The Multimodal Learning Analytics Handbook

This handbook is the first book ever covering the area of Multimodal Learning Analytics (MMLA). It is an emerging domain of Learning Analytics and plays an important role in expanding the goal of understanding and improving learning in all the different environments where it occurs. The challenge for research and practice is how to develop theories about the analysis of human behaviors during diverse learning processes and create useful tools that augment the capabilities of learners and instructors in a way that is ethical and sustainable. MMLA uses the advances in machine learning and affordable sensor technologies to act as a virtual observer/analyst of learning activities, providing new insights into learning processes that happen across multiple contexts between stakeholders, devices, and resources. However, conducting the data gathering, pre-processing, analysis, annotation, and sense-making in a way that is meaningful for learning scientists and other stakeholders still poses challenges.

Format: Paperback / softback
Length: 372 pages
Publication date: 10 October 2023
Publisher: Springer International Publishing AG

This comprehensive handbook is a groundbreaking exploration of the emerging field of Multimodal Learning Analytics (MMLA). As an integral part of Learning Analytics, MMLA plays a pivotal role in expanding the overarching goal of understanding and enhancing learning across diverse environments. The central challenge in this domain lies in developing theories and creating practical tools that can effectively analyze human behaviors during various learning processes, while also ensuring ethical and sustainable practices.

At the heart of MMLA lies the vibrant CrossMMLA research community, which actively engages in discussions on how to effectively analyze evidence from multimodal and multisystem data, extracting meaningful insights from this increasingly fluid and complex data originating from diverse transformative learning situations. The objective is to effectively communicate the results of these analyses to drive positive transformative actions within these learning processes.

Furthermore, this handbook delves into the innovative use of machine learning and affordable sensor technologies in MMLA. By adopting a "virtual observer/analyst" approach, MMLA can provide unique insights into learning activities that occur across multiple contexts, involving stakeholders, devices, and resources. Through the integration of such technologies with machine learning, Learning Analytics researchers can now conduct comprehensive analysis of text, speech, handwriting, sketches, gestures, affective states, or eye-gaze behavior. This enables them to enhance the accuracy of their predictions and learned models, as well as offer automated feedback to facilitate learner self-reflection.

However, the advent of increased data complexity brings forth.

Weight: 590g
Dimension: 155 x 234 x 24 (mm)
ISBN-13: 9783031080784
Edition number: 1st ed. 2022