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

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 map-ping 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.

Automated summary

This paper presents a system based on artificial intelligence that automates and validates the mapping process between course learning outcomes (CLOs) and program learning outcomes (PLO). The system uses natural language processing to automate the mapping process and has shown promising results in testing. A web-based tool has also been created to assist teachers and administrators in performing automatic mappings.