Title:
An artificial neural network based early prediction of failure-prone students in blended learning courses
Authors:
Otgontsetseg Sukhbaatar, Tsuyoshi Usagawa, Lodoiravsal Choimaa
Published:
International Journal of Emerging Technologies in Learning (iJET), volume 14, issue 19, pp.77-92 (2019)
doi: https://doi.org/10.3991/ijet.v14i19.10366
From the abstract:
"One of the objectives of the performance measurement of grade-based higher education is to reduce the failure rate of students. To identify and reduce the number of failing students, the learning activities and behaviors of students in the classroom must be continuously monitored; however, monitoring a large number of students is an extremely difficult task. A penetration of web-based learning systems in academic institutions revealed the posibility of evaluating student activities via these systems. In this paper, we propose an early prediction sceme to identify students at risk of failing in a blended learning course."