超越识字：基于用户特征、项目难度和Rasch分数预测可视化解释正确性
Beyond Literacy: Predicting Interpretation Correctness of Visualizations with User Traits, Item Difficulty, and Rasch Scores
AI总结：
本文提出基于用户特征、项目难度和Rasch分数预测数据可视化解释正确性的方法，通过特征选择的逻辑回归模型实现高准确率的预判，提升评估效率和培训针对性。
摘要：
数据可视化识字能力评估通常通过固定的数据可视化项目集进行，尽管不同人对相同可视化解释存在显著差异。本文提出并评估了一种预测人类对数据可视化的解释正确性（P-HIC）的方法；即在接触该可视化之前，预测特定人是否会正确解释该可视化，从而实现更个性化的评估和培训。我们将P-HIC定义为一个二元分类问题，使用22个特征涵盖人类档案、人类表现和项目难度（包括专家难度和Rasch难度）。我们评估了三种机器学习模型（逻辑回归模型、随机森林、多层感知机）以及带有和无特征选择的模型，使用1083名参与者完成的调查，参与者回答了32个数据可视化问题（每四题八个数据可视化），产生34656个项目响应。通过在每个32（项目特定）数据集上进行十次十折交叉验证，使用AUC和Cohen's kappa进行性能评估。带有特征选择的逻辑回归模型是表现最佳的方法，达到中位AUC为0.72和中位kappa为0.32。特征分析显示Rasch难度是主导预测因素，随后是专家评分和先前正确性（PercCorrect），其相关性随会话增加而增加。档案信息对P-HIC没有特别支持。我们的结果支持了预测数据可视化误解的可能性，并激励了在运行时选择适合受众的数据可视化项目，从而提高数据可视化识字能力评估和针对性培训的效率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
