A Study in the Early Prediction of ICT Literacy Ratings Using Sustainability in Data Mining Techniques

Kyungyeul Kim, Han-Sung Kim, Jaekwoun Shim and Ji Su Park
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Kyungyeul Kim: Department of Artificial Intelligence, Dongguk University, Seoul 04620, Korea
Han-Sung Kim: Software Policy & Research Institute, Seongnam-si, Gyeonggi-do 13488, Korea
Jaekwoun Shim: Korea University Center for Gifted, Seoul 02841, Korea
Ji Su Park: Department of Computer Science and Engineering, Jeonju University, Jeonju 55069, Korea

Sustainability, 2021, vol. 13, issue 4, 1-11

Abstract: It would be very beneficial to determine in advance whether a student is likely to succeed or fail within a particular learning area, and it is hypothesized that this can be accomplished by examining student patterns based on the data generated before the learning process begins. Therefore, this article examines the sustainability of data-mining techniques used to predict learning outcomes. Data regarding students’ educational backgrounds and learning processes are analyzed by examining their learning patterns. When such achievement-level patterns are identified, teachers can provide the students with proactive feedback and guidance to help prevent failure. As a practical application, this study investigates students’ perceptions of computer and internet use and predicts their levels of information and communication technology literacy in advance via sustainability-in-data-mining techniques. The technique employed herein applies OneR, J48, bagging, random forest, multilayer perceptron, and sequential minimal optimization (SMO) algorithms. The highest early prediction result of approximately 69% accuracy was yielded for the SMO algorithm when using 47 attributes. Overall, via data-mining techniques, these results will aid the identification of students facing risks early on during the learning process, as well as the creation of customized learning and educational strategies for each of these students.

Keywords: sustainability; data-mining techniques; early prediction of learning outcomes; information and communications technology literacy; education data mining (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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