Année : 2019
Lieu de publication de l'article :
Résumé de l'article
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. CCS CONCEPTS • Human-centred computing~Visualization design and evaluation methods •Information systems~Data analytics KEYWORDS Face-to-face classroom analysis, co-located learning, physical learning analytics, educational data mining, educational technologies ACM Reference format: Y. H. V. Chua, J. Dauwels and S. C. Tan, 2019. Technologies for automated analysis of co-located, real-life physical learning spaces: Where are we now? In Proceedings of the International Conference on Learning Analytics and Knowledge, Tempe, Arizona, April 2019 (LAK’19), 10 pages. DOI: 10.1145/3303772.3303811
Mots-clés
Deep learning,Feedback,Population,Real life,Pipeline (computing),Data Collection,Emoticon,Experience,Occur (action),Body of uterus,Malignant Fibrous Histiocytoma,Numerous,sensor (device),Promotion (action),Imagery,
Français
English
