Integrating Visual Serial Reaction Time and Binary Decision Trees for the Evaluation of Implicit Learning: A Methodological Proof of Concept

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Abstract
Context: This investigation assessed whether repeated exposure to visual-spatial patterns and guided micro-interactions could lead high school and first-year engineering students to internalize formal rules. By combining implicit learning tools (e.g., VSRT) with explicit tools, such as a binary tree calculator. Objective: This study aims to determine whether stable patterns and low-friction actions increase implicit learning, structured reasoning, and algebraic manipulation compared with historical baseline performance. Methodology: A concurrent mixed-methods design was used to recruit 40 participants from a purposive historical sample of 153 students. Quantitative (VSRT accuracy, interaction statistics) and qualitative (28 interviews until theoretical saturation) analyses were performed using thematic analysis and decision trees. Results: The participants achieved an accuracy of 84% in recognizing VSRT patterns, exceeding the previous average of 70% and showing clear improvement. Repetition and familiarity with tasks were found to be important factors that influenced progress. Conclusions: Implicit VSRT training combined with explicit visualization is an efficient method for teaching hierarchical content. Such dual approaches enhance academic performance as well as structural understanding and can be evidenced by repetition and familiarity as pivotal techniques. This study provides a strong model for STEM education interventions that link implicit and explicit cognitive systems.

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