Facial Recognition Techniques and Their Applicability to Student Concentration Assessment: A Survey

Roy, Mukul Lata; Malathi, D.; Jayaseeli, J. D. Dorathi

doi:10.1007/978-981-16-5652-1_18

Cited by 3 publications

(1 citation statement)

References 44 publications

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“…Rafika [13] suggested using facial recognition to track student attendance. Roy [14] and Savchenko [15] propose the use of facial emotion recognition for identifying students' attention during online learning. This is a solution to the problem of decreasing student attention due to the lack of human supervision in online courses.…”

Section: Computer Vision Technology Applicationsmentioning

confidence: 99%

Exploring Artificial Intelligence in Smart Education: Real-Time Classroom Behavior Analysis with Embedded Devices

Chen

Wang

et al. 2023

Sustainability

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Modern education has undergone tremendous progress, and a large number of advanced devices and technologies have been introduced into the teaching process. We explore the application of artificial intelligence to education, using AI devices for classroom behavior analysis. Embedded systems are special-purpose computer systems tailored to an application. Embedded system hardware for wearable devices is often characterized by low computing power and small storage, and it cannot run complex models. We apply lightweight models to embedded devices to achieve real-time emotion recognition. When teachers teach in the classroom, embedded portable devices can collect images in real-time and identify and count students’ emotions. Teachers can adjust teaching methods and obtain better teaching results through feedback on students’ learning status. Our optimized lightweight model PIDM runs on low-computing embedded devices with fast response times and reliable accuracy, which can be effectively used in the classroom. Compared with traditional post-class analysis, our method is real-time and gives teachers timely feedback during teaching. The experiments in the control group showed that after using smart devices, the classroom teaching effect increased by 9.44%. Intelligent embedded devices can help teachers keep abreast of students’ learning status and promote the improvement of classroom teaching quality.

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Section: Computer Vision Technology Applicationsmentioning

confidence: 99%

Exploring Artificial Intelligence in Smart Education: Real-Time Classroom Behavior Analysis with Embedded Devices

Chen

Wang

et al. 2023

Sustainability

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Real-Time Classroom Behavior Analysis for Enhanced Engineering Education: An AI-Assisted Approach

Hu,

Huang,

et al. 2024

Int J Comput Intell Syst

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Modern teaching has made significant progress, with many advanced equipment and technologies being introduced into the teaching process. Experimental teaching of engineering design courses is important. Due to limited teaching resources, engineering students need effective guidance during limited laboratory time. We will introduce artificial intelligence solutions to engineering education. We will use artificial intelligence technology for classroom behavior analysis to improve engineering design practice courses' teaching effectiveness. In an instructional milieu, image acquisition tools such as cameras are capable of real-time data capture, facilitating the identification and enumeration of students' emotional states. Concurrently, analytical software gauges the students' interaction patterns and performs comprehensive cluster analysis. Such multifaceted information provides valuable insights into the students' educational engagement, allowing educators to tailor their approach, thereby fostering enhanced pedagogical outcomes. The emotion recognition model we have developed, namely ERAM, demonstrates a rapid response rate coupled with dependable accuracy, making it a robust tool for classroom implementation. In contrast to the conventional post-lesson evaluations, our proposed technique furnishes immediate feedback throughout the instructional process. This real-time approach heralds a significant shift in instructional methodology, promoting timely intervention and adaptive teaching strategies. The control group experiment showed that intelligent systems improved teaching effectiveness by 8.44%. Intelligent systems can help teachers understand students' learning status and improve laboratory teaching quality in engineering design courses.

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ClassID: Enabling Student Behavior Attribution from Ambient Classroom Sensing Systems

Patidar,

Ngoon,

Zimmerman

et al. 2024

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Ambient classroom sensing systems offer a scalable and non-intrusive way to find connections between instructor actions and student behaviors, creating data that can improve teaching and learning. While these systems effectively provide aggregate data, getting reliable individual student-level information is difficult due to occlusion or movements. Individual data can help in understanding equitable student participation, but it requires identifiable data or individual instrumentation. We propose ClassID, a data attribution method for within a class session and across multiple sessions of a course without these constraints. For within-session, our approach assigns unique identifiers to 98% of students with 95% accuracy. It significantly reduces multiple ID assignments compared to the baseline approach (3 vs. 167) based on our testing on data from 15 classroom sessions. For across-session attributions, our approach, combined with student attendance, shows higher precision than the state-of-the-art approach (85% vs. 44%) on three courses. Finally, we present a set of four use cases to demonstrate how individual behavior attribution can enable a rich set of learning analytics, which is not possible with aggregate data alone.

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Facial Recognition Techniques and Their Applicability to Student Concentration Assessment: A Survey

Cited by 3 publications

References 44 publications

Exploring Artificial Intelligence in Smart Education: Real-Time Classroom Behavior Analysis with Embedded Devices

Exploring Artificial Intelligence in Smart Education: Real-Time Classroom Behavior Analysis with Embedded Devices

Real-Time Classroom Behavior Analysis for Enhanced Engineering Education: An AI-Assisted Approach

ClassID: Enabling Student Behavior Attribution from Ambient Classroom Sensing Systems

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