Data Processing Pipeline for Course-Level Outcome Analytics in Higher Education: Time-Based and Clustering-Based Hybrid Approaches
Keywords:
Quality Assurance in Higher Education, Learning Analytics, Data Processing, Hierarchical Clustering, Unsupervised Learning, Grade Distribution AnalysisAbstract
One of the key tasks of quality assurance in higher education is the systematic monitoring of course-level outcomes and the adoption of appropriate decisions based on the analysis of these results. Although quality assurance standards and guidelines place strong emphasis on data-informed monitoring and the periodic review of programmes, practical analytical approaches applied at the course level often remain diverse and unevenly formalized. This situation is largely influenced by the institutional autonomy granted to higher education institutions, which results in diverse practices across institutions.
The aim of this paper is to review and comparatively analyze contemporary data-informed and analytics-based approaches to course-level quality assurance. The study is based on a systematic examination of existing approaches, including the analysis of aggregated assessment data, the use of learning analytics and educational data mining, as well as the role of explainable analytics in supporting academic and managerial decision making. The review is complemented by a practice-oriented example based on real data, which examines the longitudinal analysis of assessment outcomes for the same course over multiple academic years.
The findings indicate that the analysis of grade distributions and performance dynamics can be effectively used to identify the need for improvements in course content, teaching methods, allocation of instructional time and assessment systems. The paper highlights the specific characteristics of different approaches, their strengths and limitations, and emphasizes the importance of using modern technologies to support course-level quality assurance processes.
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