Applying learning analytics to explore the influence of online learners' motivation on their online learning behavioral patterns

Chih-Yuan Sun; Che Tsun Lin; Chien Chou

doi:10.1109/IIAI-AAI.2016.186

Applying learning analytics to explore the influence of online learners' motivation on their online learning behavioral patterns

Chih-Yuan Sun, Che Tsun Lin, Chien Chou

Institute of Education

Research output: Contribution to journal › Conference article › peer-review

4 Scopus citations

Abstract

This study aims to apply sequential analysis to explore the effect of learning motivation on online learning behavioral patterns. The study participants were 160 graduate students, who were classified into three group types: low reading duration with low motivation, low reading duration with high motivation, and high reading duration based on a second-order cluster analysis. After performing a sequential analysis, this study revealed that highly motivated learners exhibited a relatively serious learning pattern in a multi-tasking learning environment, and that online reading duration was a significant indicator of seriousness in taking an online course. Finally, relevant recommendations were made to instructors and researchers based on the results of the present study.

Original language	English
Pages (from-to)	377-380
Number of pages	4
Journal	Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016
DOIs	https://doi.org/10.1109/IIAI-AAI.2016.186
State	Published - 31 Aug 2016
Event	5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 - Kumamoto, Japan Duration: 10 Jul 2016 → 14 Jul 2016

Keywords

Behavioral pattern
Learning analytics
Motivation
Online learning
Sequential analysis

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10.1109/IIAI-AAI.2016.186

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title = "Applying learning analytics to explore the influence of online learners' motivation on their online learning behavioral patterns",

abstract = "This study aims to apply sequential analysis to explore the effect of learning motivation on online learning behavioral patterns. The study participants were 160 graduate students, who were classified into three group types: low reading duration with low motivation, low reading duration with high motivation, and high reading duration based on a second-order cluster analysis. After performing a sequential analysis, this study revealed that highly motivated learners exhibited a relatively serious learning pattern in a multi-tasking learning environment, and that online reading duration was a significant indicator of seriousness in taking an online course. Finally, relevant recommendations were made to instructors and researchers based on the results of the present study.",

keywords = "Behavioral pattern, Learning analytics, Motivation, Online learning, Sequential analysis",

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Applying learning analytics to explore the influence of online learners' motivation on their online learning behavioral patterns. / Sun, Chih-Yuan; Lin, Che Tsun; Chou, Chien.

In: Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016, 31.08.2016, p. 377-380.

Research output: Contribution to journal › Conference article › peer-review

TY - JOUR

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AU - Sun, Chih-Yuan

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AU - Chou, Chien

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Y1 - 2016/8/31

N2 - This study aims to apply sequential analysis to explore the effect of learning motivation on online learning behavioral patterns. The study participants were 160 graduate students, who were classified into three group types: low reading duration with low motivation, low reading duration with high motivation, and high reading duration based on a second-order cluster analysis. After performing a sequential analysis, this study revealed that highly motivated learners exhibited a relatively serious learning pattern in a multi-tasking learning environment, and that online reading duration was a significant indicator of seriousness in taking an online course. Finally, relevant recommendations were made to instructors and researchers based on the results of the present study.

AB - This study aims to apply sequential analysis to explore the effect of learning motivation on online learning behavioral patterns. The study participants were 160 graduate students, who were classified into three group types: low reading duration with low motivation, low reading duration with high motivation, and high reading duration based on a second-order cluster analysis. After performing a sequential analysis, this study revealed that highly motivated learners exhibited a relatively serious learning pattern in a multi-tasking learning environment, and that online reading duration was a significant indicator of seriousness in taking an online course. Finally, relevant recommendations were made to instructors and researchers based on the results of the present study.

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KW - Learning analytics

KW - Motivation

KW - Online learning

KW - Sequential analysis

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Applying learning analytics to explore the influence of online learners' motivation on their online learning behavioral patterns

Abstract

Keywords

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