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Clutterbuck, Jennifer; Hardy, Ian; Creagh, Sue – Journal of Education Policy, 2023
In this article, we reveal the nature and effects of data infrastructures on the authorisation of data that represent students and educational practitioners, including how such data can misrepresent and govern educational policy and practices in sometimes problematic ways. To better understand the governance capacities of data infrastructures, we…
Descriptors: Data Analysis, Governance, Educational Policy, Educational Practices
Aom Perkash; Qaisar Shaheen; Robina Saleem; Furqan Rustam; Monica Gracia Villar; Eduardo Silva Alvarado; Isabel de la Torre Diez; Imran Ashraf – Education and Information Technologies, 2024
Developing tools to support students, educators, intuitions, and government in the educational environment has become an important task to improve the quality of education and learning outcomes. Information and communication technology (ICT) is adopted by educational institutions; one such instance is video interaction in flipped teaching.…
Descriptors: Academic Achievement, Colleges, Artificial Intelligence, Predictor Variables
Golden, Cindy – Brookes Publishing Company, 2018
Collecting data on behavior, academic skills, and Individualized Education Plan (IEP) goals is an essential step in showing student progress--but it can also be a complicated, time-consuming process. Take the worry and stress out of data collection with this ultra-practical resource, packed with the tools you need to organize, manage, and monitor…
Descriptors: Data Collection, Information Management, Student Records, Student Behavior
Jeon, Byungsoo; Shafran, Eyal; Breitfeller, Luke; Levin, Jason; Rosé, Carolyn P. – International Educational Data Mining Society, 2019
This paper addresses a key challenge in Educational Data Mining, namely to model student behavioral trajectories in order to provide a means for identifying students most at risk, with the goal of providing supportive interventions. While many forms of data including clickstream data or data from sensors have been used extensively in time series…
Descriptors: Online Courses, At Risk Students, Academic Achievement, Academic Failure
Aghababyan, Ani; Martin, Taylor; Janisiewicz, Philip; Close, Kevin – Journal of Learning Analytics, 2016
Learning analytics is an emerging discipline and, as such, benefits from new tools and methodological approaches. This work reviews and summarizes our workshop on microgenetic data analysis techniques using R, held at the second annual Learning Analytics Summer Institute in Cambridge, Massachusetts, on 30 June 2014. Specifically, this paper…
Descriptors: Educational Research, Data Collection, Data Analysis, Workshops
Riofrio-Luzcando, Diego; Ramirez, Jaime; Berrocal-Lobo, Marta – IEEE Transactions on Learning Technologies, 2017
Data mining is known to have a potential for predicting user performance. However, there are few studies that explore its potential for predicting student behavior in a procedural training environment. This paper presents a collective student model, which is built from past student logs. These logs are first grouped into clusters. Then, an…
Descriptors: Student Behavior, Predictive Validity, Predictor Variables, Predictive Measurement
Dvorak, Tomas; Jia, Miaoqing – Journal of Learning Analytics, 2016
This study analyzes the relationship between students' online work habits and academic performance. We utilize data from logs recorded by a course management system (CMS) in two courses at a small liberal arts college in the U.S. Both courses required the completion of a large number of online assignments. We measure three aspects of students'…
Descriptors: Online Courses, Educational Technology, Study Habits, Academic Achievement
Sorensen, Lucy C. – Educational Administration Quarterly, 2019
Purpose: In an era of unprecedented student measurement and emphasis on data-driven educational decision making, the full potential for using data to target resources to students has yet to be realized. This study explores the utility of machine-learning techniques with large-scale administrative data to identify student dropout risk. Research…
Descriptors: At Risk Students, Dropouts, Data Collection, Data Analysis
Corcoran, Sean P.; Nathanson, Lori; Kemple, James J. – Society for Research on Educational Effectiveness, 2012
This paper estimates the impact of attending a preferred high school on mediating factors of student success, including engagement, behavior, and attendance. For example, the authors ask whether students are more engaged with their school or have higher attendance when successfully matched to their first choice versus their second (or lower)…
Descriptors: School Choice, Economically Disadvantaged, Success, Learner Engagement
Hoxby, Caroline; Turner, Sarah – Education Next, 2013
For this study, the authors designed an experiment to test whether some high-achieving, low-income students would change their behavior if they knew more about colleges and, more importantly, whether a cost-effective way to help such students realize their full array of college opportunities can be implemented. This was done by randomly assigning…
Descriptors: Educational Opportunities, Intervention, High Achievement, Low Income Groups
Gottfried, Michael A. – Teachers College Record, 2013
Background/Context: Parents, policymakers, and researchers uphold that missing school has negative implications on schooling success, particularly for students in urban schools. However, it has thus far been an empirical challenge within educational research to estimate the true effect that absences have on achievement outcomes. This study…
Descriptors: Attendance Patterns, Standardized Tests, Reading Achievement, Mathematics Achievement
Butler, Bettie Ray; Lewis, Chance W.; Moore, James L., III; Scott, Malcolm E. – Journal of Negro Education, 2012
One frequently held assumption found within the school discipline literature suggests that students of color- particularly African American, male, low-income populations- are at an increased risk of receiving exclusionary discipline sanctions. Aside from race, gender, and socioeconomic status; however, less is known about other factors that…
Descriptors: Discipline, Sanctions, Discipline Policy, Educational Practices
Wong, Ovid K.; Lam, Ming-Long – Rowman & Littlefield Education, 2006
Education reforms such as "A Nation at Risk" and "Goals 2000" have come and gone. However, educators can be confident that the goal of student improvement shall not pass if the core mission is student learning. The true mission of learning has prompted educators to ponder the following questions: (1) How should each student to…
Descriptors: Student Improvement, Student Behavior, School Policy, School Activities
Wong, Ovid K.; Lam, Ming-Long – Rowman & Littlefield Education, 2006
Education reforms such as "A Nation at Risk" and "Goals 2000" have come and gone. However, we can be confident that the goal of student improvement shall not pass if the core mission is student learning. The true mission of learning has prompted educators to ponder the following questions: How do we need each student to behave?…
Descriptors: Data Analysis, Student Improvement, Educational Improvement, Educational Change
Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis