Real-time imaging-based assessment model for improving teaching performance and student experience in e-learning

Farhan, Muhammad; Aslam, Muhammad; Jabbar, Sohail; Khalid, Shehzad; Kim, Mucheol

doi:10.1007/s11554-016-0662-3

Cited by 19 publications

(5 citation statements)

References 50 publications

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“… Low Latency: Edge computing shortens the time it takes to travel to centralized cloud servers and back by processing data locally at the edge. This lowers latency and speeds up reaction times for real-time applications (Farhan et al, 2017).  Bandwidth Optimization: Edge computing maximizes bandwidth utilization and lessens network congestion by minimizing the data sent over the network.…”

Section: Advantages Of Edge Computing In Iotmentioning

confidence: 99%

Real-time Multimedia Analytics for IoT Applications: Leveraging Machine Learning for Insights

Shajahan,

Roberts,

Sandu

et al. 2024

Eng. int. (Dhaka)

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The combination of real-time multimedia analytics and Internet of Things (IoT) applications, along with machine learning techniques, has shown great potential in improving the capabilities of IoT systems. This study investigates the potential of machine learning to gain insights into IoT applications. By thoroughly examining existing literature and analyzing current trends, this study explores essential goals such as improving IoT systems' data processing, decision-making, and security. This study extensively examines the literature on real-time multimedia analytics, machine learning algorithms, and IoT applications using a systematic approach. Doing so aims to provide a comprehensive overview of the field's current state and highlight the main challenges and opportunities. The significant discoveries highlight the impressive capabilities of machine learning algorithms, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), in efficiently handling intricate multimedia data. These algorithms empower organizations to gain real-time insights and make informed decisions. Addressing challenges such as computational constraints, data privacy, and multimodal data integration is crucial for policy implications. This can be achieved through investments in edge computing infrastructure, developing low-power machine learning algorithms, and implementing robust privacy and security measures.

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Section: Advantages Of Edge Computing In Iotmentioning

confidence: 99%

Real-time Multimedia Analytics for IoT Applications: Leveraging Machine Learning for Insights

Shajahan,

Roberts,

Sandu

et al. 2024

Eng. int. (Dhaka)

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“…Then, assessing research contributions in the aforementioned authors, given the multiple approaches and disciplines, it is not possible to identify an integrated theory of Behaviorism relationship with ICTs; therefore, this paper will present from the bottom of behaviorism in its integrated relationship with education technologies for virtual training processes supported by ICT psychological approach. (Farhan, Aslam, Jabbar, Khalid, & Kim, 2017), aimed at achieving the Behaviorism of the subject's behavior. Conception based on the following principle "Man is the product of the reinforcing environmental contingencies" (Perez, A., 1995, p. 37).…”

Section: Theoretical Foundationsmentioning

confidence: 99%

Validation of Learning Theories in Their Relationship with Information and Communications Technology

Capacho¹

2018

Turkish Online Journal of Distance Education

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The paper presents the conceptual basis of the theories: Behaviorism, Gestalt, Cognitive Psychology, and the Historic-Cultural Psychology. The essential categories of these theories were validated in a population of 2704 courses at the Massachusetts Institute of Technology, MIT (USA). The research seeks the relationship of theories with course design and student learning process supported by ICTs. The results for a sample of 629 open courseware analyzed gives the conclusion that the concepts of the theory of Piaget are the most influential in the process of eLearing of students in a 49.92%.

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“…The sixth paper, entitled ''Real-time imaging-based assessment model for improving teaching performance and student experience in e-learning,'' by Farhan et al [6] explored student-focused software applications that track and recognize students and teachers in the e-Learning environment by presenting a real-time imaging-based assessment model. To track a student's visual attention and a teacher's visual engagement, they proposed algorithms for real-time scoring visual attention and engagement.…”

Section: Real-time Perceptual-inspired Imaging Systemsmentioning

confidence: 99%

GUEST EDITORIAL special issue on real-time perceptual-inspired imaging systems with computational science and aesthetics

Seo

Chen

Rho

2017

J Real-Time Image Proc

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Real-time imaging-based assessment model for improving teaching performance and student experience in e-learning

Cited by 19 publications

References 50 publications

Real-time Multimedia Analytics for IoT Applications: Leveraging Machine Learning for Insights

Real-time Multimedia Analytics for IoT Applications: Leveraging Machine Learning for Insights

Validation of Learning Theories in Their Relationship with Information and Communications Technology

GUEST EDITORIAL special issue on real-time perceptual-inspired imaging systems with computational science and aesthetics

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