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Majdi Beseiso – TechTrends: Linking Research and Practice to Improve Learning, 2025
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…
Descriptors: Prediction, Success, Academic Achievement, Artificial Intelligence
Batool, Saba; Rashid, Junaid; Nisar, Muhammad Wasif; Kim, Jungeun; Kwon, Hyuk-Yoon; Hussain, Amir – Education and Information Technologies, 2023
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…
Descriptors: Academic Achievement, Prediction, Data Use, Information Retrieval
Luna, J. M.; Fardoun, H. M.; Padillo, F.; Romero, C.; Ventura, S. – Interactive Learning Environments, 2022
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…
Descriptors: Online Courses, Student Characteristics, Classification, Student Behavior
Ping Zhao; Chunling Sun; Baojun Lv; Lan Guo; Jiansheng Gao; Xin Zhao; Fengming Jiao – International Journal of Information and Communication Technology Education, 2024
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…
Descriptors: Writing Instruction, Blended Learning, Data Analysis, Data Collection
Kearney, Christopher A.; Childs, Joshua – Preventing School Failure, 2023
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…
Descriptors: Attendance, Educational Policy, Health, Improvement
Bocala, Candice; Boudett, Kathryn Parker – Educational Leadership, 2022
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.
Descriptors: Data Use, Data Analysis, Inquiry, Equal Education
Jessica Emick; Nathan M. Griffith; Hannah Schweitzer – Psychology in the Schools, 2025
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…
Descriptors: Epilepsy, Severity (of Disability), Students with Disabilities, Parents
Nathan Lieng; Jason L. Morín; Que-Lam Huynh; Janet S. Oh – Association for Institutional Research, 2024
Higher education leaders have repeatedly called for improved diversity, equity, and inclusion efforts, but many institutions continue to fall short. Data can play an integral role in this work; key among them are data on student demographics, including race/ethnicity. Meeting diversity, equity, and inclusion goals requires a thorough and nuanced…
Descriptors: Data Collection, Data Analysis, Data Use, Minority Group Students
Reeves, Todd D.; Wei, Dan; Hamilton, Valerie – Educational Forum, 2022
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…
Descriptors: Access to Information, Data Use, Decision Making, Educational Environment
Jing Tang; Kara Ulmen; Sara Amadon; Katie Richards; Gabriella Guerra; Ja’Chelle Ball; Carlise King; Dale Richards – Child Trends, 2024
The preschool landscape is complex, consisting of several publicly funded programs supported by federal, state, and local funds. Included in this landscape is Head Start, a critical early childhood education (ECE) program that serves--in every state and territory--young children in families with incomes at or below the federal poverty line,…
Descriptors: Access to Education, Low Income Students, Social Services, Federal Programs
Ramon Flores; Daniel J. Losen – Civil Rights Project - Proyecto Derechos Civiles, 2024
Many educators in California are unaware of just how harmful out of school suspensions can be. When suspended students are barred from attending school, more often than not, the rule broken was some form of minor misconduct. This update of "Lost Instruction Time in California Schools" demonstrates that despite the important efforts by…
Descriptors: School Administration, Discipline, Homeless People, Youth
Emer Smyth; Ivan Privalko – Educational Review, 2024
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…
Descriptors: Foreign Countries, Secondary School Students, Adjustment (to Environment), Cultural Differences
Betsy Wolf – Grantee Submission, 2024
The What Works Clearinghouse (WWC) at the Institute of Education Sciences reviews rigorous research on educational practices, policies, programs, and products with a goal of identifying 'what works' and making that information accessible to the public. One critique of the WWC is the need to more closely examine 'what works' for whom, in which…
Descriptors: Data Use, Educational Research, Student Characteristics, Context Effect
Sarah Amber Evans; Lingzi Hong; Jeonghyun Kim; Erin Rice-Oyler; Irhamni Ali – Information and Learning Sciences, 2024
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…
Descriptors: Community College Students, Self Evaluation (Individuals), Data Analysis, Demography
Tzung-Ruei Tsou – SAGE Open, 2024
The importance of family background in determining a student's academic achievement has long been acknowledged by researchers. Nonetheless, the effect of schooling on this relationship has also been widely investigated. Some studies have shown that family background plays a far stronger role while the effect of schooling is minimal; others have…
Descriptors: Foreign Countries, Family Characteristics, Academic Achievement, Institutional Characteristics