A study of educational data mining: evidence from a Thai university

Ruangsak Trakunphutthirak, Yen Ping Cheung, Vincent C.S. Lee

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Educational data mining provides a way to predict student academic performance. A psychometric factor like time management is one of the major issues affecting Thai students’ academic performance. Current data sources used to predict students’ performance are limited to the manual collection of data or data from a single unit of study which cannot be generalised to indicate overall academic performance. This study uses an additional data source from a university log file to predict academic performance. It investigates the browsing categories and the Internet access activities of students with respect to their time management during their studies. A single source of data is insufficient to identify those students who are at-risk of failing in their academic studies. Furthermore, there is a paucity of recent empirical studies in this area to provide insights into the relationship between students’ academic performance and their Internet access activities. To contribute to this area of research, we employed two datasets such as web-browsing categories and Internet access activity types to select the best outcomes, and compared different weights in the time and frequency domains. We found that the random forest technique provides the best outcome in these datasets to identify those students who are at-risk of
failure. We also found that data from their Internet access activities reveals more accurate outcomes than data from browsing categories alone. The combination of two datasets reveals a better picture of students’ Internet usage and thus identifies students who are academically at-risk of failure. Further work involves collecting more Internet access log file data, analysing it over a longer period and relating the period of data collection with events during the academic year.
Original languageEnglish
Title of host publicationProceedings of AAAI19-Thirty-Third AAAI conference on Artificial Intelligence
Subtitle of host publication27 Jan-1 Feb 2019, Honolulu
EditorsPascal Van Hentenryck, Zhi-Hua Zhou
Place of PublicationPalo Alto CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Number of pages8
Publication statusAccepted/In press - 2019
EventAAAI conference on Artificial Intelligence 2019 - Honolulu, United States of America
Duration: 27 Jan 20191 Feb 2019
Conference number: 33rd
https://aaai.org/Conferences/AAAI-19/

Conference

ConferenceAAAI conference on Artificial Intelligence 2019
Abbreviated titleAAAI 2019
CountryUnited States of America
CityHonolulu
Period27/01/191/02/19
Internet address

Cite this

Trakunphutthirak, R., Cheung, Y. P., & Lee, V. C. S. (Accepted/In press). A study of educational data mining: evidence from a Thai university. In P. Van Hentenryck, & Z-H. Zhou (Eds.), Proceedings of AAAI19-Thirty-Third AAAI conference on Artificial Intelligence: 27 Jan-1 Feb 2019, Honolulu Palo Alto CA USA: Association for the Advancement of Artificial Intelligence (AAAI).
Trakunphutthirak, Ruangsak ; Cheung, Yen Ping ; Lee, Vincent C.S. . / A study of educational data mining : evidence from a Thai university. Proceedings of AAAI19-Thirty-Third AAAI conference on Artificial Intelligence: 27 Jan-1 Feb 2019, Honolulu. editor / Pascal Van Hentenryck ; Zhi-Hua Zhou. Palo Alto CA USA : Association for the Advancement of Artificial Intelligence (AAAI), 2019.
@inproceedings{637b77ce2b9d40a68992188615731790,
title = "A study of educational data mining: evidence from a Thai university",
abstract = "Educational data mining provides a way to predict student academic performance. A psychometric factor like time management is one of the major issues affecting Thai students’ academic performance. Current data sources used to predict students’ performance are limited to the manual collection of data or data from a single unit of study which cannot be generalised to indicate overall academic performance. This study uses an additional data source from a university log file to predict academic performance. It investigates the browsing categories and the Internet access activities of students with respect to their time management during their studies. A single source of data is insufficient to identify those students who are at-risk of failing in their academic studies. Furthermore, there is a paucity of recent empirical studies in this area to provide insights into the relationship between students’ academic performance and their Internet access activities. To contribute to this area of research, we employed two datasets such as web-browsing categories and Internet access activity types to select the best outcomes, and compared different weights in the time and frequency domains. We found that the random forest technique provides the best outcome in these datasets to identify those students who are at-risk offailure. We also found that data from their Internet access activities reveals more accurate outcomes than data from browsing categories alone. The combination of two datasets reveals a better picture of students’ Internet usage and thus identifies students who are academically at-risk of failure. Further work involves collecting more Internet access log file data, analysing it over a longer period and relating the period of data collection with events during the academic year.",
author = "Ruangsak Trakunphutthirak and Cheung, {Yen Ping} and Lee, {Vincent C.S.}",
year = "2019",
language = "English",
editor = "{Van Hentenryck}, {Pascal } and Zhou, {Zhi-Hua }",
booktitle = "Proceedings of AAAI19-Thirty-Third AAAI conference on Artificial Intelligence",
publisher = "Association for the Advancement of Artificial Intelligence (AAAI)",
address = "United States of America",

}

Trakunphutthirak, R, Cheung, YP & Lee, VCS 2019, A study of educational data mining: evidence from a Thai university. in P Van Hentenryck & Z-H Zhou (eds), Proceedings of AAAI19-Thirty-Third AAAI conference on Artificial Intelligence: 27 Jan-1 Feb 2019, Honolulu. Association for the Advancement of Artificial Intelligence (AAAI), Palo Alto CA USA, AAAI conference on Artificial Intelligence 2019, Honolulu, United States of America, 27/01/19.

A study of educational data mining : evidence from a Thai university. / Trakunphutthirak, Ruangsak; Cheung, Yen Ping; Lee, Vincent C.S. .

Proceedings of AAAI19-Thirty-Third AAAI conference on Artificial Intelligence: 27 Jan-1 Feb 2019, Honolulu. ed. / Pascal Van Hentenryck; Zhi-Hua Zhou. Palo Alto CA USA : Association for the Advancement of Artificial Intelligence (AAAI), 2019.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

TY - GEN

T1 - A study of educational data mining

T2 - evidence from a Thai university

AU - Trakunphutthirak, Ruangsak

AU - Cheung, Yen Ping

AU - Lee, Vincent C.S.

PY - 2019

Y1 - 2019

N2 - Educational data mining provides a way to predict student academic performance. A psychometric factor like time management is one of the major issues affecting Thai students’ academic performance. Current data sources used to predict students’ performance are limited to the manual collection of data or data from a single unit of study which cannot be generalised to indicate overall academic performance. This study uses an additional data source from a university log file to predict academic performance. It investigates the browsing categories and the Internet access activities of students with respect to their time management during their studies. A single source of data is insufficient to identify those students who are at-risk of failing in their academic studies. Furthermore, there is a paucity of recent empirical studies in this area to provide insights into the relationship between students’ academic performance and their Internet access activities. To contribute to this area of research, we employed two datasets such as web-browsing categories and Internet access activity types to select the best outcomes, and compared different weights in the time and frequency domains. We found that the random forest technique provides the best outcome in these datasets to identify those students who are at-risk offailure. We also found that data from their Internet access activities reveals more accurate outcomes than data from browsing categories alone. The combination of two datasets reveals a better picture of students’ Internet usage and thus identifies students who are academically at-risk of failure. Further work involves collecting more Internet access log file data, analysing it over a longer period and relating the period of data collection with events during the academic year.

AB - Educational data mining provides a way to predict student academic performance. A psychometric factor like time management is one of the major issues affecting Thai students’ academic performance. Current data sources used to predict students’ performance are limited to the manual collection of data or data from a single unit of study which cannot be generalised to indicate overall academic performance. This study uses an additional data source from a university log file to predict academic performance. It investigates the browsing categories and the Internet access activities of students with respect to their time management during their studies. A single source of data is insufficient to identify those students who are at-risk of failing in their academic studies. Furthermore, there is a paucity of recent empirical studies in this area to provide insights into the relationship between students’ academic performance and their Internet access activities. To contribute to this area of research, we employed two datasets such as web-browsing categories and Internet access activity types to select the best outcomes, and compared different weights in the time and frequency domains. We found that the random forest technique provides the best outcome in these datasets to identify those students who are at-risk offailure. We also found that data from their Internet access activities reveals more accurate outcomes than data from browsing categories alone. The combination of two datasets reveals a better picture of students’ Internet usage and thus identifies students who are academically at-risk of failure. Further work involves collecting more Internet access log file data, analysing it over a longer period and relating the period of data collection with events during the academic year.

M3 - Conference Paper

BT - Proceedings of AAAI19-Thirty-Third AAAI conference on Artificial Intelligence

A2 - Van Hentenryck, Pascal

A2 - Zhou, Zhi-Hua

PB - Association for the Advancement of Artificial Intelligence (AAAI)

CY - Palo Alto CA USA

ER -

Trakunphutthirak R, Cheung YP, Lee VCS. A study of educational data mining: evidence from a Thai university. In Van Hentenryck P, Zhou Z-H, editors, Proceedings of AAAI19-Thirty-Third AAAI conference on Artificial Intelligence: 27 Jan-1 Feb 2019, Honolulu. Palo Alto CA USA: Association for the Advancement of Artificial Intelligence (AAAI). 2019