Towards Evidence-Based Academic Advising Using Learning Analytics
| Autor: | Mariia Gavriushenko, Mirka Saarela, Tommi Kärkkäinen |
|---|---|
| Přispěvatelé: | Escudeiro, Paula, Costagliola, Gennaro, Zvacek, Susan, Uhomoibhi, James, McLaren, Bruce M. |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Evidence-based practice
oppiminen Computer science Process (engineering) Learning analytics 02 engineering and technology Academic advising neuvonta klusterit 0202 electrical engineering electronic engineering information engineering Mathematics education ComputingMilieux_COMPUTERSANDEDUCATION academic advising Cluster analysis Academic career learning analytics 05 social sciences 050301 education analyysimenetelmät Schedule (project management) korkea-asteen koulutus analyysi 020201 artificial intelligence & image processing klusterianalyysi robust clustering 0503 education PATH (variable) |
| Zdroj: | BASE-Bielefeld Academic Search Engine Communications in Computer and Information Science ISBN: 9783319946399 |
| Popis: | Academic advising is a process between the advisee, adviser and the academic institution which provides the degree requirements and courses contained in it. Content-wise planning and management of the student’ study path, guidance on studies and academic career support is the main joint activity of advising. The purpose of this article is to propose the use of learning analytics methods, more precisely robust clustering, for creation of groups of actual study profiles of students. This allows academic advisers to provide evidence-based information on the study paths that have actually happened similarly to individual students. Moreover, academic institutions can focus on management and updates of course schedule having an effect of clearly characterized and recognized group of students. Using this approach a model of automated academic advising process, which can determine the study profiles, is presented. The presented model shows the whole automated process, where the learners will be profiled regularly, and where the proper study path will be suggested. peerReviewed |
| Databáze: | OpenAIRE |
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