1 Introduction

Following the outbreak of the COVID-19 pandemic at the beginning of 2020, precautions were required, such as maintaining social distancing, to gain control over the pandemic. Educational institutions were physically closed to ensure social distancing—an action that brought an immediate shift into online teaching/learning solutions. This shift created a critical challenge for the higher education sector [1, 2].

To improve the effectiveness of the new pedagogical frameworks, various surveys were conducted in higher education institutions [3, 4]. These studies helped map the barriers and challenges universities encountered during this period, such as student dishonesty and conducting online classes that required practical or simulated systematic demonstrations of a process or concept [5, 6]. Most studies indicated that students felt there was minimal effective teacher-student communication compared to face-to-face teaching–learning processes [7,8,9]. Thus, the process of learning and assessment when using online platforms requires more effort than traditional methods. Students indicated that they were not motivated to learn in a virtual environment and were more likely to learn in a physical classroom [1, 10,11,12]. Nevertheless, most students were satisfied with the academic/teaching measures taken by their universities during the lockdown period [13, 14].

Although online learning has some challenges, it is still the best alternative option to face-to-face classes available today [15]. One of the biggest challenges during the COVID-19 pandemic was remote electronic exams (E-exams). To minimize the impact of the pandemic lockdowns on students' academic progression, there was a pressing need for higher educational institutions to adopt a robust exam platform within a short period. However, it was found that the remote administration of E-exams had a negative impact on students and the ability to objectively assess their progress [16].

During and after the COVID-19 pandemic restrictions, online teaching and learning continued to develop gradually, creating further opportunities for teaching and learning [17]. Thus, the pandemic led to the widespread adoption of online education, which poses a threat to the face-to-face learning model, previously considered irreplaceable and the cornerstone of every learning institution [18, 19]. The term online learning refers to any learning that takes place over a distance rather than a face-to-face platform. Furthermore, there are synchronous, in real-time, or asynchronous formats where students can access course information whenever and wherever they want [20, 21]. Providing students have access to the necessary hardware and software resources, they are then able to learn regardless of challenges, such as the restrictions due to a pandemic as well as to adapt easily to using the online learning environment during the COVID-19 pandemic [22,23,24].Article title: Kindly check and confirm the edit made in the article title. we confirm these changes

There has also been a dramatic increase in the use of open online courses worldwide in teaching–learning processes since the outbreak of the COVID-19 pandemic. Therefore, research on teaching and learning in higher education has expanded. For example, one review mapped 282 studies about the institutional processes that higher education institutions implemented during the pandemic [3]. Research on learning after a pandemic, however, remains limited as most of the world had lockdowns until the end of the academic year, only ending in the summer of 2022. To address this gap, the current study aims to present an analysis of an asynchronous course that took place after 24 months of continuous restrictions (March 2020-Feb 2022) of the COVID-19 pandemic began to fade. Herein, this case study will analyze online learning in a non-pandemic environment, when the options of face-to-face as well as an asynchronous online format were available.

A major goal of the current study was to examine whether current course hours are based on past habits, or if they should be reconsidered and adjusted to serve 21st-century students and provide them with more flexibility. This study had three main objectives: (i) to examine the learning hours of a traditional course, (ii) to examine the learning hours of an asynchronous course, and (iii) to compare and discuss the framework of learning hours.

This study's research question is: What are the preferred course scheduling hours for students in higher education post-COVID-19, following the restrictions imposed during the pandemic? This study aimed to emphasize the advantages of an asynchronous academic course vs. a traditional course, and how using Video-on-Demand (VOD) allows students to optimize their time and consume course content at any time of day, compared to studying in the traditional way. This unique format creates an information system that accompanies the course and stores its content, benefitting students in several ways. Firstly, VOD provides a convenient and flexible viewing experience, as students have the freedom to choose what, when, and where to watch course content. In this way, they can tailor their consumption of the content according to their preferences and, VOD allows for seamless streaming and instant access to content, eliminating the need to wait for downloads or physical classes to occur. This convenience schedules. Secondly, VOD offers a vast library of content with diverse options, including presentations, recorded course and external lectures, and more. Furthermore enables students to enjoy uninterrupted viewing without delays as well as the ability return to specific points in the recordings. Altogether, this can dramatically improve their concentration during the learning period [25]. Overall, VOD provides a convenient, diverse, personalized, and accessible approach to academic content, revolutionizing the way students consume it.

To the best of our knowledge, no similar studies have been conducted after the COVID-19 restrictions. Specifically, no studies have provided an alternative to traditional learning hours or addressed students’ desire for greater flexibility in higher education. Hence, the lack of innovation and alternatives that could allow students more flexible study hours should be considered. Namely, alternatives should address the hours of study throughout the day and the consumption of academic materials in the form of video on demand, which is currently highly relevant.

2 Methods

In this paper, we will review and analyze a case study of an academic course taught at an academic institution (i.e., a university) in the Faculty of Engineering during the spring semester, between February and June 2022, using an asynchronous format. Regarding the history of this course, it is important to know that this course took place for several years in a face-to-face format. However, with the outbreak of the COVID-19 pandemic, the course was transitioned to a synchronous format delivered via the ZoomFootnote 1. platform for two consecutive years (March 2020–February 2022). In February 2022, the decision for some academic institutes to return to face-to-face classes caused some reconsiderations regarding the course methodology. Thus, a decision was made to change the course to an asynchronous format. As a result, all lectures in this course were recorded during the semester, including academic lectures, guest lectures and tutorials of exercises and test preparation. The sessions were then uploaded for the students every week. This transferred the responsibility for learning the course content to the students, who then had to decide which days and hours would be most convenient for them to study the course's pre-recorded content according to their personal considerations and schedule.

The current research proposes a case study for a post-COVID-19 academic course comprising empirical data from 188 students participating in an asynchronous course at an academic institution during the spring semester of 2022. The data gathered from the asynchronous course was compared to other traditional face-to-face and online courses that took place during the same semester and in the same faculty. Specifically, an analysis was conducted regarding the learning hours of the students in the asynchronous course and their online activities compared to the coinciding traditional academic courses. The data set included: (1) hours of the traditional face-to-face and online courses, and (2) students’ learning activities and the distribution of their learning hours in an asynchronous course. As the data was collected via the course website throughout the semester, without the involvement of the researchers, their cognitive biases, personal biases, and subjectivity did not impact the data collection process.

Two learning schedules will now be presented and analyzed. The first schedule was focused on traditional courses that offered live online classes as well as face-to-face classes. The second schedule was examined according to two perspectives of an asynchronous course: (i) the students’ schedule and (ii) the students' activities. The results will compare the two schedules and examine the impact of the course format on learning hours.

2.1 The schedule of traditional academic courses

The current study was conducted in an academic institution with six teaching days and one day off per week, which was the context for the current study. The data gathered from the 106 traditional courses, in which there were two options: face-to-face lessons or lessons delivered live online, were from the same faculty during the same semester. This data showed that the Bachelor's degree classes were primarily held during the morning and afternoon. In the asynchronous course, the students could consume the content at a time that was most convenient for them, allowed by the use of VOD.Footnote 2.Thus, for the first time after two years of academic lessons being broadcast only using online platforms due to the pandemic, this allowed for the examination of students choosing their actual and optimal study hours throughout the semester.

Figure 1 presents the 106 courses offered in the Faculty of Engineering at the same academic institution during the same semester, and the number of courses taught each hour of the day. These courses were conducted during the traditional working hours of the institution. Figure 2 presents the number of students in the 106 academic courses at each hour of the day.

Fig. 1
figure 1

The schedule of traditional courses throughout the day

Full size image
Fig. 2
figure 2

The number of students taking traditional courses throughout the day

Full size image

The analysis conducted on the data presented in Fig. 1 showed that the median and mode of the hours were equal and very close to the mean (12.4). The distribution was nearly symmetrical, with the right side of the distribution being slightly more dominant and drawn to the right (Skewness less than 0.5) and low Kurtosis, which tended to have a light tail (a platykurtic distribution). These results are presented in Table 1.

Table 1 Summary statistics of schedule in the two options
Full size table

In Figs. 1 and 2, it can be seen that the first lesson of the day began at 8:00 a.m. and the last began at 8:00 p.m. Therefore, under the assumption that a double-academic lesson in university is 90 min, this means that between 9:30 p.m. and 8:00 a.m. the day after (10.5 h) there were no lessons. Moreover, it can be seen that the later it was, the fewer the number of courses that took place. Notably, there were more evening classes for the Master's degree, as it is assumed that a large portion of the students would be working while studying. However, in this study, we referred only to the Bachelor’s degree courses; therefore, Master's degree courses were omitted.

In addition, 62.3% of the courses (66 out of 106) in the faculty, in the face-to-face or live online format, were offered during morning and afternoon hours (until 2:00 p.m.); 28.3% (30 out of 106) were offered during late afternoon; and only 9.4% (10 out of 106) of the courses started after 6:00 p.m. Thus, more than 90% of the courses took place before 6:00 p.m.

2.2 A post covid-19 asynchronous course

Herein an empirical study is presented based on data from an asynchronous course that took place in the spring semester of 2022. The course was a mandatory 4.5 ECTS (European Credit Transfer and Accumulation System) course, lasting 14 weeks. In this specific course, there were 188 students who were engineering students in their final year (out of 4 years of academic studies) of a Bachelor of Science (B.Sc.) degree in engineering. The course comprised 61.17% women (115 students) and 38.82% men (73 students).

This post-COVID-19 pandemic asynchronous course used digital management tools. Each academic activity was recorded in advance, and all student activities were monitored in a detailed log file. The current research was based on 36,067 records of activities carried out during the course by its 188 participants and the lecturer in charge. This log file allowed for an in-depth understanding of the comprehensive data gathered regarding the students' activities and the course content consumption during the various hours of the day.

Due to the fact that the course was conducted in an asynchronous format, the lecturer was not present in a classroom with the students and the lectures were not broadcasted live, instead, they were pre-recorded. Therefore, a digital platform, MOODLEFootnote 3. (an open-source learning platform), was chosen to manage all course items and content. This platform was the only interface (besides emails) between the students and the lecturer in regard to consuming the course content. The content began with presentations that addressed the theoretical materials, according to the course syllabus, and recorded videos of the lecturer explaining the content. In addition, guest lecturers were invited to give a lecture (also pre-recorded) describing examples and case studies from the industry. The guest lectures started with the course lecturer conducting a short interview with the guest lecturer and continued with a presentation on a designated topic relevant to the lesson according to the course syllabus.

In this course, throughout the 14 academic lessons (14 weeks), the course content was uploaded weekly, in accordance with the course syllabus and the progression of the semester. The content was shared online at the same time the course would have taken place if it had been taught in a face-to-face format, which was also noted as such in the students' schedule (despite not physically taking place). This meant that the students could not see the course content all at once or in advance, rather, only according to the weekly progression of the semester. Therefore, each week a different topic was covered and the content connected to this topic was uploaded. A total of 69 different content details (i.e., presentations, links, videos) were available to the students at the end of the semester, once all 14 sessions were completed.

2.3 Analysis of the students’ schedule in the asynchronous course

The research analyzed the distribution of the hours of the students in the asynchronous course during the 14-week semester. The findings indicated that there was student activity occurring at every hour of the day and night. Conversely, in the traditional courses, there were no courses between 9:30 p.m. and 8:00 a.m. the next day. Nevertheless, the students in the asynchronous course chose to consume content during these hours. Notably, it was found that the late-night hours were convenient times for some students to view the course content. However, only a small number of students consumed the content consumed between 1:00 a.m. and 6:00 a.m. (less than 1% for each hour, for a total of 1.94% for the entire 5 h). Interestingly, there was a jump of 425% from 5:00 a.m. to 6:00 a.m. and another 259% jump between 6:00 a.m. and 7:00 a.m. As such, the number of active students increased in the morning hours and continued to grow, in the form of a Gaussian bell, until 1:00 a.m. (Fig. 3).

Fig. 3
figure 3

Number of active students in the asynchronous course each hour of the day

Full size image

Figure 3 presents the number of active students in the asynchronous course during the semester. An analysis of this data showed that the mean was 14.4, which was not equal to the median and mode. Skewness and Kurtosis were close to 0 (compared to a normal distribution, which equals 0). The distribution was close to symmetrical, with the left side of the distribution being slightly more dominant and drawn to the left (Skewness slightly less than 0.5) and with low Kurtosis, which tended to have a light tail (a platykurtic distribution). These results are presented in Table 1.

It can be seen that, although there were students consuming course content during the late hours of the night, the numbers only began to be meaningful at 6:00 a.m. The first point where more than 50% of the 188 participating students were active was 9:00 a.m. (113 of 188 students). The peak participation points of the day were between 1:00 p.m. and 4:00 p.m. and again between 6:00 p.m. and 7:00 p.m., with more than 85% of the students actively consuming the course content.

Broadly, it can be seen that only 27.73% of the students consumed course content between 8:00 a.m. and 1:00 p.m., compared to 60.38% of the students in the traditional course. It can be seen that 41.12% of the students preferred to consume the course content between 1:00 p.m. and 7:00 p.m., compared to 34.91% in a traditional course. Furthermore, 18.64% of the students decided to consume course content between 7:00 p.m. and 10:00 p.m. in contrast to 4.72% of the students in the traditional course, which is at a rate of four times more students consuming the content in the evening When considering the 10.5 "dead hours" of learning at the academic institution, which represents 0% of active students in a traditional course, it can be seen that 12.51% (one-eighth) of the content in the asynchronous course was consumed during these "dead-hours."

The academic institution had very few lessons between 8:00 p.m. and 10:00 p.m., with only 2.83% of the courses operating and 1.70% of the students participating in traditional courses. In contrast, in the asynchronous course, 6.30% of the students were active and consumed course content at this time. Furthermore, from all of the 188 students, we saw that during the 14-week semester, only 21.0% did not consume content at all during this timeframe. Thus 79% did consume content from 8:00 p.m. to 10:00 p.m. during the semester at least once.

2.4 Students' activities

After examining the students' activity hours, another parameter was explored: the number of activities (content pieces) that students consumed during the different hours of the day. The overall course consisted of 69 pieces of content. Thus, the analysis examined the distribution of the students’ views regarding the different content. Here too, we see content viewing throughout all hours of the day, mostly occurring at 7:00 p.m. (comprising 8.04% of all activities), versus the traditional courses where most students consumed their content during the morning and afternoon hours. The number of items viewed at 9:00 a.m. was similar to those viewed at 10:00 p.m. This was also seen when comparing the number of activities the students engaged in at 8:00 a.m. compared to 11:00 p.m. Thus, despite the late hours in the evening, when the institution’s gates were closed, the "digital academy" was open for the students and allowed them to consume content, as shown in Fig. 4.

Fig. 4
figure 4

Number of activities in the asynchronous course each hour of the day

Full size image

As mentioned, the asynchronous course had 14 weeks with 14 lessons, where new content was uploaded each week according to the course syllabus. In addition, there was one single content session related to the course's final exam guidelines, resulting in a total of 15 different sessions/topics during the semester. Out of the total 69 pieces of content uploaded to the management platform course page, on average, 4.60 pieces of information were uploaded per week. When looking at the distribution of the students' activities, it can be seen that the average student viewed the course page 35.39 times during the semester for an average of 7.77 different days. That is to say, on an average day, each student viewed 4.55 items, which was almost identical to the number of items uploaded each week. Hence, it can be concluded that each student entered the platform and viewed a single complete lesson, on average, and no more than that.

3 Results

As previously mentioned, for traditional courses offered in the same faculty, the median and mode of hours were equal and very close to the mean. The distribution was close to symmetrical, with the right side of the distribution being slightly more dominant and drawn to the right. On the other hand, in the asynchronous course, the mean was 14.4 and not equal to the median and mode. The distribution was close to symmetrical, with the left side of the distribution being slightly more dominant and drawn to the left. These results are presented in Table 1.

The percentage of students studying each hour was calculated to compare the learning hours in the traditional face-to-face and online live formats with the on-demand format. This comparison, shown in Fig. 5, emphasized the difference between the traditional and asynchronous courses. The figure showed that, when the option was available, some students engaged in learning between midnight and 8:00 a.m. (in a traditional course, this would not be an option as the institution would be closed). Furthermore, in the traditional courses, at 8:00 a.m. there was an increase of students starting to learn, which continued until 1:00 p.m.—these 5 h were when most students consumed their courses in the traditional format. Whereas, in the asynchronous course, student learning increased after 7:00 a.m., in a more linear way than the traditional courses, and continued to grow until 10:00 a.m. From that point until 8:00 p.m. there was a similar percentage of active students online, with an almost stable number of students. Only at 8:00 p.m. did the number start to decrease, albeit gradually, until midnight, unlike the drastic drop that occurred in the traditional course after 1:00 p.m. From this analysis, it can be seen that students preferred, if possible, to study and consume course content throughout the entire 24 h of the day, rather than only during the morning and early noon hours, as was offered in the traditional courses.

Fig. 5
figure 5

Comparing percentages of active students between the asynchronous and traditional courses

Full size image

4 Discussion

This research addresses the question of students’ preferred hours to schedule courses in higher education, post-COVID-19 pandemic restrictions. After the COVID-19 period, no studies have been conducted that addressed the challenges of traditional learning hours and students' desire for increased flexibility. Consequently, it is difficult to compare the findings of this study with others in the current literature. The findings of this study could assist in decision-making when building academic schedules for future semesters. On the one hand, an examination was conducted of the schedule of traditional courses and the number of students participating in these courses throughout the various hours of the day. In this format, the students were unable to choose the hours they would like to study as the schedule was set in advance by the institution with no option for adjustments. On the other hand, in parallel, a specific asynchronous course was examined where its content was uploaded to the learning platform once a week, whereby the learning hours of the course had no restrictions. Therefore, each student was free to choose their learning hours according to their desire and availability. As a result, the students were given the freedom to decide when to consume the course content during the semester, including which hours and days of each week.

5 Conclusions, limitations and suggestions

From the comparison between the learning hours in the traditional courses and the asynchronous course, it can be seen that most of the courses offered in the traditional formats at the academic institution began in the morning and afternoon hours. Afterward, there was a drastic decrease in the number of courses offered. Conversely, in the course offered in an asynchronous method, there was an interest to consume content equally throughout all 24 h of the day, with an emphasis on the hours between 8:00 a.m. and midnight and, in particular, between 10:00 a.m. and 8:00 p.m. It can be seen that students in the asynchronous course preferred to study within a broader range of hours, versus the limited morning and noon hours available for studying in traditional courses.

Hence, a fascinating conclusion can be made regarding the hours of courses and when they should be offered to students based on the current findings. Today, most academic institutions offer online learning options, particularly following COVID-19. However, attention has not previously been given to when the students consume the content. This study's findings can be used as a framework for understating that students prefer greater flexibility regarding their learning in order to manage their course schedule while in higher education institutions. Moreover, the students were found to have different preferences than the strict hours offered by the traditional courses in various academic institutions. Academic institutions should ask if there is a need for course availability in a broader way in the late hours of the day.

In this research, we examined an asynchronous academic course over a full semester where all students in the course consumed the same content, as dictated by the course syllabus, during the same timeframe with flexibility in three regards: (i) the days/hours they could consume the content, (ii) the location they wanted to learn, (iii) the pace of consumption, where all lessons were taught by the same lecturer, with additional speakers from the industry in some sessions to include a practical point of view on the theoretical topics discussed. The main advantage of this method was that the learning objectives, determined by the course syllabus, framed the desired learning outcomes. In addition, the content together with the course assignments enabled both the lecturer and the students to monitor and gain feedback regarding the progress and level of understanding of the content and take ownership of their learning. In this way, the students’ growth was supported throughout the semester, in parallel to increased instructional effectiveness. Overall, the course's Student Learning Objective (SLOs) enhanced educational clarity, effectiveness, and student engagement, which, together with the flexibility provided by the asynchronous format, led to improved learning outcomes.

It is important to mention that there are some limitations to this research. First, a limited representation of student disciplines was included in the study due to the skewed distribution of the population. As a result, the generalizability of the findings to a broader range of academic disciplines may be restricted. Future research should endeavor to include a diverse representation of student disciplines, enabling a more comprehensive understanding of the relationship between learning hours and performance across different academic fields. Second, the study’s focus was on asynchronous learning. Consequently, the findings may not be directly applicable to educational contexts that utilize different modes of online learning, such as real-time lectures or interactive synchronous sessions. To provide a more holistic view of the impact of learning hours on student performance, future investigations should explore various online learning approaches and their potential implications. Third, the study only examined Bachelor's degree classes. Extending this research to encompass multiple education levels, such as undergraduate, graduate, or professional courses, could yield valuable insights into the universality of the observed relationship. Examining the role of learning hours across different educational tiers could also provide a more nuanced perspective on how learning time influences student outcomes.

It is important to mention that this study could also offer new concepts to be examined in different fields and contexts related to specific traditional hours, for example, flexible working hours, which have revolutionized industries by granting employees the freedom to customize their schedules [26]. This paradigm shift acknowledges individual preferences, promotes a healthier work-life balance and improves time productivity [27]. As the modern workforce and higher education continue to seek more adaptable arrangements, embracing flexible working hours could lead to harmonious and efficient work and study environments.