2018Unpublished venueRequires access

Web-Based Application Design of Student Learning Style Identificator

Hernawan Sulistyanto

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Abstract

Students may have different learning goals and ways of learning that are not similar. Therefore a learning device that is adapted to the learning style of students is very feasible to be realized so that students obtain material with different presentation models according to their needs and suitability in learning. A web-based application that identifies the learning styles of students is designed to help determine the type of learning that students have. The research method uses Research & Development (R & D). System performance is tested using black-box testing methods and alpha-beta validity tests on each system module. Meanwhile the class action method and survey are used to identify the feasibility of the application design. Based on a series of observations and analyzes, it can be concluded that out of 101 students of the Faculty of Communication and Information, 27% had visual learning modalities, 16% auditory, 14% readwrite, and 43% kinesthetic. Referring to these results, the use of instructional media adapted to student learning styles is important in achieving a significant increase in mastery and understanding of learning material and this application can help teachers in preparing appropriate teaching strategies.

About this research paper

What this paper is about

Students may have different learning goals and ways of learning that are not similar. Therefore a learning device that is adapted to the learning style of students is very feasible to be realized so that students obtain material with different presentation models according to their needs and suitability in learning. A web-based application that identifies the learning styles of students is designed to help determine the type of learning that students have. The research method uses Research & Development (R & D). System performance is tested using black-box testing methods and alpha-beta validity tests on each system module. Meanwhile the class action method and survey are used to identify the feasibility of the application design. Based on a series of observations and analyzes, it can be concluded that out of 101 students of the Faculty of Communication and Information, 27% had visual learning modalities, 16% auditory, 14% readwrite, and 43% kinesthetic. Referring to these results, the use of instructional media adapted to student learning styles is important in achieving a significant increase in mastery and understanding of learning material and this application can help teachers in preparing appropriate teaching strategies.

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Available abstract

Students may have different learning goals and ways of learning that are not similar. Therefore a learning device that is adapted to the learning style of students is very feasible to be realized so that students obtain material with different presentation models according to their needs and suitability in learning. A web-based application that identifies the learning styles of students is designed to help determine the type of learning that students have. The research method uses Research & Development (R & D). System performance is tested using black-box testing methods and alpha-beta validity tests on each system module. Meanwhile the class action method and survey are used to identify the feasibility of the application design. Based on a series of observations and analyzes, it can be concluded that out of 101 students of the Faculty of Communication and Information, 27% had visual learning modalities, 16% auditory, 14% readwrite, and 43% kinesthetic. Referring to these results, the use of instructional media adapted to student learning styles is important in achieving a significant increase in mastery and understanding of learning material and this application can help teachers in preparing appropriate teaching strategies.

Key concepts: Kinesthetic learning, Learning styles, Computer science, Class (philosophy), Auditory learning, Multimedia, Active learning (machine learning), Blended learning

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