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- W4378603664 abstract "Project-Based Collaborative Learning (PBCL) enables a group to collaborate on a real-world, complex project while systematically completing learning tasks. However, maintaining everyone’s attention and interest in the collaborative process is difficult, which may affect performance. The primary purpose of this paper is to predict disengagement among PBCL learner groups. To achieve this, we developed a Collaborative Assessment Analytics Framework (CAAF), which is built on ontologies and the collection of formative assessment data. The experiment involved 312 undergraduate students registered in the first year of the degree program in transportation technology and engineering. We divided the students into diverse groups based on their Python programming skills. We conducted a collaborative assessment analysis to determine the different assessment patterns that could indicate disengagement in this situation. The assessment of the group’s behavior by our analytical layer is backed by an ontology inspired by xAPI. Based on this semantic layer, supervised machine-learning algorithms were used to predict group disengagement. Using a Decision Tree algorithm, we were able to predict group disengagement with a high degree of accuracy of 86.96%, but using Random Forest, we were able to attain an astounding degree of accuracy of 95.65%. In such instances, instructors will be able to provide early intervention and feedback based on these findings." @default.
- W4378603664 created "2023-05-29" @default.
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- W4378603664 date "2023-01-01" @default.
- W4378603664 modified "2023-09-26" @default.
- W4378603664 title "An Ontology-Based Collaborative Assessment Analytics Framework to Predict Groups’ Disengagement" @default.
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- W4378603664 doi "https://doi.org/10.1007/978-981-99-2969-6_7" @default.
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