Next Article in Journal
Information Loss Due to the Data Reduction of Sample Data from Discrete Distributions
Next Article in Special Issue
Dataset of Search Results Organized as Learning Paths Recommended by Experts to Support Search as Learning
Previous Article in Journal
Extraction of Missing Tendency Using Decision Tree Learning in Business Process Event Log
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Data Descriptor

Data on Vietnamese Students’ Acceptance of Using VCTs for Distance Learning during the COVID-19 Pandemic

Hanoi National University of Education, Dich Vong Hau, Cau Giay, Hanoi 10000, Vietnam
Vietnam National Institute of Educational Sciences, Hoan Kiem, Hanoi 1000, Vietnam
Author to whom correspondence should be addressed.
Submission received: 17 August 2020 / Revised: 4 September 2020 / Accepted: 6 September 2020 / Published: 11 September 2020
(This article belongs to the Special Issue Big Data and E-learning)




The outbreak of COVID-19 at the beginning of 2020 has heavily influenced education all around the world. In Vietnam, educational institutes were suspended, and distance learning was conducted to ensure students’ learning process, with distance learning occurring mainly via video conferencing tools (VTCs). The purpose of this paper is to provide data on Vietnamese students’ acceptance of using VCTs in distance learning during the COVID-19 pandemic through an extended technology acceptance model (TAM) and structural equation modeling (SEM) method. This study used the TAM of Venkatesh and Davis. The questionnaire was designed based on Venkatesh and Davis and Salloum et al.’s scale. An online survey with snowball sampling was selected in April. The final dataset consisted of 277 valid records. This data descriptor presented descriptive statistics (mean, standard deviation), internal consistency (Cronbach’s alpha), reliability and validity measures (composite reliability, average value extracted test), and factor loading of items of eight factors: output quality, computer playfulness, subjective norm, perceived usefulness, perceived ease of use, attitude towards to use, behavioral intention to use, and actual system to use. Results indicated that external factors such as subjective norm and computer playfulness had a significant impact on most TAM constructs. Furthermore, output quality was found to have a positive influence on students’ perceived usefulness and acceptance of VCTs in distance learning.


Dataset License

CC BY 4.0

1. Summary

The COVID-19 pandemic, which has influenced 70% of the student population worldwide [1], has directly affected teaching and learning in Vietnam’s higher education institutions (HEIs). Although educational institutions have been suspended, Vietnam’s Ministry of Education and Training (MOET) declared a new motto: “no schooling but still learning” [2], and promoted distance learning—a means of providing access to education for geographically remote learners [3] via the Internet and television, among others—as a solution for both addressing the current situation resulting from the pandemic while also helping to transform education in general [4]. At the higher education level, the MOET officially granted students of HEIs the right to study at home [5], and they also formally requested HEIs to operate distance learning as a temporary plan during COVID-19 [6]. Along with that, it was announced that the achievement academic of this training method will be officially recognized [7]. Thus, HEIs actively adapted information technology into distance learning. As a consequence, video conferencing tools (VCTs) (e.g., Zoom, Teams, Google Classroom, Facebook groups, etc.) have been widely used by Vietnams’ HEIs during COVID-19 as a solution to ensure effective responses to the distance learning requirement. Although teaching and learning with VCTs have been proven effective by many researchers [8], it is not clear what benefits and challenges they bring to higher education in Vietnam, especially concerning students’ acceptance of the use of technology in the time of unexpected events.
Due to COVID-19, the adoption of VCTs to teach and learn has become an urgent requirement for HEIs. Consequently, there is a need to study the use of VCTs, as well as their pros and cons, especially in relation to students’ acceptance of the tools. This data was gathered to examine the factors that impact Vietnamese students’ acceptance of the use of technology in distance learning during the COVID-19 pandemic through an extended technology acceptance model (TAM) and structural equation modeling (SEM) method. The data were collected over 10 days, from 14 April 2020, to 23 April 2020, and the research model and hypotheses were tested using the IBM SPSS Amos software version 23.

2. Data Description

The data comprises two main parts. The first part is the demographic information of respondents. The second part includes two major groups of variables that are related to the Vietnamese students’ adoption of the use of VCTs in distance learning during the COVID-19 pandemic: (1) external factors group, including output quality (OQ), computer playfulness (CP), and subjective norm (SN); and (2) TAM constructs group, including perceived usefulness (PU), perceived ease of use (PEU), attitude toward to use (ATT), behavioral intention to use (BI), and actual system use (ASU). In the first part, we present respondents’ demographic information, including three variables: the year in a HEI, area to access the internet, and VCTs for learning.
In the second part, the first two items required participants to respond to statements in order to measure the variable OQ: (OQ1) “The quality of the output I get from VCTs is high”; (OQ2) “I have no problem with the quality of VCTs’ output”. Items 6–8 present statements related to the variable CP: (CP1) “I feel that VCTs are enjoyable no matter what the usage purposes are”; (CP2) “I feel that VCTs help me to improve my creativity”; and (CP3) “I feel that I can have a variety of experiences without any interference”. The next two items show statements related to the variable SN: (SN1) “I should have participated in the VCTs activities, as per my instructors”; (SN2) “I should have participated in the VCTs activities, according to other students”.
The last seventeen items contained statements related to the TAM constructs as PU, PEU, ATT, BI, and ASU: (PU1) “VCTs enhance my learning performance”; (PU2) “My productivity is elevated through the utilization of VCTs in my study”; (PU3) “Using VCTs enhances my learning effectiveness; (PU4) “I find VCTs to be useful in my learning”; (PEU1) “I find it easy to get VCTs to do what I want them to do”; (PEU2) “VCTs are easy to use for me”; (PEU3) “Interacting with VCTs does not require a lot of my mental effort”; (PEU4) “My interaction with VCTs is clear and understandable”; (ATT1) “I feel positive regarding the utilization of VCTs”; (ATT2) “In general, I admire the utilization of VCTs”; (ATT3) “VCTs provide an attractive learning environment”; (BI1) “I will make use of VCTs regularly in the forthcoming time”; (BI2) “I intend to make use of functions of VCTs for providing assistance to my academic activities”; (BI3) “I will give out my recommendation to others to use VCTs”; (BI4) “I will use VCTs on a regular basis in the future”; (ASU1) “I use VCTs frequently”; and (ASU2) “I use the VCTs on a daily basis”.
Responses from the measurement scale comprised answers using a five-point Likert type scale of 1–5, ranging from strongly disagree (1) to strongly agree (5). Raw data and the questionnaire are linked to Mendeley data source. The data was analyzed and then presented through three tables and one figure.
Table 1 shows the accuracy analysis statistics with reliability and validity measures. The reliability was measured by Cronbach’s alpha and composite reliability (CR). Additionally, average value extracted (AVE) test was used to check on the validity of data [9]. From Table 1, it can be observed that all of the reliability values were higher than the recommended value of 0.7 [9], which shows levels of internal consistency. Table 2 presents respondents’ demographic information, including their year in a HEI, area to access the internet, and VCTs used for learning. Table 3 shows all hypothesis testing, of which eleven hypotheses are supported by our empirical analysis of SEM; on the other hand, the other five hypotheses are not supported. Lastly, Figure 1 demonstrates the structural equation model, showing all of the proposed hypotheses.

3. Methods

3.1. Participants

The participants of the research were students from universities in Hanoi who could not continue their traditional face-to-face learning at their school due to social distancing because of COVID-19. These students were encouraged to adopt the distance learning arranged by their institutions to avoid disruption to their education. With the exception of a few universities that had already designed their own distance learning systems, VCTs were used by the majority of students in other universities to connect with their teachers. There was no limitation to the background of students. They could have been from any faculty and academic year, used any type of device (desktops, laptops, smartphones, or tablets), been at any kind of location while learning (urban or rural), and used any form of VCT (Table 2).

3.2. Instruments

To explore the Vietnamese students’ technology acceptance model of distance learning due to the novel coronavirus SARS-CoV-2 [10] through an extended TAM based on the original TAM [11], a quantitative approach was used to analyze the data. A scale was proposed based on the modification of Venkatesh and Davis (2000) [11] and Salloum et al. (2019) [12] to match our research purposes. The scales were designed to measure external factors and TAM constructs including subjective norm, output quality, computer playfulness, perceived ease of use, perceived usefulness, attitude toward using, behavioral intention to use, and actual system use (see Table 1). For all the measures, a five-point Likert type scale was used to measure all respondents’ perceptions, ranging from strongly disagree (1) to strongly agree (5).

3.3. Data Collection

Concerning the data collection, an online survey method was considered to be the most suitable, especially during a period of social distancing. In the first step, a questionnaire was designed on Google Forms and was sent to the managerial staff and lecturers of different universities to make sure that the questions were simple enough for students to understand. Later, the questionnaire was adapted and sent directly to some students using the snowball sampling method via email and Facebook. The students who had completed the survey were encouraged to invite their peers who also used VCTs in distance learning to fill out the questionnaire. Data were collected in April 2020.
The initial set of data consisted of 294 records. Due to only providing a constant value for all of the responses, 17 records were excluded after examination. The final data set of 277 records was prepared through four phases, namely data editing, coding, capturing, and cleaning [13]. Finally, this data was analyzed with IBM SPSS Statistics software version 20. The characteristics of the respondents were shown in Table 2. An accuracy assessment with validity and reliability measures was presented in Table 1. Hypotheses decisions were presented in Table 3, and the research model was demonstrated in Figure 1.

Author Contributions

Conceptualization: D.-H.P., X.-A.N., and D.-H.L.; Data curation: X.-A.N., D.-H.L., H.-T.N., T.-P.-T.V., and T.-T.-T.N.; Formal analysis: D.-H.L., H.-T.N., T.-P.-T.V., and T.-T.-T.N.; Investigation: X.-A.N., D.-H.L., H.-T.N., T.-P.-T.V., and T.-T.-T.N.; Methodology: D.-H.P., X.-A.N., and D.-H.L.; Project administration: D.-H.P. and X.-A.N.; Supervision: D.-H.P.; Writing—original draft: D.-H.P., X.-A.N., D.-H.L., H.-T.N., T.-P.-T.V., and T.-T.-T.N.; Writing—review and editing: D.-H.P., X.-A.N., D.-H.L., H.-T.N., T.-P.-T.V., and T.-T.-T.N. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.


The authors of this data article are extremely thankful to all the students who participated in this study, along with the managerial staff and lecturers who commented on the questionnaires.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article.


  1. United Nations Educational Scientific and Cultural Organization. Distance Learning Solutions. Available online: (accessed on 14 March 2020).
  2. Bao Dien Tu Chinh Phu Nuoc Cong Hoa Xa Hoi Chu Nghia Viet Nam. Bộ GD&ĐT hợp tác với UNICEF ứng phó với COVID-19. Available online: (accessed on 18 April 2020).
  3. Moore, J.L.; Dickson-Deane, C.; Galyen, K. E-Learning, online learning, and distance learning environments: Are they the same? Internet High. Educ. 2018, 14, 129–135. [Google Scholar] [CrossRef]
  4. Bao Dien Tu Nhan Dan. Cơ hội cho đại học Việt Nam đẩy nhanh chuyển đổi số, bắt kịp xu thế thế giới. Available online: (accessed on 9 April 2020).
  5. Vietnam Ministry of Education and Training. Official Letter No. 988 / BGDĐT-GDTrH Dated 23/3/2020 about Ensure Quality of Distance Learning during the Prevention of the Covid-19 Pandemic. Available online: (accessed on 24 March 2020).
  6. Vietnam Ministry of Education and Training. Official Letter No. 795/BGDĐT-GDĐH Dated 13/3/2020 about the Implementation of Distance Learning in Response to Covid-19. Available online: (accessed on 14 March 2020).
  7. Thanh Nien. Nhiều trường đại học công nhận dạy học trực tuyến. Available online: (accessed on 16 March 2020).
  8. Al-Samarraie, H. A scoping review of videoconferencing systems in higher education: Learning paradigms, opportunities, and challenges. Int. Rev. Res. Open Distance Learn. 2019, 20, 121–140. [Google Scholar] [CrossRef]
  9. Hair, J.F.; Howard, M.C.; Nitzl, C. Assessing measurement model quality in PLS-SEM using con fi-rmatory composite analysis. J. Bus. Res. 2020, 109, 101–110. [Google Scholar] [CrossRef]
  10. Gorbalenya, A.E.; Baker, S.C.; Baric, R.S.; De Groot, R.J.; Drosten, C.; Gulyaeva, A.A.; Haagmans, B.L.; Lauber, C.; Leontovich, A.M.; Neuman, B.W.; et al. Severe acute respiratory syndrome-related coronavirus: The species and its viruses—A statement of the Coronavirus Study Group. bioRxiv 2020. [Google Scholar] [CrossRef] [Green Version]
  11. Venkatesh, V.; Davis, F.D. Theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Manag. Sci. 2000, 46, 186–204. [Google Scholar] [CrossRef] [Green Version]
  12. Salloum, S.A.; Alhamad, A.Q.M.; Al-Emran, M.; Monem, A.A.; Shaalan, K. Exploring students’ acceptance of e-learning through the development of a comprehensive technology acceptance model. IEEE Access 2019, 7, 128445–128462. [Google Scholar] [CrossRef]
  13. Hair, J.F.; Black, W.C.; Babin, B.J.; Anderson, R.E. Multivariate Data Analysis, 7th ed.; Prentice Hall: Upper Saddle River, NJ, USA, 2010. [Google Scholar]
Figure 1. Structural model.
Figure 1. Structural model.
Data 05 00083 g001
Table 1. Measurement accuracy assessment. CR: composite reliability; AVE: average value extracted.
Table 1. Measurement accuracy assessment. CR: composite reliability; AVE: average value extracted.
VariableScale ItemCronbach’s Alpha ValueCRAVEFactor Loadings
Output Quality (OQ) 0.840.850.74
OQ13.030.95 0.91
OQ23.041.08 0.81
Computer Playfulness (CP) 0.830.900.75
CP13.130.88 0.91
CP22.940.91 0.84
CP33.040.91 0.83
Subjective Norm (SN) 0.870.880.78
SN13.580.92 0.93
SN23.480.86 0.83
Perceived Usefulness (PU) 0.930.940.80
PU12.810.99 0.89
PU22.800.99 0.93
PU32.750.97 0.91
PU43.070.93 0.84
Perceived Ease of Use (PEU) 0.860.880.64
PEU13.190.87 0.78
PEU23.620.91 0.82
PEU33.311.00 0.76
PEU43.450.87 0.86
Attitude Towards to Use (ATT) 0.910.920.79
ATT13.200.87 0.87
ATT22.970.95 0.90
ATT32.960.93 0.89
Behavioral Intention to Use (BI) 0.890.900.68
BI13.230.95 0.80
BI23.340.89 0.85
BI33.200.90 0.80
BI43.100.91 0.86
Actual System Use (ASU) 0.820.850.75
ASU13.110.97 0.99
ASU22.981.00 0.71
Table 2. Sample profile. HEI: higher education institution; VCT: video conferencing tool.
Table 2. Sample profile. HEI: higher education institution; VCT: video conferencing tool.
Year in HEI277100
Area to access the internet277100.00
VCTs used for learning
Google Meet22480.87
Microsoft Teams186.50
Table 3. The structural equation model analysis.
Table 3. The structural equation model analysis.
VariablePath Coefficient (β)tHypothesesDecision
Dependent Variable: PU
SN0.060.89H1aNot supported
OQ0.436.34 ***H2Supported
PEU0.212.92 **H4aSupported
Dependent Variable: PEU
SN0.406.18 ***H1bSupported
CP0.406.26 ***H3aSupported
Dependent Variable: ATT
SN0.010.27H1dNot supported
CP0.163.08 **H3bSupported
PEU0.346.05 ***H4bSupported
PU0.519.41 ***H5aSupported
Dependent Variable: BI
SN0.122.20 *H1cSupported
CP0.334.75 ***H3cSupported
PEU0.030.4H4cNot supported
PU0.111.43H5cNot supported
ATT0.332.93 **H6Supported
Dependent Variable: ASU
PEU−0.46−0.73H4dNot supported
BI0.7810.78 ***H7Supported
Note: * p < 0.05; ** p < 0.01; *** p < 0.001.

Share and Cite

MDPI and ACS Style

Pho, D.-H.; Nguyen, X.-A.; Luong, D.-H.; Nguyen, H.-T.; Vu, T.-P.-T.; Nguyen, T.-T.-T. Data on Vietnamese Students’ Acceptance of Using VCTs for Distance Learning during the COVID-19 Pandemic. Data 2020, 5, 83.

AMA Style

Pho D-H, Nguyen X-A, Luong D-H, Nguyen H-T, Vu T-P-T, Nguyen T-T-T. Data on Vietnamese Students’ Acceptance of Using VCTs for Distance Learning during the COVID-19 Pandemic. Data. 2020; 5(3):83.

Chicago/Turabian Style

Pho, Duc-Hoa, Xuan-An Nguyen, Dinh-Hai Luong, Hoai-Thu Nguyen, Thi-Phuong-Thao Vu, and Thi-Thuong-Thuong Nguyen. 2020. "Data on Vietnamese Students’ Acceptance of Using VCTs for Distance Learning during the COVID-19 Pandemic" Data 5, no. 3: 83.

Article Metrics

Back to TopTop