Version 1
: Received: 19 September 2024 / Approved: 19 September 2024 / Online: 20 September 2024 (03:13:02 CEST)
How to cite:
Chrysafiadi, K.; Virvou, M. PerFuSIT: Personalized Fuzzy Logic Strategies for Intelligent Tutoring of Programming. Preprints2024, 2024091543. https://doi.org/10.20944/preprints202409.1543.v1
Chrysafiadi, K.; Virvou, M. PerFuSIT: Personalized Fuzzy Logic Strategies for Intelligent Tutoring of Programming. Preprints 2024, 2024091543. https://doi.org/10.20944/preprints202409.1543.v1
Chrysafiadi, K.; Virvou, M. PerFuSIT: Personalized Fuzzy Logic Strategies for Intelligent Tutoring of Programming. Preprints2024, 2024091543. https://doi.org/10.20944/preprints202409.1543.v1
APA Style
Chrysafiadi, K., & Virvou, M. (2024). PerFuSIT: Personalized Fuzzy Logic Strategies for Intelligent Tutoring of Programming. Preprints. https://doi.org/10.20944/preprints202409.1543.v1
Chicago/Turabian Style
Chrysafiadi, K. and Maria Virvou. 2024 "PerFuSIT: Personalized Fuzzy Logic Strategies for Intelligent Tutoring of Programming" Preprints. https://doi.org/10.20944/preprints202409.1543.v1
Abstract
Recent advancements in Intelligent Tutoring Systems (ITS), driven by Artificial Intelligence (AI), have attracted substantial research interest, particularly in the domain of computer programming education. Given the diversity in learners' backgrounds, cognitive abilities, and learning paces, the development of personalized tutoring strategies to support effective attainment of learning objectives has become a critical challenge. This paper introduces PerFuSIT (Personalized Fuzzy Logic Strategies for Intelligent Tutoring of Programming), an innovative fuzzy logic-based module designed to select the most appropriate tutoring strategy from five available options, based on individual learner characteristics. The available strategies include revisiting previous content, progressing to the next topic, providing supplementary materials, assigning additional exercises, or advising the learner to take a break. PerFuSIT's decision-making process incorporates a range of learner-specific parameters, such as performance metrics, error types, indicators of carelessness, frequency of help requests, and the time required to complete tasks. Embedded within the traditional ITS framework, PerFuSIT introduces a sophisticated reasoning mechanism for dynamically determining the optimal instructional approach. Experimental evaluations demonstrate that PerFuSIT significantly enhances learner performance and improves the overall efficacy of interactions with the ITS. The findings highlight the potential of fuzzy logic to optimize adaptive tutoring strategies by customizing instruction to individual learners' strengths and weaknesses, thereby providing more effective and personalized educational support in programming instruction.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
