Developing a Reading Concentration Monitoring System by Applying an Artificial Bee Colony Algorithm to E-Books in an Intelligent Classroom

Hsu, Chia-Cheng; Chen, Hsin-Chin; Su, Yen-Ning; Huang, Kuo-Kuang; Huang, Yueh‐Min

doi:10.3390/s121014158

Cited by 33 publications

(11 citation statements)

References 49 publications

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“…Many human behaviors, including head pose tracking (Ba & Odobez, 2009, 2011, face tracking (Stiefelhagen et al, 2002) and eye gaze tracking, are used in developing AAS (Toet, 2006). However, with advances in the assessment of human physiological signals, e-learning research has increasingly used physiological signals to determine students' attention levels (Chen & Huang, 2014;Hsu et al, 2012). Related efforts in recent years have assessed learners' emotions by using human physiological signals, such as heart rate variability (HRV) and EEG (Chen & Sun, 2012;Chen & Wang, 2011) and attention (Chen & Lin, 2014;Rebolledo-Mendez et al, 2009).…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing the attention levels of students by using a novel attention aware system based on brainwave signals

Chen¹,

Wang²,

Yu³

2015

Brit J Educational Tech

108

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Rapid progress in information and communication technologies (ICTs) has fueled the popularity of e‐learning. However, an e‐learning environment is limited in that online instructors cannot monitor immediately whether students remain focus during online autonomous learning. Therefore, this study tries to develop a novel attention aware system (AAS) capable of recognizing students' attention levels accurately based on electroencephalography (EEG) signals, thus having high potential to be applied in providing timely alert for conveying low‐attention level feedback to online instructors in an e‐learning environment. To construct AAS, attention responses of students and their corresponding EEG signals are gathered based on a continuous performance test (CPT), ie, an attention assessment test. Next, the AAS is constructed by using training and testing data by the NeuroSky brainwave detector and the support vector machine (SVM), a well‐known machine learning model. Additionally, based on the discrete wavelet transform (DWT), the collected EEG signals are decomposed into five primary bands (ie, alpha, beta, gamma, theta, and delta). Each primary band contains five statistical parameters (including approximate entropy, total variation, energy, skewness, and standard deviation), thus generating 25 potential brainwave features associated with students' attention level for constructing the AAS. An attempt based on genetic algorithm (GA) is also made to enhance the prediction performance of the proposed AAS in terms of identifying students' attention levels. According to GA, the seven most influential features are selected from 25 considered features; parameters of the proposed AAS are also optimized. Analytical results indicate that the proposed AAS can accurately recognize individual student's attention state as either a high or low level, and the average accuracy rate reaches as high as 89.52%. Moreover, the proposed AAS is integrated with a video lecture tagging system to examine whether the proposed AAS can accurately detect students' low‐attention periods while learning about electrical safety in the workplace via a video lecture. Four experiments are designed to assess the prediction performance of the proposed AAS in terms of identifying the periods of video lecture with high‐ or low‐attention levels during learning processes. Analytical results indicate that the proposed AAS can accurately identify the low‐attention periods of video lecture generated by students when engaging in a learning activity with video lecture. Meanwhile, the proposed AAS can also accurately identify the low‐attention periods of video lecture generated by students to some degree even when students engage in a learning activity by a video lecture with random disturbances. Furthermore, strong negative correlations are found between the students' learning performance (ie, posttest score and progressive score) and the low‐attention periods of video lecture identified by the proposed AAS. Results of this study demonstrate that the proposed...

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Section: Literature Reviewmentioning

confidence: 99%

Assessing the attention levels of students by using a novel attention aware system based on brainwave signals

Chen¹,

Wang²,

Yu³

2015

Brit J Educational Tech

108

View full text Add to dashboard Cite

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“…Due to the change of human-computer interaction between computers and users from traditional keyboard and mouse control to image-based interface and sensory control, experts and researchers in all fields attach great importance to user experience issues. (Jetter & Gerken, 2006;Mahlke, 2005) The products, system or service developed by the designer need to improve the content of the interface and enhance user's experience by observing the feedback provided by users after interacting with the product through the steps of user's experience satisfaction (Hsu et al, 2012;Su et al, 2014). In particular, the development of emerging technologies being applied to the field of education is even more necessary to understand the mastery of science and technology and the usefulness of the system through the experience of users.…”

Section: Literature Reviewmentioning

confidence: 99%

Using Augmented Reality to Enhance and Engage Students in Learning Mathematics

Chao

Chang²

2018

ASSRJ

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The study shall apply Augmented Reality (AR) technique to develop Math learning App. The learners shall apply the mobile device to scan the cards and convert the 2D floor plan into 3D objects. By manipulating virtual 3D objects, students can understand the 3D composition from different perspectives. By applying the decomposition or combinated functions of the Math learning App, students can further understand the calculation way of Volume. The study confers the learning outcomes of the school children before and after applying AR Math App through the introduction and experiment design of the teaching site, and discusses the difference between Math highaccomplished school children and ordinary children by way of digital learning. Through the ways of achievement tests, questionnaire and teachers' and childrents' interviews, the study understands the learning effects and experiencing of AR Math App. The experiment's result indicates that students can accept the AR Math learning App developed by the institute and it can also initiate their interests in learning. The result before and after studying indicates that the learning effects have significantly promoted. The result of teachers' interviews indicate that the application of this digital teaching materials and interactive experience model has effectively enhanced the students understanding the concept of Volume and 3D composition.

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“…Hsu, Chen, Su, Huang, and Huang (2012) stated that attention is the ability to focus on or sustain an action without interference from external stimuli. The most widely accepted theory about attention in general is the feature-integration theory (Treisman & Gelade, 1980).…”

Section: Literature Review Attention and Learningmentioning

confidence: 99%

“…For example, Cullen, Dan, Rogers, and Fisk (2014) measured how undergraduate students allocated their visual attention in an automated multiple-task environment. Hsu et al (2012) measured reading concentration, which refers to the attention focused on reading or learning, in terms of how actively a student pays attention to the learning materials and contents during the learning process. Moreover, if the time-on-task decreases, then the number of distractors would increase (Halperin, 1996), with Baloian et al (2008) noting the disruptions to attention caused by the use of input devices such as a keyboard and mouse while instructors use an electronic blackboard.…”

Section: Measurement Of Attentionmentioning

confidence: 99%

Students’ Attention when Using Touchscreens and Pen Tablets in a Mathematics Classroom

Chen

Chiu²,

Lin

et al. 2017

JITE:IIP

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Aim/Purpose: The present study investigated and compared students’ attention in terms of time-on-task and number of distractors between using a touchscreen and a pen tablet in mathematical problem-solving activities with virtual manipulatives. Background: Although there is an increasing use of these input devices in educational practice, little research has focused on assessing student attention while using touchscreens or pen tablets in a mathematics classroom. Methodology: A qualitative exploration was conducted in a public elementary school in New Taipei, Taiwan. Six fifth-grade students participated in the activities. Video recordings of the activities and the students’ actions were analyzed. Findings: The results showed that students in the activity using touchscreens maintained greater attention and, thus, had more time-on-task and fewer distractors than those in the activity using pen tablets. Recommendations for Practitioners: School teachers could employ touchscreens in mathematics classrooms to support activities that focus on students’ manipulations in relation to the attention paid to the learning content. Recommendation for Researchers: The findings enhance our understanding of the input devices used in educational practice and provide a basis for further research. Impact on Society: The findings may also shed light on the human-technology interaction process involved in using pen and touch technology conditions. Future Research: Activities similar to those reported here should be conducted using more participants. In addition, it is important to understand how students with different levels of mathematics achievement use the devices in the activities.

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Developing a Reading Concentration Monitoring System by Applying an Artificial Bee Colony Algorithm to E-Books in an Intelligent Classroom

Cited by 33 publications

References 49 publications

Assessing the attention levels of students by using a novel attention aware system based on brainwave signals

Assessing the attention levels of students by using a novel attention aware system based on brainwave signals

Using Augmented Reality to Enhance and Engage Students in Learning Mathematics

Students’ Attention when Using Touchscreens and Pen Tablets in a Mathematics Classroom

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