Abstract
Many students who enrol in the undergraduate program on informatics at the Hellenic Open University (HOU) fail the introductory course exams and drop out. We analyze their academic performance, derive short rules that explain success or failure in the exams and use the accuracy of these rules to reflect on specific tutoring practices that could enhance success.
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© 2006 International Federation for Information Processing
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Kalles, D., Pierrakeas, C. (2006). Using Genetic Algorithms and Decision Trees for a posteriori Analysis and Evaluation of Tutoring Practices based on Student Failure Models. In: Maglogiannis, I., Karpouzis, K., Bramer, M. (eds) Artificial Intelligence Applications and Innovations. AIAI 2006. IFIP International Federation for Information Processing, vol 204. Springer, Boston, MA . https://doi.org/10.1007/0-387-34224-9_2
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DOI: https://doi.org/10.1007/0-387-34224-9_2
Publisher Name: Springer, Boston, MA
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