Careless and insufficient effort responding (C/IER) on self‐report measures results in responses that do not reflect the trait to be measured, thereby posing a major threat to the quality of survey data. Reliable approaches for detecting C/IER aid in increasing the validity of inferences being made from survey data. First, once detected, C/IER can be taken into account in data analysis. Second, approaches for detecting C/IER support a better understanding of its occurrence, which facilitates designing surveys that curb the prevalence of C/IER. Previous approaches for detecting C/IER are limited in that they identify C/IER at the aggregate respondent or scale level, thereby hindering investigations of item characteristics evoking C/IER. We propose an explanatory mixture item response theory model that supports identifying and modelling C/IER at the respondent‐by‐item level, can detect a wide array of C/IER patterns, and facilitates a deeper understanding of item characteristics associated with its occurrence. As the approach only requires raw response data, it is applicable to data from paper‐and‐pencil and online surveys. The model shows good parameter recovery and can well handle the simultaneous occurrence of multiple types of C/IER patterns in simulated data. The approach is illustrated on a publicly available Big Five inventory data set, where we found later item positions to be associated with higher C/IER probabilities. We gathered initial supporting validity evidence for the proposed approach by investigating agreement with multiple commonly employed indicators of C/IER.

Explanatory item response modeling (EIRM) enables researchers and practitioners to incorporate item and person properties into item response theory (IRT) models. Unlike traditional IRT models, explanatory IRT models can explain common variability stemming from the shared variance among item clusters and person groups. In this tutorial, we present the R package eirm, which provides a simple and easy-to-use set of tools for preparing data, estimating explanatory IRT models based on the Rasch family, extracting model output, and visualizing model results. We describe how functions in the eirm package can be used for estimating traditional IRT models (e.g., Rasch model, Partial Credit Model, and Rating Scale Model), item-explanatory models (i.e., Linear Logistic Test Model), and person-explanatory models (i.e., latent regression models) for both dichotomous and polytomous responses. In addition to demonstrating the general functionality of the eirm package, we also provide real-data examples with annotated R codes based on the Rosenberg Self-Esteem Scale.

As universities around the world have begun to use learning management systems (LMSs), more learning data have become available to gain deeper insights into students' learning processes and make data-driven decisions to improve student learning. With the availability of rich data extracted from the LMS, researchers have turned much of their attention to learning analytics (LA) applications using educational data mining techniques. Numerous LA models have been proposed to predict student achievement in university courses. To design predictive LA models, researchers often follow a data-driven approach that prioritizes prediction accuracy while sacrificing theoretical links to learning theory and its pedagogical implications. In this study, we argue that instead of complex variables (e.g., event logs, clickstream data, timestamps of learning activities), data extracted from online formative assessments should be the starting point for building predictive LA models. Using the LMS data from multiple offerings of an asynchronous undergraduate course, we analysed the utility of online formative assessments in predicting students' final course performance. Our findings showed that the features extracted from online formative assessments (e.g., completion, timestamps and scores) served as strong and significant predictors of students' final course performance. Scores from online formative assessments were consistently the strongest predictor of student performance across the three sections

In low-stakes assessments, some students may not reach the end of the test and leave some items unanswered due to various reasons (e.g., lack of test-taking motivation, poor time management, and test speededness). Not-reached items are often treated as incorrect or not-administered in the scoring process. However, when the proportion of not-reached items is high, these traditional approaches may yield biased scores and thereby threatening the validity of test results. In this study, we propose a polytomous scoring approach for handling not-reached items and compare its performance with those of the traditional scoring approaches. Real data from a low-stakes math assessment administered to second and third graders were used. The assessment consisted of 40 short-answer items focusing on addition and subtraction. The students were instructed to answer as many items as possible within 5 minutes. Using the traditional scoring approaches, students’ responses for not-reached items were treated as either not-administered or incorrect in the scoring process. With the proposed scoring approach, students’ nonmissing responses were scored polytomously based on how accurately and rapidly they responded to the items to reduce the impact of not-reached items on ability estimation. The traditional and polytomous scoring approaches were compared based on several evaluation criteria, such as model fit indices, test information function, and bias. The results indicated that the polytomous scoring approaches outperformed the traditional approaches. The complete case simulation corroborated our empirical findings that the scoring approach in which nonmissing items were scored polytomously and not-reached items were considered not-administered performed the best. Implications of the polytomous scoring approach for low-stakes assessments were discussed.

The Rosenberg Self-Esteem Scale was administered with a 1–4, 1–5, or 0–100 scale to 819 participants, to compare score interpretations across the different versions. A rating scale utility analysis revealed that the categories in the 101-point scale were used inconsistently; based on the analysis, adjacent categories were collapsed resulting in a 7-point scale with almost identical psychometric properties as the original. The interpretations based on the 101-point scale could lead to misinterpretations when compared with the 4- and 5-point versions.