Dataset of ex-pat teachers in Southeast Asia's intention to leave due to the COVID-19 pandemic

Abstract

The COVID-19 pandemic exerted an adverse influence on the global education system, especially since starting school lockdown. The growth of teacher unemployment figures climbed double-digit and spawned these unexpected sequels. For instance, while native teachers seemed indisposed to leave the profession with the aim of seeking another more profited and seasonal jobs, many ex-pat teachers presented themselves with moving or stayed dilemma in the way the government salvaged their situation. In preference with the ex-pat teacher's case, we elucidated further throughout an e-survey in the International Baccalaureate community on Facebook from 4 to 11 April 2020 for 18,000 ex-pat teachers, who are teaching at Southeast Asia. This dataset includes 307 responses of ex-pat teachers who are staying in Singapore, Thailand, Vietnam, the Philippines, and Indonesia during the pandemic. The dataset comprises (i) Survey partakers' Demographics; (ii) Ex-pat teachers' perceptions in the relation of national, regional and school plans were afoot to the pandemic; (iii) The degree of attachment of ex-pat teacher to their current society, the ex-pat community, friends, and families during the pandemic time; (iv) Ex-pat teachers' embryo intention to reconsider their current teaching location.

Specifications table

Subject	Education, Education Management
Specific subject area	Teacher retention, Teacher engagement
Type of data	Raw data in excel file and analysed 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	This research focuses on ex-pat teachers who are teaching in several Southeast Asia countries: Singapore, Thailand, Vietnam, the Philippines, and Indonesia.
Description of data collection	An online survey has been distributed throughout the International Baccalaureate community on Facebook (18,000 ex-pat teachers worldwide) and mainly ranged within ex-pat teachers who are working in Southeast Asia.
Data source location	Information is collected from secondary student institutes in Vietnam (Latitude 16°0′N, Longitude 106°0′E), Indonesia (Latitude 5°00′N, Longitude 120°00′E), Thailand (Latitude 15°00′N, Longitude 100°00′E), Philippines (Latitude 13°00′N, Longitude 122°00′E), Singapore (Latitude 1°17′24.9702′'N, Longitude 103°51′7.0524′'E).
Data accessibility	Repository name: Harvard Dataverse Data identification number: Direct URL to data: https://doi.org/10.7910/DVN/ZB2DNH, Harvard Dataverse, V1

Value of the data

• •The dataset heralds further research into these underlying reasons why ex-pat teachers no longer keep their teaching location stayed.
• •Policymakers, schools, or even business managers can utilize this dataset to address brain drain-related phenomenon.
• •This dataset can be accessed to more corrective courses of action, which bring teachers into perceiving the policy decision.
• •The dataset offers an additional contribution to publication reviews regarding the policy's influence extended towards teacher involvement.
• •The dataset produces a scale model exploring ex-pat teachers’ changing perceptions about their current working regions, especially when these national politics formulate different policies during the COVID-19 pandemic.

1. Data description

Teacher retention and teacher engagement are strong influencers in educational institutions, especially in terms of students’ academic achievement [1,2]. Due to the COVID-19 pandemic, schools around the world had to choose distance learning with many changes in ways of teaching and learning, thus native and ex-pat teachers were both affected [3,4]. In addition, this unexpected digital transformation creates many educational problems related to learning and teaching demand [5]. This dataset focuses on ex-pat teachers’ engagement and intention to leave, which is an expansion of our recent research about Vietnamese teachers’ perceptions and student's learning habits during the pandemic [6], [7]–8].

This dataset contains two main parts, the first part is demographic information, and the second part reports on teachers’ perspective and intention. The former includes teachers’ gender, nationality, teaching country, teaching subject and grade, school type, teaching qualification and experience, and participants’ income. The later part concerns three main issues related to the pandemic: (i) Policy and regulation toward ex-pat teachers; (ii) Ex-pat teachers’ engagement with various communities; and (iii) Intention to leave of ex-pat teachers. The above variables can be used to study teacher retention, teacher engagement, impacts of policy, and teachers’ salary. Finally, the full survey, code, and measurement parameters for all variables can be found on Harvard Dataverse [9].

2. Experimental design, materials, and methods

Firstly, four experts in K-12 international education and organizational behavior were asked to pretest the validity of the assessments. Then we implemented a pilot study including 50 observations, before distributing the survey online within a Facebook community named International Baccalaureate from 4th to 11th April 2020. We only collect data from ex-pats who were teaching in Southeast Asia and recorded 528 accesses on the survey link. Among those, teachers from Indonesia, Philippines, Singapore, Thailand, and Vietnam accounted for the majority; thus, 36 responses were deleted since they were from other countries. Finally, after cleaning the dataset, there were 307 observations valid for further analysis.

Table 1 is the descriptive statistics of participants’ demographics. Table 2 shows the relationship between ex-pat teachers’ intention to leave and various indicators. The differences between participants’ retention among demographic variables are examined and presented through ANOVA analysis. Specifically, Table 3 is the summary of ANOVA analysis's significance, Table 4 is the more detailed results of between and within groups, and Table 5 shows specific robust test’ results.

Table 1.

Descriptive Statistics of Participant's Demographics.

Intend		N	Mean	Std. Deviation	Std. Error	95% Confidence Interval for Mean
						Lower Bound	Upper Bound
Gender	Male	131	2.735	0.890	0.078	2.581	2.889
	Female	176	2.801	0.880	0.066	2.670	2.932
Current country of teaching	Indonesia	34	3.441	0.700	0.120	3.197	3.685
	Thailand	66	2.904	0.882	0.109	2.687	3.121
	Philippines	51	3.157	0.823	0.115	2.925	3.388
	Vietnam	121	2.413	0.859	0.078	2.259	2.568
	Singapore	35	2.562	0.503	0.085	2.389	2.735
School type	Public school	50	2.747	0.856	0.121	2.503	2.990
	Private school	227	2.819	0.860	0.057	2.707	2.932
	Extracurricular Edu Center	29	2.448	1.070	0.199	2.041	2.855
Nationality	Australia and New Zealand	63	2.963	0.741	0.093	2.776	3.150
	Europe	106	2.840	0.932	0.091	2.660	3.019
	South Africa	11	2.091	0.870	0.262	1.506	2.676
	US and Canada	107	2.732	0.888	0.086	2.562	2.902
	Others	20	2.417	0.786	0.176	2.049	2.785
Grade level	Kindergarten	14	2.286	0.714	0.191	1.873	2.698
	Lower secondary school	111	2.775	0.841	0.080	2.617	2.933
	Upper secondary school	74	2.685	0.846	0.098	2.489	2.881
	Primary school	108	2.895	0.950	0.091	2.714	3.076
Degree	BA in Education	180	2.928	0.787	0.059	2.812	3.044
	MA in Education	78	2.628	0.912	0.103	2.423	2.834
	Teaching certificate	49	2.435	1.044	0.149	2.136	2.735
Experience at the current country	Less than a year	33	2.485	1.014	0.177	2.125	2.844
	1 year	92	3.069	0.729	0.076	2.918	3.220
	2 years	106	3.000	0.735	0.071	2.859	3.141
	3 years	29	2.483	0.699	0.130	2.217	2.749
	More than 3 years	47	2.064	0.987	0.144	1.774	2.354
Income before covid-19	Less than 1500 USD	15	2.311	0.636	0.164	1.959	2.663
	1500∼1999 USD	28	2.512	1.389	0.263	1.973	3.051
	2000∼2499 USD	46	2.696	0.913	0.135	2.425	2.967
	2500∼2999 USD	110	3.245	0.663	0.063	3.120	3.371
	3000∼3499 USD	81	2.646	0.661	0.073	2.500	2.792
	3500∼3999 USD	16	1.917	0.627	0.157	1.583	2.251
	More than 4000 USD	11	1.848	0.480	0.145	1.526	2.171
Income during covid-19	Less than 1500 USD	63	2.418	1.007	0.127	2.164	2.672
	1500∼1999 USD	36	3.102	0.956	0.159	2.779	3.425
	2000∼2499 USD	98	3.163	0.750	0.076	3.013	3.314
	2500∼2999 USD	64	2.750	0.669	0.084	2.583	2.917
	3000∼3499 USD	24	2.542	0.537	0.110	2.315	2.768
	3500∼3999 USD	12	1.806	0.658	0.190	1.387	2.224
	More than 4000 USD	10	1.867	0.502	0.159	1.508	2.226
Income after covid-19	Less than 1500 USD	23	2.406	0.899	0.187	2.017	2.794
	1500∼1999 USD	28	2.607	1.264	0.239	2.117	3.097
	2000∼2499 USD	30	2.433	0.889	0.162	2.101	2.765
	2500∼2999 USD	106	3.302	0.638	0.062	3.179	3.425
	3000∼3499 USD	87	2.655	0.658	0.071	2.515	2.795
	3500∼3999 USD	19	1.982	0.662	0.152	1.663	2.301
	More than 4000 USD	14	2.238	0.999	0.267	1.661	2.815
Total		307	2.773	0.883	0.050	2.674	2.872

Table 2.

Correlations between variables and ex-pat teacher's intention to leave the current country.

	Sum of Squares	df	Mean Square	F
Gender	.325	1	.325	.416	.520
Current country of teaching	41.050	4	10.262	15.677	.000***
School type	3.581	2	1.790	2.307	.101
Nationality	10.580	4	2.645	3.501	.008**
Grade level	5.511	3	1.837	2.386	.069*
Degree	11.533	2	5.766	7.715	.002***
Experience at the current country	42.334	4	10.584	16.273	.000***
Income before covid-19	52.377	6	8.730	14.052	.000***
Income during covid-19	47.523	6	7.921	12.426	.000***
Income after covid-19	54.071	6	9.012	14.639	.000***

* Correlation is significant at the 0.05 level; ** Correlation is significant at the 0.01 level; *** Correlation is significant at the 0.001 level.

Table 3.

Significant of ANOVA analyses.

Variable	Sig of Homogeneity test	Sig of ANOVA test	Sig of Robust Tests of Equality of Means
Nationality	.377	.008
Current country of teaching	.024		.000
Teaching qualification	.038		.002
Experience at current country	.120	.000
Income before COVID-19	.000		.000
Income during COVID-19	.003		.000
Income after COVID-19	.000		.000

Table 4.

Differences in Teachers’ Intention of Leaving during the COVID-19 Pandemic among Different Demographics (ANOVA analysis).

		Sum of Squares	df	Mean Square	F	Sig.
Nationality	Between Groups	10.580	4	2.645	3.501	.008
	Within Groups	228.166	302	.756
	Total	238.746	306
Current country of teaching	Between Groups	41.050	4	10.262	15.677	.000
	Within Groups	197.697	302	.655
	Total	238.746	306
Degree	Between Groups	11.533	2	5.766	7.715	.001
	Within Groups	227.213	304	.747
	Total	238.746	306
Experience at the current country	Between Groups	42.334	4	10.584	16.273	.000
	Within Groups	196.412	302	.650
	Total	238.746	306
Income before covid-19	Between Groups	52.377	6	8.730	14.052	.000
	Within Groups	186.369	300	.621
	Total	238.746	306
Income during covid-19	Between Groups	47.523	6	7.921	12.426	.000
	Within Groups	191.223	300	.637
	Total	238.746	306
Income after covid-19	Between Groups	54.071	6	9.012	14.639	.000
	Within Groups	184.675	300	.616
	Total	238.746	306

Table 5.

Robust Tests of Equality of Means toward Teacher's Intention of Leaving.

	Welch	Statistic	df1	df2	Sig.
Current country of teaching		17.438	4	116.594	.000
Degree		6.681	2	105.788	.002
Income before covid-19		21.872	6	59.158	.000
Income during covid-19		15.884	6	61.172	.000
Income after covid-19		17.776	6	67.821	.000

This dataset uses mainly five-point Likert scale to examine the impacts of various factors on ex-pat teachers’ retention. Based on this dataset, some research can be carried out to study the relationships between teacher engagement and external policy on teachers’ intention to leave (INTEND). First, teacher engagement (ENGAGE) is considered to have long-term influence over schools and societies [10]. This relationship becomes even more substantial and more complicated, especially in this era of globalization, when ex-pat teachers frequently face multiculturalism [11]. In this dataset, teacher engagement can be indicated by activities and communication among inter-related stakeholders [12], such as local communities (ENGAGE_LOCAL), ex-pat communities (ENGAGE_EXPAT) and families and friends at home (ENGAGE_HOME). Consequently, the relationship between teacher engagement and teacher retention can be found by using the regression model (1). Similarly, the impact of policy on teacher retention should also be investigated [13,14], 1985) as in model (2). In the questionnaire, the policy and regulation (POLICY) under examination are national policy (POLI_NATION), regional policy (POLI_REGION), and school policy (POLI_SCHOOL). Finally, model (3) can also lead to significant results. However, as policies can affect teacher engagement [15], researchers may consider using instrumental variables.

$$\text{INTEND}\sim \beta 0+\beta 1*\text{ENGAGE}+\mathrm{u}$$ (1)

$$\text{INTEND}\sim \beta 0+\beta 1*\text{POLICY}+\mathrm{u}$$ (2)

$$\text{INTEND}\sim \beta 0+\beta 1*\text{ENGAGE}+\beta 2*\text{POLICY}+\mathrm{u}$$ (3)

Declaration of Competing 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.