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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References
Alur, R., D’Antoni, L., Gulwani, S., Kini, D., Viswanathan, M.: Automated grading of DFA constructions. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI 2013), pp. 1976−1982 (2013)
Beck, J.E., Mostow, J.: How who should practice: using learning decomposition to evaluate the efficacy of different types of practice for different types of students. In: Woolf, B.P., Aimeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 353–362. Springer, Heidelberg (2008)
Cocea, M., Hershkovitz, A., Baker, R.S.J.D.: The impact of off-task and gaming behaviors on learning: immediate or aggregate?. In: Proceedings of the 14th International Conference on Artificial Intelligence in Education, pp. 507−514 (2009)
D’Antoni, L., Kini, D., Alur, R., Gulwani, S., Viswanathan, M., Hartmann, B.: How Can Automatic Feedback Help Students Construct Automata? ACM Transactions on Computer-Human Interaction 22(2), Article 9 (2015)
JFLAP website. http://www.jflap.org
Joint Task Force on Computing Curricula, Association for Computing Machinery (ACM) and IEEE Computer Society: Computer Science Curricula 2013: Curriculum Guidelines for Undergraduate Degree Programs in Computer Science. ACM, New York, NY (2013)
Rodger, S.H., Finley, T.W.: JFLAP – An Interactive Formal Languages and Automata Package. Jones and Bartlett, Sudbury (2006)
Roger, S.H., Lim, J., Reading, S.: Increasing interaction and support in the formal languages and automata theory course. In: Innovation and Technology in Computer Science Education (ITiCSE 2007), pp. 58−62 (2007)
Shekhar, V.S., Agarwalla, A., Agarwal, A., Nitish B., Kumar, V.: Enhancing JFLAP with automata construction problems and automated feedback. In: Proceedings of the 7th International Conference on Contemporary Computing (IC3 2014), pp. 19−23 (2014)
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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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