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Giora Alexandron; Aviram Berg; Jose A. Ruiperez-Valiente – IEEE Transactions on Learning Technologies, 2024
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…
Descriptors: Cheating, Identification, Online Courses, Artificial Intelligence
Juan Antonio Martinez-Carrascal; Jorge Munoz-Gama; Teresa Sancho-Vinuesa – IEEE Transactions on Learning Technologies, 2024
Academic institutions dedicate a substantial effort to ensure the academic success of their students. At the course level, teachers recommend learning paths (RLPs) for students to guarantee the achievement of their learning outcomes. In terms of performance, these kinds of approaches are deemed more effective than others based uniquely on…
Descriptors: Online Courses, Mathematics Instruction, Undergraduate Students, Mathematics Achievement
Sonja Kleter; Uwe Matzat; Rianne Conijn – IEEE Transactions on Learning Technologies, 2024
Much of learning analytics research has focused on factors influencing model generalizability of predictive models for academic performance. The degree of model generalizability across courses may depend on aspects, such as the similarity of the course setup, course material, the student cohort, or the teacher. Which of these contextual factors…
Descriptors: Prediction, Models, Academic Achievement, Learning Analytics
Kim, Hodam; Chae, Younsoo; Kim, Suhye; Im, Chang-Hwan – IEEE Transactions on Learning Technologies, 2023
Owing to the rapid development of information and communication technologies, online or mobile learning content is widely available on the Internet. Unlike traditional face-to-face learning, online learning exhibits a critical limitation: real-time interactions between learners and teachers are generally not feasible in online learning. To…
Descriptors: College Students, Control Groups, Attention, Comprehension
So, Joseph Chi-Ho; Ho, Yik Him; Wong, Adam Ka-Lok; Chan, Henry C. B.; Tsang, Kia Ho-Yin; Chan, Ada Pui-Ling; Wong, Simon Chi-Wang – IEEE Transactions on Learning Technologies, 2023
Generic competence (GC) development is an integral part of higher education to provide holistic education and enhance student career development. It also plays a critical role in complementing the curriculum. Many tertiary institutions provide various GC development activities (GCDA). Moreover, institutions strongly need to further understand…
Descriptors: Predictor Variables, Higher Education, Online Courses, Correlation
Tran, Tich Phuoc; Meacheam, David – IEEE Transactions on Learning Technologies, 2020
The use of learning management systems (LMSs) for learning and knowledge sharing has accelerated quickly both in education and corporate worlds. Despite the benefits brought by LMSs, the current systems still face significant challenges, including the lack of automation in generating quiz questions and managing courses. Over the past decade, more…
Descriptors: Integrated Learning Systems, Test Construction, Test Items, Automation
Beasley, Zachariah J.; Piegl, Les A.; Rosen, Paul – IEEE Transactions on Learning Technologies, 2021
Accurately grading open-ended assignments in large or massive open online courses is nontrivial. Peer review is a promising solution but can be unreliable due to few reviewers and an unevaluated review form. To date, no work has leveraged sentiment analysis in the peer-review process to inform or validate grades or utilized aspect extraction to…
Descriptors: Case Studies, Online Courses, Assignments, Peer Evaluation
Gupta, Anika; Garg, Deepak; Kumar, Parteek – IEEE Transactions on Learning Technologies, 2022
With the onset of online education via technology-enhanced learning platforms, large amount of educational data is being generated in the form of logs, clickstreams, performance, etc. These Virtual Learning Environments provide an opportunity to the researchers for the application of educational data mining and learning analytics, for mining the…
Descriptors: Markov Processes, Online Courses, Learning Management Systems, Learning Analytics
Wan, Han; Zhong, Zihao; Tang, Lina; Gao, Xiaopeng – IEEE Transactions on Learning Technologies, 2023
Small private online courses (SPOCs) have influenced teaching and learning in China's higher education. Learning management systems (LMSs) are important components in SPOCs. They can collect various data related to student behavior and support pedagogical interventions. This research used feature engineering and nearest neighbor smoothing models…
Descriptors: Online Courses, Learning Management Systems, Higher Education, Student Behavior
Olive, David Monllao; Huynh, Du Q.; Reynolds, Mark; Dougiamas, Martin; Wiese, Damyon – IEEE Transactions on Learning Technologies, 2019
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…
Descriptors: Prediction, Identification, At Risk Students, Online Courses
Botelho, Anthony F.; Varatharaj, Ashvini; Patikorn, Thanaporn; Doherty, Diana; Adjei, Seth A.; Beck, Joseph E. – IEEE Transactions on Learning Technologies, 2019
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…
Descriptors: Student Attrition, Student Behavior, Early Intervention, Identification