Technologies for automated analysis of co-located, real-life, physical learning spaces : where are we now?
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,...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2020
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/143249 |
| _version_ | 1811687144473755648 |
|---|---|
| author | Chua, Victoria Yi Han Dauwels, Justin Tan, Seng Chee |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Chua, Victoria Yi Han Dauwels, Justin Tan, Seng Chee |
| author_sort | Chua, Victoria Yi Han |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T05:11:39Z |
| format | Conference Paper |
| id | ntu-10356/143249 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:11:39Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1432492020-08-17T01:32:44Z Technologies for automated analysis of co-located, real-life, physical learning spaces : where are we now? Chua, Victoria Yi Han Dauwels, Justin Tan, Seng Chee School of Electrical and Electronic Engineering LAK19: Proceedings of the 9th International Conference on Learning Analytics & Knowledge Engineering::Electrical and electronic engineering Face-to-face Classroom Analysis Co-located Learning 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. Nanyang Technological University Accepted version This project is supported by a grant from Centre for Research and Development in Learning (CRADLE@NTU). 2020-08-17T01:32:44Z 2020-08-17T01:32:44Z 2019 Conference Paper 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 978-1-4503-6256-6 https://hdl.handle.net/10356/143249 10.1145/3303772.3303811 2-s2.0-85062801957 11 20 en © 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. application/pdf |
| spellingShingle | Engineering::Electrical and electronic engineering Face-to-face Classroom Analysis Co-located Learning Chua, Victoria Yi Han Dauwels, Justin Tan, Seng Chee Technologies for automated analysis of co-located, real-life, physical learning spaces : where are we now? |
| title | Technologies for automated analysis of co-located, real-life, physical learning spaces : where are we now? |
| title_full | Technologies for automated analysis of co-located, real-life, physical learning spaces : where are we now? |
| title_fullStr | Technologies for automated analysis of co-located, real-life, physical learning spaces : where are we now? |
| title_full_unstemmed | Technologies for automated analysis of co-located, real-life, physical learning spaces : where are we now? |
| title_short | Technologies for automated analysis of co-located, real-life, physical learning spaces : where are we now? |
| title_sort | technologies for automated analysis of co located real life physical learning spaces where are we now |
| topic | Engineering::Electrical and electronic engineering Face-to-face Classroom Analysis Co-located Learning |
| url | https://hdl.handle.net/10356/143249 |
| work_keys_str_mv | AT chuavictoriayihan technologiesforautomatedanalysisofcolocatedreallifephysicallearningspaceswherearewenow AT dauwelsjustin technologiesforautomatedanalysisofcolocatedreallifephysicallearningspaceswherearewenow AT tansengchee technologiesforautomatedanalysisofcolocatedreallifephysicallearningspaceswherearewenow |