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- W3134251168 abstract "Current advancements in learning technology necessitate the development of personalized software for the students, respecting their cognitive states. In the light of these novel approaches in digital education, this paper presents a framework for the automated reasoning of the students’ cognitive states and function. The cognitive states are depicted using the Revised Bloom Taxonomy, representing a continuum of increasing cognitive complexity. Moreover, the automatic reasoning is achieved using classification analysis and more specifically the k-Nearest Neighbors algorithm, which takes as input personal and cognitive students’ characteristics. As such, each new student is classified in the appropriate level of the taxonomy. To this end, a more personalized learning experience is offered to them and a student-centered learning environment in created based on the distinct pace of instruction that each student wants to receive. The presented approach has been incorporated in a learning technology system for teaching computer programming and was evaluated with promising results." @default.
- W3134251168 created "2021-03-15" @default.
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- W3134251168 date "2020-11-20" @default.
- W3134251168 modified "2023-09-26" @default.
- W3134251168 title "Automated reasoning of learners’ cognitive states using classification analysis" @default.
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- W3134251168 doi "https://doi.org/10.1145/3437120.3437285" @default.
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