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While the analysis of educational data is not itself a new practice, recent advances in educational technology, including the increase in computing power and the ability to log fine-grained data about students' use of a computer-based learning environment, have led to an increased interest in developing techniques for analyzing the large amounts of data generated in educational settings.
Dr. Wolfgang Greller and Dr. Hendrik Drachsler defined learning analytics holistically as a framework. They proposed that it is a generic design framework that can act as a useful guide for setting up analytics services in support of educational practice and learner guidance, in quality assurance, curriculum development, and in improving teacher effectiveness and efficiency.
Statistics education is the practice of teaching and learning of statistics, along with the associated scholarly research. Statistics is both a formal science and a practical theory of scientific inquiry , and both aspects are considered in statistics education.
Dawson, S., & McWilliam, E. (2008). Investigating the application of IT generated data as an indicator of learning and teaching performance: Queensland University of Technology and the University of British Columbia. (A. L. a. T. Council o. Document Number) Ferguson, R. (2012). Learning analytics: drivers, developments and challenges.
Visible learning is a meta-study that analyzes effect sizes of measurable influences on learning outcomes in educational settings. [1] It was published by John Hattie in 2008 and draws upon results from 815 other Meta-analyses. The Times Educational Supplement described Hattie's meta-study as "teaching's holy grail". [2]
The construct has since been adopted by other organizations engaged in digital library and digital learning resource projects including the Learning Registry [7] initiative spearheaded by the Office of Educational Technology at the U.S. Department of Education [8] and the Advanced Distributed Learning Initiative at the Department of Defense. [9]
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
Tukey defined data analysis in 1961 as: "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data."