Abstract
With a number of students, the purpose of collaborative learning is to assign these students to the right teams so that the promotion of skills of each team member can be facilitated. Although some team formation solutions have been proposed, the problem of extracting more effective features to describe the skill proficiency of students for better collaborative learning is still open. To that end, we provide a focused study on exploiting cognitive diagnosis to model students’ skill proficiency for team formation. Specifically, we design a two-stage framework. First, we propose a cognitive diagnosis model SDINA, which can automatically quantify students’ skill proficiency in continuous values. Then, given two different objectives, we propose corresponding algorithms to form collaborative learning teams based on the cognitive modeling results of SDINA. Finally, extensive experiments demonstrate that SDINA could model the students’ skill proficiency more precisely and the proposed algorithms can help generate collaborative learning teams more effectively.
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Notes
- 1.
There are also studies about the automatic labeling of skills [7], which is beyond the scope of our research.
- 2.
They will be publicly available after the paper acceptance.
- 3.
Unlike the real-world datasets, the simulated ones only consist of students with values between 0 and 1 on some features rather than students’ test scores.
- 4.
The latent factor getting by PMF has not been used here since it’s unexplainable.
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Acknowledgements
This research was partially supported by grants from the National Science Foundation for Distinguished Young Scholars of China (Grant No. 61325010), the Natural Science Foundation of China (Grant No. 61403358) and the Science and Technology Program for Public Wellbeing (Grant No. 2013GS340302). Qi Liu gratefully acknowledges the support of the Youth Innovation Promotion Association of CAS and acknowledges the support of the CCF-Intel Young Faculty Researcher Program (YFRP).
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Liu, Y. et al. (2016). Collaborative Learning Team Formation: A Cognitive Modeling Perspective. In: Navathe, S., Wu, W., Shekhar, S., Du, X., Wang, S., Xiong, H. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9643. Springer, Cham. https://doi.org/10.1007/978-3-319-32049-6_24
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