Abstract
Calls for widespread Computer Science (CS) education have been issued from the White House down and have been met with increased enrollment in CS undergraduate programs. Yet, these programs often suffer from high attrition rates. One successful approach to addressing the problem of low retention has been a focus on group work and collaboration. This paper details the design of a collaborative ITS (CIT) for foundational CS concepts including basic data structures and algorithms. We investigate the benefit of collaboration to student learning while using the CIT. We compare learning gains of our prior work in a non-collaborative system versus two methods of supporting collaboration in the collaborative-ITS. In our study of 60 students, we found significant learning gains for students using both versions. We also discovered notable differences related to student perception of tutor helpfulness which we will investigate in subsequent work.
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Notes
- 1.
43 students had used the non-collaborative ChiQat in a prior experiment. Their data is held out from learning gain analysis and reserved for further work.
- 2.
The human tutoring condition measured learning gains of students after one 40-minute session of working with an experienced human computer science tutor.
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Acknowledgments
This work was supported by the Abraham Lincoln Fellowship 2015–2016 from the University of Illinois at Chicago, and grant NPRP 5–939–1–155 from the Qatar National Research Fund.
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Harsley, R., Di Eugenio, B., Green, N., Fossati, D., Acharya, S. (2016). Integrating Support for Collaboration in a Computer Science Intelligent Tutoring System. In: Micarelli, A., Stamper, J., Panourgia, K. (eds) Intelligent Tutoring Systems. ITS 2016. Lecture Notes in Computer Science(), vol 9684. Springer, Cham. https://doi.org/10.1007/978-3-319-39583-8_22
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DOI: https://doi.org/10.1007/978-3-319-39583-8_22
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