Dataset of Vietnamese teachers' perspectives and perceived support during the COVID-19 pandemic Authors Cam-Tu Vu1, Anh-Duc Hoang2, Van-Quan Than3, Manh-Tuan Nguyen4, Viet-Hung Dinh5, Quynh-Anh Thi Le2, Thu-Trang Thi Le 6, Hiep-Hung Pham1, 7, Yen-Chi Nguyen2 Affiliations 1. Institute of Theoretical and Applied Research, Duy Tan University, Hanoi 100000, Vietnam 2. EdLab Asia Educational Research and Development Centre, Hanoi 100000, Vietnam 3. Political Academy, Hanoi 100000, Vietnam 4. Early Childhood Department, Hanoi National University of Education, Hanoi 100000, Vietnam 5. University of Labour and Social Affairs, Hanoi 100000, Vietnam 6. Hanoi University, Hanoi 100000, Vietnam 7. Phu Xuan University, Hue 49000, Vietnam Corresponding author(s) Anh-Duc Hoang (duc@edlabasia.org) Abstract The COVID-19 pandemic has caused unprecedented damage to the educational system worldwide. Besides the measurable economic impacts in the short-term and long-term, there is intangible destruction within educational institutions. In particular, teachers – the most critical intellectual resources of any schools – have to face various types of financial, physical, and mental struggles due to COVID-19. To capture the current context of more than one million Vietnamese teachers during COVID-19, we distributed an esurvey to more than 2,500 randomly selected teachers from two major teacher communities on Facebook from 6th to 11th April 2020. From over 373 responses, we excluded the observations which violated our cross-check questions and retained 294 observations for further analysis. This dataset includes: (i) Demographics of participants; (ii) Teachers' perspectives regarding the operation of teaching activities during the pandemic; (iii) Teachers' received support from their schools, government bodies, other stakeholders such as teacher unions, and parents' associations; and (iv) teachers' evaluation of school readiness toward digital transformation. Further, the dataset was supplemented with an additional question on the teachers' primary source of professional development activities during the pandemic. Keywords Education management; Teacher satisfaction; Teacher engagement; COVID-19; Vietnam Specifications Table Subject Education, Education Management Specific subject area Education Management; School Effectiveness; Teacher Satisfaction Type of data Raw data in excel file and analyzed data How data were acquired Data was gathered using an online survey and converted into the .xlsx format for formal analysis in SPSS v.20. Data format Raw Analyzed Parameters for data collection The target population of this work is Vietnamese teachers whose teaching profession was affected by the COVID-19 pandemic. In light of the national school closures policy, almost every educational institution has to close until the end of April 2020. As a result, approximately one million teachers of various school types and educational levels were affected. Description of data collection An online survey has been delivered to 2,500 randomly selected Pre-K to post-secondary teachers. They are members of two major teacher communities on Facebook: MIE Expert Vietnam (38,600 members) and Vietnam Innovative Education Forum (14,000 members). Data source location Information is collected from secondary student institutes in Hanoi (Latitude 21°1'28.2"N, Longitude 105°50'28.21"E), Vietnam. Data accessibility Repository name: Harvard Dataverse Data identification number: Direct URL to data: https://doi.org/10.7910/DVN/FOCPKH, Harvard Dataverse, V1 Repository Name: Mendeley Direct URL to data: https://data.mendeley.com/datasets/cy46h2rvwg/draft?a=d234e6294509-4e7f-8379-e713efca803c Value of the Data • The dataset can be used for further analysis of teacher satisfaction and online teaching effectiveness with the focus on the chaotic context of a pandemic. • The dataset can be used to construct models to evaluate educational leadership and school effectiveness in abnormal situations. • The significant differences in Vietnamese teachers' income before and during COVID-19 in this dataset can contribute to overall economic models on COVID19's damage. • The dataset will be useful for school managers and policymakers to renovate policies, regulations, and practices to enhance teacher satisfaction, engagement, and effectiveness. • The dataset presents a natural flow to measure teacher perceptions and satisfaction during COVID-19, which can be replicated in other countries. 1. Data Description School effectiveness measurements include various factors related to students, teachers, and school managers that affect students' academic achievement [1]. Although the Vietnamese government applied different systematic solutions to minimize the negative impacts of the COVID-19 pandemic [2], there is a lack of empirical evidence to support the decision-making process of school leaders. Under the chaotic circumstances caused by the pandemic, the significant shifts in learning and teaching habits require school leaders to face critical unknown-unknown issues. The formation of this dataset is an extension of our recent study on students' learning habits during the pandemic [3, 4], which contributes to the call of Elsevier on conducting research to tackle the current and potential impairments of the pandemic [5]. Regarding the sudden shift to online teaching and learning due to school closures, this dataset [6] portrayed Vietnamese teachers' perspectives and teaching effectiveness during the pandemic and schools' readiness toward the digital transformation. Besides the information about the demographics of the participants, this dataset includes two primary groups of research items: (i) Teachers' perceptions of factors associated with online teaching and learning; and (ii) Teachers' opinions on school readiness and teaching effectiveness during the pandemic. The full questionnaires, variable code, and measurement parameters for all research items have been reposited in Harvard Dataverse [6]. Integrations among those variables can examine teacher satisfaction, self-reported teaching effectiveness, and school readiness during the pandemic. Table 1. Descriptive statistics of participant demographics Teacher satisfaction N Mean Std. Deviation Std. Error Max 95% Confidence Interval for Mean Min Lower Bound Upper Bound Gender Male 46 2.772 0.828 0.122 5 2.526 3.018 2 Female 245 2.929 0.776 0.05 0 5 2.831 3.026 1 Prefer not to disclose 3 2.167 1.041 0.601 3 -0.419 4.752 1 Exp Less than 3 years 64 2.953 0.733 0.09 2 5 2.770 3.136 1 From 3 to 5 years 48 2.823 0.796 0.115 4 2.592 3.054 1 From 5 to 10 years 59 2.805 0.820 0.107 5 2.591 3.019 1 More than 10 years 123 2.939 0.804 0.07 2 5 2.796 3.082 1 Degree Diploma 13 2.615 0.506 0.140 3 2.309 2.921 2 BA 181 2.909 0.759 0.05 6 5 2.797 3.020 1 MA 89 2.888 0.878 0.09 3 5 2.703 3.073 1 Doctor 11 3.091 0.801 0.241 4 2.553 3.629 2 Grade level Pre-K 9 3.111 0.651 0.217 4 2.611 3.611 2 Primary 100 2.825 0.783 0.07 8 5 2.670 2.980 1 Lower Secondary 63 2.722 0.745 0.09 4 4 2.535 2.910 1 Upper Secondary 66 3.068 0.784 0.09 6 5 2.875 3.261 1 Post-Secondary 56 2.982 0.842 0.113 5 2.757 3.208 1 Subject Sciences-related 87 2.948 0.743 0.08 0 5 2.790 3.107 1 Social Sciencesrelated 70 2.971 0.751 0.09 0 5 2.792 3.151 1 Foreign Language 57 2.763 0.835 0.111 5 2.542 2.985 1 Others 80 2.869 0.837 0.09 4 5 2.682 3.055 1 School type Public 191 2.901 0.747 0.05 4 5 2.794 3.007 1 Private (normal) 49 3.041 0.822 0.117 5 2.805 3.277 1 Private (bilingual/intern ational) 37 2.730 0.838 0.138 4 2.450 3.009 1 Continuing Education Center 13 2.615 0.820 0.22 8 4 2.120 3.111 1 Others 4 3.375 1.493 0.747 5 0.999 5.751 2 Income before COVID-19 <214 24 3.000 0.571 0.117 4 2.759 3.241 2 214~427 124 2.927 0.823 0.07 4 5 2.781 3.074 1 pandemic (USD) 427~641 67 2.851 0.685 0.08 4 5 2.684 3.018 1 641~855 42 2.821 0.832 0.128 5 2.562 3.081 1 >855 37 2.892 0.936 0.154 5 2.580 3.204 1 Income during COVID-19 pandemic (USD) <214 100 2.905 0.695 0.07 0 4 2.767 3.043 1 214~427 133 2.868 0.790 0.06 9 5 2.733 3.004 1 427~641 35 3.071 0.768 0.130 5 2.807 3.335 2 641~855 19 2.684 1.157 0.265 5 2.126 3.242 1 >855 7 3.000 1.000 0.378 5 2.075 3.925 2 Expected income after COVID-19 pandemic (USD) <214 36 2.931 0.767 0.128 4 2.671 3.190 1 214~427 114 2.908 0.760 0.071 5 2.767 3.049 1 427~641 84 2.881 0.767 0.08 4 5 2.715 3.047 1 641~855 28 2.893 0.936 0.177 5 2.530 3.256 1 >855 32 2.859 0.882 0.156 5 2.541 3.177 1 Total 294 2.896 0.789 0.04 6 5 2.806 2.987 1 Table 2. Descriptive statistics of teachers' perceptions of factors that affect their teaching profession during COVID-19 pandemic N Range Min Max Mean Std. Deviation Variance Statistic Std. Error COVID-19 pandemic is affecting teachers'... Health (Feel_covid) 294 4 1 5 4.00 .049 .834 .696 Living habit (Feel_habit) 294 4 1 5 3.17 .045 .777 .604 Financial status (Feel_fin) 294 4 1 5 3.40 .052 .895 .801 During COVID-19 pandemic, teachers received supports from... School Board of Management (Sup_bod) 294 4 1 5 2.57 .065 1.114 1.242 Parents Association (Sup_parents) 294 4 1 5 2.15 .050 .865 .749 Teacher Union (Sup_union) 294 4 1 5 2.00 .048 .820 .672 Government (Sup_gov) 294 4 1 5 2.10 .051 .868 .754 Do not receive any support (Sup_none) 294 4 1 5 3.31 .072 1.227 1.505 Regarding online teaching tools, teachers... Mastered those ICT tools before COVID19 pandemic (ICT_before) 294 4 1 5 3.25 .052 .884 .781 Do not face difficulty during COVID-19 pandemic (ICT_difficult) 294 4 1 5 3.24 .051 .871 .759 Know many types of online teaching tools (ICT_diverse) 294 4 1 5 3.50 .062 1.057 1.118 Teachers often learn new ICT tools... Proactively (ICT_proactive) 294 3 2 5 3.62 .047 .804 .647 More than what school provides (ICT_extend) 294 3 2 5 3.73 .045 .765 .585 Table 3. Descriptive statistics of teachers' perceptions of school readiness, teaching effectiveness, and professional development during COVID-19 pandemic N Range Min Max Mean Std. Deviation Variance Statistic Std. Error Regarding online teaching activities, teachers feel that ... It's as effective as normal class (Onl_effective) 294 4 1 5 2.96 .066 1.130 1.278 Students are more active (Onl_active) 294 4 1 5 3.04 .050 .860 .739 There is more workload (Onl_workload) 294 4 1 5 3.70 .051 .874 .763 They are more stressful (Onl_stress) 294 4 1 5 3.06 .051 .878 .771 The school's readiness toward transformations during COVID-19 pandemic ICT infrastructure (Ready_ICT) 294 4 1 5 3.35 .051 .872 .761 Teacher capabilities (Ready_teacher) 294 4 1 5 3.46 .050 .861 .741 Policies and regulation (Ready_policy) 294 4 1 5 3.40 .051 .875 .766 During COVID-19 pandemic, teachers learnt new knowledge and skills on/due to... ICT (New_ICT) 294 4 1 5 3.92 .042 .728 .530 Pedagogical (New_pedagogy) 294 4 1 5 3.64 .046 .787 .619 School's supportiveness (New_by_bod) 294 4 1 5 2.88 .053 .914 .836 Colleagues (New_by_colleagues) 294 4 1 5 3.02 .055 .936 .877 Do not have time to learn new things (New_lackoftime) 294 4 1 5 2.92 .060 1.021 1.042 Table 4. Test of Homogeneity of Variances Levene Statistic df1 df2 Sig. Gender .976 2 291 .378 Exp .887 3 290 .448 Degree 1.424 3 290 .236 Grade_level .282 4 289 .889 Subject .983 3 290 .401 School type 1.416 4 289 .229 Income before 2.102 4 289 .081 Income during 3.183 4 289 .014 Income expect .582 4 289 .676 feel covid .413 4 289 .800 Feel habit .705 4 289 .589 feel fin 1.045 4 289 .384 Sup_bod 2.985 4 289 .019 Sup_parents 3.394a 3 289 .018 Sup_union 1.892 4 289 .112 Sup_gov 2.162 4 289 .073 Sup__none 4.093 4 289 .003 ICT_before 2.855 4 289 .024 ICT_difficult .490 4 289 .743 ICT_diverse 2.128 4 289 .077 ICT_proactive 2.565 3 290 .055 ICT_extend 2.732 3 290 .044 Onl_effective 1.333 4 289 .258 Onl_active 5.001 4 289 .001 Onl_workload .730 4 289 .572 Onl_stress 1.384 4 289 .239 Ready_ICT 4.785 4 289 .001 Ready_teacher 4.552 4 289 .001 Ready_policy 3.714 4 289 .006 New_ICT 2.163 4 289 .073 New_pedagogy .214 4 289 .930 New_by_bod 7.690 4 289 .000 New_by_colleagues 9.253 4 289 .000 New_lackoftime 3.597 4 289 .007 Table 5. Differences in teachers' satisfaction during COVID-19 pandemic among different demographics (ANOVA analysis) Tearcher satisfaction Sum of Squares df Mean Square F Sig. Gender Between Groups 2.566 2 1.283 2.074 .128 Within Groups 180.020 291 .619 Exp Between Groups 1.181 3 .394 .629 .597 Within Groups 181.405 290 .626 Degree Between Groups 1.478 3 .493 .789 .501 Within Groups 181.108 290 .625 Grade level Between Groups 5.195 4 1.299 2.116 .079 Within Groups 177.391 289 .614 Total 182.586 293 Subject Between Groups 1.701 3 .567 .909 .437 Within Groups 180.885 290 .624 School type Between Groups 3.996 4 .999 1.617 .170 Within Groups 178.590 289 .618 Income before Between Groups .753 4 .188 .299 .878 Within Groups 181.833 289 .629 Income expect Between Groups .121 4 .030 .048 .996 Within Groups 182.465 289 .631 Total 182.586 293 Table 6. Differences in teachers' satisfaction during COVID-19 pandemic among different examined perspectives (ANOVA analysis) Tearcher satisfaction Sum of Squares df Mean Square F Sig. Feel_covid Between Groups 7.589 4 1.897 3.133 .015 Within Groups 174.997 289 .606 Feel_habit Between Groups 18.811 4 4.703 8.299 .000 Within Groups 163.775 289 .567 Feel_fin Between Groups 11.009 4 2.752 4.636 .001 Within Groups 171.577 289 .594 Sup_union Between Groups 26.275 4 6.569 12.145 .000 Within Groups 156.310 289 .541 Sup_gov Between Groups 25.849 4 6.462 11.915 .000 Within Groups 156.737 289 .542 ICT_difficult Between Groups 3.788 4 .947 1.531 .193 Within Groups 178.798 289 .619 ICT_diverse Between Groups 2.524 4 .631 1.013 .401 Within Groups 180.062 289 .623 ICT_proactive Between Groups 1.443 3 .481 .770 .512 Within Groups 181.143 290 .625 Onl_effective Between Groups 5.463 4 1.366 2.228 .066 Within Groups 177.123 289 .613 Table 7. Robust Tests of Equality of Means toward Teacher Satisfaction Welch Statistic* df1 df2 Sig. Income_during .632 4 32.880 .643 Sup_bod 5.665 4 66.141 .001 Sup_parents** Sup_none 7.515 4 85.241 .000 ICT_before .911 4 25.462 .473 ICT_extend 2.066 3 53.860 .116 Onl_active 1.466 4 38.295 .231 Ready_ICT 6.891 4 25.282 .001 Ready_teacher 6.968 4 19.962 .001 Ready_policy 7.612 4 30.007 .000 New_by_bod 9.912 4 32.264 .000 New_by_colleagues 1.146 4 29.849 .354 New_lackoftime 5.489 4 56.265 .001 * Asymptotically F distributed. ** Robust tests of equality of means cannot be performed for Tearcher satisfaction because at least one group has the sum of case weights less than or equal to 1. 2. Experimental Design, Materials, and Methods The data was collected from 6th to 11th April 2020, the ninth week of national school suspension in Vietnam, due to the COVID-19 pandemic. Considering that there are more than one million teachers in Vietnam, it is impossible to reach all types of teachers across the country. Thus, the researchers focused on the two biggest teacher communities on Facebook: Microsoft Innovative Education Expert Vietnam MIE (38,600 members) and Vietnam Innovative Education Forum – VIEF (14,000 members). Firstly, the survey was announced by the admins of those groups and attracted around 500 interactions from members. Additionally, we randomly selected 1,000 members from each group and sent them the survey URL, separately. Overall, a total of 373 responses was collected. Couples of cross-checking questions with reversed Linkert scales were embedded in the survey Onl_workload Between Groups 2.107 4 .527 .844 .498 Within Groups 180.478 289 .624 Onl_stress Between Groups 5.803 4 1.451 2.372 .053 Within Groups 176.783 289 .612 New_ICT Between Groups 7.422 4 1.856 3.061 .017 Within Groups 175.164 289 .606 New_pedagogy Between Groups 8.382 4 2.095 3.476 .009 Within Groups 174.204 289 .603 Total 182.586 293 and helped us to eliminate 79 bias observations. Finally, we analyzed the dataset of 294 respondents. The differences between teachers' satisfaction among various demographic indicators and examined research items can be presented through ANOVA analysis. In particular, Table 4 shows the test of homogeneity of variances. Table 5 and Table 6 display the differences in teachers' satisfaction among demographic indicators and teachers' perception, respectively. The results of robust tests of equality of means are included in Table 7. Using questions with the five-points Linkert scale, this dataset demonstrated the factors associated with online teaching effectiveness, teacher satisfaction, and school effectiveness during the pandemic. Regarding the control over online teaching effectiveness (ONL_EFF), we considered four factors. First, teachers' overall perceptions of the impact of the pandemic (FEEL) are the aggregated result of the influence of the pandemic on their health; their living habits; and their financial status [7, 8]. Second, we indicated the teachers' received support (SUP) as a function of the support they receive from: School Board of Management; Parents Association; Teacher union; and Government bodies [9, 10]. The question "I do not receive any support" was included to cross-check the validity of respondents. Third, teachers' capability toward online teaching technologies (ICT_CAP) was the mean of their self-reported ICT (Information and Communication Technology) competency [10] before the pandemic emerged; the smooth of their online lesson during the pandemic; and the diversity of the tools which they mastered. Also, we added additional questions to examine the teacher's proactiveness in learning new ICT tools (ICT_ACT). We consider the influence of the above factors over online teaching effectiveness by the following regression: ONL_EFF ~ β0 + β1*FEEL + β2*(SUP) + β3*(ICT_CAP) + β4*(ICT_ACT) + u Regarding the influence over teacher satisfaction, we included teachers' self-reports among the three following constructs [11]. First, teachers' perceptions of online teaching activities (ONL_PER) were combined from the effectiveness of online class (in comparison with regular lessons – Onl_effective) [12], students' activeness (Onl_active) [13], workload increment (Onl_workload), and level of stress during the pandemic (Onl_stress) [14]. During further analytical processes, the measurement scale of increased workload and degree of stress should be reversed to ensure the consistency of the overall construct. Second, the school's readiness toward digital transformations during the pandemic (READY) was indicated by the eagerness of ICT infrastructure, teacher capabilities, policies, and regulation [15]. Third, regarding professional development, we included types and sources of new know-how that teachers absorbed during the pandemic (PD). A cross-checking question was added to exclude invalid answers "I do not have time to learn new things." If the response of this question is not consistent with the previous three, we will eliminate that observation. Considering teacher satisfaction as the primary outcome, the influence of those other factors listed above can be examined by the following regression: SAT ~ β0 + β1*(ONL_PER) + β2*(READY) + β3*(PD) + u Acknowledgments A great thanks to all the teachers who participated in this study, as well as the community managers of Microsoft Innovative Education Forum Vietnam (MIE Vietnam) and Vietnam Innovative Education Forum (VIEF), school leaders, and instructional coaches who contributed to elevating the data collection process. Competing Interests 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. References [1] Gamoran, A., & Long, D. A. (2007). Equality of educational opportunity a 40 year retrospective. In International studies in educational inequality, theory and policy (pp. 23-47). Springer, Dordrecht. [2] La, V., Pham, T., Ho, T. M., Hoàng, N. M., Linh, N. P. K., Vuong, T., ... Vuong, Q. (2020). Policy response, social media and science journalism for the sustainability of the public health system amid COVID-19 outbreak: The Vietnam lessons. Sustainability, 12, 2931. https://doi.org/10.3390/su12072931 [3] Tran, T., Hoang, A. D., Nguyen, T. T., Dinh, V.H., Nguyen, Y. C., Pham, H. H. (2020). Dataset of Vietnamese student's learning habits during COVID-19. Data in Brief, 105682 https://doi.org/10.1016/j.dib.2020.105682 [4] Tran, T., Hoang, A. D., Nguyen, Y. C., Nguyen, L. C., Ta, N. T., Pham, Q. H., Pham, C. X., Le, Q. A., Dinh, V. H., Nguyen, T. T. (2020). Toward Sustainable Learning during School Suspension: Socioeconomic, Occupational Aspirations, and Learning Behavior of Vietnamese Students during COVID-19. 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