In organizational and academic settings, the strategic formation of teams is paramount, necessitating an approach that transcends conventional methodologies. This study introduces a novel application of multicriteria integer programming (MCIP), which simultaneously accommodates multiple criteria, thereby innovatively addressing the complex task of…

Attribute hierarchy, the underlying prerequisite relationship among attributes, plays an important role in applying cognitive diagnosis models (CDM) for designing efficient cognitive diagnostic assessments. However, there are limited statistical tools to directly estimate attribute hierarchy from response data. In this study, we proposed a…

Cognitive diagnosis models (CDMs) are the assessment tools that provide valuable formative feedback about skill mastery at both the individual and population level. Recent work has explored the performance of CDMs with small sample sizes but has focused solely on the estimates of individual profiles. The current research focuses on obtaining…

Random item effects item response theory (IRT) models, which treat both person and item effects as random, have received much attention for more than a decade. The random item effects approach has several advantages in many practical settings. The present study introduced an explanatory multidimensional random item effects rating scale model. The…

This article is concerned with a subset of numerically stable and scalable algorithms useful to support computationally complex psychometric models in the era of machine learning and massive data. The subset selected here is a core set of numerical methods that should be familiar to computational psychometricians and considers whitening transforms…

Physics education researchers are interested in using the tools of machine learning and natural language processing to make quantitative claims from natural language and text data, such as open-ended responses to survey questions. The aspiration is that this form of machine coding may be more efficient and consistent than human coding, allowing…

In the context of a rapid digital transformation, digital competence is now regarded as a fourth cultural skill complementing reading, writing, and arithmetic. We argue that a well-structured and sound competence model is needed as a shared foundation for learning, teaching, pedagogical diagnostics and evaluative schemes in the school system.…

Computational modelling is widely used in biological science. Therefore, biology students need to learn computational modelling. However, there is a lack of evidence about how to teach computational modelling in biology and what the effects are on student learning. The purpose of this intervention-control study was to investigate how knowledge in…

Focusing efficiently on potential weaknesses in the validity argument of writing assessments--such as writing subjectivity, content coverage, criteria vagueness, and raters' incompetence--has been shown to positively enhance teachers' overall writing assessment competence (AC). In this study, we propose a computational bootstrapping model of…

Purpose: Dyslexia is characterized by its diverse causes and heterogeneous manifestations. Chinese children with dyslexia exhibit orthographic, phonological, and semantic deficits across character and radical levels when writing. However, whether character dictation can be used to distinguish children with dyslexia from their typically developing…

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…

Every educator knows the sinking feeling of a lesson gone wrong. As teachers look around the room and realize that many of their students are just not getting it, they often feel like failures. However, the struggle students experience as they persevere through high-quality challenging tasks is not a sign of failure, but rather a key aspect of…

Contribution: A problem-solving approach (PSA) model derived from major computational thinking (CT) concepts. This model can be utilized to formulate solutions for different algorithmic problems and translate them into effective active learning methods. Background: Different teaching approaches for programming are widely available; however, being…

Analyzes the amount of compression possible when arithmetic coding is used for text compression in conjunction with various input models. Algorithms are analyzed; modeling effects are considered; scaling is discussed; higher order models are examined, including prediction by partial matching; and coding effects are described. (34 references) (LRW)

Compares traditional mathematical models with computer simulations. Shows the strengths and flexibility of algorithmic computational simulations through a program designed to investigate and extend understanding in one of the most enduring questions in social choice research. Discusses solutions to this problem from each approach--analytic and…