Using data mining for predicting relationships between online question theme and final grade
M. Abdous, W. He, and C. Yen. Educational Technology & Society, 15 (3):
77-88(2012)
Abstract
As higher education diversifies its delivery modes, our ability to use the predictive and analytical power of
educational data mining (EDM) to understand students’ learning experiences is a critical step forward. The
adoption of EDM by higher education as an analytical and decision making tool is offering new opportunities to
exploit the untapped data generated by various student information systems (SIS) and learning management
systems (LMS). This paper describes a hybrid approach which uses EDM and regression analysis to analyse live
video streaming (LVS) students’ online learning behaviours and their performance in their courses. Students’
participation and login frequency, as well as the number of chat messages and questions that they submit to their
instructors, were analysed, along with students’ final grades. Results of the study show a considerable variability
in students’ questions and chat messages. Unlike previous studies, this study suggests no correlation between
students’ number of questions / chat messages / login times and students’ success. However, our case study
reveals that combining EDM with traditional statistical analysis provides a strong and coherent analytical
framework capable of enabling a deeper and richer understanding of students’ learning behaviours and
experiences.
%0 Journal Article
%1 abdous2012using
%A Abdous, M'hammed
%A He, Wu
%A Yen, Cherng-Jyh
%D 2012
%J Educational Technology & Society
%K analytics data educational learning mining
%N 3
%P 77-88
%T Using data mining for predicting relationships between online question theme and final grade
%U http://ifets.info/journals/15_3/6.pdf
%V 15
%X As higher education diversifies its delivery modes, our ability to use the predictive and analytical power of
educational data mining (EDM) to understand students’ learning experiences is a critical step forward. The
adoption of EDM by higher education as an analytical and decision making tool is offering new opportunities to
exploit the untapped data generated by various student information systems (SIS) and learning management
systems (LMS). This paper describes a hybrid approach which uses EDM and regression analysis to analyse live
video streaming (LVS) students’ online learning behaviours and their performance in their courses. Students’
participation and login frequency, as well as the number of chat messages and questions that they submit to their
instructors, were analysed, along with students’ final grades. Results of the study show a considerable variability
in students’ questions and chat messages. Unlike previous studies, this study suggests no correlation between
students’ number of questions / chat messages / login times and students’ success. However, our case study
reveals that combining EDM with traditional statistical analysis provides a strong and coherent analytical
framework capable of enabling a deeper and richer understanding of students’ learning behaviours and
experiences.
@article{abdous2012using,
abstract = {As higher education diversifies its delivery modes, our ability to use the predictive and analytical power of
educational data mining (EDM) to understand students’ learning experiences is a critical step forward. The
adoption of EDM by higher education as an analytical and decision making tool is offering new opportunities to
exploit the untapped data generated by various student information systems (SIS) and learning management
systems (LMS). This paper describes a hybrid approach which uses EDM and regression analysis to analyse live
video streaming (LVS) students’ online learning behaviours and their performance in their courses. Students’
participation and login frequency, as well as the number of chat messages and questions that they submit to their
instructors, were analysed, along with students’ final grades. Results of the study show a considerable variability
in students’ questions and chat messages. Unlike previous studies, this study suggests no correlation between
students’ number of questions / chat messages / login times and students’ success. However, our case study
reveals that combining EDM with traditional statistical analysis provides a strong and coherent analytical
framework capable of enabling a deeper and richer understanding of students’ learning behaviours and
experiences. },
added-at = {2015-02-14T17:44:07.000+0100},
author = {Abdous, M'hammed and He, Wu and Yen, Cherng-Jyh},
biburl = {https://www.bibsonomy.org/bibtex/208dd076c954989ddfaac3e52cbb7fe4d/yish},
interhash = {6696269474f5488f7da83351790eb19a},
intrahash = {08dd076c954989ddfaac3e52cbb7fe4d},
journal = {Educational Technology & Society},
keywords = {analytics data educational learning mining},
number = 3,
pages = {77-88},
timestamp = {2015-02-14T17:44:07.000+0100},
title = {Using data mining for predicting relationships between online question theme and final grade},
url = {http://ifets.info/journals/15_3/6.pdf},
volume = 15,
year = 2012
}