Research on self-assessment has overwhelmingly conceptualized it as a product and treated students as a homogeneous group, restraining our understanding of the topic. To address this gap, this study aimed to identify different student profiles based on their self-assessment and to examine how it related to their learning achievement over time.…

Learning programming has become increasingly popular, with learners from diverse backgrounds and experiences requiring different support. Programming-process analysis helps to identify solver types and needs for assistance. The study examined students' behavior patterns in programming among beginners and non-beginners to identify solver types,…

The reliability of assessment tools is critical for accurately monitoring student performance in various educational contexts. When multiple assessments are combined to form an overall evaluation, each assessment serves as a data point contributing to the student's performance within a broader educational framework. Determining composite…

Three quantitative methods are compared for their ability to understand different COVID-19 fatality ratios in 33 OECD countries. Linear regression provides a limited overview without sensitivity to the diversity of cases. Cluster Analysis and Dynamic Patterns Synthesis (DPS) gives scrutiny to the granularity of case similarities and differences,…

Using R to conduct univariate meta-analyses is becoming common for publication. However, R can also conduct multivariate meta-analysis (MMA). However, newcomers to both R and MMA may find using R to conduct MMA daunting. Given that, R may not be easy for those unfamiliar with coding. Likewise, MMA is a topic of advanced statistics. Thus, it may be…

There has been a growing interest in approaches based on machine learning (ML) for detecting test collusion as an alternative to the traditional methods. Clustering analysis under an unsupervised learning technique appears especially promising to detect group collusion. In this study, the effectiveness of hierarchical agglomerative clustering…

Cluster-randomized trials have been widely used to evaluate the treatment effects of interventions on student outcomes. When interventions are implemented by teachers, researchers need to account for the nested structure in schools (i.e., students are nested within teachers nested within schools). Schools usually have a very limited number of…

Solving organic chemistry reactions requires reasoning with multiple concepts and data (i.e., multivariate reasoning). However, studies have reported that organic chemistry students typically demonstrate univariate reasoning. Case comparisons, where students compare two or more tasks, have been reported to support students' multivariate reasoning.…

As ideas from data science become more prevalent in secondary curricula, it is important to understand secondary teachers' content knowledge and reasoning about complex data structures and modern visualizations. The purpose of this case study is to explore how secondary teachers make sense of mappings between data and visualizations, especially…

The development of a Computerized Adaptive Test (CAT) for operational use begins with several important steps, such as creating a large-size item bank, piloting the items on a sizable and representative sample of examinees, dimensionality assessment of the item bank, and estimation of item parameters. Among these steps, testing the dimensionality…

Predictive analytics is widely used in various domains, including education, to inform decision-making and improve outcomes. However, many predictive models are proprietary and inaccessible for evaluation or modification by researchers and practitioners, limiting their accountability and ethical design. Moreover, predictive models are often opaque…

Based on a brief analysis of the current situation of university education management and research on intelligent algorithms, this article constructs a university education management system based on big data. For the clustering and prediction modules in higher education management, corresponding algorithms are used for optimization design. A…

In this article, I present a new multivariate regression model for analyzing outcomes with network dependence. The model is capable to account for two types of outcome dependence including the mean dependence that allows the outcome to depend on selected features of a known dependence network and the error dependence that allows the outcome to be…

Meta-analytic studies of second language (L2) learning typically employ a classic approach to meta-analysis. Although the classic approach can clarify findings, a multivariate, multilevel meta-analysis (3M) approach increases transparency by accounting for (a) dependencies in the evidence presented by primary studies, (b) methodological…

The purpose of the current paper is to provide a step-by-step tutorial for employing the list experiment to control for Social Desirability Bias (SDB). Sport management scholars routinely rely on self-report data in cross-sectional settings to test research hypotheses. However, concerns exist regarding the use of self-reports without adjusting for…