Multilevel factor mixture modeling (FMM) is a hybrid of multilevel confirmatory factor analysis (CFA) and multilevel latent class analysis (LCA). It allows researchers to examine population heterogeneity at the within level, between level, or both levels. This tutorial focuses on explicating the model specification of multilevel FMM that considers…

The traditional system focuses excessively on physical skills and physical fitness assessment, with problems such as single indicator, static approach, subject limitation and inefficient data utilization, making it difficult to assess students in a comprehensive and fair manner. The rise of big data technology has brought about a turnaround, from…

Carefully designed evaluations of informal science events are key to gaining insights into their efficacy and impact on audiences. However, traditional approaches to evaluation, such as interviews and questionnaires, can be disruptive to the audience experience of science events. There is therefore a need to develop and implement new evaluation…

Self-regulated learning (SRL) theory comprises cognitive, metacognitive, and affective aspects that enable learners to autonomously manage their learning processes. This article presents a systematic literature review on the measurement of SRL in digital platforms, that compiles the 53 most relevant empirical studies published between 2015 and…

Despite the increasing interest in data analytics applications within postgraduate education research, there remains a significant gap in research dedicated to exploring mixed methods research for such investigations. This study undertook to bridge this gap by exploring the application and use of mixed methods research to examine data analytics…

This article provides clear and practical guidelines for researchers seeking to use national secondary datasets to conduct evidence-based research. Drawing from our own experiences, we discuss a six-step research process model (Bryan et al., 2010, 2017) to help researchers navigate the use of these datasets. We present examples from the school…

With technological advances, institutional stakeholders are considering evidence-based developments such as Curriculum Analytics (CA) to reflect on curriculum and its impact on student learning, dropouts, program quality, and overall educational effectiveness. However, little is known about the CA state of the art in Higher Education Institutions…

How are educational values and pedagogical approaches inscribed into automated education technologies and their data visualizations? In this article we analyze the design process and technical and pedagogical debates of a team of researchers and developers working on an automated scoring tool for primary school students' early writing as part of…

Students' sense of belonging in the mathematics classroom can be supported and increased by building mathematics tasks that connect to and incorporate aspects of students' identities and sense of selves (e.g., their names, images, and ages). This article shares one commonly used mathematics task and highlights how it can be used to create…

The Australian Universities Accord Final Report offers a historic yet insufficient opportunity to advance Indigenous self-determination in higher education. Its goals will remain hollow without dismantling the entrenched colonial foundations embedded in universities' governance and data practices. This paper demands that Indigenous data…

Data-driven marketing analytics courses are integral to modern business management degrees in universities, yet many graduates focus solely on single, separated data analysis techniques during their learning process, hindering effective integration and practical performance. This study proposes that employing the Fishbowl method, which divides…

Interest in applied educational research methodology is growing as educators and researchers strive to seek empirical evidence about what is effective teaching within distinctive contexts. However, for beginner researchers who are interested in conducting case studies within educational settings and are looking for an appropriate starting point,…

In this article, we discuss the methodological implications of data and theory integration for Theory-Based Evaluation (TBE). TBE is a family of approaches to program evaluation that use program theories as instruments to answer questions about whether, how, and why a program works. Some of the groundwork about TBE has expressed the idea that a…

Data extraction is a time-consuming and resource-intensive task in the systematic review process. Natural language processing (NLP) artificial intelligence (AI) techniques have the potential to automate data extraction saving time and resources, accelerating the review process, and enhancing the quality and reliability of extracted data. In this…

This article presents a general-purpose method for detecting cheating in online courses, which combines anomaly detection and supervised machine learning. Using features that are rooted in psychometrics and learning analytics literature, and capture anomalies in learner behavior and response patterns, we demonstrate that a classifier that is…