Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143249
Title: Technologies for automated analysis of co-located, real-life, physical learning spaces : where are we now?
Authors: Chua, Victoria Yi Han
Dauwels, Justin
Tan, Seng Chee
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Chua, V. Y. H., Dauwels, J., & Tan, S. C. (2019). Technologies for automated analysis of co-located, real-life, physical learning spaces : where are we now? LAK19: Proceedings of the 9th International Conference on Learning Analytics & Knowledge, 11-20. doi:10.1145/3303772.3303811
Conference: LAK19: Proceedings of the 9th International Conference on Learning Analytics & Knowledge
Abstract: The motivation for this paper is derived from the fact that there has been increasing interest among researchers and practitioners in developing technologies that capture, model and analyze learning and teaching experiences that take place beyond computer-based learning environments. In this paper, we review case studies of tools and technologies developed to collect and analyze data in educational settings, quantify learning and teaching processes and support assessment of learning and teaching in an automated fashion. We focus on pipelines that leverage information and data harnessed from physical spaces and/or integrates collected data across physical and digital spaces. Our review reveals a promising field of physical classroom analysis. We describe some trends and suggest potential future directions. Specifically, more research should be geared towards a) deployable and sustainable data collection set-ups in physical learning environments, b) teacher assessment, c) developing feedback and visualization systems and d) promoting inclusivity and generalizability of models across populations.
URI: https://hdl.handle.net/10356/143249
ISBN: 978-1-4503-6256-6
DOI: 10.1145/3303772.3303811
Schools: School of Electrical and Electronic Engineering 
Rights: © 2019 Association for Computing Machinery. All rights reserved. This paper was published in the LAK19: Proceedings of the 9th International Conference on Learning Analytics & Knowledge and is made available with permission of Association for Computing Machinery.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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