A four-country cross-case analysis of academic staff expectations about learning analytics in higher education
| Autor: | Dragan Gašević, Adolfo Ruiz Calleja, Maren Scheffel, Alexander Whitelock-Wainwright, Carlos Delgado Kloos, Tobias Ley, Pedro J. Muñoz-Merino, Kairit Tammets, Kaire Kollom, Hendrik Drachsler, Yi-Shan Tsai, Pedro Manuel Moreno-Marcos, Ioana Jivet |
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| Přispěvatelé: | Department of Online Learning and Instruction, RS-Research Line Online Learning and Instruction (part of ERA program) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Higher education
STRATEGIES Computer Networks and Communications Educación Learning analytics Education 0502 economics and business DESIGN IMPLICATIONS Curriculum Cross case analysis Informática Ideal (set theory) business.industry Questionnaire 05 social sciences Perspective (graphical) 050301 education Academic staff Expectations Public relations Focus groups Focus group Computer Science Applications business Psychology 0503 education 050203 business & management |
| Zdroj: | The Internet and Higher Education, 49(2021):100788. Elsevier Science Inc. Kollom, K, Tammets, K, Scheffel, M, Tsai, Y-S, Jivet, I, Muñoz-Merino, P J, Moreno-Marcos, P M, Whitelock-Wainwright, A, Calleja, A R, Gasevic, D, Kloos, C D, Drachsler, H & Ley, T 2021, ' A four-country cross-case analysis of academic staff expectations about learning analytics in higher education ', The Internet and Higher Education, vol. 49, no. 2021, 100788 . https://doi.org/10.1016/j.iheduc.2020.100788 Internet and Higher Education, 49 |
| ISSN: | 1096-7516 |
| DOI: | 10.1016/j.iheduc.2020.100788 |
| Popis: | The purpose of this paper is to explore the expectations of academic staff to learning analytics services from an ideal as well as a realistic perspective. This mixed-method study focused on a cross-case analysis of staff from Higher Education Institutions from four European universities (Spain, Estonia, Netherlands, UK). While there are some differences between the countries as well as between ideal and predicted expectations, the overarching results indicate that academic staff sees learning analytics as a tool to understand the learning activities and possibility to provide feedback for the students and adapt the curriculum to meet learners' needs. However, one of the findings from the study across cases is the generally consistently low expectation and desire for academic staff to be obligated to act based on data that shows students being at risk of failing or under-performing. The data underlying this manuscript was collected in the SHEILA project co-funded by the Erasmus+ Programme of the European Union. |
| Databáze: | OpenAIRE |
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