How to Forecast the Students' Learning Outcomes Based on Factors of Interactive Activities in a Blended Learning Course

Abstract

This paper summarizes the research results of identifying the influencing factors in the online learning phase of a blended learning course. From such factors, we propose a model for predicting student outcomes. In our study, we have conducted several models in order to predict the student's learning outcomes, using a course of 231 participants. Obtained data from the logs file of an LMS system is analyzed using learning analytics and machine learning techniques, and the results propose that the four factors are the number of views, the number of posts, the number of forum views, and the number of on-time submitted assignments impact on the student's learning outcomes. For the forecast of the final exam grade based on the results of the formative assessment tests, Bayesian Ridge is the most accurate among the four conducted models (Linear Regression, KNR, SVM, Bayesian Ridge). Our study can be a useful material for lecturers and course designers in effectively organizing blended learning courses.