Analytics of Real-world Learning by Re-constructing Time-series Occurrence of Qualitatively Different Learning and 3D Human Attention
Masaya Okada, Graduate School of Informatics, Shizuoka University, Japan ; Masahiro Tada, Faculty of Science and Engineering, Kindai University, Japan
E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, in New Orleans, LA, USA ISBN 978-1-939797-12-4 Publisher: Association for the Advancement of Computing in Education (AACE), San Diego, CA
Unlike classroom desktop learning, it is hard to assess the situation of real-world learning in which collaborative learners autonomously and diversely conduct themselves in an outdoor field. This paper proposes a new concept of sensor-based analytics for real-world learning by selecting environmental learning in nature as a model case. Our originality is concentrated on our viewpoints: (1) a learner three-dimensionally uses his/her body to search, notice, and examine diverse pieces of information embedded in the world, (2) knowledge and behavior are co-constructed through inter-system interactions between a human and the environment. From these viewpoints, we analyzed the time-series occurrence of learning activities in a natural field and then obtained observation results showing that qualitatively different learning (e.g., superficial and concrete-level observation, abstract and meta-level consideration) is accompanied by different time-series patterns of the 3D transition of human
Okada, M. & Tada, M. (2014). Analytics of Real-world Learning by Re-constructing Time-series Occurrence of Qualitatively Different Learning and 3D Human Attention. In T. Bastiaens (Ed.), Proceedings of World Conference on E-Learning (pp. 1476-1485). New Orleans, LA, USA: Association for the Advancement of Computing in Education (AACE).
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