Predicting students' success is crucial in educational settings to improve academic performance and prevent dropouts. This study aimed to improve student performance prediction by combining advanced machine learning (ML) approaches. Convolutional Neural Networks (CNNs) and attention mechanisms were used for extracting relevant features from…

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…

The aim of this paper is to categorize and describe different types of learners in massive open online courses (MOOCs) by means of a subgroup discovery (SD) approach based on MapReduce. The proposed SD approach, which is an extension of the well-known FP-Growth algorithm, considers emerging parallel methodologies like MapReduce to be able to cope…

While the field advocates for a diversified and comprehensive professional role (National Association of School Psychologists, 2020), school psychologists have long spent most of their time in assessment-related activities (Farmer et al., 2021), averaging about eight cognitive evaluations monthly (Benson et al., 2020). Assessment practices have…

In Part 1 of this series, the authors briefly reviewed some challenges inherent in using standardized tools with students who are not well represented in norming data. To help readers clearly conceptualize the framework steps, the authors present two case studies that showcase how a nonstandardized approach to assessment can be individualized to…

This paper discusses the application value of the writing teaching mode combined with the mixed teaching mode in college English writing teaching against the background of big data. Focusing on production-oriented approach (POA) theory, this paper proposes a mixed learning writing model for English teaching and applies the POA mixed learning…

School attendance/absenteeism (SA/A) is a crucial indicator of health and development in youth but educational policies and health-based practices in this area rely heavily on a simple metric of physical presence or absence in a school setting. SA/A data suffer from problems of quality (reliability, construct validity, data integrity) and utility…

Epilepsy is one of the most common neurological disorders in young people, which disrupts daily life and results in an increased risk of victimization. Archival data from the 2018/2019 National Survey of Children's Health (NSCH), a nationally representative cross-sectional survey, were used. Data from the NSCH were collected via parent reports and…

Collaborative data inquiry can help schools serve their students better and improve student outcomes--but only if equity is prioritized. Researchers from Harvard's Data Wise Project discuss the importance of using an equity lens when engaging in collaborative data inquiry and what this can mean in terms of disrupting system inequities.

Non-academic factors such as school climate, grit, and growth mindset are receiving much attention in recent education policy and practice. Within this context, this study (N = 425) describes the distribution of U.S. in-service teachers' access to and use of 10 categories of non-academic data. Findings indicate that in-service teachers vary widely…

Accuracy and precision are measures of experimental error and are fundamental to most chemical analysis laboratory classes. Assessment of accuracy and precision is often based on the comprehension of the results generated by students rather than on the quality of the data generated. This activity focused on developing a chemical analysis…

There is a large body of research exploring the difficulties young people experience on the transition to secondary education. However, there has been little comparative research yielding insights into how these experiences vary by institutional context. This article explores differences in school transition difficulties among young people in…

This article evaluates the applicability at the state and district level of web scraping--an automated data-extraction technique that regularly exports and refreshes data from the Internet--to provide a low-cost way to get a close-to-real-time snapshot of the demand side of the teacher labor market. Once set up, web scraping can quickly build and…

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…

Accompanied with the development of storage and processing capacity of modern technology, educational data increases sharply. It is difficult for educational researchers to derive useful information from much educational data. Therefore, educational data mining techniques are important for the development of modern education field. Recently,…