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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.
Value-added modeling (also known as value-added measurement, value-added analysis and value-added assessment) is a method of teacher evaluation that measures the teacher's contribution in a given year by comparing the current test scores of their students to the scores of those same students in previous school years, as well as to the scores of other students in the same grade.
Consequently, academic analytics can be rooted in data from various sources such as a CMS, and financial systems (Campbell, Finnegan, & Collins, 2006). Additionally, the data comes in various different formats for example spread sheets. Also, data can be got from the institution's external environment.
Revolution Analytics – production-grade software for the enterprise big data analytics; RStudio – GUI interface and development environment for R; ROOT – an open-source C++ system for data storage, processing and analysis, developed by CERN and used to find the Higgs boson; Salstat – menu-driven statistics software
Analytics is the systematic computational analysis of data or statistics. [1] It is used for the discovery, interpretation, and communication of meaningful patterns in data, which also falls under and directly relates to the umbrella term, data science. [2] Analytics also entails applying data patterns toward effective decision-making.
Ooms, Marius (2009). "Trends in Applied Econometrics Software Development 1985–2008: An Analysis of Journal of Applied Econometrics Research Articles, Software Reviews, Data and Code". Palgrave Handbook of Econometrics. Vol. 2: Applied Econometrics. Palgrave Macmillan. pp. 1321– 1348. ISBN 978-1-4039-1800-0. Renfro, Charles G. (2004).
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."
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]