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Published Paper Details:
"Focus Plus: Detect Learner's Distraction by Web Camera in Distance Teaching"
Authors
Dr.Manav Thakur,Pratik Berad,Prathmesh Dighe,Harsh Yewle,Sahil Ovhal
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"Focus Plus: Detect Learner's Distraction by Web Camera in Distance Teaching"
Abstract
In the era of digital education, maintaining student engagement has become a pressing challenge. Traditional classroom methods for observing student attention are not viable in online settings. This paper presents Focus Plus, an intelligent system designed to detect learner distraction in real-time using webcam feeds. Utilizing computer vision techniques such as face detection, eye tracking, and head pose estimation, the system identifies whether a student is focused or distracted. The data is logged and visualized to generate reports that help instructors assess learner engagement. The implementation leverages Python with OpenCV and MediaPipe, providing a lightweight, real-time solution suitable for integration with existing learning platforms. Virtual reality (VR) can potentially enhance student engagement and memory retention in the classroom. However, distraction among participants in a VR-based classroom is a significant concern. Several factors, including mind wandering, external noise, stress, etc., can cause students to become internally and/or externally distracted while learning. To detect distractions, single or multi-modal features can be used. A single modality is found to be insufficient to detect both internal and external distractions, mainly because of individual variability. Using eye-tracking data and EEG data from the same participants, we can classify different forms of internal and external distractions while they are participating in an educational VR experience. We created an educational VR experience environment and added equipment to collect multi-modal data from participants. In order to classify the data collected from participants using various machine-learning methods, we used four different machine-learning models: k-nearest neighbors (kNN), Random Forest (RF), one-dimensional convolutional neural network-long short-term memory (1D-CNN-LSTM), and two-dimensional convolutional neural network (2D-CNN).In this work, we investigated multi- to classify participants' internal and external distraction states using the multi-modal features. We performed cross-subject, cross-session, and gender-based grouping tests to evaluate our models. We found that the RF classifier achieves the highest accuracy over 83% in the cross-subject test, around 68% to 78% in the cross-session test, and around 90% in the gender-based grouping test compared to other models. SHAP analysis of the extracted features illustrated greater contributions from the occipital and prefrontal regions of the brain, as well as gaze angle, gaze origin, and head rotation features from the eye tracking data.
IJCRT's Publication Details
Unique Identification Number - IJCRTBV02006
Paper ID - 308374
Author type - Indian Author
Page Number(s) - 44-50
Pubished in - Volume 14 | Issue 5 | May 2026
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No Of Downloads - 7
Author Country - India, -, -, -, -, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
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Dr.Manav Thakur,Pratik Berad,Prathmesh Dighe,Harsh Yewle,Sahil Ovhal, ""Focus Plus: Detect Learner's Distraction by Web Camera in Distance Teaching"", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.14, Issue 5, pp.44-50, May 2026, Available at :http://www.ijcrt.org/papers/IJCRTBV02006.pdf
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