In modeling missing data, the missing data latent variable of the confirmatory factor model accounts for systematic variation associated with missing data so that replacement of what is missing is not required. This study aimed at extending the modeling missing data approach to tetrachoric correlations as input and at exploring the consequences of…

In order to study the big data of college students' employment, this paper takes the big data of college students' employment as the premise, analyzes the current employment data by establishing a DBN model, and puts forward relevant management measures, aiming to provide scientific basis for the management of graduates' employment data. The…

Background: In educational settings, individuals are often best served by an intervention that is adapted over sequential stages to suit their initial and changing needs. The salience of an adaptive intervention is, perhaps, most clear in the classroom. Learning itself is a sequential process: mastering a given concept or technique frequently…

This dissertation examines the effectiveness of machine learning algorithms and feature engineering techniques for analyzing process data and predicting test performance. The study compares three classification approaches and identifies item-specific process features that are highly predictive of student performance. The findings suggest that…

Educators have long been awash in a sea of standardized test score data, with the understanding that their engagement with these data will lead to improvement in teaching and learning. But, in practice, these data have often been too infrequent, too lagging, and too distant from day-to-day practice to inform actionable next steps. To improve…

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…

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)…

Purpose: The research purpose had been to investigate the dominant factors that influenced the success of the development of entrepreneurship courses in cultivating entrepreneurial mindsets and entrepreneurial skills into educational frameworks in universities. Design/methodology/approach: The case study used in-depth interviews with key…

It is common to find mixed-format data results from the use of both multiple-choice (MC) and constructed-response (CR) questions on assessments. Dealing with these mixed response types involves understanding what the assessment is measuring, and the use of suitable measurement models to estimate latent abilities. Past research in educational…

Automated detection of facial emotions has been an interesting topic for multiple decades in social and behavioral research but is only possible very recently. In this tutorial, we review three popular artificial intelligence based emotion detection programs that are accessible to R programmers: Google Cloud Vision, Amazon Rekognition, and…

With notions of data visualization expanding to include contemporary art and design, data visualization represents an important new dimension for transdisciplinary art education. The pedagogical potential of these practices has begun to be recognized in many fields, including art education. However, despite substantial interest, little research…

Information design is the theory and practice of presenting information through the pairing of graphic and verbal content. It is important in the sciences because of the centrality of data visualization in research and teaching. This paper seeks to build the case for the inclusion of information design within both the Geography and university- or…

The robustness of qualitative comparative analysis (QCA) results features high on the agenda of methodologists and practitioners. This article aims at advancing this debate on several fronts. First, in line with the extant literature, we take a comprehensive view on robustness arguing that decisions on calibration, consistency, and frequency…

Identifying motivation for enrollment in MOOCs has been an important way to predict participant success rates. But themes for motivation have largely centered around themes for enrolling in any MOOC, and not ones specific to the course being studied. In this study, qualitatively coding discussion forums was combined with topic modeling to identify…