This article presents a general-purpose method for detecting cheating in online courses, which combines anomaly detection and supervised machine learning. Using features that are rooted in psychometrics and learning analytics literature, and capture anomalies in learner behavior and response patterns, we demonstrate that a classifier that is…

A fixed learning path for all learners is a major drawback of virtual learning systems. An online learning path recommendation system has the advantage of offering flexibility to select appropriate learning content. Learning Analytics Intervention (LAI) provides several educational benefits, particularly for low-performing students. Researchers…

Both educational data mining and learning analytics aim to understand learners and optimise learning processes of educational settings like Moodle, a learning management system (LMS). Analytics in an LMS covers many different aspects: finding students at risk of abandoning a course or identifying students with difficulties before the assessments.…

Video clickstream behaviors such as pause, forward, and backward offer great potential for educational data mining and learning analytics since students exhibit a significant amount of these behaviors in online courses. The purpose of this study is to investigate the predictive relationship between video clickstream behaviors and students' test…

Students organize and manage their own learning time, choosing when, what, and how to study due to the flexibility of online learning. Each person has unique learning habits that define their behaviours and distinguish them from others. To investigate the temporal behaviour of students in online learning environments, we seek to identify suitable…

Background: Small private online courses (SPOCs) are one of the strategies to introduce the massive open online courses (MOOCs) within the university environment and to have these courses validates for academic credit. However, numerous researchers have highlighted that academic dishonesty is greatly facilitated by the online context in which…

Massive Open Online Massive Open Online Courses (MOOCs) have been transitioning slowly from being completely open and without clear recognition in universities or industry, to private settings through the emergence of Small and Massive Private Online Courses (SPOCs and MPOCs). Courses in these new formats are often for credit and have clear market…

In order to enhance the experience of learning, many educators applied learning analytics in a classroom, the major principle of learning analytics is targeting at-risk student and given timely intervention according to the results of student behavior analysis. However, when researchers applied machine learning to train a risk identifying model,…

During the COVID-19 pandemic, an adult noncredit program in the California Community College system partnered with Ease Learning to help convert face-to-face courses to an online modality. Subsequent data revealed a misalignment in the courses' Student Learning Outcomes and Instructional Objectives which became a barrier to student success. Wile's…

The widespread adoption of online courses opens opportunities for analysing learner behaviour and optimising web-based learning adapted to observed usage. Here, we introduce a mathematical framework for the analysis of time-series of online learner engagement, which allows the identification of clusters of learners with similar online temporal…

A significant amount of research effort has been put into finding variables that can identify students at risk based on activity records available in learning management systems (LMS). These variables often depend on the context, for example, the course structure, how the activities are assessed or whether the course is entirely online or a…

In this paper we show how we can utilize human-guided machine learning techniques coupled with a learning science practitioner interface (DataShop) to identify potential improvements to existing educational technology. Specifically, we provide an interface for the classification of underlying Knowledge Components (KCs) to better model student…

The increased usage of computer-based learning platforms and online tools in classrooms presents new opportunities to not only study the underlying constructs involved in the learning process, but also use this information to identify and aid struggling students. Many learning platforms, particularly those driving or supplementing instruction, are…