Reducing Extraneous Cognitive Load to Improve Analytic Geometry Problem Solving among Undergraduate Mathematics Students - Damar Rais; Yue Wen Yan; Silvie Rama Yuliane; Deswandhana AS (Journal of Multidisciplinary Science: MIKAILALSYS, 2026)
Mathematical problem solving in higher education requires students to coordinate symbolic, graphical, and conceptual information within limited cognitive resources. This study examines the effect of extraneous cognitive load on undergraduate mathematics students’ problem-solving performance in an Analytic Geometry course and determines whether cognitively optimized task presentation can improve performance by reducing unnecessary mental effort during mathematical reasoning. A within-subject explanatory mixed-methods design was employed with 25 mathematics department students. Participants completed equivalent Analytic Geometry problem-solving tasks under two presentation conditions: a conventional format and a cognitively optimized format. Data were collected through problem-solving tests, perceived extraneous cognitive load ratings, time-on-task records, written solution analysis, and semi-structured interviews. The results showed that students achieved higher problem-solving scores in the cognitively optimized condition than in the conventional condition. They also reported lower extraneous cognitive load and completed the tasks in less time. Correlation and regression analyses indicated that higher extraneous cognitive load was associated with lower problem-solving performance, suggesting that unnecessary processing demands constrained students’ mathematical reasoning. Qualitative findings supported these results by showing that students benefited from integrated diagrams, clearer symbolic representations, and reduced information search. The study concludes that extraneous cognitive load is a critical factor in undergraduate mathematical problem solving and that optimizing instructional presentation can improve cognitive efficiency without reducing mathematical rigor. These findings contribute to research on cognitive load, mathematical thinking, and educational technology, while offering practical implications for designing Analytic Geometry learning materials that support efficient and meaningful mathematical reasoning.