Building Smart Learning Analytics System for Smart University
| Autor: | Venkat Sumanth Guduru, Jeffrey P. Bakken, Annie Benitha Thomas, Durga Poojitha Bodduluri, Colleen Heinemann, Vladimir Uskov, Rama Rachakonda |
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| Rok vydání: | 2017 |
| Předmět: |
010302 applied physics
Computer science business.industry 05 social sciences Learning analytics 050301 education 01 natural sciences Data science Data type Academic analytics Conceptual design Anticipation (artificial intelligence) Analytics 0103 physical sciences Smart learning Internet of Things business 0503 education |
| Zdroj: | Smart Education and e-Learning 2017 ISBN: 9783319594507 |
| DOI: | 10.1007/978-3-319-59451-4_19 |
| Popis: | The performed analysis of innovative learning analytics systems clearly shows that in the near future those systems will be actively deployed by academic institutions. The on-going research project described here is focused on in-depth analysis of hierarchical levels of learning analytics and academic analytics, types of data to be collected, main features, and the conceptual design of smart learning analytics for smart university. Our vision is that modern analytics systems should strongly support smart university’s “smartness” levels such as adaptivity, sensing, inferring, anticipation, self-learning, and self-organization. This paper presents the up-to-date research outcomes of a research project on the design and development of smart learning analytics systems for smart universities. |
| Databáze: | OpenAIRE |
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