The Covid-19 pandemic procedures negatively affected all areas of life, and these effects increased with the presence of comprehensive and repeated closures, perhaps the education sector in all its stages is one of the most affected sectors. That is why decision-makers in most countries of the world have resorted to switching to distance education in order to reduce the negative effects of the Covid-19 procedures. With the continuity of distance education during the repeated closure procedures, it was found that there are shortcomings in this type of education, so educational institutions around the world seek to develop this type of education and make it a synonym for formal education. In order to raise the efficiency of distance education, a mechanism should be found to monitor students' facial expressions during online lectures and their interaction and response during the lecture. Therefore, from knowing the statistics of students’ facial expressions, it is possible to guess the extent of their understanding of the lecture, as these statistics can be used as feedback for teachers, in order to change a particular method of teaching or maintain another method. This research presents a system that is able to automatically distinguish students' facial expressions, and these expressions include eight expressions (happiness, sadness, anger, fear, disgust, focus, neutrality, and surprise), the data for this research was collected from pictures of students' facial expressions at the university from 70 students, the data included 6720 pictures of students' faces distributed among the eight expressions mentioned above, and that is equally, that is, for each category 840 pictures.
The Covid-19 pandemic procedures
negatively
affected
all areas of life, and these effects increased with the presence of comprehensive and repeated closures, perhaps the
education
sector in all its stages is one of the most
affected
sectors.
That is
why decision-makers in most countries of the world have resorted to switching to distance
education
in order to
reduce
the
negative
effects of the Covid-19 procedures. With the continuity of distance
education
during the repeated closure procedures, it
was found
that there are shortcomings in this type of
education
,
so
educational institutions around the world seek to develop this type of
education
and
make
it a synonym for formal
education
. In order to raise the efficiency of distance
education
, a mechanism should
be found
to monitor
students'
facial
expressions
during online lectures and their interaction and response during the lecture.
Therefore
, from knowing the statistics of students’
facial
expressions
, it is possible to guess the extent of their understanding of the lecture, as these statistics can be
used
as feedback for teachers, in order to
change
a particular method of teaching or maintain another method. This research presents a system
that is
able to
automatically
distinguish
students'
facial
expressions
, and these
expressions
include eight
expressions
(happiness, sadness, anger, fear, disgust, focus, neutrality, and surprise), the data for this research
was collected
from pictures of
students'
facial
expressions
at the university from 70 students, the data included 6720 pictures of
students'
faces distributed among the eight
expressions
mentioned above, and
that is
equally
,
that is
, for each category 840 pictures.