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In the educational sector, automated learning analytics is becoming an important subject that requires efficient systems to track the learning process and provide feedback to the instructor. The COVID-19 pandemic has recently impacted educational systems around the world, contributing to the near-to
2025-06-28 16:29:42 - Adil Khan
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Project Area of Specialization Artificial IntelligenceProject SummaryIn the educational sector, automated learning analytics is becoming an important subject that requires efficient systems to track the learning process and provide feedback to the instructor. The COVID-19 pandemic has recently impacted educational systems around the world, contributing to the near-total closing of schools, colleges and universities. For continuity of learning, many educational institutions are switching to various online platforms. Despite the many advantages that online study has on transforming the learning process, there are some challenges imposed by the method. One challenge is the availability of Internet services. The Internet is not accessible in rural areas, as a result of which students will not be able to turn on their webcam, which will make it difficult for the lecturer to assess the student's attention and presence. In addition, there is a lack of impersonal contact, as students are not able to completely comprehend the ongoing academic sessions. This system will make it easier for us to minimize these issues. Recent advances in methods of deep learning and computer vision allow automated monitoring of learner’s actions and affective states at various levels, from university to preschool. The purpose of this study is to develop an automatic system that enabled the instructor to capture and summarize student behaviours and attention in the online class session. In the entire session, the machine feeds the captured picture of the student with distinct intervals to the deep learning model and determine the attention level of the student in the entire session.
Project ObjectivesGoals and Objective
- An online class room management portal
- An attentiveness detection model to enhance interactive nature of the online session
- The real time monitoring of the student’s expression and activities
- Enhance the learning capabilities of the online learning systems.
Implementation Method
- Frontend - React Framework
- Backend - Django Framework
- Face Recognition
- Drowsiness Detection
- Emotion Detection
- An attentiveness detection model to enhance interactive nature of the online learning
- The real time monitoring of the student’s expression and activities can be effitiently monitored.
- Enhance the learning capabilities of the online learning systems.
System Architecture and Features
The presence and attentiveness of the learner are the most critical factors in online learning. In a classroom, this process is not complicated, but it's a major challenge for online classrooms. The objective of this system is to use these technologies like deep learning to detect the face of the student to check whether the student is in front of the webcam or not. It also detects emotions and drowsiness.
Online courses have a negative influence on our education system because there is no supervision of students. The other major flaw in online learning is that there is a lack of student teacher interaction. The core objective of this system is to solve these issues. This system will imitate the conventional classes in online classes.
- Features
- Face Detection
- Drowsiness Detection
- Emotions Detection
- Detect Internet Speed
- System Architecture
The system captures the image of the student locally with random interval of time and pass it to our deep learning model which can evaluate the attentiveness of the student and report the result to the lecturer.
Figure 1. Block diagram of the proposed system
Figure 2. Training process of the proposed model
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 80000 | |||
| High Resolution Image Device | Equipment | 1 | 40000 | 40000 |
| Cloud Service | Equipment | 1 | 20000 | 20000 |
| API Service | Equipment | 1 | 10000 | 10000 |
| Zong MBB Device | Miscellaneous | 1 | 3500 | 3500 |
| Internet Service | Miscellaneous | 1 | 5000 | 5000 |
| Printing & Stationary | Miscellaneous | 1 | 1500 | 1500 |