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Commit 156a284f authored by LiniEisha's avatar LiniEisha
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index.html
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...@@ -381,23 +381,9 @@ ...@@ -381,23 +381,9 @@
<ul class="list-group"> <ul class="list-group">
<li class="mx-4"> <li class="mx-4">
“What is the optimum way of tracking student attendance in a much efficient way “What are the main problems faced by university students and lecturers in the classroom
and how does the lecturer analyze reasons for student absenteeism?” (Q1). environment and how to address those issues with the aid of technologies in the domain
</li> of Artificial Intelligence, Machine Learning & Computer Vision?”
<li class="mx-4">
“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).
</li>
<li class="mx-4">
“How to summarize the lecture content to enable students to pay more attention
to the lecture and reduce time spent for taking notes?” (Q3).
</li>
<li class="mx-4">
“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).
</li> </li>
</ul> </ul>
</div> </div>
...@@ -504,7 +490,39 @@ ...@@ -504,7 +490,39 @@
</div> </div>
<div class="card-body"> <div class="card-body">
<p>Hello</p> <ul class="list-group">
<li class="mx-4">
Tracking student attendance using face detection and facial recognition.
</li>
<li class="mx-4">
Notify and get opinion from the students who are absent and students who are
leaving the lecture during a specified time period.
</li>
<li class="mx-4">
Provide an overview for the lecturer regarding student behavior during the lecture periods
through a web interface.
</li>
<li class="mx-4">
Identifying the student activities, emotion expressions and concentration levels
of students within 20-minute lecture duration using activity recognition, emotion
recognition and gaze estimation by employing computer vision technologies.
</li>
<li class="mx-4">
Convert the filtered audio through lecture audio into the text format using the speech-to-text approach
in order to identify the important parts of the lecture and to summarize the lecture.
</li>
<li class="mx-4">
Summarize the converted text and provide the summary in a pdf format for students.
</li>
<li class="mx-4">
Monitoring lecturers’ evaluation by analyzing the audio data recorded in a
lecture hall during the lecture hours.
</li>
<li class="mx-4">
Monitoring the lecturers’ interaction by tracking their posture movements such as sitting, standing
and walking.
</li>
</ul>
</div> </div>
</div> </div>
</div> </div>
...@@ -883,7 +901,7 @@ ...@@ -883,7 +901,7 @@
<h3 class="font-weight-bold">Marks</h3> <h3 class="font-weight-bold">Marks</h3>
<div class="justify-content-center"> <div class="justify-content-center">
?% 10%
</div> </div>
</div> </div>
...@@ -910,7 +928,7 @@ ...@@ -910,7 +928,7 @@
<h3 class="font-weight-bold">Description</h3> <h3 class="font-weight-bold">Description</h3>
<div class="justify-content-center"> <div class="justify-content-center">
<p class="card-text">A completed product demonstration was expected</p> <p class="card-text">Each student will be individual evaluated.</p>
</div> </div>
<h3 class="font-weight-bold">Date</h3> <h3 class="font-weight-bold">Date</h3>
...@@ -923,7 +941,7 @@ ...@@ -923,7 +941,7 @@
<h3 class="font-weight-bold">Marks</h3> <h3 class="font-weight-bold">Marks</h3>
<div class="justify-content-center"> <div class="justify-content-center">
?% 10%
</div> </div>
</div> </div>
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