Automating Transfer Credit Assessment-A Natural Language Processing-Based Approach

Student mobility or academic mobility involves students moving between institutions during their post-secondary education,and one of the challenging tasks in this process is to assess the transfer credits to be offered to the incoming student.In general,this process involves domain experts comparing the learning outcomes of the courses,to decide on offering transfer credits to the incoming students.This manual implementation is not only labor-intensive but also influenced by undue bias and administrative complexity.The proposed research article focuses on identifying a model that exploits the advancements in the field of Natural Language Processing(NLP)to effectively automate this process.Given the unique structure,domain specificity,and complexity of learning outcomes(LOs),a need for designing a tailor-made model arises.The proposed model uses a clustering-inspired methodology based on knowledge-based semantic similarity measures to assess the taxonomic similarity of LOs and a transformer-based semantic similarity model to assess the semantic similarity of the LOs.The similarity between LOs is further aggregated to form course to course similarity.Due to the lack of quality benchmark datasets,a new benchmark dataset containing seven course-to-course similarity measures is proposed.Understanding the inherent need for flexibility in the decision-making process the aggregation part of the model offers tunable parameters to accommodate different levels of leniency.While providing an efficient model to assess the similarity between courses with existing resources,this research work also steers future research attempts to apply NLP in the field of articulation in an ideal direction by highlighting the persisting research gaps.

Investigation of the Academic Performance of College-to-University Transfer Students

Over the last decade, many universities/colleges have developed formal agreements which permit students from recognized college programs to be able to seamlessly transfer to a closely-related university program with advance standing. There has been some concerned raised that students that come to university from college may not be academically (or emotionally) prepared for the faster-paced university programs. This research, which was funded by an Ontario Council on Articulation and Transfer Faculty Fellowship, examines the academic performance of students in computer-related disciplines with a focus on comparing students who come to a university through a formalized college-to-university transfer agreement relative to students who enroll directly from high school. The comparisons will be based on metrics such as graduation rates, course failure rates, overall averages, course-level averages, and course-subject averages.