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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
Miranda Kucera; K. Kawena Begay – Communique, 2025
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…
Descriptors: Equal Education, Student Evaluation, Evaluation Methods, Standardized Tests
Miranda Kucera; K. Kawena Begay – Communique, 2025
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…
Descriptors: Equal Education, Student Evaluation, Evaluation Methods, Standardized Tests
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
Carrie Klein; Jessica Colorado – State Higher Education Executive Officers, 2024
Since 2010, the State Higher Education Executive Officers Association's (SHEEO) Strong Foundations survey has reported on the evolution and value of postsecondary student unit record systems (PSURSs) by illuminating the condition of state postsecondary data in the U.S. In the "Strong Foundations 2023" survey, which was administered from…
Descriptors: College Students, Student Records, Data Collection, Databases
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
Díaz, Victoria E.; McKeown, Stephanie; Peña, Camilo – British Columbia Council on Admissions and Transfer, 2023
This project reviews data collection practices regarding race, ethnicity and ancestry (REA) in post-secondary institutions (PSIs) in Canada, as well as in other relevant sectors (e.g., health, K-12 education, government agencies). The goal of the project was to identify promising practices and to develop recommendations to guide REA data…
Descriptors: Data Collection, Data Use, Student Characteristics, Race
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
Casey Gogno; Scott Burden; Wyntre Stout – Association for Institutional Research, 2024
Creating a welcoming community is key for an academic environment to thrive. This approach includes accurately representing community members' identities to understand their experiences, and establishing procedures for recording and utilizing individuals' names to support their ability to express their identities freely and without fear of…
Descriptors: Data Collection, Information Storage, Student Characteristics, Identification
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
Bhavik Anil Patel – Journal of Chemical Education, 2022
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…
Descriptors: Chemistry, Science Laboratories, Accuracy, Data