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Lee, Chia-An; Tzeng, Jian-Wei; Huang, Nen-Fu; Su, Yu-Sheng – Educational Technology & Society, 2021
Massive open online courses (MOOCs) provide numerous open-access learning resources and allow for self-directed learning. The application of big data and artificial intelligence (AI) in MOOCs help comprehend raw educational data and enrich the learning process for students and instructors. Thus, we created two deep neural network models. The first…
Descriptors: Grade Prediction, Online Courses, Student Behavior, Independent Study
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Orchard, Ryan K. – Journal of Educational Technology Systems, 2019
Learning management systems (LMS) allow for a variety of ways in which online multiple-choice assessments ("tests") can be configured, including the ability to allow for multiple attempts and options for which of and how the attempts will count. These options are usually chosen according to the instinct of the instructor; however, LMS…
Descriptors: Integrated Learning Systems, Data Use, Electronic Learning, Assignments
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Edwards, John; Hart, Kaden; Shrestha, Raj – Journal of Educational Data Mining, 2023
Analysis of programming process data has become popular in computing education research and educational data mining in the last decade. This type of data is quantitative, often of high temporal resolution, and it can be collected non-intrusively while the student is in a natural setting. Many levels of granularity can be obtained, such as…
Descriptors: Data Analysis, Computer Science Education, Learning Analytics, Research Methodology
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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
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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