A Data-Driven Analysis of Cognitive Learning and Illusion Effects in University Mathematics

Rodolfo Bojorque (), Fernando Moscoso, Miguel Arcos-Argudo and Fernando Pesántez
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Rodolfo Bojorque: Campus El Vecino, Universidad Politécnica Salesiana, Cuenca 010102, Ecuador
Fernando Moscoso: Campus El Vecino, Universidad Politécnica Salesiana, Cuenca 010102, Ecuador
Miguel Arcos-Argudo: Campus El Vecino, Universidad Politécnica Salesiana, Cuenca 010102, Ecuador
Fernando Pesántez: Campus El Vecino, Universidad Politécnica Salesiana, Cuenca 010102, Ecuador

Data, 2025, vol. 10, issue 11, 1-16

Abstract: The increasing adoption of video-based instruction and digital assessment in higher education has reshaped how students interact with learning materials. However, it also introduces cognitive and behavioral biases that challenge the accuracy of self-perceived learning. This study aims to bridge the gap between perceived and actual learning by investigating how illusion learning—an overestimation of understanding driven by the fluency of instructional media and autonomous study behaviors—affects cognitive performance in university mathematics. Specifically, it examines how students’ performance evolves across Bloom’s cognitive domains (Understanding, Application, and Analysis) from midterm to final assessments. This paper presents a data-driven investigation that combines the theoretical framework of illusion learning, the tendency to overestimate understanding based on the fluency of instructional media, with empirical evidence drawn from a structured and anonymized dataset of 294 undergraduate students enrolled in a Linear Algebra course. The dataset records midterm and final exam scores across three cognitive domains (Understanding, Application, and Analysis) aligned with Bloom’s taxonomy. Through paired-sample testing, descriptive analytics, and visual inspection, the study identifies significant improvement in analytical reasoning, moderate progress in application, and persistent overconfidence in self-assessment. These results suggest that while students develop higher-order problem-solving skills, a cognitive gap remains between perceived and actual mastery. Beyond contributing to the theoretical understanding of metacognitive illusion, this paper provides a reproducible dataset and analysis framework that can inform future work in learning analytics, educational psychology, and behavioral modeling in higher education.

Keywords: data-driven education; cognitive learning; illusion learning; metacognition; higher education; learning analytics; behavioral data; open dataset (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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