Visual or Textual: Effects of Explanation Format and Personal Characteristics on the Perception of Explanations in an Educational Recommender System

2026-03-26

AI Summary

This study investigates how visual and textual explanation formats in educational recommender systems interact with users’ individual characteristics to influence their perceptions of transparency, trust, sense of control, and satisfaction. Drawing on a within-subjects experiment with 54 participants, the research integrates multidimensional user traits—including the Big Five personality dimensions, need for cognition, decision-making styles, familiarity with visualizations, and technical expertise—and analyzes the data using robust linear mixed-effects models. The findings reveal, for the first time, that the moderating effects of these personal characteristics on explanation preferences are limited. Instead, the study proposes general design principles for visual explanations: those that are concise, interactive, highly selective, and intuitive consistently enhance user perceptions across diverse individuals, with minimal susceptibility to individual differences.

Technology Category

• Knowledge Representation and Reasoning: Preferences
• Machine Learning: Learning Preferences or Rankings
• Data Mining & Knowledge Management: Recommender Systems

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

Explanations are central to improving transparency, trust, and user satisfaction in recommender systems (RS), yet it remains unclear how different explanation formats (visual vs. textual) are suited to users with different personal characteristics (PCs). To this end, we report a within-subject user study (n=54) comparing visual and textual explanations and examine how explanation format and PCs jointly influence perceived control, transparency, trust, and satisfaction in an educational recommender system (ERS). Using robust mixed-effects models, we analyze the moderating effects of a wide range of PCs, including Big Five traits, need for cognition, decision making style, visualization familiarity, and technical expertise. Our results show that a well-designed visual, simple, interactive, selective, easy to understand visualization that clearly and intuitively communicates how user preferences are linked to recommendations, fosters perceived control, transparency, appropriate trust, and satisfaction in the ERS for most users, independent of their PCs. Moreover, we derive a set of guidelines to support the effective design of explanations in ERSs.

Problem

• explanation format
• personal characteristics
• educational recommender system
• user perception
• visual vs. textual

Innovation

• explanation format
• visual explanation
• personal characteristics
• educational recommender system
• user study