Abstract
Several educational software tools allow students to hone their problem-solving skills using practice problems and feedback in the form of hints. If one can meaningfully define the “distance” between any incorrect student attempt and the correct solution, it is possible to define the student’s learning velocity for that problem: the rate at which the student is able to decrease this distance. In this paper, we present an extension to one such educational software tool (JFLAP) that permits us to compute learning velocities for each student on practice problems involving finite automata construction. These learning velocities are helpful in at least two ways: (1) instructors can rank students (e.g., by identifying students whose learning velocities on most problems are significantly below the class average, and who may therefore require the instructor’s attention), and (2) instructors can rank problems according to difficulty (e.g., while designing a question paper, a “difficult” problem might be one where only a few students have quickly converged to the correct solution).
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Ananya, H.A., Akhilesh Hegde, I., Joshi, A.G., Kumar, V. (2016). Ranking Student Ability and Problem Difficulty Using Learning Velocities. In: Berretti, S., Thampi, S., Srivastava, P. (eds) Intelligent Systems Technologies and Applications. Advances in Intelligent Systems and Computing, vol 384. Springer, Cham. https://doi.org/10.1007/978-3-319-23036-8_17
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DOI: https://doi.org/10.1007/978-3-319-23036-8_17
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