By Ben Kei Daniel
This booklet specializes in the makes use of of massive info within the context of upper schooling. The publication describes a variety of administrative and operational information collecting techniques geared toward assessing institutional functionality and development on the way to are expecting destiny functionality, and identifies power matters regarding educational programming, study, instructing and learning. giant info refers to facts that is essentially too massive and intricate and strikes too quickly for the processing skill of traditional database structures. the price of massive information is the power to spot important information and switch it into useable details by way of opting for styles and deviations from patterns.
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Additional info for Big Data and Learning Analytics in Higher Education: Current Theory and Practice
2014c). “Editorial: A conceptual framework for business intelligence/analytics”, submitted to INFORMS Analytics. , & Frank, M. ). (2010). Knowledge management and E-learning. New York: Taylor & Francis. Nadasen, D. (2013). “Data mining and data integration: A community college and university partnership to improve transfer student success” summary slides. Adelphi, MD: University of Maryland University College, Ofﬁce of Institutional Research. Pena-Ayala, A. (2013). Educational data mining: A review of recent works and a data mining-based analysis of the state-of-the-art, Expert Systems With Applications: An Int.
For example, the University of Maryland University College (UMUC), Kresge Foundation grant applies data mining to identify the critical “at-risk” transfer students in the ﬁrst or second semester of their UMUC programs in order to provide the necessary support services to help them succeed toward graduation, as explained shortly. Pena-Ayala (2013) performed a recent review of the educational data mining (EDM) ﬁeld by examining 240 EDM papers from 2010 through the ﬁrst quarter of 2013. Usually, descriptive or predictive approaches are applied to EDM.
In the next step, students develop the research methodology including instruments and procedures. Depending on the status of the overall research project, instruments are provided by the lecturer or are developed as pilot instruments by the students. ). The data analysis is performed within groups in the tutorials, while problems and outcomes are addressed in the lectures to enable students to develop a broader understanding of the issues emerging across all the projects. As a ﬁnal outcome of the course, students produce a research project report following scientiﬁc guidelines (Ifenthaler & Gosper, 2014).
Big Data and Learning Analytics in Higher Education: Current Theory and Practice by Ben Kei Daniel