Student Drop-out Modelling Using Virtual Learning Environment Behaviour Data

Abstract

With the rapid advancement of Virtual Learning Environments (VLE) in higher education, the amount of available student data grows. Universities collect the information about students, their demographics, their study results and their behaviour in the online environment. By applying modelling and predictive analysis methods it is possible to predict student outcome or detect bottlenecks in course design. Our work aims at statistical simulation of student behaviour in the VLE in order to identify behavioural patterns leading to drop-out or passive withdrawal i.e. the state when a student is not studying, but he has not actively withdrawn from studies. For that purpose, the method called Markov chain modelling has been used. Recorded student activities in VLE (VLE logs) has been used for constructing of probabilistic representation that students will perform some activity in the next week based on their activities in the current week. The result is an instance of the family of absorbing Markov chains, which can be analysed using the property called time to absorption. The preliminary results show that interesting patterns in student VLE behaviour can be uncovered, especially when combined with the information about submission of the first assessment. Our analysis has been performed using Open University Learning Analytics dataset (OULAD) and research notes are available online 1 .

Early prediction of student engagement in virtual learning environments using machine learning techniques

“…The Open University Learning Analytics Dataset (OULAD) is used in this study to identify the engagement characteristics of students in a VLE (Kuzilek et al, 2017(Kuzilek et al, , 2018. This anonymized dataset comprises data about courses, students, and their interactions with Open University Virtual Learning Environment for seven selected courses (called modules).…”

First-Year Engineering Students’ Strategies for Taking Exams

“…In 2018 Kuzilek et al (2018) used Markov chains on the same task as Hlosta et al (2014) with the students' VLE activities generalised to the higher level of abstraction, leading to uncovering a passive withdrawal pattern within the VLE behavioural data. Marques and Belo (2011) applied Markov chains on analysis of behavioural profiles at a Portuguese university.…”

Assignments as Influential Factor to Improve the Prediction of Student Performance in Online Courses

“…Considering the factors used to carry out the prediction tasks in OULAD, it can be observed slight differences in the most used factors with respect to the previous general work. Thus, a 39% of studies use the number of accesses to resources (clickstreams) [26,[29][30][31][32][33][34][35], while a 25% of studies combine this information with demographic data from the students [27,28,32,[36][37][38][39]. Focusing solely on assignment information, only one study [40] uses exclusively this factor.…”

“…Regarding the purpose of the different works, under the main task of predicting student performance, it can be found that the majority of studies pretend to predict whether the student will pass or fail a course [3,27,[30][31][32][37][38][39][40][41][42][43][44][45]. Other approaches focus on the dropout rate [26,29,32,33], while others follow an early prediction study [33,35,36,46]. It is also notable that most studies distinguish among courses or even presentations to make these predictions.…”

“…With regard to the algorithms used, it can be appreciated that different supervised learning techniques have been used, mainly trees-based methods [28,30,31,33,36,40,44,46], although Bayesian methods have also been applied [38], as well as support vector machines [27], generative methods based on sequences [26,32] and neural networks or deep learning [3,34,35,39,[41][42][43]45].…”