Developing a sensor-based learning concentration detection system. Issue 2 (25th February 2014)

• Record Type: Journal Article
• Authors: Su, Yen-Ning; Hsu, Chia-Cheng; Chen, Hsin-Chin; Huang, Kuo-Kuang; Huang, Yueh-Min
• Editors: Hsieh, Wen-Hsiang
• Is Part Of: Engineering computations. Volume 31:Issue 2(2014)
• Page Start: 216
• Page End: 230
• Publication Date: 2014-02-25
• Subjects: Sensors -- Artificial bee colony algorithm -- Learning concentration; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-01-2013-0010
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: – This study aims to use sensing technology to observe the learning status of learners in a teaching and learning environment. In a general instruction environment, teachers often encounter some teaching problems. These are frequently related to the fact that the teacher cannot clearly know the learning status of students, such as their degree of learning concentration and capacity to absorb knowledge. In order to deal with this situation, this study uses a learning concentration detection system (LCDS), combining sensor technology and an artificial intelligence method, to better understand the learning concentration of students in a learning environment. Design/methodology/approach: – The proposed system uses sensing technology to collect information about the learning behavior of the students, analyzes their concentration levels, and applies an artificial intelligence method to combine this information for use by the teacher. This system includes a pressure detection sensor and facial detection sensor to detect facial expressions, eye activities and body movements. The system utilizes an artificial bee colony (ABC) algorithm to optimize the system performance to help teachers immediately understand the degree of concentration and learning status of their students. Based on this, instructors can give appropriate guidance to several unfocused students at the same time. Findings: – The fitness value and computation time were used to evaluate the LCDS. Comparing the results of the proposed ABC algorithm with those from the random search method, the algorithm was found to obtain better solutions. The experimental results demonstrate that the ABC algorithm can quickly obtain near optimal solutions within a reasonable time. Originality/value: – A learning concentration detection method of integrating context-aware technologies and an ABC algorithm is presented in this paper. Using this learning concentration detection method, teachers can keep abreast of their students' learning status in a teaching environment and thus provide more appropriate instruction.