The Application of Gaussian Mixture Models for the Identification of At-Risk Learners in Massive Open Online Courses

With high learner withdrawal rates in the setting of MOOC plat-forms, the early identification of at risk student groups has be-come increasingly important. Although many prior studies con-sider the dropout issue in form of a sequence classification prob-lem, such works address only a limited set of behavioral dynamics, typically recorded as sequance of weekly interval, neglecting important contextual factors such as assignment deadlines that may be important components of student latent engagement. In this paper we therefore aim to investigate the use of Gaussian Mixture Models for the incorporation such im-portant dynamics, providing an analytical assessment of the in-fluence of latent engagement on students and their subsequent risk of leaving the course. Additionally, linear regression and , k- nearest neighbors classifiers were used to provide a performance comparison. The features used in the study were constructed from student behavioral records, capturing activity over time, which were subsequently organized into six time intervals, corre-sponding to assignment submission dates. Results obtained from the classification procedure yielded an F1-Measure of 0.835 for the Gaussian Mixture Model, indicating that such an approach holds promise for the identification of at risk students within the MOOC setting

Predicting Student Success in a Self-Paced Mathematics MOOC

abstract: While predicting completion in Massive Open Online Courses (MOOCs) has been an active area of research in recent years, predicting completion in self-paced MOOCS, the fastest growing segment of open online courses, has largely been ignored. Using learning analytics and educational data mining techniques, this study examined data generated by over 4,600 individuals working in a self-paced, open enrollment college algebra MOOC over a period of eight months. Although just 4% of these students completed the course, models were developed that could predict correctly nearly 80% of the time which students would complete the course and which would not, based on each student’s first day of work in the online course. Logistic regression was used as the primary tool to predict completion and focused on variables associated with self-regulated learning (SRL) and demographic variables available from survey information gathered as students begin edX courses (the MOOC platform employed). The strongest SRL predictor was the amount of time students spent in the course on their first day. The number of math skills obtained the first day and the pace at which these skills were gained were also predictors, although pace was negatively correlated with completion. Prediction models using only SRL data obtained on the first day in the course correctly predicted course completion 70% of the time, whereas models based on first-day SRL and demographic data made correct predictions 79% of the time.Dissertation/ThesisDoctoral Dissertation Educational Technology 201

The application of Machine Learning for Early Detection of At -Risk Learners in Massive Open Online Courses

With the rapid improvement of digital technology, Massive Open Online Courses (MOOCs) have emerged as powerful open educational learning platforms. MOOCs have been experiencing increased use and popularity in highly ranked universities in recent years. The opportunity to access high-quality courseware content within such platforms, while eliminating the burden of educational, financial and geographical obstacles has led to a growth in participant numbers. Despite the increasing participation in online courses, the low completion rate has raised major concerns in the literature. Identifying those students who are at-risk of dropping out could be a promising solution in solving the low completion rate in the online setting. Flagging at-risk students could assist the course instructors to bolster the struggling students and provide more learning resources. Although many prior studies have considered the dropout issue in the form of a sequence classification problem, such works only address a limited set of retention factors. They typically consider the learners’ activities as a sequence of weekly intervals, neglecting important learning trajectories. In this PhD thesis, my goal is to investigate retention factors. More specifically, the project seeks to explore the association of motivational trajectories, performance trajectories, engagement levels and latent engagement with the withdrawal rate. To achieve this goal, the first objective is to derive learners’ motivations based on Incentive Motivation theory. The Learning Analytic is utilised to classify student motivation into three main categories; Intrinsically motivated, Extrinsically motivated and Amotivation. Machine learning has been employed to detect the lack of motivation at early stages of the courses. The findings reveal that machine learning provides solutions that are capable of automatically identifying the students’ motivational status according to behaviourism theory. As the second and third objectives, three temporal dropout prediction models are proposed in this research work. The models provide dynamic assessment of the influence of the following factors; motivational trajectories, performance trajectories and latent engagement on students and the subsequent risk of them leaving the course. The models could assist the instructor in delivering more intensive intervention support to at-risk students. Supervised machine learning algorithms have been utilised in each model to identify the students who are in danger of dropping out in a timely manner. The results demonstrate that motivational trajectories and engagement levels are significant factors, which might influence the students’ withdrawal in online settings. On the other hand, the findings indicate that performance trajectories and latent engagement might not prevent students from completing online courses