Demir, E., Ediz, Ç, & Turan, A. H. (2023). Online course viewings and their effects on performances in Covid-19 distance
education period, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(2), 247-
It is believed that increasing class attendance enhances class engagement and success (Moores,
Birdi and Higson, 2019; Wongtrakul and Dangprapai, 2020). Hence, minimum attendance requirement
to classes is mandatory in most universities. While some studies support this fact, some studies have
concluded that attendance does not affect course success (Gomis-Porqueras and Rodrigues-Neto,
2018; Kaushik, Kumar and Kumar, 2021). Even if the students physically attend the lessons, they may
not provide behavior engagement, emotional engagement, and cognitive engagement, which are three
dimensions of class engagement (Hu and Li, 2017; Qiping Kong, 2003). Compulsory attendance to the
course may lead the students to attend the course without listening to the course, such as surng the
internet, and so this does not contribute to their success (Nieuwoudt, 2020). Kaushik, Kumar and Kumar
(2021), stating that compulsory attendance may hinder academic success, dened the reasons for this as
students wasting the interval when the course intervals are long, spending too much time on their way to
and from school, and thinking that asynchronous courses would be sufcient for some courses. Some of
these reasons do not apply to distance education. Because in online education, time is not spent going to
school; the students can watch the lectures anywhere and anytime they want. So the effects of physical
attendance on success can be different for online courses.
So far, the comparison of live and recorded lecture viewing is mostly about students’ preferences,
and studies measuring their effects on academic achievement are limited (Islam, Kim and Kwon, 2020;
Howard, Meehan and Parnell, 2018; Trenholm, Alcock and Robinson, 2012; Nieuwoudt, 2020; Kahui et
al., 2022; Le, 2022). In addition, generally studies in literature are limited mostly with schools or lectures.
We want to evaluate student live and recorded course viewings, which are very important parts of the
distance education system, especially in the Covid-19 period. We hope that this study will be benecial
for policy makers, education system developers and educators interested in online lesson viewings in
Online Course Viewings and Their Effects on Performances in Covid-19
Distance Education Period
Erdem Demir
, Çağla Ediz
, Aykut Hamit Turan
Management Information Systems, Sakarya University, Sakarya, Turkey,
e-mail: erdem.demir@skampus365.com, cediz@sakarya.edu.tr, ahturan@sakarya.edu.tr
Abstract: Despite numerous studies examining student preferences in terms of live and recorded lecture viewings,
the effects of lesson viewings on online platforms have been limitedly studied. In this study, the rates of attending live lectures
and viewing lecture recordings in the Covid-19 era were examined, and attendance and viewings effects on nal scores in
these courses were evaluated. For this purpose, data from online education systems of live and record viewings for Turkish
Literature, mathematics, and biostatistics classes in the spring semester of 2021-2022, belonging to 13 Turkish universities
and 2082 students, were utilized. We found that (1) Thirteen percent of the students did not view any live or recorded courses,
and approximately one-third did not enter the nal exam; (2) The students in state universities have signicantly higher record
viewing rates than those in private universities with medium effect size, (3) Females present signicantly higher live viewings and
record viewing rates than males with small effect sizes; (4) Biostatistics has moderate-high correlations between viewing rates
and nal scores. On the other hand, there are no or weak relationships between the viewing rates and nal scores for Turkish
literature and mathematics, in which study materials can be widely accessed from many sources different from biostatistics.
Keywords: distance education, nal scores, live lecture attendance, recorded lecture viewing.
Original scientic paper
Received: March, 12.2023.
Revised: May, 12.2023.
Accepted: May, 24.2023.
© 2023 by the authors. This article is an open access article distributed under the terms and conditions of the
Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Corresponding author: cediz@sakarya.edu.tr
Demir, E., Ediz, Ç, & Turan, A. H. (2023). Online course viewings and their effects on performances in Covid-19 distance
education period, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(2), 247-
universities’ distance education. This study is important for the evaluation of selected three courses in
13 universities. First we have seen that in the literature (For example: Shahabadi and Uplane, 2015),
students have different perspectives for synchronized and recorded lectures. Hence, we classied our
viewed classes in two classes as synchronized and live lectures. Existing studies indicated that based on
gender, there would be some differences in distance education as well (Gupta and Saks, 2013; Kahui et
al., 2022). Hence, we created a category for gender. On the other hand, we have not come up with any
literature regarding the differences in public and private university students’ interests in viewing the online
lectures. In order to provide support to the literature, we also investigated public and private university
difference in our study. Finally, whether students attend the nal or not was the third category in our study.
We also investigated the participants online class participation in all these categories specically for Covid
19 period. Hence, we propose the following hypothesis.
RQ1- What are the rates of viewings (live & record) and entering nal exams of the students in the
Covid-19 in Turkey?
RQ2- Are there any signicant differences between viewings (live & record) based on gender,
university type, and nal taking tendency?
RQ3- Are there signicant relations between viewings (live & record) and nal scores?
The remain of this study continues with literature review. In the third section of this study, download
and data preparation processes are explained. In the fourth section, analyzes and ndings are provided.
In this section, rst of all, descriptive statistics about lesson viewings, and the distribution of student
lecture viewings and entering the nal exam are given. Afterward, we investigated whether there were
signicant differences in lesson viewings according to gender, type of university, and entering the nal
exam. Later, it was investigated whether the lectures’ viewing rates had an effect on nal scores. In the
last section, the ndings were also elaborated.
Literature Review
With Covid-19, face-to-face teaching was suspended in schools to a large extent, and distance
education decisions were taken not to interrupt education during this period. Universities in Turkey also
followed this suit and carried out the 2020-2021 spring academic period with online courses to prevent
the risk of Covid-19 contamination in the classroom environment and to ensure the continuation of the
education. In distance education, teachers and students work on educational materials in different places
and sometimes at different times (Gunawardena and McIsaac, 2013). Distance education, which was
previously carried out through channels such as radio and television, continues with web-based training
widely with the development of information technology and the spread of the Internet. Web-based online
courses can be given as synchronously or asynchronously. While synchronous education offers the
opportunity to interact between the teacher and the student, asynchronous education offers the option
of using course records that can be watched at any time by adjusting the video speed and moving back
and forth. When these educations with different advantages are compared, students generally preferred
recorded video lectures to live lectures (Islam, Kim and Kwon, 2020; Howard, Meehan and Parnell, 2018;
Trenholm, Alcock and Robinson, 2012). However, students stated that they still attach importance to live
lectures for existence of sense of community and quick feedback (Trenholm, Alcock and Robinson, 2012).
Motivations and cognitive strategies affect students’ decision whether to attend the courses face-to-face
or online (Bassili, 2008; McKenna and Kopittke, 2018). Since most students consider the interaction in
face-to-face courses important, they continue to attend the lessons even though online accessible class
records are uploaded to the system (Yoon, Oates and Sneddon, 2014; Fei et al., 2013; Gysbers et al.,
2011; Alamer and Alharbi, 2021). For example, only 58 per cent of university students, who can take
distance or face-to-face education, preferred and participated in face-to-face education, and less than
15 per cent of those who did not take the any preference to downloaded courses, yet did not watch them
(McKenna and Kopittke, 2018).
At the beginning of the Covid-19 period in Turkey, students stated that both theoretical and
practical courses would be insufcient with distance education. They did not think of suspending study,
but thought that the school time would be extended (Kursuncu and Kurt, 2020). Although the students did
not encounter any technical problems in the distance exams, they stated that they were worried because
they would deal with power cuts and internet connection problems before the exam (Ilgaz and Afacan
Adanır, 2020). Different results were obtained to student satisfaction in the studies conducted during the
Covid period. In a survey evaluating the distance education of undergraduate dentistry students in Turkey
during pandemic, students complained that practical training could not be given online and the lectures
Demir, E., Ediz, Ç, & Turan, A. H. (2023). Online course viewings and their effects on performances in Covid-19 distance
education period, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(2), 247-
were inefcient (Cirakoglu and Ozbay, 2022). On the other hand, Tayem et al. (2022) reported that most
of the students were satised with distance education and would prefer the theoretical courses to be
given remotely and the practical courses to be given face-to-face. With a similar inference, educators who
give anatomy education think that the loss of quality experienced in the distance teaching in theoretical
courses will be less than that of distance practical courses (Ozen, Erdoğan and Malas, 2022). For some
courses, distance education can be as effective as traditional education, if it is supported by simultaneous
education (Alamer and Alharbi, 2021). In the distance education system, it was observed that especially
male students were more satised and found the lectures more effective and exible (Buluk and Equalti,
2020; Turan, Kucuk and Cilligol Karabey, 2022). In another survey conducted with undergraduate
students, students stated that they were provided time exibility and course content exibility in distance
education. Yet, student satisfaction was generally low because of the complexity of teaching materials
(Turan, Kucuk and Cilligol Karabey, 2022). On the other hand, in Nieuwoudt’s study, it is found that
viewing live lectures or watching videos of course recordings can have the same effect as face-to-face
education. It is emphasized that there may be different reasons for students not attending the live courses
in distance education, therefore they should be given the right to attend the course by watching the lecture
records (Nieuwoudt, 2020).
Data Preprocessing
ALMS is one of the two most used learning management systems in Turkey (Durak, Çankaya and
İzmirli, 2020). The system records data of the students and teachers in order to measure the efciency of
their online activities. To examine the course viewing rates of students and their effects on their success
in the 2020-2021 spring semester of Covid period, we selected the courses that are commonly given in
different departments of universities. We preferred these courses to be in different categories: numerical,
verbal, and departmental. These courses were mathematics, Turkish literature and biostatistics. We
downloaded the records of these courses given in 13 different universities and 21 different departments.
We ltered the dataset in such a way that the names of the courses included as mathematics, Turkish
literature, biostatistics, or the words with the same meaning as them, such as calculus. In addition, we
have removed the courses that evaluate the nal of the courses as homework from the dataset. All
characters in the downloaded dataset texts are converted to lowercase. Since some of the gender data of
the users was missing from the database, the missing places were added manually.
Each lecture recorded by the teachers in the dataset is added to the system as a separate record
for each student. These records include student times of viewing live lectures. Viewing recorded lectures
are assigned as zero at rst. As viewing activities of students’ change, these values are altered. We
summed students’ live attendance times and also summed up their replay (record viewing) times for each
lecture and saved them in a new database. So in the new database, there was a single record for each
student-lecture. In addition, we summed the recording times of the teacher in the live lectures for each
lecture during 14-weeks and we found the total live time for each lecture. In some lectures, we observed
that the live lectures were not given, instead, the videos uploaded or recorded by the teacher to the system
were watched. We removed these course records from the prepared dataset. Afterward, we divided the
total live viewing time and the total record viewing time of the courses by the total recording time of the
live lectures and multiplied by 100 (1, 2). So we normalized the viewings by replacing them with their
percentages. These records were joined on the dataset containing the nal scores of the students where
student numbers and course numbers were equal, and the data set preparation process was completed.
Live Attendance (%)= ∑Live Viewing Time / ∑Live Lecture Time x 100 (1)
Record Viewing Rate= ∑Record Viewing Time/∑Live Lecture Time x 100 (2)
Analyzes and Findings
Descriptive Analyses with Student Rates of Viewing Lessons and Entering
There are 2082 records in the prepared dataset. In this prepared dataset, the numbers of records
for mathematics, Turkish literature, and biostatistics are respectively; 251, 1277 and 604. While the
numbers of females and males are 1374 and 708, respectively, there are 1434 state, 648 private university
Demir, E., Ediz, Ç, & Turan, A. H. (2023). Online course viewings and their effects on performances in Covid-19 distance
education period, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(2), 247-
students. At the end of the semester, 1410 of the 2082 students entered the nal exam, 672 of them did
not. All variables are not normally distributed. The average percentage of viewing for live lectures is 28.1
per cent and for viewing recorded lectures’ range is 46.4. The highest average viewings, belonging to
biostatistics, female, state schools’ courses and students entered the nal are presented below (Table 1).
Table 1
Descriptive Statistics
We want to investigate students’ attention to the online lectures. To evaluate this, we examined
viewing and entering nal rates. We observed that,13,4 per cent of the students did not attend any live
classes in the chosen courses and did not view any video lectures (Table 2). The percentage of students
in private universities not viewing any lectures is the highest with 32.25 per cent. In the Turkish literature
course, 19.8 per cent of the students did not view any lecture. On the other hand, biostatistics has the
lowest percentages of not to watch course with 2.48 per cent. One third of the students approximately
(32.28 per cent), did not enter the nal. 251 of 708 (35.45 per cent) male students, 421 of 1374 (30.64
per cent) female students, 326 of the 1434 (24.73 per cent) state university students, and 346 of the 648
(53.4 per cent) private university students did not take the nal exam (Table 2).
Table 2
Rates of students who didn’t watched any online classes and did not enter the nals
Differences Analyses
We want to analyze differences for viewing rates with different features. These features are male
and female, type of universities as state and private universities, entering nal exam and not entering nal
exam. Each student attends only one course in the dataset, so each record is independent from the other
courses and the data values in each group are nonparametric. Thus, we applied the Mann-Whitney U
Test for all to see if there was a signicant difference between two groups in live attendance rates (record
viewing rates).
Demir, E., Ediz, Ç, & Turan, A. H. (2023). Online course viewings and their effects on performances in Covid-19 distance
education period, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(2), 247-
H1: There is a signicant difference between male and female students’ live attendances.
H2: There is a signicant difference between male and female students’ record viewings.
H3: There is a signicant difference between state and private university students’ live attendances.
H4: There is a signicant difference between state and private university students’ record viewings.
H5: There is a signicant difference between the students’ who taking nal and who the students’
not taking nal live attendances.
H6: There is a signicant difference between the students’ who taking nal and who the students’
not taking nal students’ record viewings.
We evaluated the signicance level at the 0.01 value and we calculated the effect sizes (r) of
through division of Z on N square (Corder and Foreman, 2009) for signicant tests. It was seen that the
group with the most different viewing rates, with a medium effect size (r=- 0.371), was between state
and private university students’ record viewings (Table 3). That is, the rate of record viewings of students
at state universities (15.70) is signicantly higher than those at private universities (0.13) (U=250635,
p=0.00). Also, the percentage of female students’ live attendance (24.23 per cent) is signicantly higher
than the percentage of male students’ live attendance (10.64 per cent) (U=403653, p=0.00). Likewise,
the rate of female students’ record viewing (11.945) is signicantly higher than the record viewing rates of
male students (3.195) (U=414842, p=0.00). However, the effect sizes of gender differences for both live
and record viewings are small (r=-0.141 and -0.121sequentially).