A Data Mining based Optimization of Selecting Learning Material in an Intelligent Tutoring System for Advancing STEM Education

Subsequent to the data deluge of the internet era and the recent advancement in big data technologies, it is easy to affirm the continuous application of such technological innovation to tackling a wide array of students' educational needs. The field of artificial intelligence and machine learn...

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Bibliographic Details
Published in2020 International Conference on Computational Science and Computational Intelligence (CSCI) pp. 904 - 909
Main Authors Ogunkunle, Olanrewaju, Qu, Yanzhen
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2020
Subjects
Analytical models
Computational modeling
data mining
Education
intelligent tutoring system
knowledge discovery
Machine learning
Scientific computing
Software
STEM education
teaching and learning analytics
Technological innovation
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DOI10.1109/CSCI51800.2020.00169

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Summary:Subsequent to the data deluge of the internet era and the recent advancement in big data technologies, it is easy to affirm the continuous application of such technological innovation to tackling a wide array of students' educational needs. The field of artificial intelligence and machine learning have improved education learning outcomes. However, the problem of generalized traditional supportive collaboration scripts for all students irrespective of the student's learning traits and position on the learning spectrum leads to less than optimum result in their educational pursuits. This paper presents a novel approach that uses data mining algorithm to optimize the selection of educational resources for students based on their learning traits and the six factors that cofound instructional content and delivery with a focus on students with learning disabilities for STEM subjects.
DOI:10.1109/CSCI51800.2020.00169