Vark learning style classification using physiological signals

Name: Lawal Ibrahim Dutsin Ma Faruk

Organization : Mae Fah Luang University

keyword: Machine Learning

ThaSH: Skin temperature

; Learning Style

ThaSH: Physiological signal -- Learning

; VARK Model

ThaSH: Machine Learning

Abstract: Learning style is very important aspect in knowledge management, it brings out the best of every individual propelling them to greatness. Learning requires cognitive actions which are part of the central nervous system .The central nervous system is connected to physiological signals , making them proper tools for measuring mental tasks . The VARK learning style have been used by numerous researchers and educators in learning and other related aspect, however the model only uses the questionnaire data collection tools. Questionnaire methodology are susceptible to distractions, often labor exhaustive, and also the results might be possible subjective, which makes them unsuitable for measuring cognitive activities. This study proposes a way to use the physiological signals as attribute for the classification of the VARK learning style method. Physiological signals are more suitable in displaying human engagement in mental task, such as learning, also they are faster and accurate to analyze. These makes the physical signal perfect tools for understanding and classifying the VARK model. Four experiment were designed for this study, each representing one type of the VARK learning style model. The experiments were carried out to record the participant heart rate, blood pressure and skin temperature to understand their engagement and mental tasks during each individual learning. The decision tree machine learning was employed to analyze the classification outcome based on the collected data attributes and questionnaire results. Fifty participant’s skin temperature and thirty participant’s heart rate and blood pressure were used for this study. There was 88 percent accuracy when using skin temperature to measure the participant engagement and 90 percent accuracy when using the heart rate and temperature respectively to measure the participant engagement. The study proves physiological signals could be used to measure participant mental task engagement during VARK learning style activities, which means they are suitable to be used as attribute for learning style classification.

Mae Fah Luang University. Learning Resources and Educational Media Center

Address: CHIANG RAI

Email: library@mfu.ac.th

Name: Santichai Wicha

Role: Advisor

Name: Punnarumol Temdee

Role: Co-Advisor

Created: 2021

Modified: 2021-02-04

Issued: 2021-02-04

วิทยานิพนธ์/Thesis

application/pdf

CallNumber: Thesis T58.6 L416v 2019

eng

DegreeName: Master of Science

Level: Master's Degree

Descipline: Information Technology

Grantor: Mae Fah Luang University

©copyrights Mae Fah Luang University

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