Abstract
There exist several online applications for automated testing of the computer programs that students write in computer science education. Use of such systems enables self-paced learning with automated feedback delivered by the application. However, due to the complexity of programming languages, even the easiest tasks made available through such systems require understanding of several programming concepts and formatting. Therefore, a student’s initial work in an introductory computer science course may be highly challenging, especially for students with no previous programming background.
To address this challenge, a highly-decomposed micro-task module has been developed and made available on an automated assessment platform with programming assignments. Impact of its introduction has been examined within an introductory programming university course with 239 participants. We investigated the micro-task module’s impact on student affect, student performance on the platform, and student learning outcomes. Results of the experiment show that students in the experimental group (with micro-tasks enabled) significantly less frequently reported frustration, confusion and boredom, needed less time to solve tasks on the platform and achieved significantly better results on the final test.
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Pankiewicz, M., Baker, R., Ocumpaugh, J. (2023). Using Intelligent Tutoring on the First Steps of Learning to Program: Affective and Learning Outcomes. In: Wang, N., Rebolledo-Mendez, G., Dimitrova, V., Matsuda, N., Santos, O.C. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. AIED 2023. Communications in Computer and Information Science, vol 1831. Springer, Cham. https://doi.org/10.1007/978-3-031-36336-8_92
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DOI: https://doi.org/10.1007/978-3-031-36336-8_92
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