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Xieling Chen; Di Zou; Gary Cheng; Haoran Xie – Education and Information Technologies, 2024
The rise of massive open online courses (MOOCs) brings rich opportunities for understanding learners' experiences based on analyzing learner-generated content such as course reviews. Traditionally, the unstructured textual data is analyzed qualitatively via manual coding, thus failing to offer a timely understanding of the learner's experiences.…
Descriptors: Artificial Intelligence, Semantics, Course Evaluation, MOOCs
Abdullahi Yusuf; Norah Md Noor; Shamsudeen Bello – Education and Information Technologies, 2024
Studies examining students' learning behavior predominantly employed rich video data as their main source of information due to the limited knowledge of computer vision and deep learning algorithms. However, one of the challenges faced during such observation is the strenuous task of coding large amounts of video data through repeated viewings. In…
Descriptors: Learning Analytics, Student Behavior, Video Technology, Classification
Macak, Martin; Kruzelova, Daniela; Chren, Stanislav; Buhnova, Barbora – Education and Information Technologies, 2021
Understanding the processes in education, such as the student learning behavior within a specific course, is a key to continuous course improvement. In online learning systems, students' learning can be tracked and examined based on data collected by the systems themselves. However, it is non-trivial to decide how to extract the desired students'…
Descriptors: Student Projects, Learning Analytics, Data Collection, Computer Science Education

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