引入眼动注视点的联合-交叉负载多模态认知诊断建模

多模态数据为实现对认知结构的精准诊断及其他认知特征(如, 认知风格)的全面反馈提供了可能性。为实现对题目作答精度、作答时间(RT)和视觉注视点数(FC)的联合分析, 本文基于联合-交叉负载建模法提出3个多模态认知诊断模型。实证研究及模拟研究结果表明: (1)联合分析比分离分析更适用于多模态数据; (2)新模型可直接利用RT和FC中信息提高潜在能力或潜在属性的估计准确性; (3)新模型的参数估计返真性较好; (4)忽略交叉负载所导致的负面结果比冗余考虑交叉负载所导致的更严重。

Joint-cross-loading multimodal cognitive diagnostic modeling incorporating visual fixation counts

Students’ observed behavior (e.g., learning behavior and problem-solving behavior) comprises of activities that represent complicated cognitive processes and latent conceptions that are frequently systematically related to one another. Cognitive characteristics such as cognitive styles and fluency may differ between students with the same cognitive/knowledge structure. However, practically all cognitive diagnosis models (CDMs) that merely assess item response accuracy (RA) data are currently incapable of estimating or inferring individual differences in cognitive traits. With advances in technology-enhanced assessments, it is now possible to capture multimodal data, such as outcome data (e.g., response accuracy), process data (e.g., response times (RTs), and biometric data (e.g., visual fixation counts (FCs)), automatically and simultaneously during the problem-solving activity. Multimodal data allows for precise cognitive structure diagnosis as well as comprehensive feedback on various cognitive characteristics.