Tracking At-Risk Student Groups from Teaching and Learning Activities in Engineering Education

Christopher Chung Lim Kwan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


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.

Original languageEnglish
Title of host publicationInnovative Technologies and Learning - Third International Conference, ICITL 2020, Proceedings
EditorsTien-Chi Huang, Ting-Ting Wu, João Barroso, Frode Eika Sandnes, Paulo Martins, Yueh-Min Huang
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
ISBN (Print)9783030638849
Publication statusPublished - 2020
Event3rd International Conference on Innovative Technologies and Learning, ICITL 2020 - Porto, Portugal
Duration: 23 Nov 202026 Nov 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12555 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference3rd International Conference on Innovative Technologies and Learning, ICITL 2020


  • At-risk student
  • F-measure
  • Hierarchical clustering
  • K-means clustering
  • Precision
  • Recall

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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