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Kisling, Reid; Peterson, Andrew; Nisbet, Robert – Strategic Enrollment Management Quarterly, 2021
Data analytics is undergoing an evolution through effective data use to support both operational and learning analytics models. However, this evolution will require that institutional leaders transform their data systems to best support the needs of application modeling and use their intuition to help drive the development of better analytical…
Descriptors: Higher Education, Learning Analytics, Models, Instructional Leadership
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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
Carlson, Tiffany; Crepeau-Hobson, Franci – Communique, 2021
When the coronavirus pandemic was declared a public health crisis in March 2020, school psychologists were forced into situations where face-to-face interaction with their students was discouraged and in some cases, prohibited. Consequently, the traditional practice of school psychology abruptly ended. Individualized Education Plans (IEP) and…
Descriptors: Cognitive Tests, Ethics, Decision Making, Models
Smith, Brent; Milham, Laura – Advanced Distributed Learning Initiative, 2021
Since 2016, the Advanced Distributed Learning (ADL) Initiative has been developing the Total Learning Architecture (TLA), a 4-pillar data strategy for managing lifelong learning. Each pillar describes a type of learning-related data that needs to be captured, managed, and shared across an organization. Each data pillar is built on a set of…
Descriptors: Learning Analytics, Computer Software, Metadata, Learning Activities
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Wise, Alyssa Friend; Shaffer, David Williamson – Journal of Learning Analytics, 2015
It is an exhilarating and important time for conducting research on learning, with unprecedented quantities of data available. There is a danger, however, in thinking that with enough data, the numbers speak for themselves. In fact, with larger amounts of data, theory plays an ever-more critical role in analysis. In this introduction to the…
Descriptors: Learning Theories, Predictor Variables, Data, Data Analysis
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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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de Freitas, Sara; Gibson, David; Du Plessis, Coert; Halloran, Pat; Williams, Ed; Ambrose, Matt; Dunwell, Ian; Arnab, Sylvester – British Journal of Educational Technology, 2015
With digitisation and the rise of e-learning have come a range of computational tools and approaches that have allowed educators to better support the learners' experience in schools, colleges and universities. The move away from traditional paper-based course materials, registration, admissions and support services to the mobile, always-on and…
Descriptors: Higher Education, Student Records, Data Analysis, Information Utilization
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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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Keller, Eileen Weisenbach; Hughes, Stephanie; Hertz, Giles – Journal of Educational Administration, 2011
Purpose: An increase in the number of disruptive and violent events on college and university campuses instigated this review of the methods used to interrupt the trend, with the goal of identifying a preliminary model for systematic management of such threats. The intent is to instigate research, review and discussion in order to decrease the…
Descriptors: Higher Education, Campuses, School Safety, Violence
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Hamblin, David J.; Phoenix, David A. – Journal of Higher Education Policy and Management, 2012
There are increasing demands for higher levels of data assurance in higher education. This paper explores some of the drivers for this trend, and then explains what stakeholders mean by the concept of data assurance, since this has not been well defined previously. The paper captures insights from existing literature, stakeholders, auditors, and…
Descriptors: Higher Education, Educational Technology, Stakeholders, Quality Assurance
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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Bradley-Johnson, Sharon; Johnson, C. Merle; Vladescu, Jason C. – Journal of Psychoeducational Assessment, 2008
Autism is a low-incidence disability that is complex to assess and for which rates continue to increase. Assessment options for autism are reviewed and presented in the context of recent research and a comprehensive, multidisciplinary assessment model. The model involves three levels that yield data progressing from more subjective and general to…
Descriptors: Autism, Children, Models, Evaluation
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Gutierrez, Antonio; Dantes, Janice – Community College Journal of Research and Practice, 2009
Documenting student outcomes is a priority for higher education institutions. Federally-mandated graduation rates are often used as the sole measure of student success. However, this measure fails to document outcomes for students with multiple educational objectives and career paths. Driven by this challenge, the City Colleges of Chicago…
Descriptors: Community Colleges, Graduation Rate, College Outcomes Assessment, Evaluation Methods
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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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