当前和未来的多模式学习分析数据挑战

Daniel Spikol, L. Prieto, M. Rodríguez-Triana, M. Worsley, X. Ochoa, M. Cukurova
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引用次数: 12

摘要

多模式学习分析(MMLA)捕获、集成和分析来自不同来源的学习痕迹,以便更全面地了解学习过程,无论它发生在哪里。MMLA利用了越来越广泛的各种传感器、高频数据收集技术以及复杂的机器学习和人工智能技术。本次研讨会的目的有两个:首先,让参与者接触并开发不同的多模态数据集,这些数据集反映了MMLA如何为研究复杂的学习过程和环境带来新的见解和机会;第二,在先前关于该主题的研讨会的基础上,共同确定进一步MMLA研究的一系列重大挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Current and future multimodal learning analytics data challenges
Multimodal Learning Analytics (MMLA) captures, integrates and analyzes learning traces from different sources in order to obtain a more holistic understanding of the learning process, wherever it happens. MMLA leverages the increasingly widespread availability of diverse sensors, high-frequency data collection technologies and sophisticated machine learning and artificial intelligence techniques. The aim of this workshop is twofold: first, to expose participants to, and develop, different multimodal datasets that reflect how MMLA can bring new insights and opportunities to investigate complex learning processes and environments; second, to collaboratively identify a set of grand challenges for further MMLA research, built upon the foundations of previous workshops on the topic.
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