Identifying At-risk Students from Course-specific Predictive Analytics

Chung Lim Christopher Kwan

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

Abstract

Identifying at-risk students in a large class of an engineering mathematics course during the delivery of teaching and learning activities is not an easy task to be accomplished by many instructors, particularly in the first few weeks of their studies. In the paper, course-specific predictive analytics, called the multiple linear regression model, the logistic regression model and the classification and regression tree (CART) model are trained, tested and compared with the use of LMS data in the first semester of the academic year 2017-18 such as the level of achievements in online class activities, the mini-project, the mid-term test, assignments, and the final examination for classifying at-risk students as early as possible during the course of study. A feature selection method is used to select statistically significant variables in the development of multiple linear regression and logistic regression models for enhancing the generalizability of both models. It is found that 3 key variables such as the level of achievements in the 6th online class activity, the mid-term test and assignment 2, which may have pedagogically meaningful information, are crucial for classifying at-risk students. Despite the highest accuracy of the CART model, the logistic regression model significantly outperforms the multiple linear regression and the CART models in terms of the recall and f-measure of the testing set. Instead of selecting 3 key variables, the present logistic regression model which only comprises 2 statistically significant variables such as the level of achievements in the 6th online class activity and the mid-term test can be employed to identify at-risk students for early intervention of their studies once the results of the mid-term test and the 6th online class activity are made available at the end of week 7.

Original languageEnglish
Title of host publicationICCE 2019 - 27th International Conference on Computers in Education, Proceedings
EditorsMaiga Chang, Hyo-Jeong So, Lung-Hsiang Wong, Ju-Ling Shih, Fu-Yun Yu, Michelle P. Banawan, Ben Chang, Weiqin Chen, Andrei D. Coronel, Swapna Gottipati, H. Ulrich Hoppe, Morris S.Y. Jong, Calvin Liao, Jon Mason, Fan Ouyang, Patcharin Panjaburee, Ma. Mercedes T. Rodrigo, Yanjie Song, Niwat Srisawasdi, Ahmed Tlili, Chengjiu Yin
PublisherAsia-Pacific Society for Computers in Education
Pages356-360
Number of pages5
ISBN (Electronic)9789869721448
Publication statusPublished - 19 Nov 2019
Event27th International Conference on Computers in Education, ICCE 2019 - Kenting, Taiwan
Duration: 2 Dec 20196 Dec 2019

Publication series

NameICCE 2019 - 27th International Conference on Computers in Education, Proceedings
Volume2

Conference

Conference27th International Conference on Computers in Education, ICCE 2019
Country/TerritoryTaiwan
CityKenting
Period2/12/196/12/19

Keywords

  • At-risk Students
  • Classification and Regression Tree
  • F-measure
  • Logistic Regression Model
  • Multiple Linear Regression Model
  • Recall

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science Applications
  • Information Systems
  • Hardware and Architecture
  • Education

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