Smart Classroom: A Deep Learning Approach towards Attention Assessment through Class Behavior Detection

Parambil, Medha Mohan Ambali; Ali, Luqman; Alnajjar, Fady; Gochoo, Munkhjargal

doi:10.1109/aset53988.2022.9735018

Cited by 14 publications

(7 citation statements)

References 22 publications

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“…Additionally, AI can help grade student work, giving teachers more time to work on more complex educational tasks [24]. Parambil et al (2022) talk about an AI-powered system that can track students' emotions and attention in real time in the classroom. This system gives teachers instant graphical feedback, which helps them accurately measure and respond to the level of student engagement [25].…”

Section: Real-time Feedback and Assessmentmentioning

confidence: 99%

Artificial Intelligence’s Opportunities and Challenges in Engineering Curricular Design: A Combined Review and Focus Group Study

Mosly

2024

Societies

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This study explores the opportunities and challenges of integrating artificial intelligence (AI) into engineering education. Through a review of the literature and a qualitative focus group study, an assessment was made for the role of AI in personalizing learning, enhancing simulation engagement, providing real-time feedback, and preparing students for AI-integrated workplaces. The study emphasizes how AI may significantly improve educational experiences by making them more dynamic, interactive, and successful. It also draws attention to important issues, such as moral questions, algorithmic biases in AI, infrastructure constraints, the need for AI literacy training for educators, and a range of student perspectives on AI engineering education. The results support a systematic approach to AI integration, highlighting the necessity of cooperative efforts by educators, legislators, curriculum designers, and technologists in order to overcome these obstacles. The study makes the case that AI can transform engineering education by negotiating these challenges and providing students with the information and skills needed for the digital future, all the while assuring fair and moral access to technology-enhanced learning.

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Section: Real-time Feedback and Assessmentmentioning

confidence: 99%

Artificial Intelligence’s Opportunities and Challenges in Engineering Curricular Design: A Combined Review and Focus Group Study

Mosly

2024

Societies

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“…The ability to recognize human behavior can be extremely important inside a smart classroom (Wang, 2021 ) as it enables the recognition of student behavior and emotions allowing the detection of uncomfortable situations for students, such as high anxiety or reduced concertation levels. Smart classrooms with developed AI-powered surveillance system can detect students who are not paying attention in class and alert the teacher (Parambil et al, 2022 ). In addition, action recognition systems can analyse students’ behaviour during the course and estimate their engagement (Thomas & Jayagopi, 2017 ).…”

Section: Key Technologies Related To Smart Classesmentioning

confidence: 99%

A critical evaluation, challenges, and future perspectives of using artificial intelligence and emerging technologies in smart classrooms

Dimitriadou

Lanitis

2023

Smart Learn. Environ.

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The term "Smart Classroom" has evolved over time and nowadays reflects the technological advancements incorporated in educational spaces. The rapid advances in technology, and the need to create more efficient and creative classes that support both in-class and remote activities, have led to the integration of Artificial Intelligence and smart technologies in smart classes. In this paper we discuss the concept of Artificial Intelligence in Education and present a literature review related to smart classroom technology, with an emphasis on emerging technologies such as AI-related technologies. As part of this survey key technologies related to smart classes used for effective class management that enhance the convenience of classroom environments, the use of different types of smart teaching aids during the educational process and the use of automated performance assessment technologies are presented. Apart from discussing a variety of technological accomplishments in each of the aforementioned areas, the role of AI is discussed, allowing the readers to comprehend the importance of AI in key technologies related to smart classes. Furthermore, through a SWOT analysis, the Strengths, Weaknesses, Opportunities, and Threats of adopting AI in smart classes are presented, while the future perspectives and challenges in utilizing AI-based techniques in smart classes are discussed. This survey targets educators and AI professionals so that the former get informed about the potential, and limitations of AI in education, while the latter can get inspiration from the challenges and peculiarities of educational AI-based systems.

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“…The attention of students in the classroom is crucial in online learning environments where face-to-face interaction is limited. Assessing the attention of students is the main point to improve both instructing and learning [12]. Kainat et al [13] declared that attention is core for effective learning, but analyzing attention is a complex task.…”

Section: Online Educationmentioning

confidence: 99%

The Attention of Students in Online Education

Barba-Guamán

Valdiviezo-Díaz

2022

Int. J. Emerg. Technol. Learn.

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The level of attention of students who receive classes through videoconferencing platforms is troubling. Different throughout an entire lecture, the instructor can perceive the participants' behavior, while in an online class, it is difficult to determine if they are attentive to the instructions given. An innovative method that helps solve this problem is the use of computer vision algorithms, with methods such as face detection, facial landmarks, face recognition, and head pose detection based on deep learning networks. In this paper, a neural network was trained using facial landmarks to estimate head position and thus attention levels. The model was applied in five classes using different videoconferencing platforms in the Intelligent Systems subject of a private university in Ecuador. Some limitations, such as lighting and video quality, affected the level of accuracy. The number of registered participants was 12, of which between 30% and 80% attended. The maximum level of attention detected was 91.9%, while the minimum level was 86.6%. This case study proves that the head position detection function employed by many videoconferencing platforms is a useful parameter that aids the instructor in this type of context.

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Smart Classroom: A Deep Learning Approach towards Attention Assessment through Class Behavior Detection

Cited by 14 publications

References 22 publications

Artificial Intelligence’s Opportunities and Challenges in Engineering Curricular Design: A Combined Review and Focus Group Study

Artificial Intelligence’s Opportunities and Challenges in Engineering Curricular Design: A Combined Review and Focus Group Study

A critical evaluation, challenges, and future perspectives of using artificial intelligence and emerging technologies in smart classrooms

The Attention of Students in Online Education

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