Background and Context: Students' programming projects are often assessed on the basis of their tests as well as their implementations, most commonly using test adequacy criteria like branch coverage, or, in some cases, mutation analysis. As a result, students are implicitly encouraged to use these tools during their development process (i.e., so…

Example-generation tasks have been suggested as an effective way to both promote students' learning of mathematics and assess students' understanding of concepts. E-assessment offers the potential to use example-generation tasks with large groups of students, but there has been little research on this approach so far. Across two studies, we…

Process Analysis is an emerging approach to discover meaningful knowledge from temporal educational data. The study presented in this paper shows how we used Process Analysis methods on the National Assessment of Educational Progress (NAEP) test data for modeling and predicting student test-taking behavior. Our process-oriented data exploration…

Factor analysis is often used to model scales created to measure latent constructs, and internal structure validity evidence is commonly assessed with indices like SRMR, RMSEA, and CFI. These indices are essentially effect size measures and definitive benchmarks regarding which values connote reasonable fit have been elusive. Simulations from the…

We first review existing literature on cumulative logit models along with various ways to test the parallel lines assumption. Building on the traditional frequentist framework, we introduce a method of Bayesian assessment of null values to provide an alternative way to examine the parallel lines assumption using highest density intervals and…

Scientific modeling is a vital educational practice that helps students apply scientific knowledge to real-world phenomena. Despite advances in AI, challenges in accurately assessing such models persist, primarily due to the complexity of cognitive constructs and data imbalances in educational settings. This study addresses these challenges by…

Thurstonian forced-choice modeling is considered to be a powerful new tool to estimate item and person parameters while simultaneously testing the model fit. This assessment approach is associated with the aim of reducing faking and other response tendencies that plague traditional self-report trait assessments. As a result of major recent…

Diagnostic classification models (DCMs) are psychometric models that can be used to estimate the presence or absence of psychological traits, or proficiency on fine-grained skills. Critical to the use of any psychometric model in practice, including DCMs, is an evaluation of model fit. Traditionally, DCMs have been estimated with maximum…

In the current higher education context, the development of academics' competencies seems to be a crucial issue, with a strong focus on teaching, learning and assessment digital skills (Redecker & Punie, 2017). In connection with the framework of DigCompEdu (2017), it seems important to understand how to better sustain academics' new…

Evidence-based assessment (EBA) requires that investigators employ scientific theories and research findings to guide decisions about what domains to measure, how and when to measure them, and how to make decisions and interpret results. To implement EBA, investigators need high-quality assessment tools along with evidence-based processes. We…

Over the past 8 years, U.S. kindergarten classrooms have been impacted by policies mandating or recommending the administration of a specific kindergarten entry assessment (KEA) in the initial months of school as well as the increasing reliance on digital technology in the form of mobile apps, touchscreen devices, and online data platforms. Using…

Researchers strategically assess youth mental health by soliciting reports from multiple informants. Typically, these informants (e.g., parents, teachers, youth themselves) vary in the social contexts where they observe youth. Decades of research reveal that the most common data conditions produced with this approach consist of discrepancies…

The number of articles showing dissatisfaction with the null hypothesis statistical testing (NHST) framework has been progressively increasing over the years. Alternatives to NHST have been proposed and the Bayesian approach seems to have achieved the highest amount of visibility. In this last part of the special issue, a few alternative…

Due to COVID-19, the world has encountered new challenges regarding pedagogy, learning, assessment, and evaluation. In meeting these challenges, there have been rapid changes in learning, and the gap between pedagogy and evaluation has grown. The purpose of this paper is to develop a new evaluative model suitable for the technologically enhanced,…

Currently available (classical) testing procedures for the network autocorrelation can only be used for falsifying a precise null hypothesis of no network effect. Classical methods can be neither used for quantifying evidence for the null nor for testing multiple hypotheses simultaneously. This article presents flexible Bayes factor testing…