Learning analytics: International perspectives, policies, and contributions

Yau, Jane Yin-Kim ; Ifenthaler, Dirk

DOI: https://doi.org/10.1007/978-981-13-2262-4_123-1
Document Type: Book chapter
Year of publication: 2020
Book title: Encyclopedia of educational innovation
Page range: 1-6
Publisher: Peters, Michael A. ; Heraud, Richard
Place of publication: Singapore
Publishing house: Springer
ISBN: 978-981-13-2262-4
Publication language: English
Institution: Business School > Wirtschaftspädagogik V (Ifenthaler 2015-)
Subject: 370 Education
Abstract: Sociodemographic information, higher education entrance qualification grades, or pass and fail rates, i.e., educational data, have been widely used by higher education institutions. Given the availability of educational data in digital formats, learning analytics arose as a research field from a combined number of topics including educational data mining, adaptive and intelligent tutoring systems, personal learning environments, insights into student engagement, retention monitoring, progress and performance indicators, resource recommendation systems, as well as insights into effective curriculum design and pedagogical strategies. Learning analytics have been defined as the use of static and dynamic information about learners and learning environments, assessing, eliciting, and analyzing it for real-time modeling, prediction and optimization of learning processes, learning environments, as well as educational decision-making (Ifenthaler 2015). Currently, most learning analytics research are concentrated in the UK, the USA, and Australia; smaller scale of learning analytics research can be located in Europe and countries such as Brazil, China, Columbia, India, Israel, Japan, South Korea, and Taiwan. There are several concepts closely linked to processing, analyzing, and visualizing educational information. However, these concepts are often confused and lack universally agreed as well as applied definitions. Educational data mining refers to the process of extracting useful information out of a large collection of complex educational datasets (Berland et al. 2014). Academic analytics are the identification of meaningful patterns in educational data in order to inform academic issues (e.g., retention, success rates) and produce actionable strategies (e.g., budgeting, human resources). Teaching analytics focus on data from classroom management and interactions between teachers and students. School analytics capture organizational data including information about management, decision-making, and staff capabilities. Assessment or measurement analytics emphasize the importance of the diagnostic opportunities of educational data.
Additional information: Online-Ressource

Dieser Eintrag ist Teil der Universitätsbibliographie.

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