TY  - GEN
AB  - This book focuses on research and development aspects of building data analytics workflows that address various challenges of e-learning applications. This book represents a guideline for building a data analysis workflow from scratch. Each chapter presents a step of the entire workflow, starting from an available dataset and continuing with building interpretable models, enhancing models, and tackling aspects of evaluating engagement and usability. The related work shows that many papers have focused on machine learning usage and advancement within e-learning systems. However, limited discussions have been found on presenting a detailed complete roadmap from the raw dataset up to the engagement and usability issues. Practical examples and guidelines are provided for designing and implementing new algorithms that address specific problems or functionalities. This roadmap represents a potential resource for various advances of researchers and practitioners in educational data mining and learning analytics.
AU  - Mihăescu, Marian Cristian,
CN  - LB1028.43
DO  - 10.1007/978-3-030-96644-7
DO  - doi
ID  - 1445408
KW  - Artificial intelligence
KW  - Machine learning.
KW  - Intelligence artificielle
KW  - Apprentissage automatique.
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-96644-7
N2  - This book focuses on research and development aspects of building data analytics workflows that address various challenges of e-learning applications. This book represents a guideline for building a data analysis workflow from scratch. Each chapter presents a step of the entire workflow, starting from an available dataset and continuing with building interpretable models, enhancing models, and tackling aspects of evaluating engagement and usability. The related work shows that many papers have focused on machine learning usage and advancement within e-learning systems. However, limited discussions have been found on presenting a detailed complete roadmap from the raw dataset up to the engagement and usability issues. Practical examples and guidelines are provided for designing and implementing new algorithms that address specific problems or functionalities. This roadmap represents a potential resource for various advances of researchers and practitioners in educational data mining and learning analytics.
SN  - 9783030966447
SN  - 3030966445
T1  - Data analytics in e-learning :approaches and applications /
TI  - Data analytics in e-learning :approaches and applications /
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-96644-7
VL  - volume 220
ER  -