Sage Journals: Discover world-class research

Abstract

This study aimed to investigate university students’ evaluations of pre-recorded video and live online lectures, focusing on the factors that influence these assessments, particularly students’ studying behaviors. We conducted a web-based survey to gather data from undergraduate students. The results revealed that students rated live online lectures higher than pre-recorded video lectures in learning efficiency, student–teacher connectivity, and student–student connectivity. However, a contrasting trend was observed for academic performance. The multivariate analysis indicated that students who reported higher levels of restraint from distractions while listening to online lectures evaluated both pre-recorded video and live online lectures significantly more favorably. Moreover, students who regularly turned on their cameras during live lectures tended to rate online lectures more positively. These findings underscore the significance of students’ studying behavior as a critical factor influencing the effectiveness and outcomes of online lectures. It is important to foster and encourage students’ active engagement with online lectures and promote the development of self-learning skills.

Keywords

evaluation live online lectures pre-recorded video lectures study behaviors university students

Introduction

The COVID-19 pandemic caused widespread disruptions in education systems worldwide because it required the adoption of alternative methods of delivering education to prevent the spread of the virus. Consequently, online teaching has become increasingly prevalent, with asynchronous pre-recorded video lectures and synchronous live online teaching—delivered via platforms such as Zoom and Google Meet—being the two most common approaches.

Each of these teaching methods has advantages and disadvantages. Synchronous live online teaching enables real-time interaction between teachers and students, fostering a sense of social presence and facilitating immediate feedback. However, managing large groups of students online and technical issues that arise can present challenges and disrupt the learning process. In contrast, asynchronous pre-recorded video lectures offer greater flexibility and convenience by allowing students to access lectures at their own pace and preferred time. However, the absence of real-time interaction between teachers and students may result in a less-engaging learning experience (Le, 2022; Park, 2021; Peterson et al., 2018).

The objective of this paper is to investigate university students’ preferences for different online teaching methods by analyzing their evaluations across four categories: learning efficiency, academic performance, student–teacher connectivity, and student–student connectivity. By examining students’ preferences and experiences in these four categories, this paper aims to provide insights into the effectiveness of asynchronous pre-recorded video and synchronous live-online teaching methods and identify areas that can be improved to create a more effective and engaging online learning experience.

Furthermore, this paper explores the factors that influence students’ evaluations of online lectures. While previous research (Butnaru et al., 2021; Engzell et al., 2021; Le, 2022; Romanov & Nevgi, 2007) has primarily focused on demographic variables such as gender, major, and years of schooling, this study also examines students’ studying behaviors. These behaviors include the location where students listen to lectures, their approach to pre-recorded lectures (e.g., binge-watching or following a set schedule), whether they activate video cameras during live-lectures, and their level of engagement with distractions such as social media and texting during lectures. While a study conducted by Cho and Kim (2022) examined students’ studying behaviors in Korea, its scope was limited to architectural university students. To the authors’ knowledge, there is no prior research in Korea examining the outcomes of online learning that considers the studying behaviors of students across all academic disciplines. Hence, this paper aims to provide a more comprehensive understanding of students’ engagement with online learning environments by considering demographic factors as well as studying behaviors across all academic disciplines.

As online teaching continues to grow in prevalence, it is crucial to understand the benefits and drawbacks of different teaching methods. Thus, this paper examines students’ assessments of pre-recorded and live online lectures, as well as the factors that influence these evaluations by focusing on students’ studying behaviors. The study aims to offer valuable insights into how online teaching can be improved to enhance student learning outcomes.

Literature Review

Existing empirical research comparing live online and pre-recorded video lectures has focused primarily on students’ preferences, but the findings have been inconclusive. Some studies (e.g., Chundur & Prakash, 2009; Peterson et al., 2018; Rockinson-Szapkiw & Wendt, 2015) have found a preference for live online lectures among students. However, Buxton (2014) and Griffiths and Graham (2010) found the opposite preference. Additionally, studies such as those of Hrastinski (2008) and Mabrito (2006) have reported students’ indifference between live online lectures and pre-recorded video lectures. Students’ preferences in these studies often stem from the specific advantages associated with each method, such as the interactive nature of live online education and the convenience offered by pre-recorded online education (Le, 2022). In addition, pre-recorded lectures have been reported to be particularly beneficial for introverted, hesitant, or language-challenged students (Belcher, 1999).

Brockfeld et al. (2018) examined students’ evaluations of live online and pre-recorded video lectures across multiple categories, including the learning atmosphere, concentration ability, usefulness for examinations, structure, content, acoustic intelligibility, clarity, degree of interest, lecture script, optical discernibility, and tempo. The results indicated that students’ ratings varied depending on specific categories. Notably, pre-recorded video lectures received significantly better reviews than live online lectures in categories such as learning atmosphere, concentration capacity, presence of other students, and acoustic intelligibility.

Examining students’ learning achievement during the COVID-19 pandemic, Islam et al. (2020) conducted a study evaluating live online and pre-recorded video lectures among students majoring in business management across three categories: learning the content, learning the objectives, and perceiving the value of tuition. The findings consistently revealed that students rated pre-recorded videos higher than live online lectures in all three categories. However, the authors suggested that the effectiveness of pre-recorded video lectures depends on students’ motivation to independently engage with the class materials, highlighting the significant role of students’ attitudes and learning behaviors.

Methodology

Data Collection

Between August 1 and 30, 2021, we conducted a web-based survey to gather data from 98 undergraduate students at H University in the Republic of Korea. The sample consisted of students who had experience attending offline face-to-face classes before the COVID-19 pandemic and live online and pre-recorded video lectures during and after the pandemic. The selection criteria ensured that students had experience with all three types of classes, allowing for a comprehensive comparison of their evaluations. The survey was administered anonymously through the university’s online student bulletin board. Participants were requested to complete a 42-question survey specifically designed for this study, focusing on their evaluations of live online and pre-recorded lectures. To ensure the appropriateness and validity of the questionnaire, a pilot test and a focus group interview were conducted before the main survey. The pilot test involved seven students who provided feedback on the questionnaire. This process helped assess the suitability of the questions and estimate the average time required to complete the survey, which was found to be approximately 15–20 min. As a token of appreciation for their participation, respondents were offered a Starbucks gift voucher valued at KRW5,000 (approximately US$5).

Measures

Evaluation of Live Online Lectures and Pre-Recorded Lectures

In this study, the evaluation of live online lectures and pre-recorded video lectures was measured through four categories: learning efficiency compared with offline face-to-face lectures, academic performance, student–teacher connectivity, and student–student connectivity. The questionnaire items were developed based on previous literatures. For learning efficiency and academic performance, we drew from Loveland and Loveland’s (2003) study and made modifications to suit our research context. To assess student–teacher connectivity, we utilized Cho et al.’s (2023) study, which was based on the Arbaugh and Benbunan-Fich (2006) Community of inquiry survey. For student–student connectivity, we incorporated items from Cho et al.’s (2023) study, which utilized the Hartmann et al. (2016) Spatial Presence Experience Scale. The four categories of evaluation are explained in detail in the following paragraphs.

Learning efficiency refers to students’ effectiveness in understanding lectures and maintaining a level of study comparable to the level they had with traditional face-to-face lectures. A five-point Likert scale consisting of eight questions was employed for eight questions of measuring learning efficiency. These questions assessed whether online classes have the following characteristics: (a) more difficult to understand; (b) more convenient; (c) more difficult to concentrate in; (d) result in less interaction with the instructor; (e) result in less interaction with other students; (f) lead to more absents of classes; (g) lead to less time spent studying; and (h) have an overall educational impact similar to offline face-to-face lectures. Questions 2 and 8 were not reverse coded, while the remaining questions were reverse coded, meaning that a rating of 1 refers to online classes being poorly rated, while a rating of 5 refers to them being better than offline face-to-face lectures. The average of the eight questions was computed as a continuous variable. The internal consistency of the eight questions measuring learning efficiency was assessed using Cronbach’s alpha, resulting in a score of .82 for live online lectures and .86 for pre-recorded video lectures. These scores exceed the threshold of 0.70, indicating a high level of internal consistency and reliability (Cortina, 1993).

Academic performance in this study reflects how well students are able to learn and excel academically through online lectures, including assignments and tests. A five-point Likert scale was used, consisting of four questions. These questions assessed the following factors of online lectures: (a) whether students felt they had a good understanding of the class; (b) whether students learned enough; (c) whether students experienced difficulties with assignments; and (d) whether students experienced high learning fatigue due to the use of digital devices. Questions 3 and 4 were reverse coded, meaning that a rating of 1 referred to poor academic performance and a rating of 5 referred to high academic performance. The Cronbach’s alpha scores for academic performance were .66 for live online lectures and .74 for pre-recorded video lectures, indicating acceptable internal consistency. Despite the slightly lower Cronbach’s alpha score for live online lectures, these items were included to enable parallel comparison with the items measuring the pre-recorded video lectures.

Student–teacher connectivity measures the degree of interaction and engagement between students and teachers during online lectures. A five-point Likert scale comprising five questions was employed. The questions assessed the following factors related to online lectures: (a) whether students were able to understand teachers’ teaching plans and procedures clearly; (b) whether teachers provided adequate feedback on class context and assignments; (c) whether teachers’ feedback on questions was timely; (d) whether teachers’ grading was fair and appropriate; and (e) whether teachers had a good grasp of students’ progress and learning. Cronbach’s alpha test scores were .88 for live online lectures and .83 for pre-recorded video lectures, indicating good internal consistency.

Student–student connectivity refers to the level of interaction and engagement among students in online lectures. A five-point Likert scale was employed for total of three questions for this category. The questions assessed the following factors related to online lectures: (a) whether students felt they were learning with their peers; (b) whether students found it difficult to interact with other students; and (c) whether students were able to easily share their ideas and opinions on class bulletin boards or group chat rooms. Question 2 was reverse coded, so that a rating of 1 referred to poor student–student connectivity and a rating of 5 referred to high connectivity. We averaged the three questions and measured it as a continuous variable. The Cronbach’s alpha scores were .63 for live online lectures and .80 for pre-recorded video lectures. Although the Cronbach’s alpha score for live online lectures was slightly below the threshold of .70, we decided to include these items to enable parallel comparison with the items measuring pre-recorded video lectures.

Students’ Demographic Factors

We included several demographic factors as control variables. Gender was measured as a binary variable with two categories: male and female. Students’ majors were categorized into three disciplines: science and engineering, humanities and social sciences, and creative arts and sports. The amount of time spent in school was included as a continuous variable, representing the number of semesters the students had attended university at the time of the survey. It is important to note that our sample consists of students who experienced attending offline physical face-to-face classes before the COVID-19 outbreak, as well as live online and pre-recorded video classes during and after the pandemic. Therefore, the minimum number of semesters attended by the students in our sample is two semesters. We also included other demographic variables such as age and location of residence; however, these variables were shown to be statistically insignificant and the difference in R-squared values, which measures the goodness of fit of the regression, was very minimal, indicating that adding variables age and location did not significantly improve model fit. Hence, in our final model, we excluded these variables because a more parsimonious model is statistically recommended (Glen, 2023).

Students’ Studying Behavior

We analyzed students’ studying behaviors based on five questions. First, we assessed where students usually listen to lectures. In the survey, this item was measured as a five-item categorical variable: personal space at home (i.e., a private room not shared with other family members, etc.), home but not personal space (i.e., living room, etc.), dormitory, school space (i.e., library, empty classroom, etc.), and café. Due to lack of observations, however, we collapsed the categories into a binary variable, distinguishing between personal space at home and all other locations when analyzing. Second, we examined students' level of concentration with other distractions, such as social media, internet surfing, and texting, while listening to lectures. We asked students to rate their level of restraint from engaging in other activities on a 5-point Likert scale, with 1 indicating poor restraint and 5 indicating high restraint. Third, for online live lectures, we asked students whether they usually turned on their video cameras during live lectures on a binary yes or no scale. Fourth, for pre-recorded video lectures, we asked students whether they binge-watched or followed a set schedule when watching the lectures. Lastly, we asked students whether they usually watched pre-recorded video lectures repeatedly or not.

Analytical Strategy

To compare students’ evaluations of pre-recorded video and live online lectures, we employed various analytical approaches. First, we utilized paired t-tests to examine if students’ evaluations between the two types of lectures were statistically signficantly different. Second, we employed two-sample t-tests and analysis of variance (ANOVA) to investigate how students’ evaluations differed based on their studying behaviors. Third, we conducted ordinary least squares (OLS) regression analysis to explore how individual variables influenced students’ evaluations of pre-recorded and live online lectures. The OLS regression results were presented in standardized beta coefficients, allowing for a comparison of the relative strengths of each individual factor in relation to the dependent variable.

Results

Descriptive Analysis

Table 1 presents the descriptive results of our survey of university students. Most of the participants were female (81.63%), pursuing majors in creative arts and sports (50.00%), and had attended university for an average of 3.50 years, corresponding to the junior level at the time of the survey.

Table 1.

Descriptive Results of Variables (N = 98).

Variables		N (%) or Mean (SE)
Demographic factors	Gender
	Male	18 (18.37%)
	Female	80 (81.63%)
	Major
	Science and engineering	27 (27.55%)
	Humanities and social sciences	22 (22.45%)
	Creative arts and sports	49 (50.00%)
	Amount of time in university (semester)	3.50 (0.07)
Evaluation of online lectures	Learning efficiency (average)	3.33 (0.06)
	Academic performance (average)	3.41 (0.07)
	Student–teacher connectivity (average)	3.60 (0.08)
	Student–student connectivity (average)	2.91 (0.08)
Students’ studying behavior	Online class location
	Personal space at home	84 (85.71%)
	All other	14 (14.29%)
	Average restraint from distraction (1 = poor to 5 = high)	3.20 (0.10)
	Camera turned on during live online lectures
	Turned on	52 (53.06%)
	Turned off	46 (46.94%)
	Binge-watch pre-recoded video lectures
	Binge-watch	72 (74.22%)
	Followed a set schedule	25 (25.77%)
	Watched pre-recoded video lectures repeatedly
	Watched repeatedly	33 (33.67%)
	Did not watch repeatedly	65 (66.33%)

For the evaluation of online lectures using the five-point Likert scale, students provided average ratings of 3.33 for learning efficiency, 3.41 for academic performance, 3.60 for student–teacher connectivity, and 2.91 for student–student connectivity. Notably, students rated student–teacher connectivity the highest, while student–student connectivity received the lowest rating.

For students’ studying behaviors during online lectures, the results showed that, on average, 85.71% of students reported listening to lectures in personal spaces at home, such as private rooms, which were not shared with other family members. Students rated their level of restraint from engaging in other activities while listening to lectures at an average of 3.20 on the five-point Likert scale. A rating of 1 indicated poor restraint, while a rating of 5 indicated high restraint. For live online lectures, 53.06% of students indicated that they usually turned on their video cameras during the lectures, while 46.94% responded that they did not. For pre-recorded lectures, 74.22% of students reported that they usually binge-watched the lectures, while the remaining 25.77% reported following a set schedule. Additionally, 33.67% of students reported frequently watching lectures repeatedly, whereas 66.33% did not.

Bivariate Analysis

Students’ Evaluation of Pre-Recoded Video Lectures Versus Live Online Lectures

Table 2 presents a comparison of students’ evaluations of pre-recorded video lectures and live online lectures across the different evaluation categories. First, for learning efficiency, students slightly favored live online lectures (rating of 3.38) over pre-recorded video lectures (rating of 3.29). However, a t-test analysis revealed that this difference was not statistically significant. Second, in the category of academic performance, students rated pre-recorded video lectures (rating of 3.48) more favorably than live online lectures (rating of 3.34). However, similar to the previous category, the t-test results indicated that the difference in ratings was not statistically significant. Third, for student–teacher connectivity, students rated live online lectures (rating of 3.69) more favorably than pre-recorded video lectures (rating of 3.50), and the t-test result revealed a statistically significant difference (t = −2.97, p < 0.01). Fourth, for student–student connectivity, students also rated live online lectures (rating of 3.28) more favorably than pre-recorded video lectures (rating of 2.54), and the t-test result revealed a statistically significant difference (t = −7.68, p < 0.001).

Table 2.

Paired t-test Results Evaluation of Pre-recorded Video Versus Live Online Lectures.

Evaluation category	Pre-recorded video lectures	Live online lectures	T-test score
Learning efficiency	3.29	3.38	−1.19
Academic performance	3.48	3.34	1.91⁺
Student–teacher connectivity	3.50	3.69	−2.97**
Student–student connectivity	2.54	3.28	−7.68***

Note. +p< 0.1. *p < 0.05. **p < 0.01. ***p < 0.001.

Students’ Evaluation of Pre-recoded Video Lectures and Live Online Lectures Based on Studying Behaviors

Table 3 presents how students’ ratings of online lectures differed based on their studying behavior. First, for learning efficiency, students who reported a higher level of restraint from engaging in other activities while listening to lectures consistently rated learning efficiency higher, and this finding was statistically significant for both pre-recorded video lectures (F = 12.37, p < 0.001) and live online lectures (F = 5.75, p < 0.01). For the use of cameras during live online lectures, students who reported turning on their cameras during lectures rated learning efficiency significantly higher than those who reported not turning on their cameras (t = −2.37, p < 0.05). In relation to pre-recorded video lectures, students who reported watching pre-recorded video lectures repeatedly rated learning efficiency higher than those who did not, and this difference was statistically significant (t = −2.00, p < 0.05).

Table 3.

Students’ Rating of Online Lectures Based on Their Studying Behavior.

	Learning efficiency		Academic performance		Student–teacher connectivity		Student–student connectivity
	Pre-recorded video lectures	Live online lectures	Pre-recorded video lectures	Live online lectures	Pre-recorded video lectures	Live online lectures	Pre-recorded video lectures	Live online l ectures
Online class location
Personal space at home	3.40	3.33	3.55	3.36	3.51	3.69	2.60	3.27
All other	3.26	3.04	3.09	3.25	3.43	3.73	2.17	3.31
T-test	−0.70	−1.28	−1.94⁺	−0.49	−0.36	0.17	−1.45	0.16
Restraint from distraction
Poor	2.81	3.10	2.92	3.20	2.99	3.28	2.13	2.93
Middle	3.34	3.31	3.61	3.06	3.60	3.65	2.68	3.38
High	3.63	3.63	3.85	3.60	3.85	4.04	2.79	3.49
ANOVA F-test	12.37***	5.75**	14.81***	4.86***	12.38***	8.82***	4.04*	4.57*
Camera turned on during live lectures
Turned onTurned off		3.55 3.23		3.54 3.17		3.96 3.46		3.55 3.03
T-test	–	−2.37*	–	−2.44*	–	−3.07**	–	−3.15**
Binge-watched pre-recoded lectures
Binge-watched	3.27		3.48		3.60		2.52
Followed a set schedule	3.38		3.49		3.20		2.64
T-test	0.72	–	0.04	–	−2.11**	–	0.49	–
Watched pre-recoded lectures repeatedly
Watched repeatedly	3.07		3.61		3.59		2.70
Did not watch repeatedly	3.39		3.23		3.33		2.23
T-test	−2.00*	–	−2.14*	–	−1.48	–	−2.11*	–

Note.*p < .05. **. p < .01. ***p < .001.

Second, in relation to academic performance, students who reported a higher level of restraint from engaging in other activities while listening to lectures consistently rated academic performance higher. This finding was statistically significant for both pre-recorded video lectures (F = 14.81, p < 0.001) and live online lectures (F = 4.86, p < 0.01). For the use of cameras during live online lectures, students who reported turning on their cameras during lectures rated academic performance significantly higher than those who reported turning off their cameras (t = −2.44, p < 0.05). For, pre-recorded video lectures, students who reported watching pre-recorded video lectures repeatedly rated academic performance higher than those who did not, and this difference was statistically significant (t = −2.14, p < 0.05).

Third, for student–teacher connectivity, the study found that students who demonstrated a higher level of restraint reported greater student–teacher connectivity for both pre-recorded video lectures (F = 12.38, p < 0.001) and live online lectures (F = 8.82, p < 0.001), indicating a statistically significant relationship. For the use of cameras during live online lectures, students who reported turning on their cameras during lectures rated student–teacher connectivity significantly higher than those who reported not turning on their cameras (t = −3.07, p < 0.01). For pre-recorded video lectures, students who reported listening to the lectures following a set schedule provided higher ratings of student–teacher connectivity compared with those who reported binge-watching. This finding was statistically significant (t = −2.11, p < 0.01).

Fourth, for student–student connectivity, the study found that students who demonstrated a higher level of restraint from engaging in other activities while listening to lectures reported higher student-student connectivity for both pre-recorded video lectures (F = 4.04, p < 0.05) and live online lectures (F = 4.57, p < 0.05). For the use of cameras during live online lectures, students who reported turning on their cameras during lectures rated student–student connectivity significantly higher than those who reported not turning on their cameras (t = −3.15, p < 0.01). For pre-recorded video lectures, students who reported repeatedly watching pre-recorded video lectures rated student–student connectivity higher than those who did not, and this difference was statistically significant (t = −2.11, p < 0.05).

Multivariate Analysis

Table 4 presents the OLS analysis results. Here, we present the coefficients in standardized beta coefficients to compare the strength of the effect of each individual independent variable with the dependent variable (i.e., students’ evaluation of live online lectures and pre-recorded video lectures).

Table 4.

OLS Multivariate Regression Results.

	Learning efficiency		Academic performance		Student–teacher connectivity		Student–student connectivity
	Pre-recorded video lectures	Live online lectures	Pre-recorded video lectures	Live online lectures	Pre-recorded video lectures	Live online lectures	Pre-recorded video lectures	Live online lectures
	Beta Coeff. (SE)	Beta Coeff. (SE)	Beta Coeff. (SE)	Beta Coeff. (SE)	Beta Coeff. (SE)	Beta Coeff. (SE)	Beta Coeff. (SE)	Beta Coeff. (SE)
Gender (ref. male) Female	0.17(0.19)+	0.15(0.18)	0.09(0.21)	0.16(0.21)	0.03(0.20)	0.08(0.22)	0.05(0.28)	0.06(0.22)
Major (ref. science and engineering)Humanities and social scienceCreative arts and sports	−0.03(0.22)3.16e-3(0.18)	0.05(0.21) 0.13(0.16)	0.03(0.23) 0.02(0.19)	−0.05(0.24)−0.01(0.19)	0.05(0.23) 0.10(0.18)	−1.13e-4(0.25)−0.05(0.20)	−0.07(0.32) 0.21(0.26)+	−0.13(0.25) 0.03(0.20)
Amount of time in university	−1.27e-3 (0.12)	0.21 (0.11)*	−0.01 (0.13)	0.10 (0.13)	0.04 (0.12)	0.09 (0.14)	0.08 (0.18)	0.23 (0.13)*
Online class location (ref. personal space at home)All other	−3.82e-3 (0.22)	0.05 (0.20)	0.10 (0.23)	0.04 (0.24)	0.02 (0.23)	0.09 (0.14)	0.07(0.32)	−0.08 (0.25)
Restraint from distraction (ref. poor)MiddleHigh	0.31 (0.20) 0.53 (0.17)*	0.19 (0.19) + 0.38 (0.16)**	0.36 (0.21) 0.54 (0.18)*	−0.05 (0.22) 0.22 (0.19)+	0.38 (0.21) 0.58 (0.18)*	0.18 (0.23) 0.38 (0.20)**	0.28(0.29)* 0.36 (0.25)**	0.26(0.23)* 0.30 (0.20)*
Binge-watched pre-recoded lectures (ref. binge-watched)Followed a set schedule	−0.07 (0.16)	–	0.02 (0.17)	–	0.22 (0.17)*	–	−0.05 (0.23)	–
Watched pre-recoded lectures repeatedly (ref. did not watch repeatedly)Watched repeatedly	0.18 (0.15) +	–	0.19 (0.16)*	–	0.09 (0.15)	–	0.17 (0.22)+	–
Camera turned on during live lectures (ref. turned off) Turned on	–	0.17 (0.14) +	–	0.21 (0.16) +	–	0.20(0.17)+	–	0.28 (0.17)**
R-squared	0.263	0.221	0.289	0.160	0.303	0.199	0.200	0.213
F value	3.42**	3.13**	3.89***	2.10*	4.16***	2.73**	2.39*	2.98

Note.+p < .1. *p < .05. **. p < .01. ***p < .001.

First, for learning efficiency, it was found that students who reported a higher level of restraint from engaging in other activities while listening to pre-recorded video lectures significantly rated higher learning efficiency (poor vs. middle: β = 0.31, p < 0.01; poor vs. high: β = 0.53, p < 0.001). Furthermore, students who listened to pre-recorded video lectures repeatedly rated learning efficiency higher than those who did not (β = 0.18, p < 0.1). When examining the beta coefficients, it was observed that restraint from distraction had the highest coefficient among all factors, indicating that restraint from engaging in other activities while listening to lectures had the most significant influence on students’ evaluation of learning efficiency in relation to pre-recorded video lectures. For live online lectures, years in university (β = 0.21, p < 0.05); restraint from distraction (poor vs. middle: β = 0.19, p < 0.1; poor vs. high: β = 0.38, p < 0.01) had a significant influence on students’ evaluation of learning efficiency. The beta coefficient results indicated that, similar to the results for pre-recorded video lectures, restraint from distraction had the highest coefficient, suggesting that the level of restraint from engaging in other activities while listening to lectures had the most significant effect on students’ evaluation of learning efficiency. Interestingly, years in university also exhibited relatively high beta coefficients, indicating that senior students were more likely to rate live online lectures favorably in relation to learning efficiency.

Second, for academic performance, only the variables restraint from distraction (poor vs. middle: β = 0.36, p < .01; poor vs. high: β = 0.54, p < 0.001), and whether students listened to lectures repeatedly (β = 0.19, p < 0.05) had a significant effect on students’ evaluations for pre-recorded video lectures. For live online lectures, none of the variables were shown to have a statistically significant effect at the p < 0.05 level.

Third, for student–teacher connectivity, the results revealed that students who had a higher level of restraint from engaging in other activities while listening to pre-recorded video lectures rated student–teacher connectivity higher (poor vs. middle: β = 0.38, p < 0.01; poor vs. high: β = 0.58, p < 0.001). Additionally, students who followed a set schedule when listening to pre-recorded video lectures reported higher student–teacher connectivity than those who binge-watched the lectures (β = 0.22, p < 0.05). Similarly, for live online lectures, students who reported a higher level of restraint from engaging in other activities while listening to lectures rated student–teacher connectivity higher (poor vs. high: β = 0.38, p < .01).

Fourth, for student–student connectivity, the results showed students who reported a higher level of restraint from engaging in other activities while listening to pre-recorded video lectures rated student–student connectivity higher (poor vs. middle: β = 0.28, p < 0.05; poor vs. high: β = 0.36, p < 0.01). For live online lectures, years in university (β = 0.23, p < 0.05); restraint from distractions (poor vs. middle: β = 0.26, p < 0.05; poor vs. high: β = 0.30, p < 0.05) had a significant correlation with students’ evaluation of student–student connectivity. Also, students who turned on their cameras during live lectures indicated significantly higher sutent-student connectivity than those who didn’t (β=0.28, p < 0.01).

Conclusion

In conclusion, this study examined students’ evaluations of pre-recorded and live online lectures and investigated the factors influencing these assessments, particularly focusing on students’ studying behaviors.

First, the results revealed that most students (85.71%) listened to online lectures in their personal spaces at home and reported moderate restraint from engaging in other activities while listening to lectures (average rating of 3.20 on a five-point Likert scale). For live online lectures, over half of the students (53.06%) usually turned on their cameras. For pre-recorded lectures, results showed that the majority of students (74.22%) engaged in binge-watching rather than following a set schedule to watch the lectures, and most (66.33%) usually watched the lectures only once rather than repeatedly.

Second, the results revealed that students rated live online lectures higher than pre-recorded video lectures in the categories of learning efficiency, student–teacher connectivity, and student–student connectivity. However, a contrasting result was observed for academic performance, with students perceiving pre-recorded video lectures as more effective in enhancing their academic performance than live online lectures.

Third, the OLS regression analysis results revealed that students who reported higher levels of restraint from distractions such as social media and texting while listening to online lectures evaluated both pre-recorded video and live online lectures significantly more favorably. The beta coefficients showed that this effect was found to be the strongest determinants than other studying behaviors or students’ demographic characteristics. Also, results showed that students who usually turned on their cameras during live online lectures rated the lectures more positively, particularly in relation to student–student connectivity. Furthermore, students who repeatedly watched pre-recorded lectures tended to rate the lectures more favorably than those who did not, and those who watched lectures on a set schedule rated student-teacher connectivity significantly more favorably than those who binge-watched them.

These findings underscore the significance of students’ studying behavior as a critical factor influencing the effectiveness and outcomes of online lectures. As a result, it is important to foster and encourage students’ active engagement with online lectures and promote the development of self-learning skills. Educators should emphasize the importance of students’ roles and responsibilities in online learning by creating an environment that supports and motivates students to maximize the benefits of online lectures and cultivate their own learning capabilities. Our results emphasize that refraining from activities such as texting and browsing social media or other websites has a significant effect on students’ evaluations. Vonderwell and Savery (2004) emphasize that simply instructing students to be more attentive in their learning is insufficient and stress the importance of interaction among students in facilitating effective learning. Therefore, establishing a learning community that extends beyond lectures is important. We propose that establishing a complementary online community will allow students to share their experiences and seek peer assistance. Hence, when designing online classes, schools should also prioritize the development of online platforms for student-student communication.

In addition, our findings emphasize that for live online lectures, turning on video cameras has a particularly strong positive influence on live online lectures. The use of cameras enables communication through nonverbal cues, allowing teachers to effectively assess their teaching in real-time and make necessary adjustments based on nonverbal feedback such as smiles, frowns, head nods, and indications of boredom (Miller, 1988; Mottet & Richmond, 2002). Additionally, camera usage has been reported to enhance student–student relationships (Mottet, 2000; Olson et al., 1995). However, studies indicate that many students are hesitant to turn on their cameras due to concerns about personal appearance; privacy (including the visibility of their physical surroundings); and issues related to weak internet connections (Castelli & Sarvary, 2020). Considering these reasons, it is important to explain the benefits of having cameras turned on and actively encourage their usage. Castelli and Sarvary (2020) suggest including camera use policies in the syllabus and having instructors explicitly communicate their encouragement of camera usage on the first day of class, with reminders throughout the semester if necessary. Moreover, platforms such as Zoom and Google Meet provide options for background blurring, addressing privacy concerns. As online teaching continues to gain prevalence, it is crucial to further develop and implement privacy-enhancing programs on all platforms.

Furthermore, results revealed that individuals who adhered to a set schedule when viewing pre-recorded lectures and revisited them multiple times reported significantly enhanced student-teacher connectivity and academic performance, respectively. These findings offer valuable insights for the design of online recorded classes. We propose that to facilitate students in following a structured approach to lectures and discourage last-minute binge-watching, faculties may consider providing access to lectures for a defined period (perhaps one or 2 weeks) rather than granting unlimited access. This approach will encourage students to participate in lectures on a set schedule. Additionally, by implementing a time-limited access policy, students will also have the opportunity to revisit lecture times. Also, faculties may consider scheduling regular check-ins or progress assessments to monitor student’s learning pace, which will help students to stay on schedule and avoid binge-listening.

For future studies, we recommend conducting a nationwide study with more sample size to increase the generalizability of the findings. Due to economic constraints, the sample size was limited to 98 students for this study. To achieve a more statistically representative sample, it is important to employ a random sampling method that incorporates a nationally representative sample. In addition, the authors acknowledge the potential challenge of distinguishing between the effects of live and pre-recorded lectures when a class incorporates both formats. Nonetheless, even in such instances, respondents were encouraged to make every effort to discern the distinct effects of each. Although this study examined students’ experience of pre-recorded and live online lectures during COVID-19, the results can also be applicable to post-COVID-19. The landscape of online classes is likely to continue after COVID-19, and many institutions are considering adopting hybrid learning models that combine online and in-person factors. Therefore, results from this study provide important insights. To the best of the authors’ knowledge, this study is the first exploratory study in Korea to examine how university students’ studying behavior influences online lectures, associating both synchronous live online lectures and asynchronous pre-recorded video lectures. Additionally, by including students who have experienced attending both offline face-to-face classes, live online lectures, and pre-recorded video classes, students were given opportunities to compare their experiences and evaluate different forms of online lectures.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Hanyang Humanities Enhancement Research Fund of Hanyang University (HY- 202400000000394). This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2022S1A5A2A01038779).

Ethics Statement

confirm exemption for the Study from the Institutional Review Board on Human Subjects Research and Ethics Committees, Hanyang University, Seoul, Korea (HYUIRB-202107-007)

ORCID iDs

Eun Jung Kim

Mi Jeong Kim

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author, M. J. Kim.

References

Arbaugh

J. B.

Benbunan-Fich

(2006). An investigation of epistemological and social dimensions of teaching in online learning environments. Academy of Management Learning & Education, 5(4), 435–447. https://doi.org/10.5465/amle.2006.23473204

Belcher

(1999). Authentic interaction in a virtual classroom: Leveling the playing field in a graduate seminar. Computers and Composition, 16(2), 253–267. https://doi.org/10.1016/S8755-4615(99)00006-7

Brockfeld

Müller

de Laffolie

(2018). Video versus live lecture courses: A comparative evaluation of lecture types and results. Medical Education Online, 23, 1. https://doi.org/10.1080/10872981.2018.1555434

Butnaru

G. I.

Niţă

Anichiti

Brînză

(2021). The effectiveness of online education during Covid 19 pandemic—A comparative analysis between the perceptions of academic students and high school students from Romania. Sustainability, 13(9), 5311. https://doi.org/10.3390/su13095311

Buxton

E. C.

(2014). Pharmacists’ perception of synchronous versus asynchronous distance learning for continuing education programs. American Journal of Pharmaceutical Education, 78(1), 8. https://doi.org/10.5688/ajpe7818.

Castelli

F. R.

Sarvary

M. A.

(2020). Why students do not turn on their video cameras during online classes and an equitable and inclusive plan to encourage them to do so. Academic Practice in Ecology and Evolution, 11(8), 3565–3576. https://doi.org/10.1002/ece3.7123.

Cho

M. E.

Kim

M. J.

(2022). Exploratory study of online education experiences of architectural students during the Covid-19 pandemic. Journal of the Korean Institute of Interior Design [in Korean], 31(6), 23–31. http://dx.doi.org/10.14774/JKIID.2022.31.6.023

Cho

M. E.

Lee

J. H.

Kim

M. J.

(2023). Identifying online learning experience of architecture students for a smart education environment. Journal of Asian Architecture and Building Engineering, 22(4), 1903–1914. https://doi.org/10.1080/13467581.2022.2145216

Chundur

Prakash

(2009). Synchronous vs asynchronous communications—What works best in an online environment? Lessons learnt. In Siemens

Fulford

(Eds.), World Conference on Educational Multimedia, Hypermedia, and Telecommunications (pp. 3541–3545). Chesapeake, VA: Association for the Advancement of Computing in Education.

10.

Cortina

J. M.

(1993). What is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology, 78(1), 98–104. https://doi.org/10.1037/0021-9010.78.1.98

11.

Engzell

Frey

Verhagen

M. D.

(2021). Learning loss due to school closures during the COVID-19 pandemic. PNAS, 118(17), e2022376118 https://doi.org/10.1073/pnas.2022376118

12.

Glen

(2023). Parsimonious model: Definition, ways to compare models. Statistics How To. https://www.statisticshowto.com/parsimonious-model/

13.

Griffiths

Graham

(2010). Using asynchronous video to achieve instructor immediacy and closeness in online classes: Experiences from three cases. International Journal on E-learning, 9(3), 325–340.

14.

Hartmann

Wirth

Schramm

Klimmt

Vorderer

Gysbers

Böcking

Ravaja

Laarni

Saari

Gouveia

Sacau

A. M.

(2016). The Spatial Presence Experience Scale (SPES): A short self-report measure for diverse media settings. Media Psychology, 28(1), 1–15. https://doi.org/10.1027/1864-1105/a000137

15.

Hrastinski

(2008). Asynchronous and synchronous e-learning. Educause Quarterly, 31(4), 51–55.

16.

Islam

Kim

D.-A.

Kwon

(2020). A comparison of two forms of instruction: Pre-recorded video lectures vs. live Zoom lectures for education in the business management field. Sustainability, 12(19), 8149. https://doi.org/10.3390/su12198149

17.

(2022). Pre-recorded lectures, live online lectures, and student academic achievement. Sustainability, 14(5), 2910. https://doi.org/10.3390/su14052910

18.

Loveland

K. A.

Loveland

J. P.

(2003). Student evaluations of online classes versus on-campus classes. Journal of Business & Economics Research, 1(4). https://doi.org/10.19030/jber.v1i4.2993

19.

Mabrito

(2006). A study of synchronous versus asynchronous collaboration in an online business writing class. The American Journal of Distance Education, 20(2), 93–107. https://doi.org/10.1207/s15389286ajde2002_4

20.

Miller

P. W.

(1988). Nonverbal communication. Third Edition. What research says to the teacher. Washington, DC: National Education Association Professional Library.

21.

Mottet

T. P.

(2000). Interactive television instructors’ perceptions of students’ nonverbal responsiveness and their influence on distance teaching. Communication Education, 49(2), 146–164. https://doi.org/10.1080/03634520009379202

22.

Mottet

T. P.

Richmond

V. P.

(2002). Student nonverbal communication and its influence on teachers and teaching. In Chesebro

J. L.

McCroskey

J. C.

(Eds.), Communication for teachers (pp. 47–61). Boston, MA: Allyn & Bacon.

23.

Olson

J. S.

Olson

G. M.

Meader

D. K.

(1995). What mix of video and audio is useful for small groups doing remote real-time design work? In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 362–368). https://doi.org/10.1145/223904.223951.

24.

Park

(2021). Undergraduate student perceptions of synchronous and video-recorded classes: Differences by year of study. The Society of Korean Language And Literature [in Korean], 69, 385–417. https://dx.doi.org/10.15711/WR.69.0.13

25.

Peterson

Beymer

Putnam

(2018). Synchronous and asynchronous discussions: Effects on cooperation, belonging, and affect. Online Learning, 22(4), 7–25. https://doi.org/10.24059/olj.v22i4.1517

26.

Rockinson-Szapkiw

Wendt

(2015). Technologies that assist in online group work: A comparison of synchronous and asynchronous computer mediated communication technologies on students’ learning and community. Journal of Educational Multimedia and Hypermedia, 24(3), 263–279.

27.

Romanov

Nevgi

(2007). Do medical students watch video clips in eLearning and do these facilitate learning? Medical Teacher, 29(5), 490–494. https://doi.org/10.1080/01421590701542119

28.

Vonderwell

Savery

J. R.

(2004). Online learning: Student role and readiness. Turkish Online Journal of Educational Technology, 3, 38–42.

Students’ Evaluation of Live Online Lectures Versus Pre-Recorded Video Lectures During COVID-19: Focusing on Students’ Study Behaviors