Abstract
Online learning platforms such as Massive Open Online Course (MOOC), Virtual Learning Environments (VLEs), and Learning Management Systems (LMS) facilitate thousands or even millions of students to learn according to their interests without spatial and temporal constraints. Besides many advantages, online learning platforms face several challenges such as students’ lack of interest, high dropouts, low engagement, students’ self-regulated behavior, and compelling students to take responsibility for settings their own goals. In this study, we propose a predictive model that analyzes the problems faced by at-risk students, subsequently, facilitating instructors for timely intervention to persuade students to increase their study engagements and improve their study performance. The predictive model is trained and tested using various machine learning (ML) and deep learning (DL) algorithms to characterize the learning behavior of students according to their study variables. The performance of various ML algorithms is compared by using accuracy, precision, support, and f-score. The ML algorithm that gives the best result in terms of accuracy, precision, recall, support, and f-score metric is ultimately selected for creating the predictive model at different percentages of course length. The predictive model can help instructors in identifying at-risk students early in the course for timely intervention thus avoiding student dropouts. Our results showed that students’ assessment scores, engagement intensity i.e. clickstream data, and time-dependent variables are important factors in online learning. The experimental results revealed that the predictive model trained using Random Forest (RF) gives the best results with averaged precision =0.60%, 0.79%, 0.84%, 0.88%, 0.90%, 0.92%, averaged recall =0.59%, 0.79%, 0.84%, 0.88%, 0.90%, 0.91%, averaged F-score =0.59%, 0.79%, 0.84%, 0.88%, 0.90%, 0.91%, and average accuracy =0.59%, 0.79%, 0.84%, 0.88%, 0.90%, 0.91% at 0%, 20%, 40%, 60%, 80% and 100% of course length.
Highlights
Rapid innovations in the design and development of online learning platforms such as Massive Open Online Course (MOOC), Virtual Learning Environments (VLEs), and Learning Management System (LMS) have overcome the limitations of space and time but have made access to education easy and affordable
In an online learning environment, where a large amount of data is generated every day, machine learning (ML) techniques could help in analyzing the variables that define the students and come up with the results that better describe their learning behavior, ML may reveal information that is beneficial for both instructors and students [4], [5]
A substantial improvement was noted in the performance of predictive models for Pass, Withdrawn, Distinction, and Fail classes when assessment data was added for constructing the predictive models
Summary
Rapid innovations in the design and development of online learning platforms such as Massive Open Online Course (MOOC), Virtual Learning Environments (VLEs), and Learning Management System (LMS) have overcome the limitations of space and time but have made access to education easy and affordable. In an online learning environment, where a large amount of data is generated every day, machine learning (ML) techniques could help in analyzing the variables that define the students and come up with the results that better describe their learning behavior, ML may reveal information that is beneficial for both instructors and students [4], [5]. Educational Data Mining (EDM) tools, techniques, and products have progressed significantly, helping educators to make education easy and effective [7] These techniques lack in identifying at-risk students earlier in the course timeline, compelling instructors to perform significant manual work for students problem identification to keep them on track.
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