A key commitment of computer-supported collaborative learning research is to study how people learn in collaborative settings to guide development of methods for capture and design for learning. Computer-supported collaborative learning research has a tradition of studying how the physical world plays a part in collaborative learning. Within the…

Computational thinking (CT) is a fundamental ability for learners in today's society. Although CT assessments and interventions have been studied widely, little is known about CT predictions. This study predicted students' CT achievement in the ICILS 2018 using five machine learning models. These models were trained on the data from five European…

Explanatory item response models (EIRMs) have been applied to investigate the effects of person covariates, item covariates, and their interactions in the fields of reading education and psycholinguistics. In practice, it is often assumed that the relationships between the covariates and the logit transformation of item response probability are…

Explanatory item response models (EIRMs) have been applied to investigate the effects of person covariates, item covariates, and their interactions in the fields of reading education and psycholinguistics. In practice, it is often assumed that the relationships between the covariates and the logit transformation of item response probability are…

Computational thinking (CT) is an emerging and multifaceted competence important to the computing era. However, despite the growing consensus that CT is a competence domain, its theoretical and empirical account remain scarce in the current literature. To address this issue, rigorous psychometric evaluation procedures were adopted to investigate…

Computational thinking (CT) is an important 21st-century skill. This paper aims at more useful CT assessment. Available evaluation instruments are reviewed; two generally accepted CT evaluation tools are selected for a comprehensive CT assessment: the CTt, a performance test, and the CTS, a self-assessment instrument. The sample comprises 202 high…

With the growing attention on large-scale educational testing and assessment, the ability to process substantial volumes of response data becomes crucial. Current estimation methods within item response theory (IRT), despite their high precision, often pose considerable computational burdens with large-scale data, leading to reduced computational…

Computational thinking is a strategy of thinking to tackle complex problems. There is a paucity of conceptualization and instruments that cogitate on computational thinking disposition and attitudes. This study reacts to these constraints by establishing an instrument to test computational thinking related dispositions and attitudes. The…

This paper describes a study that focused on developing, validating and standardizing a dyscalculia test, henceforth called the Dyscalculia Test. Out of the 4,758,800 students in Nigeria's upper primary and junior secondary schools, I randomly drew a sample of 2340 students, using a multistage sampling procedure that applied various sampling…

The integration of visual programming in early formal education has been found to promote computational thinking of students. Teachers' intuitive perspectives about optimal learning processes -- "folk psychology" -- impact their perspectives about teaching "folk pedagogy" and play a significant role in integrating educational…

Computational Thinking (CT), entailing both domain-general and domain-specific skills, is a competency fundamental to computing education and beyond. However, as a cross-domain competency, appropriate assessment design and method remain equivocal. Indeed, the majority of the existing assessments have a predominant focus on measuring programming…

This article presents "concept landscapes"--a novel way of investigating the state and development of knowledge structures in groups of persons using concept maps. Instead of focusing on the assessment and evaluation of single maps, the data of many persons is aggregated and data mining approaches are used in analysis. New insights into…

Item nonresponses are prevalent in standardized testing. They happen either when students fail to reach the end of a test due to a time limit or quitting, or when students choose to omit some items strategically. Oftentimes, item nonresponses are nonrandom, and hence, the missing data mechanism needs to be properly modeled. In this paper, we…

This study investigated the effectiveness of statistical adjustments applied to rater bias in many-facet Rasch analysis. Some changes were first made in the dataset that did not include "rater × examinee" bias to cause to have "rater × examinee" bias. Later, bias adjustment was applied to rater bias included in the data file,…

Mechanisms causing item nonresponses in large-scale assessments are often said to be nonignorable. Parameter estimates can be biased if nonignorable missing data mechanisms are not adequately modeled. In trend analyses, it is plausible for the missing data mechanism and the percentage of missing values to change over time. In this article, we…