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Kevin Slonka; Matthew North; Neelima Bhatnagar; Anthony Serapiglia – Information Systems Education Journal, 2025
Continuing to fill the literature gap, this research replicated and expands a prior study of student performance in database normalization in an introductory database course. The data was collected from four different universities, each having different prerequisite courses for their database course. Student performance on a database normalization…
Descriptors: Required Courses, Academic Achievement, Information Systems, Databases
Williamson, Ben – Journal of Education Policy, 2016
Educational institutions and governing practices are increasingly augmented with digital database technologies that function as new kinds of policy instruments. This article surveys and maps the landscape of digital policy instrumentation in education and provides two detailed case studies of new digital data systems. The Learning Curve is a…
Descriptors: Visualization, Synchronous Communication, Governance, Data Collection
Livieris, Ioannis E.; Mikropoulos, Tassos A.; Pintelas, Panagiotis – Themes in Science and Technology Education, 2016
Educational data mining is an emerging research field concerned with developing methods for exploring the unique types of data that come from educational context. These data allow the educational stakeholders to discover new, interesting and valuable knowledge about students. In this paper, we present a new user-friendly decision support tool for…
Descriptors: Predictive Measurement, Decision Support Systems, Academic Achievement, Exit Examinations
DeRocchis, Anthony M.; Michalenko, Ashley; Boucheron, Laura E.; Stochaj, Steven J. – Grantee Submission, 2018
This Innovative Practice Category Work In Progress paper presents an application of machine learning and data mining to student performance data in an undergraduate electrical engineering program. We are developing an analytical approach to enhance retention in the program especially among underrepresented groups. Our approach will provide…
Descriptors: Engineering Education, Data Analysis, Undergraduate Students, Artificial Intelligence