Learning Analytics Methods and Tutorials
| Přispěvatelé: | Saqr, Mohammed, López-Pernas, Sonsoles |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: |
learning analytics methods
educational data mining quantitative methods in education social network analysis sequence analysis Process mining machine learning in education artificial intelligence in education temporal networks epistemic networks thema EDItEUR::J Society and Social Sciences::JN Education::JNV Educational equipment and technology computer-aided learning (CAL) thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQE Expert systems / knowledge-based systems thema EDItEUR::U Computing and Information Technology::UX Applied computing::UXJ Computer applications in the social and behavioural sciences |
| Druh dokumentu: | book |
| DOI: | 10.1007/978-3-031-54464-4 |
| Popis: | This open access comprehensive methodological book offers a much-needed answer to the lack of resources and methodological guidance in learning analytics, which has been a problem ever since the field started. The book covers all important quantitative topics in education at large as well as the latest in learning analytics and education data mining. The book also goes deeper into advanced methods that are at the forefront of novel methodological innovations. Authors of the book include world-renowned learning analytics researchers, R package developers, and methodological experts from diverse fields offering an unprecedented interdisciplinary reference on novel topics that is hard to find elsewhere. The book starts with the basics of R as a programming language, the basics of data cleaning, data manipulation, statistics, and analytics. In doing so, the book is suitable for newcomers as they can find an easy entry to the field, as well as being comprehensive of all the major methodologies. For every method, the corresponding chapter starts with the basics, explains the main concepts, and reviews examples from the literature. Every chapter has a detailed explanation of the essential techniques and basic functions combined with code and a full tutorial of the analysis with open-access real-life data. A total of 22 chapters are included in the book covering a wide range of methods such as predictive learning analytics, network analysis, temporal networks, epistemic networks, sequence analysis, process mining, factor analysis, structural topic modeling, clustering, longitudinal analysis, and Markov models. What is really unique about the book is that researchers can perform the most advanced analysis with the included code using the step-by-step tutorial and the included data without the need for any extra resources. This is an open access book. |
| Databáze: | OAPEN Library |
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