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Showing 1 to 15 of 16 results Save | Export
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Motz, Benjamin; Busey, Thomas; Rickert, Martin; Landy, David – International Educational Data Mining Society, 2018
Analyses of student data in post-secondary education should be sensitive to the fact that there are many different topics of study. These different areas will interest different kinds of students, and entail different experiences and learning activities. However, it can be challenging to identify the distinct academic themes that students might…
Descriptors: Data Collection, Data Analysis, Enrollment, Higher Education
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Mimis, Mohamed; El Hajji, Mohamed; Es-saady, Youssef; Oueld Guejdi, Abdellah; Douzi, Hassan; Mammass, Driss – Education and Information Technologies, 2019
The educational recommendation system to provide support for academic guidance and adaptive learning has always been an important issue of research for smart education. A bad guidance can give rise to difficulties in further studies and can be extended to school dropout. This paper explores the potential of Educational Data Mining for academic…
Descriptors: Educational Counseling, Guidance, Educational Research, Data Collection
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Liu, Sanya; Ni, Cheng; Liu, Zhi; Peng, Xian; Cheng, Hercy N. H. – International Journal of Distance Education Technologies, 2017
Nowadays, Massive Open Online Courses (MOOCs) have obtained a rapid development and drawn much attention from the areas of learning analytics and artificial intelligence. There are lots of unstructured data being generated in online reviews area. The learning behavioral data become more and more diverse, and they prompt the emergence of big data…
Descriptors: Online Courses, Student Records, Learning Strategies, Cognitive Style
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Williamson, Ben – Research in Education, 2017
Schools are increasingly involved in diverse forms of student data collection. This article provides a sociotechnical survey of a data assemblage used in education. ClassDojo is a commercial platform for tracking students' behaviour data in classrooms and a social media network for connecting teachers, students, and parents. The hybridization of…
Descriptors: Educational Research, Data Collection, Technology Uses in Education, Student Records
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Phillips, Brad C.; Horowitz, Jordan E. – New Directions for Community Colleges, 2013
The completion agenda is in full force at the nation's community colleges. To maximize the impact colleges can have on improving completion, colleges must organize around using student progress and outcome data to monitor and track their efforts. Unfortunately, colleges are struggling to identify relevant data and to mobilize staff to review…
Descriptors: Community Colleges, Academic Persistence, College Role, Data Collection
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Raju, Dheeraj; Schumacker, Randall – Journal of College Student Retention: Research, Theory & Practice, 2015
The study used earliest available student data from a flagship university in the southeast United States to build data mining models like logistic regression with different variable selection methods, decision trees, and neural networks to explore important student characteristics associated with retention leading to graduation. The decision tree…
Descriptors: Student Characteristics, Higher Education, Graduation Rate, Academic Persistence
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Ola, Ade G.; Bai, Xue; Omojokun, Emmanuel E. – Research in Higher Education Journal, 2014
Over the years, companies have relied on On-Line Analytical Processing (OLAP) to answer complex questions relating to issues in business environments such as identifying profitability, trends, correlations, and patterns. This paper addresses the application of OLAP in education and learning. The objective of the research presented in the paper is…
Descriptors: Profiles, Database Management Systems, Information Management, Progress Monitoring
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Crawford, Lindy – Preventing School Failure, 2014
This article discusses the role of assessment in a response-to-intervention model. Although assessment represents only 1 component in a response-to-intervention model, a well-articulated assessment system is critical in providing teachers with reliable data that are easily interpreted and used to make instructional decisions. Three components of…
Descriptors: Intervention, Models, Response to Intervention, Student Evaluation
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Mah, Dana-Kristin – Technology, Knowledge and Learning, 2016
Learning analytics and digital badges are emerging research fields in educational science. They both show promise for enhancing student retention in higher education, where withdrawals prior to degree completion remain at about 30% in Organisation for Economic Cooperation and Development member countries. This integrative review provides an…
Descriptors: Educational Research, Data Collection, Data Analysis, Recognition (Achievement)
Sirinides, Philip; Fink, Ryan – Regional Educational Laboratory Mid-Atlantic, 2014
Faced with rising demand for information about early childhood programs, states need to find tools and strategies to monitor the progress of and to identify high-quality early childhood programs. In response, REL Mid-Atlantic convened a regional workgroup for state personnel who work with the systems containing early childhood data. The workgroup…
Descriptors: Early Childhood Education, Educational Strategies, Educational Practices, Effective Schools Research
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Lillibridge, Fred – New Directions for Community Colleges, 2008
This chapter presents a sophisticated approach for tracking student cohorts from entry through departure within an institution. It describes how a researcher can create a student tracking model to perform longitudinal research on student cohorts. (Contains 3 tables and 2 figures.)
Descriptors: Academic Persistence, Longitudinal Studies, Models, Research Methodology
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Lee, Chien-Sing – Computers & Education, 2007
Models represent a set of generic patterns to test hypotheses. This paper presents the CogMoLab student model in the context of an integrated learning environment. Three aspects are discussed: diagnostic and predictive modeling with respect to the issues of credit assignment and scalability and compositional modeling of the student profile in the…
Descriptors: Intelligent Tutoring Systems, Distance Education, Integrated Learning Systems, Hypermedia
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Marco, Gary L.; And Others – Journal of Educational Measurement, 1976
Special emphasis is given to the kinds of control that can be exercised over initial status, including the use of proxy input data. A rationale for the classification scheme is developed, based on (1) three one-shot, one cross-sectional, and two longitudinal data types and (2) two types of referencing: criterion referencing and norm referencing.…
Descriptors: Classification, Data Collection, Evaluation Methods, Methods
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Clemson, Barry – Journal of Educational Administration, 1980
The proper function of the computer is as a filter to suppress irrelevant data and highlight critical data, to make short-term projections, to display the dynamics of system interactions, and to allow managers and teachers to interact with models of the system. Outlines the components of such a system. (Author/IRT)
Descriptors: Computer Managed Instruction, Computers, Data Collection, Educational Administration
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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
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