerical Methods course. Our analysis included aggregate statistics, frequency analysis, and Principal Component Analysis. We used this analysis to identify the variables that exhibited the most variability and provided the most information in the data. We then explored naturally occurring clusters of students using the Partitioning Around Medoids clustering approach. We found that overall performance in the course, as measured by the final course grade, is strongly associated with two factors: (1) the behavioral interactions of students with the adaptive platform and (2) their performance on the adaptive learning assessments. We also discovered distinct student clusters that exhibited different behaviors and success in the course. This information can be used to identify students who require more support and to design evidence-based strategies to support these students.
Reference:
Yalcin, Ali, Autar Kaw, and Renee Clark. “On learning platform metrics as markers for student success in a course.” Computer Applications in Engineering Education (2023). https://doi.org/10.1002/cae.22653