Description
Fridolin Wild explores the intersection of learning analytics and data science, focusing on R as a powerful tool for educational research. Through engaging methodologies such as Social Network Analysis (SNA), Latent Semantic Analysis (LSA), and Multi-Perspective Implicit Assessment (MPIA), the work provides a comprehensive guide for those looking to harness data in educational contexts.
The book delves into practical applications of these analytical techniques, offering readers insights into how data can enhance the learning experience. Wild's expertise in the field allows for a nuanced understanding of the complexities of learning environments and the role data can play in shaping educational outcomes.
By bridging theory and practice, this resource serves as a valuable asset for educators, researchers, and data analysts. It encourages a hands-on approach to learning analytics, enabling practitioners to gain deeper insights into student behavior and learning patterns, thus paving the way for targeted interventions and improved educational practices.
The book delves into practical applications of these analytical techniques, offering readers insights into how data can enhance the learning experience. Wild's expertise in the field allows for a nuanced understanding of the complexities of learning environments and the role data can play in shaping educational outcomes.
By bridging theory and practice, this resource serves as a valuable asset for educators, researchers, and data analysts. It encourages a hands-on approach to learning analytics, enabling practitioners to gain deeper insights into student behavior and learning patterns, thus paving the way for targeted interventions and improved educational practices.
Détails du livre
Format
Relié
Langue
Anglais
Éditeur
Springer