TY - GEN
T1 - Tracking At-Risk Student Groups from Teaching and Learning Activities in Engineering Education
AU - Kwan, Christopher Chung Lim
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - Tracking student groups, in particular, at-risk student group is a challenging but meaningful work in a large class of an engineering mathematics course, enabling instructors to ascertain how well students are learning and when they need interventions of their studies during the delivery of teaching and learning activities. In the paper, two unsupervised learning algorithms, hierarchical clustering and k-means clustering, are used and compared with the use of LMS data such as the level of achievements in online class activities, assignments, a mini-project and a mid-term test for tracking at-risk student groups at the end of weeks 3, 5, 7, 9 and 11 in a 13-week semester of an academic year. Notwithstanding the higher accuracy of both clustering, the k-means clustering significantly outperforms the hierarchical clustering in terms of the precision, recall and f-measure at the end of week 11. It is found that the k-means clustering can be employed to track at-risk students with the recall of 0.640 and the f-measure of 0.533 for the initial intervention of their studies by the end of week 7.
AB - Tracking student groups, in particular, at-risk student group is a challenging but meaningful work in a large class of an engineering mathematics course, enabling instructors to ascertain how well students are learning and when they need interventions of their studies during the delivery of teaching and learning activities. In the paper, two unsupervised learning algorithms, hierarchical clustering and k-means clustering, are used and compared with the use of LMS data such as the level of achievements in online class activities, assignments, a mini-project and a mid-term test for tracking at-risk student groups at the end of weeks 3, 5, 7, 9 and 11 in a 13-week semester of an academic year. Notwithstanding the higher accuracy of both clustering, the k-means clustering significantly outperforms the hierarchical clustering in terms of the precision, recall and f-measure at the end of week 11. It is found that the k-means clustering can be employed to track at-risk students with the recall of 0.640 and the f-measure of 0.533 for the initial intervention of their studies by the end of week 7.
KW - At-risk student
KW - F-measure
KW - Hierarchical clustering
KW - K-means clustering
KW - Precision
KW - Recall
UR - http://www.scopus.com/inward/record.url?scp=85097563188&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-63885-6_23
DO - 10.1007/978-3-030-63885-6_23
M3 - Conference article published in proceeding or book
AN - SCOPUS:85097563188
SN - 9783030638849
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 196
EP - 205
BT - Innovative Technologies and Learning - Third International Conference, ICITL 2020, Proceedings
A2 - Huang, Tien-Chi
A2 - Wu, Ting-Ting
A2 - Barroso, João
A2 - Sandnes, Frode Eika
A2 - Martins, Paulo
A2 - Huang, Yueh-Min
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Innovative Technologies and Learning, ICITL 2020
Y2 - 23 November 2020 through 26 November 2020
ER -