A systematic review of intelligent tutoring systems based on Gross body movement detected using computer vision

Ashwin, T. S.; Prakash, Vijay; Rajendran, Ramkumar

doi:10.1016/j.caeai.2023.100125

Cited by 8 publications

(3 citation statements)

References 59 publications

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“…The data obtained from the in-classroom environment enables to capture the data related to student's behavioral activities (physical domain), including attendance (Bhattacharya et al, 2018), (Chango et al,2021) posture (Henderson et al, 2020), body movements (Ashwin et al, 2023), yawning (sleepy) (Omidyeganeh et al, 2016) interaction with peers (Liu et.al., 2019) interaction with teachers (Liu et.al., 2019), detection of malpractices (Prathish, S., & Bijlani, K, 2016) On the other hand, the data from the online settings enables the capture of student data beyond behavior characteristics and extends to the cognitive and affective domains. Figure 2 shows the tree diagram representing various data sources in formal and informal learning settings.…”

Section: A How Technology-enabled Data Sources Are Capturing the Late...mentioning

confidence: 99%

Capturing Latent Learning Indicators Through Technology: New Paradigms for Measuring Student Learning

Mallibhat,

Mudenagudi

2024

Research in Engineering Education Symposium

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ContextTechnology has been helpful in the field of education for the design, delivery, and assessment of courses. Though academicians quickly adopted the new technology for delivery, they still use the traditional written exams to assess student learning, even in professional courses, including medical, engineering, yoga, and music education systems. PurposeThe paper focuses on the investigation of how recent technological advancements help capture the hidden and accurate learning indicators of student learning, what devices are found helpful by researchers towards capturing the latent learning indicators, what the trends are, and what are the publicly available datasets that can catalyze the research in the field of learning analytics. MethodsThe study was carried out by adopting the PRISMA template of Systematic Literature Review (SLR), and the four phases, including identification, screening, selection, and inclusion methods, were carried out towards investigating the research questions. OutcomesThis paper helps academicians and researchers in the field of education and learning analytics to get an overview of the current trend and identify the research gaps towards integrating data from multiple sources and connecting the educational theories with the captured parameters. ConclusionA drift has been observed from unimodal data sources to multimodal sources, capturing the data from the perceived behavior of the student to the hidden cognitive and affective domain characteristics.

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Section: A How Technology-enabled Data Sources Are Capturing the Late...mentioning

confidence: 99%

Capturing Latent Learning Indicators Through Technology: New Paradigms for Measuring Student Learning

Mallibhat,

Mudenagudi

2024

Research in Engineering Education Symposium

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“…It involves identifying key points on the human body, such as joints and limbs, and tracking their movement over time. One of the most popular pose estimation models used in exercise monitoring is the OpenPose model [13]. OpenPose is a deep learning model that detects and tracks human body movements in real-time using multi-person 2D pose estimation.…”

Section: Related Workmentioning

confidence: 99%

“…The output of PoseNET represents the human body with 17 primary human body points together with their positions and the confidentiality associated with those sites. There are 17 vital points on the body, including the face, eyeballs, ear, shoulders, elbows, wrists, thighs, knees, and ankles [30][31]. Fig.…”

Section: A Proposed Approachmentioning

confidence: 99%

A Novel Deep Neural Network to Analyze and Monitoring the Physical Training Relation to Sports Activities

Omarov,

Nurmash,

Doskarayev

et al. 2023

IJACSA

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In the research paper, authors meticulously detail the development, testing, and application of an innovative deep learning model aimed at monitoring physical activities of students in real-time. Drawing upon the advanced capabilities of convolutional neural networks (CNNs), the proposed system exhibits an exceptional ability to track, analyze, and evaluate the physical exercises performed by students, thereby providing an unprecedented scope for customization in physical education strategies. This piece of scholarly work bridges the gap between physical education and cutting-edge technology, highlighting the burgeoning role of artificial intelligence in health and fitness sector. With an expansive study spanning various cohorts of physical culture students, the paper provides compelling empirical evidence that underlines the superiority of the deep learning system over conventional methods in aspects of accuracy, speed, and efficiency of monitoring. The authors demonstrate the transformative potential of their system, capable of facilitating personalized and optimized physical training strategies based on real-time feedback. Moreover, the potential implications of the study extend beyond the realm of education and into wider public health applications, with the possibility of fostering improved health outcomes on a larger scale. This research paper makes a significant contribution to the burgeoning field of AI in physical education, embodying a paradigm shift in the approach towards physical fitness and health monitoring. It underscores the potential of AI-driven technology to revolutionize traditional methods in physical education, paving the way for more personalized and effective teaching and training regimes, and ultimately contributing to enhanced health and fitness outcomes among students.

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Sample-Based Human Movement Detection for Interactive Videos Applied to Performing Arts

Rodrigues,

Diogo,

Jurgens

et al. 2023

Lecture Notes in Computer Science

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A systematic review of intelligent tutoring systems based on Gross body movement detected using computer vision

Cited by 8 publications

References 59 publications

Capturing Latent Learning Indicators Through Technology: New Paradigms for Measuring Student Learning

Capturing Latent Learning Indicators Through Technology: New Paradigms for Measuring Student Learning

A Novel Deep Neural Network to Analyze and Monitoring the Physical Training Relation to Sports Activities

Sample-Based Human Movement Detection for Interactive Videos Applied to Performing Arts

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