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

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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.

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Data availability

The datasets used during the current study are available in the GitHub repository, [https://github.com/nzaki02/CLO-PLO].

Abbreviations

CLO :

Course learning outcome

PLO :

Program learning outcome

NLP :

Natural language processing

AI :

Artificial Intelligence

QA :

Quality assurance

HEI :

Higher education institutional

ILO :

Institutional learning outcomes

ABET :

Accreditation Board for Engineering and Technology

BERT :

Bidirectional Encoder Representations from Transformers

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This work was done based on self-funding.

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Authors and Affiliations

  1. Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, 15551, Al Ain, United Arab Emirates

    Nazar Zaki & Sherzod Turaev

  2. Big Data Analytics Center, United Arab Emirates University, Al Ain, United Arab Emirates

    Nazar Zaki & Anusuya Krishnan

  3. Department of Info. Systems and Security, College of Information Technology, United Arab Emirates University, 15551, Al Ain, United Arab Emirates

    Khaled Shuaib

  4. Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates

    Elfadil Mohamed

Authors
  1. Nazar Zaki
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  2. Sherzod Turaev
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  3. Khaled Shuaib
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  4. Anusuya Krishnan
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  5. Elfadil Mohamed
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Correspondence to Nazar Zaki.

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Zaki, N., Turaev, S., Shuaib, K. et al. Automating the mapping of course learning outcomes to program learning outcomes using natural language processing for accurate educational program evaluation. Educ Inf Technol 28, 16723–16742 (2023). https://doi.org/10.1007/s10639-023-11877-4

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  • DOI: https://doi.org/10.1007/s10639-023-11877-4

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