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Gabriela Trindade Perry; Marlise Bock Santos – Journal of Computer Assisted Learning, 2024
Background: Instances of academic dishonesty are common in online learning environments because difficulties in their detection result in considerably low degrees of risks. However, if not identified, the noise introduced by dishonest learners in MOOCs' clickstream data could lead to biased results and conclusions in scientific research.…
Descriptors: Foreign Countries, MOOCs, Distance Education, Electronic Learning
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Saman Rizvi; Bart Rienties; Jekaterina Rogaten; René F. Kizilcec – Journal of Computer Assisted Learning, 2024
Background: Extensive research on massive open online courses (MOOCs) has focused on analysing learners' behavioural trace data to understand navigation and activity patterns, which are known to vary systematically across geo-cultural contexts. However, the perception of learners regarding the role of different learning design elements in…
Descriptors: MOOCs, Inclusion, Instructional Design, Participant Characteristics
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Ishari Amarasinghe; Konstantinos Michos; Francisco Crespi; Davinia Hernández-Leo – Journal of Computer Assisted Learning, 2024
Background: Data-driven educational technology solutions have the potential to support teachers in different tasks, such as the designing and orchestration of collaborative learning activities. When designing, such solutions can improve teacher understanding of how learning designs impact student learning and behaviour; and guide them to refine…
Descriptors: Learning Activities, Educational Technology, Design, Cooperative Learning