Many large-scale assessments model proficiency with a latent regression on contextual variables. Item-response data are used to estimate the parameters of the latent variable model and are used in conjunction with the contextual data to generate plausible values of individuals' proficiency attributes. These models typically incorporate numerous…

Due to the unprecedented growth in available data collected by e-learning platforms, including platforms used by massive open online course (MOOC) providers, important opportunities arise to structurally use these data for decision making and improvement of the educational offering. Student retention is a strategic task that can be supported by…

Educational data mining is an emerging interdisciplinary research area involving both education and informatics. It has become an imperative research area due to many advantages that educational institutions can achieve. Along these lines, various data mining techniques have been used to improve learning outcomes by exploring large-scale data that…

Socially shared regulation contributes to the success of collaborative learning. However, the assessment of socially shared regulation of learning (SSRL) faces several challenges in the effort to increase the understanding of collaborative learning and support outcomes due to the unobservability of the related cognitive and emotional processes.…

This study elaborates on the pivotal roles of mathematical and statistical models in data-driven predictions in an integrated STEM context using the case of Year 4 students: (?) "a descriptive means" to describe the features of trends and variability of data and (?) "an explanatory means" to explain causal relationships behind…

Enabling college graduates to achieve career success is increasingly considered a major responsibility of universities. Many studies have developed models of predicting students' career decisions and have sought to provide appropriate treatments or early support for students to achieve this goal. Most studies, however, have focused on using…

The purpose of this study was to explore and identify patterns of risk predictors of maltreatment recurrence using predictive risk modeling (PRM). This study used the administrative dataset from the National Child Maltreatment Information System recorded by Korean CPS (Child Protective Service) workers. The information, including recurrent…

Academic dropout remains a significant challenge for education systems, necessitating rigorous analysis and targeted interventions. This study employs machine learning techniques, specifically random forest (RF) and feature tokenizer transformer (FTT), to predict academic attrition. Utilizing a comprehensive dataset of over 40 000 students from an…

The continuous development of Educational Data Mining (EDM) and Learning Analytics (LA) technologies has provided more effective technical support for accurate early warning and interventions for student academic performance. However, the existing body of research on EDM and LA needs more empirical studies that provide feedback interventions, and…

Since schools increasingly use digital platforms that provide educational data in digital formats, teacher data use, and data literacy have become a focus of educational research. One main challenge is whether teachers use digital data for pedagogical purposes, such as informing their teaching. We conducted a survey study with N = 1059 teachers in…

Purpose: This study leverages social network analysis (SNA) to visualise the way students interacted with online resources and uses the data obtained from SNA as features for supervised machine learning algorithms to predict whether a student will successfully complete a course. Design/methodology/approach: The exploration and visualisation of the…

This study investigates current approaches to learning analytics (LA) dashboarding while highlighting challenges faced by education providers in their operationalization. We analyze recent dashboards for their ability to provide actionable insights which promote informed responses by learners in making adjustments to their learning habits. Our…

Randomized A/B tests within online learning platforms represent an exciting direction in learning sciences. With minimal assumptions, they allow causal effect estimation without confounding bias and exact statistical inference even in small samples. However, often experimental samples and/or treatment effects are small, A/B tests are underpowered,…

As a digital learning medium, serious games can be powerful, immersive educational vehicles and provide large data streams for understanding player behavior. Educational data mining and learning analytics can effectively leverage big data in this context to heighten insight into student trajectories and behavior profiles. In application of these…

Teacher-level factors are theoretically linked to student outcomes in data-based instruction (DBI; Lembke et al., 2018). Professional development and ongoing support can increase teachers' knowledge, skills, and beliefs related to DBI, as well as their instructional fidelity (McMaster et al., 2020). However, less is known about how each of these…