| 书目名称 | The Multimodal Learning Analytics Handbook |
| 编辑 | Michail Giannakos,Daniel Spikol,Rawad Hammad |
| 视频video | |
| 概述 | First comprehensive resource in the area of multimodal data for learning.State of the art machine learning and AI methods for making sense of complex learning data.Exploring the role and impact of mul |
| 图书封面 |  |
| 描述 | This handbook is the first book ever covering the area of Multimodal Learning Analytics (MMLA). The field of MMLA is an emerging domain of Learning Analytics and plays an important role in expanding the Learning Analytics goal of understanding and improving learning in all the different environments where it occurs. The challenge for research and practice in this field is how to develop theories about the analysis of human behaviors during diverse learning processes and to create useful tools that could augment the capabilities of learners and instructors in a way that is ethical and sustainable. Behind this area, the CrossMMLA research community exchanges ideas on how we can analyze evidence from multimodal and multisystem data and how we can extract meaning from this increasingly fluid and complex data coming from different kinds of transformative learning situations and how to best feed back the results of these analyses to achieve positive transformative actions on those learning processes..This handbook also describes how MMLA uses the advances in machine learning and affordable sensor technologies to act as a virtual observer/analyst of learning activities. The book describes |
| 出版日期 | Book 2022 |
| 关键词 | learning analytics; multimodal learning analytics; multimodal teaching; complex learning data; affective |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-031-08076-0 |
| isbn_softcover | 978-3-031-08078-4 |
| isbn_ebook | 978-3-031-08076-0 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
发表于 2025-3-21 17:01:20
