TY - GEN
T1 - Correlation between course tracking variables and academic performance in blended online courses
AU - Lin, Che Cheng
AU - Chiu, Chiung Hui
PY - 2013
Y1 - 2013
N2 - The purpose of this research was to identify which course tracking variables correlate significantly with academic performance in blended asynchronous online courses through an empirical analysis of Learning Management System (LMS) data. In this study, course tracking variables refers to number of online sessions, number of original posts created, number of follow-up posts created, number of content pages viewed and number of posts read. Academic performance defined as how well a student's final grad is. These five variables were collected from 15 undergraduate courses in the first semester of academic year 2012 at one national university in Taiwan. A total of 528 related final scores were transformed to z score and analyzed to investigate the correlation between course tracking variables and academic performance. A multiple regression analysis was used to evaluate how well course tracking variables measure predicted academic performance. Results indicated that approximately 16.4% of the variability in academic performance was accounted for by student's course tracking variables measure, and three of the five variables were statistically significant.
AB - The purpose of this research was to identify which course tracking variables correlate significantly with academic performance in blended asynchronous online courses through an empirical analysis of Learning Management System (LMS) data. In this study, course tracking variables refers to number of online sessions, number of original posts created, number of follow-up posts created, number of content pages viewed and number of posts read. Academic performance defined as how well a student's final grad is. These five variables were collected from 15 undergraduate courses in the first semester of academic year 2012 at one national university in Taiwan. A total of 528 related final scores were transformed to z score and analyzed to investigate the correlation between course tracking variables and academic performance. A multiple regression analysis was used to evaluate how well course tracking variables measure predicted academic performance. Results indicated that approximately 16.4% of the variability in academic performance was accounted for by student's course tracking variables measure, and three of the five variables were statistically significant.
KW - Academic performance
KW - ICT
KW - academic analytics
KW - data mining
UR - http://www.scopus.com/inward/record.url?scp=84885224657&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84885224657&partnerID=8YFLogxK
U2 - 10.1109/ICALT.2013.57
DO - 10.1109/ICALT.2013.57
M3 - Conference contribution
AN - SCOPUS:84885224657
SN - 9780769550091
T3 - Proceedings - 2013 IEEE 13th International Conference on Advanced Learning Technologies, ICALT 2013
SP - 184
EP - 188
BT - Proceedings - 2013 IEEE 13th International Conference on Advanced Learning Technologies, ICALT 2013
T2 - 2013 IEEE 13th International Conference on Advanced Learning Technologies, ICALT 2013
Y2 - 15 July 2013 through 18 July 2013
ER -