Early Detection of At-Risk Students in a Calculus Course

Akshay Kumar Dileep, Ajay Bansal, James Cunningham

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Calculus as a math course is an important subject students need to succeed in, to venture into STEM majors. The paper focuses on the early detection of at-risk students in a calculus course which can provide the proper intervention that might help them succeed. Calculus has high failure rates which corroborate with the data collected from our University that shows us that 40% of the 3266 students whose data were used failed in their calculus course. Some existing studies similar to our paper make use of open-scale data that are lower in data count and perform predictions on low-impact MOOC-based courses. Paper proposes, an automatic detection method of academically at-risk students by using Learning Management Systems (LMS) activity data along with the student information system (SIS) data from our University for the Math course. The proposed method will detect students at risk by employing machine learning to identify key features that contribute to the success of a student. The model developed has a predictive accuracy of 73.5 % on the online modality of the Math course and has 87.8 % accuracy on the face-2-face (F2F) modality of the same class. Transfer student, a binary feature attributed to the highest feature importance.

Original languageEnglish (US)
Title of host publicationProceedings - 2022 IEEE 46th Annual Computers, Software, and Applications Conference, COMPSAC 2022
EditorsHong Va Leong, Sahra Sedigh Sarvestani, Yuuichi Teranishi, Alfredo Cuzzocrea, Hiroki Kashiwazaki, Dave Towey, Ji-Jiang Yang, Hossain Shahriar
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages187-194
Number of pages8
ISBN (Electronic)9781665488105
DOIs
StatePublished - 2022
Event46th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2022 - Virtual, Online, United States
Duration: Jun 27 2022Jul 1 2022

Publication series

NameProceedings - 2022 IEEE 46th Annual Computers, Software, and Applications Conference, COMPSAC 2022

Conference

Conference46th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2022
Country/TerritoryUnited States
CityVirtual, Online
Period6/27/227/1/22

Keywords

  • Feature Engineering
  • Learning Analytic
  • Machine Learning
  • Predictive Modelling

ASJC Scopus subject areas

  • Computer Science Applications
  • Hardware and Architecture
  • Software
  • Media Technology
  • Education

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