Automated Alignment of Course Outcomes with Program Outcomes and Program Educational Objectives Using NLP Techniques

This research introduces a systematic method for mapping Course Outcomes (CO) to Program Outcomes (PO), employing text preprocessing and the calculation of semantic similarity. Sentence-BERT was used to create vector embeddings for COs and POs, effectively capturing their essential meanings. The semantic similarity scores were scaled to a range of 1–3 to measure the strength of alignment between COs and POs. The resulting CO-PO mapping table provides data-driven insights into how course content aligns with program objectives, thereby enhancing the precision of curriculum design and ensuring better alignment with educational objectives. In addition to CO-PO mapping, K-means clustering and TF-IDF were implemented to evaluate the educational objectives of the program (PEOs), providing an additional layer of analysis. This methodology offers a more meaningful understanding of how different courses contribute to overarching educational goals, promoting enhanced alignment and optimization of the curriculum. The study helps improve the curriculum through this approach, fostering stronger connections between individual courses and the broader program objectives.