Customized rule-based model to identify at-risk students and propose rational remedial actions

Balqis Albreiki, Tetiana Habuza, Zaid Shuqfa, Mohamed Adel Serhani, Nazar Zaki, Saad Harous

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Detecting at-risk students provides advanced benefits for improving student retention rates, effective enrollment management, alumni engagement, targeted marketing improvement, and institutional effectiveness advancement. One of the success factors of educational institutes is based on accurate and timely identification and prioritization of the students requiring assistance. The main objective of this paper is to detect at-risk students as early as possible in order to take appropriate correction measures taking into consideration the most important and influential attributes in students’ data. This paper emphasizes the use of a customized rule-based system (RBS) to identify and visualize at-risk students in early stages throughout the course delivery using the Risk Flag (RF). Moreover, it can serve as a warning tool for instructors to identify those students that may struggle to grasp learning outcomes. The module allows the instructor to have a dashboard that graphically depicts the students’ performance in different coursework components. The at-risk student will be distinguished (flagged), and remedial actions will be communicated to the student, instructor, and stakeholders. The system suggests remedial actions based on the severity of the case and the time the student is flagged. It is expected to improve students’ achievement and success, and it could also have positive impacts on under-performing students, educators, and academic institutions in general.

Original languageEnglish
Article number71
JournalBig Data and Cognitive Computing
Volume5
Issue number4
DOIs
Publication statusPublished - Dec 2021
Externally publishedYes

Keywords

  • At-risk student
  • Early detection
  • Educational data mining
  • Rule-based system
  • Student evaluation

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Computer Science Applications
  • Artificial Intelligence

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