Clustered data structures are common in many areas of educational and psychological research (e.g., students clustered in schools, patients clustered by clinician). In the course of conducting research, questions are often administered to obtain scores reflecting latent constructs. Multilevel measurement models (MLMMs) allow for modeling…

Principal Component Analysis (PCA) is a powerful statistical technique for reducing the complexity of data and making patterns and relationships within the data more easily understandable. By using PCA, students can learn to identify the most important features of a data set, visualize relationships between variables, and make informed decisions…

When performing an aggregate data meta-analysis of a continuous outcome, researchers often come across primary studies that report the sample median of the outcome. However, standard meta-analytic methods typically cannot be directly applied in this setting. In recent years, there has been substantial development in statistical methods to…

Purpose: This study aims to analyze the platforming scenario at a Brazilian university as well as the data security process for students and professors. Design/methodology/approach: This research brings an analysis through a qualitative approach of the platformization process in a Brazilian teaching institution. Findings: The results point to a…

Purpose: Data literacy empowers college students, equipping them with essential skills necessary for their personal lives and careers in today's data-driven world. This study aims to explore how community college students evaluate their data literacy and further examine demographic and educational/career advancement disparities in their…

Knowledge tracing aims to model and predict students' knowledge states during learning activities. Traditional methods like Bayesian Knowledge Tracing (BKT) and logistic regression have limitations in granularity and performance, while deep knowledge tracing (DKT) models often suffer from lacking transparency. This paper proposes a…

In sparse data meta-analyses (with few trials or zero events), conventional methods may distort results. Although better-performing one-stage methods have become available in recent years, their implementation remains limited in practice. This study examines the impact of using conventional methods compared to one-stage models by re-analysing…

This dissertation documented how undergraduate students made sense of data in news media. The participants were 30 undergraduate students enrolled in a course called "Numbers and Social Justice." The study used argument analysis (Toulmin, 2003) and ethnographic methodology to examine students' written work in a naturalistic setting.…

Career guidance, which strongly influences the world labor market and social structure, is studied in many countries on different levels. This study aims to explore the number, growth trajectories, and geographic distribution of studies on Career guidance in general schools and identify prominent influential authors, sources, publications, and new…

Most existing solutions to the current replication crisis in science address only the factors stemming from specific poor research practices. We introduce a novel mechanism that leverages the experts' predictive abilities to analyze the root causes of replication failures. It is backed by the principle that the most accurate predictor is the most…

Poor academic performance remains among the most concerning educational issues, especially in higher education and online learning. To address the concern, institutions like the University of Southeastern Philippines (USeP) leverage educational data mining (EDM) techniques to generate relevant information from learning management systems (LMS)…

Visual analysis and nonoverlap-based effect sizes are predominantly used in analyzing single case experimental designs (SCEDs). Although they are popular analytical methods for SCEDs, they have certain limitations. In this study, a new effect size calculation model for SCEDs, named performance criteria-based effect size (PCES), is proposed…

In single-case research designs (SCDs) to determine a functional relation a time-series graph is constructed. Preliminary evidence suggest the approach used to scale the vertical axis and the data points per x- to y-axis ratio (DPPXYR) impact visual analysts' decisions. We conducted a systematic review to evaluate time-series graphs published in…

The ability to analyze large data sets ("Big Data") is an increasingly important skill in modern science. In Biochemistry, the increased volume and velocity of data is particularly evident in the rapid expansion of biological databases. We present a modular bioinformatics course to survey the analysis of genomic data for advanced…

In response to research indicating a need for an increased emphasis on aspects of corporate accounting, including corporate taxation, in the accounting curriculum as well as employer expectations that entry-level accounting position applicants possess Excel skills, this classroom exercise uses Excel to compare the financial impacts of three…