Academic stress is intrinsically imprecise: students often report linguistic states such as low, moderate, or high, while academic performance is represented by GPA, which may be rounded and heterogeneous across programs. Conventional correlation converts linguistic stress to crisp ordinal scores and therefore hides measurement vagueness. This paper proposes a novel Certainty-Weighted α-Cut Fuzzy Correlation (CWαFC) framework. Stress categories are represented by overlapping triangular fuzzy numbers, GPA is represented by a narrow fuzzy number around its reported value, and uncertainty is propagated through α-cuts. A certainty parameter contracts or expands the fuzzy support according to classification reliability. The framework is demonstrated with the public Student Lifestyle Dataset containing 2,000 records. Independent analyses of the same real dataset report 297 low-stress, 674 moderate-stress, and 1,029 high-stress students; mean GPAs of 2.82, 3.02, and 3.26; and an ordinal stress–GPA correlation of about 0.5504. CWαFC preserves this positive central tendency while replacing a single coefficient by nested correlation intervals. The method gives a more defensible interpretation of linguistic educational data and explicitly separates vagueness from sampling uncertainty. Because the dataset is observational and its stress label is derived partly from lifestyle variables, the positive association is not interpreted causally.

C. G. Magadum, B. A. Sajane, "A Certainty-Weighted α-Cut Fuzzy Correlation Framework for Analyzing Academic Stress and Academic Performance" International Journal of Scientific Research and Engineering Development, V9(5): Page(646-651) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.