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

Access to Document

10.1109/IIAI-AAI.2016.186

Fingerprint Dive into the research topics of 'Applying learning analytics to explore the influence of online learners' motivation on their online learning behavioral patterns'. Together they form a unique fingerprint.

Cite this

@article{260f2723233941f9a4f94b867ca7e069,

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",

author = "Chih-Yuan Sun and Lin, {Che Tsun} and Chien Chou",

year = "2016",

month = aug,

day = "31",

doi = "10.1109/IIAI-AAI.2016.186",

language = "English",

pages = "377--380",

journal = "Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016",

note = "null ; Conference date: 10-07-2016 Through 14-07-2016",

}

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

T1 - Applying learning analytics to explore the influence of online learners' motivation on their online learning behavioral patterns

AU - Sun, Chih-Yuan

AU - Lin, Che Tsun

AU - Chou, Chien

PY - 2016/8/31

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.

KW - Behavioral pattern

KW - Learning analytics

KW - Motivation

KW - Online learning

KW - Sequential analysis

UR - http://www.scopus.com/inward/record.url?scp=84988851734&partnerID=8YFLogxK

U2 - 10.1109/IIAI-AAI.2016.186

DO - 10.1109/IIAI-AAI.2016.186

M3 - Conference article

AN - SCOPUS:84988851734

SP - 377

EP - 380

JO - Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016

JF - Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016

Y2 - 10 July 2016 through 14 July 2016

ER -