The EEG-Based Attention Analysis in Multimedia m-Learning

Ni, Dan; Wang, Shuo; Liu, Guocheng

doi:10.1155/2020/4837291

Cited by 32 publications

(16 citation statements)

References 35 publications

(36 reference statements)

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“…The differences were reflected in the fact that the EG performed better in terms of learning and in terms of the intensity and time of concentration. A similar study recently carried out by Ni et al (2020) used a NeuroSky EEG device to analyse concentration when using multimedia materials (text, text and graphics, video), and found that participants learning via mobile devices had the highest concentration value. The learners expressed greater attention in text media, but this possibly resulted from the fact that text media had only words that were easy to concentrate; however, the difference did not reach statistical significance.…”

Section: Discussionmentioning

confidence: 86%

Stem Education in Eco-Farming Supported by Ict and Mobile Applications

Pajk¹,

Isacker²,

Aberšek

et al. 2021

JBSE

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The presented research focused on developing and testing an innovative interdisciplinary STEM didactic model. The developed didactic model was introduced in the field of eco-farming. To the participants, it offers the possibility for non-formal training, which can take place anywhere and anytime. Participants require some knowledge of STEM subjects (especially chemistry and biology) as well as knowledge of ecology, technology, and engineering, in order to provide answers and solutions to environmental challenges while using knowledge of mathematics (especially combinatorics and statistics) to search for optimal solutions (in our case, a lean business plan). The model was tested in non-formal education settings, based on an interdisciplinary approach and modern technologies, such as monitoring the effectiveness of training using electroencephalography (EEG) and mobile applications. In the presented didactic model, special emphasis was placed on an interdisciplinary STEM approach to environmental protection, ecology, connatural forms of production and sustainable development. The presented research confirms the hypotheses that non-formal education is becoming an increasingly important form of education and training, and that the use of the interdisciplinary didactic model, contemporary technologies, and mobile applications, increases the time and intensity of concentration in learning and thus improves learning effectiveness. Keywords: eco-farming, electroencephalography, environment protection, mobile learning, non-formal education, sustainable development

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Section: Discussionmentioning

confidence: 86%

Stem Education in Eco-Farming Supported by Ict and Mobile Applications

Pajk¹,

Isacker²,

Aberšek

et al. 2021

JBSE

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show abstract

“…The EEG device supports two output formats: the raw EEG signal generated from Fp1, and the derived index of attention and meditation generated from the built-in algorithm of Neurosky. While the latter was used in some research works [ 46 ], conflicting results on the validity of derived attention index were reported [ 47 , 48 , 49 ]. The concern of the raw signal focused on the influence of eye blinks on the raw EEG signal recording [ 50 ].…”

Section: Methodsmentioning

confidence: 99%

Moment-to-Moment Continuous Attention Fluctuation Monitoring through Consumer-Grade EEG Device

Zhang

Yan

Sapkota

et al. 2021

Sensors

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While numerous studies have explored using various sensing techniques to measure attention states, moment-to-moment attention fluctuation measurement is unavailable. To bridge this gap, we applied a novel paradigm in psychology, the gradual-onset continuous performance task (gradCPT), to collect the ground truth of attention states. GradCPT allows for the precise labeling of attention fluctuation on an 800 ms time scale. We then developed a new technique for measuring continuous attention fluctuation, based on a machine learning approach that uses the spectral properties of EEG signals as the main features. We demonstrated that, even using a consumer grade EEG device, the detection accuracy of moment-to-moment attention fluctuations was 73.49%. Next, we empirically validated our technique in a video learning scenario and found that our technique match with the classification obtained through thought probes, with an average F1 score of 0.77. Our results suggest the effectiveness of using gradCPT as a ground truth labeling method and the feasibility of using consumer-grade EEG devices for continuous attention fluctuation detection.

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“…As clarified by the Association of College and Research Libraries (2013), "Instagram is dedicated to the mobile experience, and part of its growth was fueled by the rapid adoption of smartphones with high-functioning cameras". Although it is possible to browse Instagram using the Desktop Web, it is still "primarily" regarded as a mobile application (Moreau, 2020). A number of critical functionalities such as uploading photos or videos, posting on a profile, deleting and archiving posts, and modifying certain accounts settings is not accessible to computer users (Moreau, 2020).…”

Section: Instagram As a Mobile Learning Toolmentioning

confidence: 99%

“…Although it is possible to browse Instagram using the Desktop Web, it is still "primarily" regarded as a mobile application (Moreau, 2020). A number of critical functionalities such as uploading photos or videos, posting on a profile, deleting and archiving posts, and modifying certain accounts settings is not accessible to computer users (Moreau, 2020). This manifests as one of the prominent examples for how this particular social networking tool differs from its counterparts, namely Facebook and Twitter, both of which do offer full desktop experiences (Alba, 2017).…”

Section: Instagram As a Mobile Learning Toolmentioning

confidence: 99%

Educationalizing Instagram for Virtual Instruction in COVID-19

Ghobrini

Benzert

Balaş³

2021

International Journal of Web-Based Learning and Teaching Technologies

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Learning through social media platforms is a nascent pedagogy that opens up new virtual online e-instructional modalities and avenues to be explored especially in these challenging emergency times of COVID-19. This research focuses on a self-directed initiative of a math teacher who taught her students in an open virtual class via Instagram. This study explores how the main features of Instagram -inherently used as social interaction platform - were maximized for educational purposes. It also investigates the effects, be they positive or negative, on the learning-teaching process in terms of engagement and communication. For this, a mixed-method sequential exploratory design was opted for to conduct the study which surveyed 100 students across 22 different high schools who took part in the virtual open math classes. The findings highlight the different patterns of Instagram use and platform features that lend this social media website the requisite feasibility to educationalize it. Furthermore, the results reveal both the favourable and disadvantageous aspects of Instagram.

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The EEG-Based Attention Analysis in Multimedia m-Learning

Cited by 32 publications

References 35 publications

Stem Education in Eco-Farming Supported by Ict and Mobile Applications

Stem Education in Eco-Farming Supported by Ict and Mobile Applications

Moment-to-Moment Continuous Attention Fluctuation Monitoring through Consumer-Grade EEG Device

Educationalizing Instagram for Virtual Instruction in COVID-19

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