Learning Analytics

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F24%3A43926306" target="_blank" >RIV/62156489:43110/24:43926306 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Learning Analytics

Popis výsledku v původním jazyce

This study material provides a detailed exploration of learning analytics, focusing on how data-driven insights can improve educational outcomes. The content begins with a comprehensive introduction to the fundamental concepts, objectives, and challenges of learning analytics, followed by an examination of its essential elements and data resources. The book delves into various methodological frameworks, data analysis methods, and preprocessing techniques to equip readers with the skills needed to perform effective exploratory data analysis. Key machine learning models such as linear regression, random forests, clustering using K-means, and association rule mining are thoroughly explained and contextualized within the realm of learning analytics. The material also highlights current research topics and the development of educational technology (EdTech) using AI, machine learning, and deep learning models. Practical insights into software development, open architecture frameworks, and project management are provided to support the successful implementation of learning analytics in educational settings.

Název v anglickém jazyce

Learning Analytics

Popis výsledku anglicky

This study material provides a detailed exploration of learning analytics, focusing on how data-driven insights can improve educational outcomes. The content begins with a comprehensive introduction to the fundamental concepts, objectives, and challenges of learning analytics, followed by an examination of its essential elements and data resources. The book delves into various methodological frameworks, data analysis methods, and preprocessing techniques to equip readers with the skills needed to perform effective exploratory data analysis. Key machine learning models such as linear regression, random forests, clustering using K-means, and association rule mining are thoroughly explained and contextualized within the realm of learning analytics. The material also highlights current research topics and the development of educational technology (EdTech) using AI, machine learning, and deep learning models. Practical insights into software development, open architecture frameworks, and project management are provided to support the successful implementation of learning analytics in educational settings.

Druh

B - Odborná kniha

CEP obor

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OECD FORD obor

20202 - Communication engineering and systems

Projekt

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Návaznosti

O - Projekt operacniho programu

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

ISBN

978-80-558-2232-7

Počet stran knihy

217

Název nakladatele

Univerzita Konštantína Filozofa v Nitre

Místo vydání

Nitra

Kód UT WoS knihy

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