@@ -84,74 +84,29 @@ The main objectives of the research project could be further split into individu
...
@@ -84,74 +84,29 @@ The main objectives of the research project could be further split into individu
As mentioned in the given figure, this system will mainly consist of a mobile and web application. When the students and the lecturer enter the classroom, their behavior will be monitored by two separate video-cameras and a microphone. The collected data will transmit to the server through raspberry-pi and stores the gathered data in the MongoDB cloud database service. While the video camera tracks both facial expression and body movements, the microphone will record the lecture for further analysis. The student attendance will register by capturing facial identity using deep learning and computer vision mechanisms. Students will be monitored by capturing their facial and body movements by Computer Vision and Deep Learning algorithms. The lecturer audio and video details will be gathered and stored for analyzing as such lecture summarizing and monitoring lecturer performance using machine learning, deep learning, and computer vision technologies. While the students have access to the mobile application, the lecturers and the higher management have the authority to manage the web-application. Frontend components will communicate with the backend using Django REST API.
As mentioned in the given figure, this system will mainly consist of a mobile and web application. When the students and the lecturer enter the classroom, their behavior will be monitored by two separate video-cameras and a microphone. The collected data will transmit to the server through raspberry-pi and stores the gathered data in the MongoDB cloud database service. While the video camera tracks both facial expression and body movements, the microphone will record the lecture for further analysis. The student attendance will register by capturing facial identity using deep learning and computer vision mechanisms. Students will be monitored by capturing their facial and body movements by Computer Vision and Deep Learning algorithms. The lecturer audio and video details will be gathered and stored for analyzing as such lecture summarizing and monitoring lecturer performance using machine learning, deep learning, and computer vision technologies. While the students have access to the mobile application, the lecturers and the higher management have the authority to manage the web-application. Frontend components will communicate with the backend using Django REST API.
SLPES Mobile Application
## Module Description
Introduction
### Branch structure
This project will be created under the guidelines provided by the Research Project module offered in 4th Year by the Faculty of Computing. The title of the project is "Student and Lecturer Performance Enhancement System". This Research Project comes under the domain of Artifical Intelligence and Machine Learning along with the support of Computer Vision.
Main Objective
Main Objectives that will be targetted in this Research Project will be listed as follows.
Tracking student attendance using facial detection and facial recognition and notify and getting opinion from the students who are absent and students who are leaving the lecture in the during a specified time period (Attendance Register).
Monitoring student behavior within the classroom during lecture periods to identify potential problem areas in retaining student attention and adapting the lecturing styles to suit the student needs (Monitoring Student Behavior).
Summarize the converted text and present as a document and identify the important points of the lecture to display them highlighted (Lecture Summarizing).
Monitoring lecturers’ behavior by analyzing the teaching style in a lecture hall during the lecture hours (Monitor Lecturer Performance).
Main Research Questions
This Research Project will be conducted to find answers for the following 4 research questions.
“What is the optimum way of tracking student attendance in a much efficient way and how does the lecturer analyze reasons for student absenteeism?” (Q1).
“Does a correlation exist between lecturing style and student behavior in the classroom and how can Computer Vision and Artificial Intelligence be incorporated in determining this relationship?” (Q2).
“How to summarize the lecture content to enable students to pay more attention to the lecture and reduce time spend for taking notes?” (Q3).
“How to evaluate lecture performance by tracking their behavior during a lecture and analyzing the quality of the lecture content which is delivered by the lecturer?” (Q4).
Individual Research Questions
The main research questions came up for the project were based on each individual component that will be implemented through the course of the project. Hence, the individual research questions could be listed down as follows.
Attendance Register
What is the optimum way of tracking student attendance in a much efficient way and how does the lecturer analyze reasons for student absenteeism?
Monitoring Student Behavior
Does a correlation exist between lecturing style and student behavior in the classroom and how can Computer Vision and Artificial Intelligence be incorporated in determining this relationship?.
Lecture Summarizing
How to summarize the lecture content to enable students to pay more attention to the lecture and reduce time spend for taking notes?
Monitoring Lecturer Performance
How to evaluate lecture performance by tracking their behavior during a lecture and analyzing the quality of the lecture content which is delivered by the lecturer?”
Individual Objectives
The main objectives of the research project could be further split into individual objectives which elaborates purpose of the project in much detail.
Attendance Register
Tracking student attendance using facial detection and facial recognition.
Notify and getting opinion from the students who are absent and students who are leaving the lecture in the during a specified time period.
Setting up student data and respective course data in an adaptive manner.
Provide an overview for the lecturer regarding student attendance through a dashboard and predicting future attendance.
Monitoring Student Behavior
The following list contains the respective branch names for each individual
Monitoring student behavior within the classroom during lecture periods to identify potential problem areas in retaining student attention and adapting the lecturing styles to suit the student needs.
* db_and_monitoring - IT17097284
Identifying the concentration level, emotion expressions and general behavior of students within 2-hour lecture duration using facial recognition, emotion recognition and motion detection employing computer vision technologies.
Determining the dependency between student behavior and the lecturer behavior by analyzing overall behavior status of students and lecturer for a given time frame during the lecture.
* IT17100908 - IT17100908
Note that the **QA_RELEASE** branch is the merged branch of all the working components. The **master** contains only the common folder strcuture of the project.
Lecture Summarizing
### Folder structure
Understand lecturer’s voice and filter the lecturer’s voice by removing background noises and silent pauses
This folder structure adheres to the general project structure of a django web project. The following list describes the folders and files in use throughout the entire project.
Convert the noise-filtered audio into text
Summarize the converted text and present as a document.
Identify the important points of the lecture to display them highlighted.
Display important notices mentioned in the lecture.
* AttendanceApp - comprises the work of *Attendance Register* component.
* FirstApp - contains the implementations of *Monitoring Student Behavior* component.
* LectureSummarizingApp - constitutes the workings of the *Lecture Summarization* component.
* MonitorLecturerApp - comprises the implementations of *Monitoring Lecturer Performance* component.
* integrated_slpes - this contains the general settings and url configurations of the entire project.
* manage.py - this file is required to run the project (done by the comman *python manage.py runserver*)
* requirements.txt - contains the list of dependencies required to run the project.
Monitoring Lecturer Performance
Monitoring lecturers’ behavior by analyzing the teaching style in a lecture hall during the lecture hours.
Identifying the lectures emotional state when the lecture is conducting.
Generating an end-semester feedback of the lecturer giving a summary of their performance.
