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- ArticleJune 2024
Explaining Problem Recommendations in an Intelligent Tutoring System
Generative Intelligence and Intelligent Tutoring SystemsPages 291–299https://doi.org/10.1007/978-3-031-63028-6_23AbstractStudents learning with intelligent tutoring systems (ITS) do not always trust system recommendations. One solution for this is explainable AI (XAI), which is shown to increase trust in AI. Our research focuses on how students’ personality traits ...
- ArticleJune 2016
Do Erroneous Examples Improve Learning in Addition to Problem Solving and Worked Examples?
ITS 2016: Proceedings of the 13th International Conference on Intelligent Tutoring Systems - Volume 9684Pages 13–22https://doi.org/10.1007/978-3-319-39583-8_2Learning from Problem Solving PS, Worked Examples WE and Erroneous Examples ErrEx have all proven to be effective learning strategies. However, there is still no agreement on what kind of assistance in terms of different learning activities should be ...
- ArticleJune 2012
Modeling the affective states of students using SQL-Tutor
- Thea Faye G. Guia,
- Ma. Mercedes T. Rodrigo,
- Michelle Marie C. Dagami,
- Jessica O. Sugay,
- Francis Jan P. Macam,
- Antonija Mitrovic
ITS'12: Proceedings of the 11th international conference on Intelligent Tutoring SystemsPages 634–635https://doi.org/10.1007/978-3-642-30950-2_97We attempted to build models of affect of students using SQL-Tutor. Most exhibited states are engaged concentration, confusion and boredom. Though none correlated with achievement, boredom and frustration persisted. Using linear regression, we arrived ...