Literature DB >> 32395572

Dataset of Vietnamese student's learning habits during COVID-19.

Tran Trung1, Anh-Duc Hoang2, Trung Tien Nguyen3, Viet-Hung Dinh4, Yen-Chi Nguyen2, Hiep-Hung Pham2,5.   

Abstract

A dataset was constructed to examine Vietnamese student's learning habits during the time schools were suspended due to the novel coronavirus - SARS-CoV-2 (COVID-19), in response to a call for interdisciplinary research on the potential effects of the coronavirus pandemic (Elsevier, 2020). The questionnaires were spread over a network of educational communities on Facebook from February 7 to February 28, 2020. Using the snowball sampling method, researchers delivered the survey to teachers and parents to provide formal consent before they forwarded it to their students and children. In order to measure the influence of students' socioeconomic status and occupational aspirations on their learning habits during school closures, the survey included three major groups of questions: (1) Individual demographics, including family socioeconomic status, school type, and occupational aspirations; (2) Student's learning habits, including hours of learning before and during the period of school suspension, with and without other people's support; and (3) Students' perceptions of their self-learning during the school closures. There was a total of 920 clicks on the survey link, but only 460 responses accompanied by consent forms were received. Non-credible answers (e.g., year of birth after 2009, more than 20 hours of learning per day) were eliminated. The final dataset included 420 valid observations.
© 2020 The Author(s).

Entities:  

Keywords:  COVID-19; Learning habits; Occupational Aspiration; School closure; Secondary school; Socioeconomic; Vietnam

Year:  2020        PMID: 32395572      PMCID: PMC7207136          DOI: 10.1016/j.dib.2020.105682

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


Specifications Table

Value of the data

The dataset will be useful for researchers who want to compare students’ habits in a normal situation and unusual situations such as a pandemic. The dataset will be valuable to researchers who want to examine relationships between socioeconomic status, occupational aspirations, and students’ learning habits. The dataset will be useful for researchers who want to conduct comparative studies on students’ learning habits in different countries. The results of this dataset also contribute to enhancing educational leaders’ and policymakers’ awareness of the effects of sudden changes in educational scenarios, so education systems may be better prepared for similar situations in the future.

Data Description

Students’ learning habits are not the same during the school year and holidays. While a decrease in students’ formal learning habits during holidays is seasonal and predictable [1], the adjustments in their learning habits during a sudden pandemic are still unresearched. The preparation of this dataset is a response to the call for inter-disciplinary research about the effects of the novel coronavirus pandemic [2]. As a country that dealt with the COVID-19 outbreak very early and productively, Vietnam is a notable case study of instantaneous and conspicuous collaboration between the government and society [3]. However, the shift in the educational system was unforeseen and caused significant side effects [4]. This dataset [5] focused on the learning habits of 420 secondary students (Grade 6-12) in Hanoi during the first two weeks of school closures due to COVID-19. The dataset includes three major groups of variables: (A) Individual demographics, including family socioeconomic status (SES), school type, and occupational aspirations; (B) Students’ learning habits, including hours of learning before and during the period of school suspension, with and without other people's support; and (C) Students’ perceptions of their self-learning during the school closures. In addition, we added a question to measure the integration of online lessons during this time with sustainability topics. Detailed descriptions of all variables, together with the questions for each variable, and descriptive tables and figures can be found in the Mendeley data repository [5]. Tables 1, 2, 3 and 4.
Table 1

Descriptive statistics of demographics and students’ learning habits

Learning hoursNMeanStd. DeviationStd. ErrorMax95% Confidence Interval for Mean
Min
Lower BoundUpper Bound
A. Students’ demographic
GenderMale1661.57.699.05431.471.681
Female2391.59.704.04631.501.681
Not public151.47.640.16531.111.821
Total4201.58.699.03431.511.641
Grade levelSecondary school2341.61.687.04531.521.701
High school1861.54.714.05231.431.641
Total4201.58.699.03431.511.641
School typePublic school (normal)1861.50.668.04931.401.601
Public school (Gifted)1321.65.741.06531.521.781
Private school (normal)941.63.672.06931.491.771
International school81.50.926.3273.732.271
Total4201.58.699.03431.511.641
SiblingsOne381.53.797.12931.261.791
Two2471.60.684.04431.521.691
Three571.51.685.09131.331.691
Four or more781.56.713.08131.401.721
Total4201.58.699.03431.511.641
Father's jobSTEM-related1411.59.687.05831.471.701
Social Science1721.64.724.05531.531.751
Free731.51.710.08331.341.671
Others341.35.544.09331.161.541
Total4201.58.699.03431.511.641
Mother's jobSTEM-related321.59.712.12631.341.851
Social Science2701.62.715.04431.531.701
Free631.43.665.08431.261.601
Others551.53.634.08531.361.701
Total4201.58.699.03431.511.641
Family incomeLess than 430 USD621.52.671.08531.351.691
From 430 to under 860 USD1411.48.628.05331.371.581
From 860 to under 1,290 USD971.60.745.07631.451.751
From 1,290 to under 1,720 USD501.80.700.09931.602.001
From 1,720 to under 2,150 USD301.70.794.14531.402.001
More than 2,150 USD401.60.744.11831.361.841
Total4201.58.699.03431.511.641
University Entrance Exam subject groupA (Math, Physics, Chemistry)521.48.641.08931.301.661
A1 (Math, Physics, English)641.84.672.08431.682.011
B (Math, Biology, Chemistry)231.70.559.11731.451.941
C (Literature, History, Geography)221.41.734.15731.081.731
D (Literature, Foreign Language, Mathematics)1871.55.727.05331.441.651
Other721.50.671.07931.341.661
Total4201.58.699.03431.511.641
Self-evaluation of Academic performanceBelow Average71.14.378.1432.791.491
Average1091.41.596.05731.301.531
Good2511.62.702.04431.531.701
Excellent531.77.824.11331.552.001
Total4201.58.699.03431.511.641
English language proficiencyBelow Average351.43.655.11131.201.651
Average1351.46.620.05331.351.561
Good1911.62.721.05231.521.731
Excellent591.78.767.10031.581.981
Total4201.58.699.03431.511.641
B. Students’ learning habits
Learning time before COVID-19under 4h3121.38.560.03231.321.441
from 4 to 7h932.09.732.07631.942.241
over 7h152.53.743.19232.122.941
Total4201.58.699.03431.511.641
Learning time during COVID-19under 4h2291.08.292.01931.041.121
from 4 to 7h1401.12.388.03331.061.191
over 7h511.39.666.09331.201.581
Total4201.13.398.01931.101.171
Online learning time during COVID-19under 4h3041.37.593.03431.301.431
from 4 to 7h881.97.535.05731.852.081
over 7h282.64.731.13832.362.931
Total4201.58.699.03431.511.641
Learning time with instructionunder 4h3731.53.666.03431.461.601
from 4 to 7h381.82.834.13531.542.091
over 7h92.44.726.24231.893.001
Total4201.58.699.03431.511.641
Table 2

Descriptive statistics of students’ perceptions of their self-learning during school closures

C. Students’ perception of self-learning during COVID-19NRangeMinMaxMean
Std. Deviation
StatisticStd. Error
Self-learning during school closure due to COVID-19 is necessary because…
I can ensure my learning progress4204153.90.047.965
I can maintain my learning habits4204153.88.045.926
My teachers show me it is necessary4204153.66.0501.031
My parents show me it is necessary4204153.73.0501.019
My siblings show me it is necessary4204153.27.0551.125
My friends show me it is necessary4204153.25.0541.113
I consider my self-learning activities are effective because…
I have motivation for self-learning4204153.44.049.998
I have good concentration skills4204153.36.047.970
I have support from my family4204153.35.0531.090
I have an effective learning environment4204153.55.0501.034
I can define my daily learning objectives4204153.44.0501.017
I have various learning resources4204153.66.048.983
I communicate and collaborate with my friends about learning4204153.21.0551.129
Table 3

Correlations among variables and students’ total learning hours during COVID-19

VariablesTotal Learning hours during COVID-19
P-valure
Sum of SquaresdfMean SquareF
Students’ demographics
Gender.2042.102.209.812
Grade level.4961.4961.017.314
School type2.1243.7081.455.226
Siblings.5463.182.371.774
Father's job2.7583.9191.895.061**
Mother's job1.9983.6661.368.252
Family income4.6955.9391.945.086
University Entrance Exam subject group24.148212.0744.208.018***
Self-evaluation of Academic performance6.71732.2394.708.002***
English language proficiency5.47031.8233.810.014***
Learning hour before COVID-1950.145225.07267.708.000***
Students’ perceptions about the necessity of learning during COVID-19
I can ensure my learning progress3.3604.8401.733.061**
I can maintain my learning habits11.88442.9716.399.001***
My teachers show me it is necessary2.8794.7201.481.207
My parents show me it is necessary5.13541.2842.672.032***
My siblings show me it is necessary3.8654.9661.998.094
My friends show me it is necessary3.1214.7801.607.171
Students’ perception about factors that support learning during COVID-19
I have motivation for self-learning20.71145.17811.687.000***
I have good concentration skills13.66843.4177.428.000***
I have support from my family6.08341.5213.180.014***
I have an effective learning environment12.05443.0136.496.000***
I can define my daily learning objectives21.51445.37812.194.000***
I have various learning resources12.96343.2417.019.000***
I communicate and collaborate with my friends about learning6.03541.5093.154.014***
Table 4

Integration of online sessions with sustainability topics

NRangeMinMaxMean
Std. Deviation
StatisticStd. Error
General Preventive Health care4204153.85.048.985
Coronaviruses4204153.93.047.959
Sustainable Environment Development4204153.58.0490.995
Sustainable Society Development4204153.49.0501.033
E-learning tools and techniques4204153.35.0531.081
Descriptive statistics of demographics and students’ learning habits Descriptive statistics of students’ perceptions of their self-learning during school closures Correlations among variables and students’ total learning hours during COVID-19 Integration of online sessions with sustainability topics

Experimental Design, Materials, and Methods

The survey was conducted between February 7 and February 28, 2020, the first three weeks of nationwide school closures due to COVID-19. Initially, online questionnaires were delivered to parents and teachers who were active in various educational forums on Facebook. Thereafter, it was spread by parents’ and teachers’ referrals. Parents or teachers were required to complete the consent form before forwarding the URL to the student. A total of 460 responses were received, but only 420 valid observations were accepted for further analysis, due to the elimination of obviously invalid answers (e.g. more than 20 hours of learning per day). Overall, the influence of SES and students’ occupational aspirations on their learning habits during COVID-19 was examined using ordinary least squares (OLS) regression: Theoretically, the survey was designed based on prior literature on transformative learning, with the focus on socioeconomic differences. Variables in group A related to students’ demographics, including SES factors and students’ self-evaluated competencies. Scholars have pointed out that SES factors such as monthly family income, parents’ occupations, number of siblings, school type, and grade level have significant influences on students’ learning habits [6,7]. This study complements the conventional notion of SES with additional variables about students’ competencies. Specifically in the case of Vietnam, we added subjects for university entrance, which demonstrate students’ occupational aspirations, and English, which is a crucial competency in today's world. Variables in group B measured students’ learning habits by their learning hours per day [8]. In particular, students were asked their total hours of self-learning before and during COVID-19. With regard to the total number of learning hours during COVID-19, there were sub-questions about the total hours of off-line and online study modes, as well as the total hours of learning with instruction or without instruction from other people. Variables in group C were mainly designed for this specific data collection. All items in this section were measured using a five-point Likert scale (1: Totally Disagree, 5: Totally Agree). First, we examined students’ perceptions on the necessity for self-learning during COVID-19. According to the literature on transformative learning, students’ learning practices are influenced by their beliefs about learning and influences from teachers, parents, and peers [9]. Thus, we constructed the variable of “students’ necessity for self-learning” using the following items: (i) to ensure my learning progress; (ii) to maintain my learning habits; (iii) influenced by teachers; (iv) influenced by parents; (v) influenced by siblings; (vi) influenced by friends. Second, we measured students’ self-reports on factors that influence self-learning effectiveness. This variable consisted of different physical factors (the availability of learning resources [10], learning space [11]), psychological factors (self-motivation [12], family support [13]), and behavioural factors (concentration, goal setting [14], communication and peer collaboration [15]). In addition, with regard to the unique context of school closures due to COVID-19, we measured the integration of students’ online lessons with sustainability topics. Students were asked whether they were taught any of those topics or not: (i) General Preventive Health care; (ii) Coronaviruses; (iii) Sustainable Environment Development; (iv) Sustainable Society Development; (v) E-learning tools and techniques.
SubjectEducation, Secondary Education
Specific subject areaLearning analytics, Socioeconomic, Occupational orientation
Type of dataTableFigureExcel fileSav file
How data were acquiredData was gathered using an online survey and converted into .xlsx format for formal analysis in SPSS v.20
Data formatRawAnalyzed
Parameters for data collectionThe target population of the survey was students in Hanoi who are learning online due to the effect of COVID-19. Only Grade 6-12 students were selected as they can evaluate their learning activities, and have more explicit occupational aspirations. Only students who had parental approval could access the survey.
Description of data collectionThe data was conducted through an online questionnaire, which was delivered to Grade 6-12 students in Hanoi using the snowball sampling method.
Data source locationInformation was collected from secondary schools in Hanoi (Latitude 21°1′28.2"N, Longitude 105°50′28.21"E), Vietnam
Data accessibilityRepository name: Mendeley repositoryData identification number:Direct URL to data: http://dx.doi.org/10.17632/2pzvmnb2km.3
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