| Autor: |
Shane Dawson, Vitomir Kovanović, Abhinava Barthakur |
| Přispěvatelé: |
Barthakur, Abhinava, Dawson, Shane, Kovanovic, Vitomir, Thirteenth International Conference on Learning Analytics & Knowledge Texas, USA 13-17 March 2023 |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
LAK23: 13th International Learning Analytics and Knowledge Conference. |
| DOI: |
10.1145/3576050.3576083 |
| Popis: |
The term Learner Profile has proliferated over the years, and more recently, with the increased advocacy around personalising learning experiences. Learner profiles are at the center of personalised learning, and the characterisation of diversity in classrooms is made possible by profiling learners based on their strengths and weaknesses, backgrounds and other factors influencing learning. In this paper, we discuss three common approaches of profiling learners based on students’ cognitive knowledge, skills and competencies and behavioral patterns, all latter commonly used within Learning Analytics (LA). Although each approach has its strengths and merits, there are also several disadvantages that have impeded adoption at scale. We propose that the broader adoption of learner profiles can benefit from careful combination of the methods and practices of three primary approaches, allowing for scalable implementation of learner profiles across educational systems. In this regard, LA can leverage from other aligned domains to develop valid and rigorous measures of students' learning and propel learner profiles from education research to more mainstream educational practice. LA could provide the scope for monitoring and reporting beyond an individualised context and allow holistic evaluations of progress. There is promise in LA research to leverage the growing momentum surrounding learner profiles and make a substantial impact on the field's core aim - understanding and optimising learning as it occurs. Refereed/Peer-reviewed |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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