Assessment Disparities in MOOCs: A Data-Driven Study

Massive Open Online Courses (MOOCs), are online courses that are accessible and adaptable and are intended for massive enrolments. They offer low-cost access to high-quality education, self-paced learning and a variety of subject options that can improve job prospects. Through platforms like Coursera, edX, and NPTEL, universities and other organisations provide MOOCs, enabling learners all over the world to access high-quality education. Without the limitations of conventional classroom instruction, MOOCs also assist professionals in improving their skills and staying current with industry developments. Therefore, it is crucial to understand the difference in the performance of technical and non-technical students in different MOOCs to effectively design pedagogical tactics in today’s rapidly changing educational landscape. This study is aimed at investigating the performance of students on internal and external assessments from two different backgrounds i.e. technical and non-technical MOOCs. K -means clustering is used to compare the results of students from different courses. The result comprises of internal and proctored test scores of two different courses in a dataset. Clustering was performed with careful data preparation, including outlier removal to get the accurate results. We demonstrate how students from different backgrounds behaved in analogous assessment situations by contrasting the clustered groups. Our results indicate different performance patterns between technical and non-technical students with substantial implications on higher education’s curriculum development and resources for supporting student needs. The variation in performance emphasizes a need for varied teaching designs tailored to different learning approaches than present in nontechnical classes.