| Autor: |
Carmel Kent, Abayomi Akanji, Benedict du Boulay, Ibrahim Bashir, Thomas G. Fikes, Sue A. Rodríguez De Jesús, Alysha Ramirez Hall, Paul Alvarado, Jennifer E. Jones, Mutlu Cukurova, Varshita Sher, Canan Blake, Arthur Fisher, Juliet Greenwood, Rosemary Luckin |
| Rok vydání: |
2022 |
| DOI: |
10.4018/978-1-7998-9644-9.ch001 |
| Popis: |
Many universities aim to improve students' 'learning to learn' (LTL) skills to prepare them for post-academic life. This requires evaluating LTL and integrating it into the university's curriculum and assessment regimes. Data is essential to provide evidence for the evaluation of LTL, meaning that available data sources must be connected to the types of evidence required for evaluation. This chapter describes a case study using an LTL ontology to connect the theoretical aspects of LTL with a university's existing data sources and to inform the design and application of learning analytics. The results produced by the analytics indicate that LTL can be treated as a dimension in its own right. The LTL dimension has a moderate relationship to academic performance. There is also evidence to suggest that LTL develops at an uneven pace across academic terms and that it exhibits different patterns in online as compared to face-to-face delivery methods. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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