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Data-driven instruction is an educational approach that relies on information to inform teaching and learning. The idea refers to a method teachers use to improve instruction by looking at the information they have about their students.
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.
Artificial intelligence in education (AIEd) is another vague term, [4] and an interdisciplinary collection of fields which are bundled together, [5] inter alia anthropomorphism, generative artificial intelligence, data-driven decision-making, ai ethics, classroom surveillance, data-privacy and Ai Literacy. [6]
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.
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.
Assessment in higher education is a form of data-driven decision-making aimed at using evidence of what students learn to improve curriculum, student learning, and teaching. [9] Standardized tests, grades, and student work scored by rubrics are forms of student learning outcomes assessment.
Educational assessment or educational evaluation [1] is the systematic process of documenting and using empirical data on the knowledge, skill, attitudes, aptitude and beliefs to refine programs and improve student learning. [2] Assessment data can be obtained by examining student work directly to assess the achievement of learning outcomes or ...
Analysis is composed of five basic steps: capture, report, predict, act and refine. Capture: All analytic efforts are centred on data.Consequently, academic analytics can be rooted in data from various sources such as a CMS, and financial systems (Campbell, Finnegan, & Collins, 2006).