Beyond Literacy: Predicting Interpretation Correctness of Visualizations with User Traits, Item Difficulty, and Rasch Scores

TL;DR

This study develops a machine learning approach to predict whether individuals will interpret data visualizations correctly, using user traits, item difficulty, and Rasch scores, to enable personalized visualization assessments and training.

Contribution

It introduces a predictive model for interpretation correctness that incorporates diverse user and item features, advancing personalized data visualization literacy evaluation.

Findings

Logistic Regression with feature selection performs best with AUC of 0.72.

RaschDifficulty is the most influential predictor.

Prediction accuracy improves with session progress.

Abstract

Data Visualization Literacy assessments are typically administered via fixed sets of Data Visualization items, despite substantial heterogeneity in how different people interpret the same visualization. This paper presents and evaluates an approach for predicting Human Interpretation Correctness (P-HIC) of data visualizations; i.e., anticipating whether a specific person will interpret a data visualization correctly or not, before exposure to that DV, enabling more personalized assessment and training. We operationalize P-HIC as a binary classification problem using 22 features spanning Human Profile, Human Performance, and Item difficulty (including ExpertDifficulty and RaschDifficulty). We evaluate three machine-learning models (Logistic Regression model, Random Forest, Multi Layer Perceptron) with and without feature selection, using a survey with 1,083 participants who answered 32 Data Visualization items (eight data visualizations per four items), yielding 34,656 item responses. Performance is assessed via a ten-time ten-fold cross-validation in each 32 (item-specific) datasets, using AUC and Cohen's kappa. Logistic Regression model with feature selection is the best-performing approach, reaching a median AUC of 0.72 and a median kappa of 0.32. Feature analyses show RaschDifficulty as the dominant predictor, followed by experts' ratings and prior correctness (PercCorrect), whose relevance increases across sessions. Profile information did not particularly support P-HIC. Our results support the feasibility of anticipating misinterpretations of data visualizations, and motivate the runtime selection of data visualizations items tailored to an audience, thereby improving the efficiency of Data Visualization Literacy assessment and targeted training.