Volume 5 , Issue 2 , PP: 32–40, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Agnes Osagie 1 *
Doi: https://doi.org/10.54216/NIF.050204
Assessment records in digital mathematics platforms contain a form of uncertainty that is not sufficiently expressed by binary correctness labels. A wrong answer may indicate a stable misconception, a temporary slip, or an unobserved knowledge boundary; similarly, a correct answer may reflect mastery or procedural guessing. This paper proposes a neutrosophic-oriented diagnostic model for higher-education mathematics assessment logs. Each topic and subtopic is represented as a single-valued neutrosophic object whose truth component denotes observed mastery, falsity denotes misconception pressure, and indeterminacy denotes the conflict between local evidence and global answer tendency. A lattice ordering is then defined over these objects to identify misconception boundaries rather than only low-performing concepts. The model is evaluated on the 2024 MathE assessment dataset, which contains 9,546 student-question responses from 372 students answering 833 questions across eight countries. Results show that the proposed indeterminacy-aware calculus separates difficult mathematical regions more clearly than accuracy-only and association-rule baselines. Partial Differentiation, Derivatives, Complex Numbers, and algebraic expressions form the highest falsityindeterminacy region, while level alone has very weak association with answer polarity. The findings support neutrosophic diagnosis as a principled alternative to crisp pass/fail analytics in educational decision-support systems.
Single-valued neutrosophic set , Educational data mining , Mathematics assessment , Indeterminacy lattice , Misconception diagnosis , Information fusion
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