SALIM ABID: Welcome, the team from E-Owl,
a virtual education platform that
helps professors create virtual meeting examiners.
With E-Owl, a student can also check their grades
and assignments online.
E-Owl was created using Firebase Google Cloud
platform and TensorFlow by Ahmed, Kerolos, Khalid,
and Mahmoud from Future Academy in Egypt.
SPEAKER: People expected to get bored by e-learning.
Our mission is to show them it doesn't have to be like that.
We would love to introduce E-Owl.
In 2020, around 1.7 billion students
were impacted by the coronavirus outbreak.
Schools are closed and the students
moved to online learning.
But problems arose, and one of them
is a lack of interaction and engagement
between the instructor and the student.
An unproductive learning environment
arises when the instructor is unable to observe each student.
In our solution, we provide a real-time focus and emotion
detection to all students in the virtual meeting.
The instructor can create a meeting from the dashboard.
When students join, the system will
start detecting the focus and the emotion of each student.
And based on these data, we will start
recommending hints or decisions to the instructor,
or even warn him about a problem in the class.
All these hints aim to increase engagement during the meeting.
After the end of each meeting, the instructor
can see full analyzed reports about all his meetings.
For a specific classmate, the report
includes the average of emotion and focus and some hints.
Providing a new way of helping instructors in the meeting
is what we focus on.
It's well-known that assessing a student's progress
with a low percentage of cheating
is critical to a successful education.
Find a new way of managing your progress,
whereas an instructor can create an [INAUDIBLE] quiz.
And the students can join, but with their camera
open for continuous detection during all quiz time,
while adding other restrictions, such as detecting
tab changes and other.
If the student violates the rules, the quiz will be ended
and we will inform the instructor of violation.
ERICA: Hi.
Hi, Sami.
Hi, team E-Owls.
SAMI KIZILBASH: Hello.
ERICA: Hi.
Good to see you.
Cool.
Awesome.
Well, I'll let Sami introduce himself again.
And I know he has a couple of questions for you,
so let's get straight to it.
SAMI KIZILBASH: Thanks, Erica Hi, everyone.
It's Sami here again.
I lead Google's global accelerator and experts
programs.
I also look after our developer programs in Asia-Pacific.
And I'm based in Singapore.
I was very excited about your project.
And I have to say, I love the concept
of flagging emotional states during classroom,
class time in real-time, and then
being able to give these individual reports
to the teachers.
I do have a couple of questions.
The first one is related to that facial expression
or facial emotion recognition.
What were some of the biggest challenges
that you faced using that technology
in the context of a classroom?
And what have you done to overcome
or are you working on to overcome those challenges?
AHMED MOSTAFA IBRAHIEM: Thank you for your question.
It's known that engagement and the feeling of a student
play a significant role in their academic process.
In face-to-face education, the instructor can see all
the students' reaction.
And if there is unfocused student or negative reaction,
the instructor can easily determine whether or not
to slow down, or speed up, or in some other way
modify his presentation.
But the situation is much different
and harder for an instructor in online education
to get the reaction and the emotion.
Thus, it affects learning process and the all the
[INAUDIBLE] that the instructor need,
and preparing and teaching become ineffective.
One of our challenges was finding an [INAUDIBLE] data set
was really challenging for us.
When we started looking at the image which
was published online, it was even harder
for a human to tell whether the image is sad or natural.
So we decided to move with a one published online,
but it's not big enough.
So we decided to do some machine learning techniques
such as data augmentation.
Also, one of the biggest challenges
is that human emotions are very individual and latent
in nature.
Not all humans-- not all emotions
can be translated and decoded exactly.
This seems to be, also, challenging for us.
And we plan to do more research with experts
to identify more on how we can overcome
this challenge in the future.
Thank you.
SAMI KIZILBASH: Thank you.
Thank you.
That makes a lot of sense.
I did notice that you had started
testing with students to see, to get
their feedback on the platform.
What's your plan to get more feedback from teachers?
KHALED ABDEL-FATTAH AHMED: Yes, we successfully
tested our project with teachers.
This test covered create meetings,
reports, and quiz creation, and all other features,
then started receiving the feedback from the instructor.
One of them was there is a delay in showing dashboard data.
But in general, they like the idea
that there is an assistant for them virtually
helping in managing the class and determining
which students are involved more and which ones are doing great.
MAHMOUD SAID RAMADAN GAD: For our next steps,
there is still much room for technical improvement
in the project, especially in meeting function, where
we are deciding to improve the accuracy,
and adding more hands, and make it
more easier with the students with disabilities
to interact with meetings and quizzes.
Besides all that, we are planning
to contact a secondary school to try
our project with its students.
SAMI KIZILBASH: Thank you.
Very exciting.
Back to you, Erica.
ERICA: That's great.
Yeah, we have an audience question.
And while we pull that up, I just want
to remind everyone who's watching live right now
with us, you can ask this team, E-Owl, a question,
and all the other teams questions as well.
So go to slido.com and enter in that code, GDSE21.
So let's pull up the question.
So the question is, what is the benefit to the student who
is enabling their camera?
Many students might feel uncomfortable with doing so
for a variety of reasons.
So we wanted to learn a little bit more
about what you thought about that.
AHMED MOSTAFA IBRAHIEM: At our last test,
we found that some student is feeling
uncomfortable with opening the camera all the meeting time.
So we are deciding to talk more with more experts
to get to know how to solve this problem in the future.
This program will benefit them and provide them
a uniquely e-learning experience that
was not found before for them.
Instructors, they can get all data and all emotions
that they need to create a successful e-learning
environment.
ERICA: Great.
Thank you.
It's great that you're getting user feedback.
That's super important.
So thanks for that.
And, Sami, did you have any final thoughts or comments?
SAMI KIZILBASH: Absolutely.
Actually, it's a relief that you're
thinking about the cultural biases,
and the individual aspects of emotion,
and how we express that facially.
And so I'm really happy that you already have that in mind.
That would have been one of my biggest concerns,
I suppose, regarding this, is making sure
that the teachers are making a fair assessment
of the students.
So I'm glad that's top of mind for you.
Part of me is very thankful that this technology didn't
exist when I was in class, so that the teachers wouldn't
have a full analysis of my face throughout a whole day.
But to be honest, I'm pretty excited about something
like this happening given the state of the world
right now, given that online is enabling so much more
education.
So I just wish you all lots of luck,
and congrats on all the progress,
and keep building out those great features.
Thank you so much.
SALIM ABID: Thank you.
ERICA: Thank you.
It was great meeting with you.
It was fun learning more about your project.
Thanks, everyone.
Bye.
And thank you, Sami.
SAMI KIZILBASH: Bye.
Thank you.
ERICA: Bye.