Mental imagery is often difficult to express and interpret, especially in educational settings where mismatches in perception—termed "image gaps"—can occur between learners and instructors or among learners themselves. This study proposes a novel method for visually representing mental imagery to make such gaps more transparent and actionable. Traditionally, psychological and cognitive constructs derived from statistical techniques like factor analysis are presented as numerical data or abstract matrices, which are challenging for non-specialists to interpret. To address this, we developed an algorithm that transforms factor analysis results into intuitive graphical representations using Chernoff face graphs. These visualizations allow users to grasp complex latent structures at a glance, facilitating shared understanding and dialogue.
Our approach aims to support decision-making in areas such as course evaluation, career guidance, and learner profiling by making implicit perceptions visible and comparable. Preliminary applications suggest that visualizing image gaps can foster more interactive and empathetic communication between stakeholders in education. By bridging the divide between statistical insight and practical interpretation, this method contributes to both psychological assessment and educational technology.We will present the algorithm, sample visualizations, and explore its potential impact on decision-making in educational contexts.