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Challenges and opportunities of multimodal data in human learning: The computer science students' perspective
Journal of Computer Assisted Learning ( IF 3.761 ) Pub Date : 2021-03-02 , DOI: 10.1111/jcal.12542
Katerina Mangaroska 1 , Roberto Martinez‐Maldonado 2 , Boban Vesin 3 , Dragan Gašević 2
Affiliation  

Multimodal data have the potential to explore emerging learning practices that extend human cognitive capacities. A critical issue stretching in many multimodal learning analytics (MLA) systems and studies is the current focus aimed at supporting researchers to model learner behaviours, rather than directly supporting learners. Moreover, many MLA systems are designed and deployed without learners' involvement. We argue that in order to create MLA interfaces that directly support learning, we need to gain an expanded understanding of how multimodal data can support learners' authentic needs. We present a qualitative study in which 40 computer science students were tracked in an authentic learning activity using wearable and static sensors. Our findings outline learners' curated representations about multimodal data and the non-technical challenges in using these data in their learning practice. The paper discusses 10 dimensions that can serve as guidelines for researchers and designers to create effective and ethically aware student-facing MLA innovations.

中文翻译:

人类学习中多模态数据的挑战与机遇:计算机科学专业学生的视角

多模态数据有可能探索扩展人类认知能力的新兴学习实践。许多多模态学习分析 (MLA) 系统和研究中的一个关键问题是当前的重点是支持研究人员对学习者行为进行建模,而不是直接支持学习者。此外,许多 MLA 系统是在没有学习者参与的情况下设计和部署的。我们认为,为了创建直接支持学习的 MLA 接口,我们需要深入了解多模态数据如何支持学习者的真实需求。我们提出了一项定性研究,其中使用可穿戴和静态传感器在真实的学习活动中跟踪 40 名计算机科学专业的学生。我们的发现概述了学习者的 策划关于多模态数据的表示以及在他们的学习实践中使用这些数据的非技术挑战。该论文讨论了 10 个维度,这些维度可以作为研究人员和设计师创建有效且具有道德意识的面向学生的 MLA 创新的指南。
更新日期:2021-03-02
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