Qiwei He

Item quality is crucial to psychometric analyses for cognitive diagnosis. In cognitive diagnosis models (CDMs), item quality is often quantified in terms of item parameters (e.g., guessing and slipping parameters). Calibrating the item parameters with only item response data, as a common practice, could result in challenges in identifying the cause of low-quality items (e.g., the correct answer is easy to be guessed) or devising an effective plan to improve the item quality. To resolve these challenges, we propose the item explanatory CDMs where the CDM item parameters are explained with item features such that item features can serve as an additional source of information for item parameters. The utility of the proposed models is demonstrated with the Trends in International Mathematics and Science Study (TIMSS)-released items and response data: around 20 item linguistic features were extracted from the item stem with natural language processing techniques, and the item feature engineering process is elaborated in the paper. The proposed models are used to examine the relationships between the guessing/slipping item parameters of the higher-order DINA model and eight of the item features. The findings from a follow-up simulation study are presented, which corroborate the validity of the inferences drawn from the empirical data analysis. Finally, future research directions are discussed.

Background Data‐driven investigations of how students transit pages in digital reading tasks and how much time they spend on each transition allow mapping sequences of navigation behaviours into students' navigation reading strategies. Objectives The purpose of this study is threefold: (1) to identify students' navigation patterns in multiple‐source reading tasks using a sequence clustering approach; (2) to examine how students' navigation patterns are associated with their reading performance and socio‐demographic characteristics; (3) to showcase how the navigation sequences could be clustered on the similarity measure by dynamic time warping (DTW) methods. Methods This study draws on process data from a sample of 16,957 students from 69 countries participating in the PISA 2018 study to identify how students navigate through a multiple‐source reading item. Students' navigation sequences were characterized by two indicators: the page sequence that tracks the page transition path and the time sequence that records the time duration on each visited page. K‐medoid partitioning clustering analyses were conducted on pairwise distance similarity measures computed by the DTW method. Results and conclusions Students' navigation patterns were found moderately associated with their reading proficiency levels. Students who visited all the pages and spent more time reading without rush transitions obtained the highest reading scores. Girls were more likely to achieve higher scores than boys when longer navigation sequences were used with shorter reading time on transited pages. Students who navigated only limited pages and spent shorter reading time were averagely at the lowest rank of socio‐economic status. Implications This study provides evidence for the exploration of students' navigation patterns and the examination of associations between navigation patterns and reading scores with the use of process data.