An Ontology and Management System for Learning Outcomes and Student Mastery

Universities, faculty, and students use Learning Outcomes (LO) to create a shared understanding of the content provided in an individual course, known as Outcome-Based Education (OBE). One area of interest in OBE is evaluating whether the instructor and individual student performance have met the LO, which is integral to ensuring all invested parties are on the same page about class content and student performance. This work proposes a system for the management and evaluation of LO. Primarily, this work defines an ontology to support the management and evaluation of LO via Knowledge Graphs (KG). The KG links individual LO with individual assessment items. Two state-of-the-art Natural Language Processing models, BERT and ChatGPT, are evaluated in respect to their effectiveness in automating this linking. This data allows the educational professional to reflect on how well their assessments match the course's LO. The second part of this system harnesses student data to measure performance in relation to LO. In this Work-in-Progress paper, the system is prototyped and tested on the midterm results of a course in the Software Engineering curriculum. Student performance is documented in relation to each assessment question on the exams to measure student mastery of course material. Through this approach, courses can be evaluated and improved to deliver better quality education to all students. This includes improvements at the course level and possibilities for early intervention to ensure student success. This paper details the development of this system and through its implementation shows how it benefits engineering educators and their students.