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

Analyzing Learners Behavior in MOOCs: An Examination of Performance and Motivation Using a Data-Driven Approach

Massive Open Online Courses (MOOCs) have been experiencing increasing use and popularity in highly ranked universities in recent years. The opportunity of accessing high quality courseware content within such platforms, while eliminating the burden of educational, financial and geographical obstacles has led to a rapid growth in participant numbers. The increasing number and diversity of participating learners has opened up new horizons to the research community for the investigation of effective learning environments. Learning Analytics has been used to investigate the impact of engagement on student performance. However, extensive literature review indicates that there is little research on the impact of MOOCs, particularly in analyzing the link between behavioral engagement and motivation as predictors of learning outcomes. In this study, we consider a dataset, which originates from online courses provided by Harvard University and Massachusetts Institute of Technology, delivered through the edX platform [1]. Two sets of empirical experiments are conducted using both statistical and machine learning techniques. Statistical methods are used to examine the association between engagement level and performance, including the consideration of learner educational backgrounds. The results indicate a significant gap between success and failure outcome learner groups, where successful learners are found to read and watch course material to a higher degree. Machine learning algorithms are used to automatically detect learners who are lacking in motivation at an early time in the course, thus providing instructors with insight in regards to student withdrawal

Machine Learning Approaches to Predict Learning Outcomes in Massive Open Online Courses

With the rapid advancements in technology, Massive Open Online Courses (MOOCs) have become the most popular form of online educational delivery, largely due to the removal of geographical and financial barriers for participants. A large number of learners globally enrol in such courses. Despite the flexible accessibility, results indicate that the completion rate is quite low. Educational Data Mining and Learning Analytics are emerging fields of research that aim to enhance the delivery of education through the application of various statistical and machine learning approaches. An extensive literature survey indicates that no significant research is available within the area of MOOC data analysis, in particular considering the behavioural patterns of users. In this paper, therefore, two sets of features, based on learner behavioural patterns, were compared in terms of their suitability for predicting the course outcome of learners participating in MOOCs. Our Exploratory Data Analysis demonstrates that there is strong correlation between click steam actions and successful learner outcomes. Various Machine Learning algorithms have been applied to enhance the accuracy of classifier models. Simulation results from our investigation have shown that Random Forest achieved viable performance for our prediction problem, obtaining the highest performance of the models tested. Conversely, Linear Discriminant Analysis achieved the lowest relative performance, though represented only a marginal reduction in performance relative to the Random Forest

Detecting At-Risk Students with Early Interventions Using Machine Learning Techniques

Massive Open Online Courses (MOOCs) have shown rapid development in recent years, allowing learners to access high-quality digital material. Because of facilitated learning and the flexibility of the teaching environment, the number of participants is rapidly growing. However, extensive research reports that the high attrition rate and low completion rate are major concerns. In this paper, the early identification of students who are at risk of withdrew and failure is provided. Therefore, two models are constructed namely at-risk student model and learning achievement model. The models have the potential to detect the students who are in danger of failing and withdrawal at the early stage of the online course. The result reveals that all classifiers gain good accuracy across both models, the highest performance yield by GBM with the value of 0.894, 0.952 for first, second model respectively, while RF yield the value of 0.866, in at-risk student framework achieved the lowest accuracy. The proposed frameworks can be used to assist instructors in delivering intensive intervention support to at-risk students

Towards the Differentiation of Initial and Final Retention in Massive Open Online Courses

Following an accelerating pace of technological change, Massive Open Online Courses (MOOCs) have emerged as a popular educational delivery platform, leveraging ubiqui-tous connectivity and computing power to overcome longstanding geographical and financial barriers to education. Consequently, the demographic reach of education delivery is extended towards a global online audience, facilitating learning and development for a continually ex-panding portion of the world population. However, an extensive literature review indicates that the low completion rate is the major issue related to MOOCs. Due to a lack of in-person inter-action between instructors and learners in such courses, the ability of tutors to monitor learners is impaired, often leading to learner withdrawals. To address this problem, learner drop out patterns across five courses offered by Harvard and MIT universities are investigated in this paper. Learning Analytics is applied to address key factors behind participant dropout events through the comparison of attrition during the first and last weeks of each course. The results show that the number of attired participants during the first week of the course is higher than during the last week, low percentages of attired learners are found prior to course closing dates. It is indicated therefore that assessment fees may not represent a significant reason for learners withdrawal. We introduce supervised machine learning algorithms for the analysis of learner retention and attrition within MOOC platform. Results show that machine learning represents a viable direction for the predictive analysis of MOOCs, with highest performances yielded by Boosted Tree classification for initial attrition and Neural Network based classification for final attrition

Lossy and Lossless Video Frame Compression: A Novel Approach for the High-Temporal Video Data Analytics

The smart city concept has attracted high research attention in recent years within diverse application domains, such as crime suspect identification, border security, transportation, aerospace, and so on. Specific focus has been on increased automation using data driven approaches, while leveraging remote sensing and real-time streaming of heterogenous data from various resources, including unmanned aerial vehicles, surveillance cameras, and low-earth-orbit satellites. One of the core challenges in exploitation of such high temporal data streams, specifically videos, is the trade-off between the quality of video streaming and limited transmission bandwidth. An optimal compromise is needed between video quality and subsequently, recognition and understanding and efficient processing of large amounts of video data. This research proposes a novel unified approach to lossy and lossless video frame compression, which is beneficial for the autonomous processing and enhanced representation of high-resolution video data in various domains. The proposed fast block matching motion estimation technique, namely mean predictive block matching, is based on the principle that general motion in any video frame is usually coherent. This coherent nature of the video frames dictates a high probability of a macroblock having the same direction of motion as the macroblocks surrounding it. The technique employs the partial distortion elimination algorithm to condense the exploration time, where partial summation of the matching distortion between the current macroblock and its contender ones will be used, when the matching distortion surpasses the current lowest error. Experimental results demonstrate the superiority of the proposed approach over state-of-the-art techniques, including the four step search, three step search, diamond search, and new three step search

Exploiting Time in Adaptive Learning from Educational Data

Virtual Learning Environments (VLEs) are web platforms where educational content is delivered, along with tools to support individual study. Logs that record how students interact with the platform are collected daily, so automated methods can be used to extract useful knowledge from these data. All stakeholders involved in the learning activities of the VLEs, especially students and teachers, can benefit from the insights derived from the educational data and valuable information can be extracted using machine learning algorithms. Usually, educational data are examined as stationary data using conventional batch methods. However, these data are non-stationary by nature and could be better treated as data streams. This paper reports the results of a classification study in which Random Forests, applied in both batch and adaptive mode, are used to build a model for predicting student exam failure/success. In addition, an analysis of the most important features is performed to detect the most discriminating attributes related to the student’s result. Experiments conducted on a subset of the Open University Learning Analytics (OULAD) dataset demonstrate the reliability of the adaptive version of Random Forest in accurately classifying the evolving educational data