AI Summary & Key Takeaways
AI InsightThe results show that the LGM with the best model fit indicies, for both items, was the quadratic model using response variable as a covariate.
📌 3 Key Takeaways
Abstract
This study illustrates how eye-tracking data can be translated to “item process data” for multiple-choice test items to study the relationship between subjects’ item responses and choice reading behavior. Several modes of analysis were used to test the hypothesized added value of using process data to identify choice reading patterns of subjects. In addition to the cross-sectional analises of agggregate measurements derived from the time series eye tracking data, Latent Growth Curve Model Analises were undertaken to test if the the shape of change observed in the sequential choice reading patterns differed for subjects depending on their responses to the item being correct or incorrect. Application data were from an experimental study and included seventy-one subjects’ responses to two multiple-choice test items measuring reading comprehension ability in English as a second language. Analyses were carried out for one item at a time. For each item, first, each subject’s recorded eye movements were coded into a set of Area of Interests (AOIs), segmenting the lines in the stem and the individual choices. Next, each subject’s fixation times on the AOIs were time stamped into seconds, indicating when and in what order each subject’s gaze had fixated on each AOI until a choice was marked as the correct answer, which ended the item encounter. A set of nested Latent Growth Curve models were considered for the choice-related AOIs to deliniate if distinct choice-process sequences were evident for correct and incorrect respoders. Model fit indices, random intercepts, slopes, and residuals were computed using the mean log fixation times over item encounter time. The results show that the LGM with the best model fit indicies, for both items, was the quadratic model using response variable as a covariate. Albeit limited due to the two-item – seventy-one subjects experimental setting of the study, the findings are promising and show that utilizing item-level process data can be very useful for defining distinct choice processing (task-oriented reading) patterns of examinees. Over all, the results warrant further study of choice derived AOIs using longitudinal statistical models. It is argued that, the screening methodology desribed in this study can be a useful tool to investigate speededness, distractor functioning, or even to flag subjects with irregular choice processing behavior, such as providing a direct mark on a choice, without any significant reading activity on any of the choices presented (i.e., whether cheating might have occurred.)