Literature DB >> 32632376

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

Anh-Duc Hoang1, Ngoc-Thuy Ta1, Yen-Chi Nguyen1, Cong-Kien Hoang2, Tien-Trung Nguyen3, Hiep-Hung Pham4, Linh-Chi Nguyen1, Phuong-Thuc Doan1, Quynh-Anh Dao1, Viet-Hung Dinh5.   

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.
© 2020 Elsevier Inc.

Entities:  

Keywords:  COVID-19; Education Management; Ex-pat Teacher; International school; Southeast Asia; Teacher engagement; Teacher retention

Year:  2020        PMID: 32632376      PMCID: PMC7309813          DOI: 10.1016/j.dib.2020.105913

Source DB:  PubMed          Journal:  Data Brief        ISSN: 2352-3409


Specifications table

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.

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].

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.

IntendNMeanStd. DeviationStd. Error95% Confidence Interval for Mean
Lower BoundUpper Bound
GenderMale1312.7350.8900.0782.5812.889
Female1762.8010.8800.0662.6702.932
Current country of teachingIndonesia343.4410.7000.1203.1973.685
Thailand662.9040.8820.1092.6873.121
Philippines513.1570.8230.1152.9253.388
Vietnam1212.4130.8590.0782.2592.568
Singapore352.5620.5030.0852.3892.735
School typePublic school502.7470.8560.1212.5032.990
Private school2272.8190.8600.0572.7072.932
Extracurricular Edu Center292.4481.0700.1992.0412.855
NationalityAustralia and New Zealand632.9630.7410.0932.7763.150
Europe1062.8400.9320.0912.6603.019
South Africa112.0910.8700.2621.5062.676
US and Canada1072.7320.8880.0862.5622.902
Others202.4170.7860.1762.0492.785
Grade levelKindergarten142.2860.7140.1911.8732.698
Lower secondary school1112.7750.8410.0802.6172.933
Upper secondary school742.6850.8460.0982.4892.881
Primary school1082.8950.9500.0912.7143.076
DegreeBA in Education1802.9280.7870.0592.8123.044
MA in Education782.6280.9120.1032.4232.834
Teaching certificate492.4351.0440.1492.1362.735
Experience at the current countryLess than a year332.4851.0140.1772.1252.844
1 year923.0690.7290.0762.9183.220
2 years1063.0000.7350.0712.8593.141
3 years292.4830.6990.1302.2172.749
More than 3 years472.0640.9870.1441.7742.354
Income before covid-19Less than 1500 USD152.3110.6360.1641.9592.663
1500∼1999 USD282.5121.3890.2631.9733.051
2000∼2499 USD462.6960.9130.1352.4252.967
2500∼2999 USD1103.2450.6630.0633.1203.371
3000∼3499 USD812.6460.6610.0732.5002.792
3500∼3999 USD161.9170.6270.1571.5832.251
More than 4000 USD111.8480.4800.1451.5262.171
Income during covid-19Less than 1500 USD632.4181.0070.1272.1642.672
1500∼1999 USD363.1020.9560.1592.7793.425
2000∼2499 USD983.1630.7500.0763.0133.314
2500∼2999 USD642.7500.6690.0842.5832.917
3000∼3499 USD242.5420.5370.1102.3152.768
3500∼3999 USD121.8060.6580.1901.3872.224
More than 4000 USD101.8670.5020.1591.5082.226
Income after covid-19Less than 1500 USD232.4060.8990.1872.0172.794
1500∼1999 USD282.6071.2640.2392.1173.097
2000∼2499 USD302.4330.8890.1622.1012.765
2500∼2999 USD1063.3020.6380.0623.1793.425
3000∼3499 USD872.6550.6580.0712.5152.795
3500∼3999 USD191.9820.6620.1521.6632.301
More than 4000 USD142.2380.9990.2671.6612.815
Total3072.7730.8830.0502.6742.872
Table 2

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

Sum of SquaresdfMean SquareF
Gender.3251.325.416.520
Current country of teaching41.050410.26215.677.000***
School type3.58121.7902.307.101
Nationality10.58042.6453.501.008**
Grade level5.51131.8372.386.069*
Degree11.53325.7667.715.002***
Experience at the current country42.334410.58416.273.000***
Income before covid-1952.37768.73014.052.000***
Income during covid-1947.52367.92112.426.000***
Income after covid-1954.07169.01214.639.000***

* 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.

VariableSig of Homogeneity testSig of ANOVA testSig 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 SquaresdfMean SquareFSig.
NationalityBetween Groups10.58042.6453.501.008
Within Groups228.166302.756
Total238.746306
Current country of teachingBetween Groups41.050410.26215.677.000
Within Groups197.697302.655
Total238.746306
DegreeBetween Groups11.53325.7667.715.001
Within Groups227.213304.747
Total238.746306
Experience at the current countryBetween Groups42.334410.58416.273.000
Within Groups196.412302.650
Total238.746306
Income before covid-19Between Groups52.37768.73014.052.000
Within Groups186.369300.621
Total238.746306
Income during covid-19Between Groups47.52367.92112.426.000
Within Groups191.223300.637
Total238.746306
Income after covid-19Between Groups54.07169.01214.639.000
Within Groups184.675300.616
Total238.746306
Table 5

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

WelchStatisticdf1df2Sig.
Current country of teaching17.4384116.594.000
Degree6.6812105.788.002
Income before covid-1921.872659.158.000
Income during covid-1915.884661.172.000
Income after covid-1917.776667.821.000
Descriptive Statistics of Participant's Demographics. Correlations between variables and ex-pat teacher's intention to leave the current country. * Correlation is significant at the 0.05 level; ** Correlation is significant at the 0.01 level; *** Correlation is significant at the 0.001 level. Significant of ANOVA analyses. Differences in Teachers’ Intention of Leaving during the COVID-19 Pandemic among Different Demographics (ANOVA analysis). Robust Tests of Equality of Means toward Teacher's Intention of Leaving. 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.

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.
SubjectEducation, Education Management
Specific subject areaTeacher retention, Teacher engagement
Type of dataRaw data in excel file and analysed data
How data were acquiredData was gathered using an online survey and converted into the .xlsx format for formal analysis in SPSS v.20.
Data formatRawAnalyzed
Parameters for data collectionThis research focuses on ex-pat teachers who are teaching in several Southeast Asia countries: Singapore, Thailand, Vietnam, the Philippines, and Indonesia.
Description of data collectionAn 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 locationInformation 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 accessibilityRepository name: Harvard DataverseData identification number:Direct URL to data: https://doi.org/10.7910/DVN/ZB2DNH, Harvard Dataverse, V1
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