Automating the mapping of course learning outcomes to program learning outcomes using natural language processing for accurate educational program evaluation

Nazar Zaki, Sherzod Turaev, Khaled Shuaib, Anusuya Krishnan, Elfadil Mohamed

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Quality control and assurance plays a fundamental role within higher education contexts. One means by which quality control can be performed is by mapping the course learning outcomes (CLOs) to the program learning outcomes (PLO). This paper describes a system by which this mapping process can be automated and validated. The proposed AI-based system automates the mapping process through the use of natural language processing. The framework underwent testing using two actual datasets from two educational programs, and the findings were promising. A testament to the potential of the suggested framework was the precision of the mapping detected (83.1% and 88.1% for the two programs, respectively) compared to the mapping performed by the domain experts. A web-based tool was created to help teachers and administrators execute automatic mappings (https://dsaluaeu.github.io/mapper.html). The data and software used in this research project can be found at the following URL: https://github.com/nzaki02/CLO-PLO .

Original languageEnglish
Pages (from-to)16723-16742
Number of pages20
JournalEducation and Information Technologies
Volume28
Issue number12
DOIs
Publication statusPublished - Dec 2023

Keywords

  • Academic mapping
  • Artificial Intelligence
  • Course learning outcomes
  • Natural language processing
  • Program learning outcomes
  • Quality assurance in higher education

ASJC Scopus subject areas

  • Education
  • Library and Information Sciences

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