Towards Emotionally Aware AI Smart Classroom: Current Issues and Directions for Engineering and Education

Kim, Yelin; Soyata, Tolga; Feyzi-Behnagh, Reza

doi:10.1109/access.2018.2791861

Cited by 163 publications

(68 citation statements)

References 134 publications

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“…Some devices can be brought to the Smart Classroom according to the Bring Your Own Device (BYOD) initiative, promoting the use personal devices by students. It is increasingly common to find wearable devices, such as smart watches, brainwave detection, and emotion recognition [53,54].…”

Section: Technological Solutionsmentioning

confidence: 99%

“…It is not limited to self-corrections of closed questionnaires, but real-time recommendations to the students [54] and intelligent tutoring systems [84]. Feedback can also be reported to the teacher informing them of the overall students' moods, giving advice on non-verbal behaviour [53] or other inputs to make teaching more efficient.…”

Section: Performed Processesmentioning

confidence: 99%

“…In the short term, new developments should go in the direction of allowing a new learning paradigm [88], new pedagogies and new teaching methodologies for smart citizens of the 21st century, and for developing new skills and new competences [83]. Concerning technology, the implementation of Smart watches or RFID tag sensors for teachers and students [53][54][55][56][57]64], interactive whiteboards or touch screen displays with multiple students [49,52] or interaction screens that recognise body movements [51,52], and full access to knowledge through mobile devices and full internet access can be considered. It should be noted that there are existing initiatives and solutions of cloud storage [59,62] allowing easy access and information management from anywhere, and devices with collaborative data and writing software tools that allow knowledge sharing, management and co-creation [66], considering students' needs [63] and student-centred learning methods [85], which allow student self-regulation to foster autonomous and lifelong learning.…”

Section: Technology Environment Processesmentioning

confidence: 99%

“…The long run is more centred on taking data and using Artificial Intelligence [63,65]. Sensors could measure and monitor the students' and teachers' cognitive and behavioural processes [53,54,56]. It will be a real revolution because the system will provide real time information [54,84] and will help teachers to make fast decisions to conduct and adjust the students' learning pathways [65,80] and student-centred learning methods [85], which will allow student self-regulation to foster autonomous and lifelong learning.…”

Section: Technology Environment Processesmentioning

confidence: 99%

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The Smart Classroom as a Means to the Development of ESD Methodologies

2020

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Educational institutions are envisioned as principal agents for addressing the current sustainability challenge that society is facing. Education for Sustainable Development (ESD) is transformational and concerns learning content and outcomes, pedagogy and the learning environment in itself. ESD entails rethinking the learning environment (physical and virtual) in line with sustainable development, which implies classrooms' transformation towards learner engagement, formative assessments and active methodologies. This paper responds to this need through exploring the relationship between Smart Classrooms and four widely used ESD methodologies (project or problem-based learning, case study, simulation and cooperative inquiry), identifying how the dimensions and categories of the characteristics of Smart Classrooms can contribute and lead to the implementation of ESD methodologies in real teaching practice in an effective way. The method used in this study consisted of a literature review of both theoretical frameworks separately, ESD and Smart Classrooms, and a subsequent expert analysis to identify the interrelation between both. The Smart Classroom shows a high level of adequacy for using problem and project-based learning, case study and cooperative inquiry methods because of its characteristics in terms of technology developments, environmental conditions and processes. Simulation is the ESD methodology with the lowest level of adequacy in a Smart Classroom, because it is primarily held online rather than through face-to-face teaching. Smart Education facilitates the putting in practice of ESD processes as it enables the creation of intelligent, sustainable, resource-efficient, personalised and adaptive learning environments. Further empirical research is needed to explore the influence that the Smart Classroom has in enabling ESD processes and practices, and to identify students' and teachers' needs at different education levels. Additionally, teacher training programmes focused on the correct use of Smart Classrooms and on the digital competence of teachers are critical to its successful implementation.

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Section: Technological Solutionsmentioning

confidence: 99%

Section: Performed Processesmentioning

confidence: 99%

Section: Technology Environment Processesmentioning

confidence: 99%

Section: Technology Environment Processesmentioning

confidence: 99%

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The Smart Classroom as a Means to the Development of ESD Methodologies

2020

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“…Gesture recognition has attracted increasing research interest in many fields, including human-computer interaction, human-robot collaboration, video retrieval and so on. 10,16,19,39 Gestures can be categorized into three types: body gestures, hand and arm gestures, and head and facial gestures. 19 In this paper, we mainly focus on the first type of gesture in the classroom, or rather upper body gesture.…”

Section: Introductionmentioning

confidence: 99%

Student body gesture recognition based on Fisher broad learning system

Shi

Wei

Pan

et al. 2019

Int. J. Wavelets Multiresolut Inf. Process.

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Observing student body gesture has been widely used to assess teaching effectiveness over the past few decades. However, manual observation is not suitable for the automatic data analysis in the field of learning analytics. Consequently, a student body gesture recognition method based on Fisher Broad Learning System (FBLS) and Local Log-Euclidean Multivariate Gaussian (L 2 EMG) is proposed in this paper. FBLS is designed by introducing the discriminative information into the hidden layer of Broad Learning System (BLS) and reducing the dimensionality of hidden-layer representations. FBLS has superiorities in accuracy and speed. In addition, L 2 EMG, which is a highly distinctive descriptor, characterizes the local image with a multivariate Gaussian distribution. So L 2 EMG features are fed into the FBLS for recognition in this paper. Extensive † Corresponding author. 1950001-1 Int. J. Wavelets Multiresolut Inf. Process. 2019.17. Downloaded from www.worldscientific.com by 44.224.250.200 on 07/03/20. Re-use and distribution is strictly not permitted, except for Open Access articles. Y. Shi et al.experimental results on self-built dataset show that the proposed student body gesture recognition method obtains better results than other benchmarking methods.

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Improved fuzzy‐assisted multimedia‐assistive technology for engineering education

Chen

Subramani

et al. 2020

Comp Applic In Engineering

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Presently, technological developments within the academic and online education sectors have enabled mobile data‐based interactive strategies to impact transformative learning innovations for improving the learning experience. Nowadays, fuzzy logic has become a part of engineering educational systems, and it provides uncertainty‐defining logic. In this paper, the following method has been proposed for the engineering educational system: Recommending groups in E‐learning‐based fuzzy learning approach with multimedia‐assistive technology. In the recent past, assistive technologies have served as a tool or platform for resolving problems and improved learning through many factors, such as critical thinking, multimodal communication, and motivational approaches for students. Currently, the application of fuzzy logic in education has helped in making decisions in an effective manner. These technologies and experiments have significant roles in student success, because a classical student evaluation and a group study approach for high‐quality learning are no longer sufficient.

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Towards Emotionally Aware AI Smart Classroom: Current Issues and Directions for Engineering and Education

Cited by 163 publications

References 134 publications

The Smart Classroom as a Means to the Development of ESD Methodologies

The Smart Classroom as a Means to the Development of ESD Methodologies

Student body gesture recognition based on Fisher broad learning system

Improved fuzzy‐assisted multimedia‐assistive technology for engineering education

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