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An HCI-Based Cognitive Architecture for Learning Process Observation

An HCI-Based Cognitive Architecture for Learning Process Observation
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Author(s): Guimin Shi (The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education, Beijing, China), Sheng Yang (The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education, Beijing, China), Changyong Liu (The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education, Beijing, China), Shimin Meng (The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education, Beijing, China), Zhiming Luo (Xiamen University, Fujian, China) and Shaozi Li (Xiamen University, Fujian, China)
Copyright: 2020
Volume: 18
Issue: 1
Pages: 18
Source title: International Journal of Distance Education Technologies (IJDET)
Editor(s)-in-Chief: Maiga Chang (Athabasca University, Canada)
DOI: 10.4018/IJDET.2020010101

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Abstract

In this article, a cognitive framework for observing learning activities based on human-computer coupling is proposed. The observation is based on the vectorization of a learning situation along with human-computer interaction factors. An evolutionary high-dimensional topology of learning cognitive flow is introduced for human-computer interaction. In addition, the authors have selected a tree topology as the topological structure of a low-dimensional learning space to process the observations for online learning. Furthermore, the mechanism for the BSM (brain cognitive body-situation of coupling-manifold of information) the coupling morphism is presented. The principle for the coupled observation of objects in a cognitive or learning manifold is proposed. Finally, a special system for teaching and learning is programmed to observe and evaluate learning and mental arithmetic training processes. This system not only provides students with a new ergonomic learning model but also records the students' learning processes. Thus, the teachers can summarize the knowledge points automatically rather than manually.

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