DP2019-27

A Review of Data Used in
Education Research: Focus on
Empirical Studies in
Developing Countries
Phal CHEA

December 26, 2019

A Review of Data Used in Education Research:
Focus on Empirical Studies in Developing Countries
Phal Chea*
December 2019

Abstract
This paper intends to review education-related datasets, including data from household surveys,
learning assessments and field experiments, publicly available for researchers and students
interested in conducting education research. It also presents ideas on how those data can be used inempirical studies and identifies some major potential sources of those datasets. Issues in education
have shifted from access to quality learning and at the same time, randomized control trial (RCT)
has become the gold standard in measuring the impacts of education programs. The paper also
notices the emerging field of educational data mining employed to predict student performance or
to identify at-risk students.
Keywords: Education data, household survey, learning assessment, field experiment, educational
data mining
JEL Codes: A20, C80, I21, I25*

Adjunct Researcher, Research Institute for Economics and Business Administration (RIEB), Kobe University

1. Introduction
Despite good progress in expanding educational access over the past decades in developing countries
around the world, it is estimated that more than 120 million children and 137 million adolescentsand youth are still out of school in 2018 globally (UIS, 2019). The progress in reducing the global
number of out-of-school children has stagnated in recent years, regardless of the adoption of
Education for All (EFA) in 2000 and Sustainable Development Goal 4 (SDG 4) in 2015 aiming at
realizing universal primary and secondary education. In addition, recent studies indicate that many
children fail to acquire basic knowledge and skills needed to be productive after leaving school. As
a result, there has been a shift from the emphasis on access (years of schooling) toward the qualityof learning using learning achievement as a benchmark. At the same time, there is an increasing
number of studies in the past two decades employing more rigorous research designs using the field
experiment approach to measure casual impacts rather than relationships between educationinterventions and education outcomes. Educational Data Mining is another new emerging
interdisciplinary research field, combining computer science, statistics, and education, to better
understand and predict students’ success or failure.
To monitor progress toward SDG 4 and to make well-informed decisions based on evidence,
reliable and high-quality data are indispensable. Key sources of education data include
administrative datasets collected through education system, household surveys, learningassessments, and data collected through specific education projects. This paper intends to review
education-related datasets which are publicly accessible for researchers and students interested in
conducting research in the field of education development. It also provides some examples ofexisting literature that have employed those datasets in the empirical analysis as well as identifying
the potential sources where those datasets can be obtained.

2. Household Surveys and Population Censuses
Household surveys are an important source of data on educational access, participation, andattainment. Most countries conduct household surveys to collect a wide range of information fromhealth, education, employment to consumption on a regular basis. In most cases, information suchas education level, grade completion, school participation and literacy of household members aged
five and older can be found in household surveys (Education Policy and Data Center, 2009). Somehousehold surveys, including the Living Standards Measurement Study (LSMS), also provide
information about household expenditure on education. Combining with other information of1households characteristics (e.g. household size, gender of household head, parental education, andhousehold wealth) and of individuals (e.g. age, gender, relationship to household head, andemployment status), household surveys are frequently used to assess the relationships between
education access and other demand-side factors to understand the education system as well as to
identify educational issues of particular populations. However, since the scope of household surveys
is very broad, normally information related to supply-side factors, such as characteristics of schools
and teachers, is scarcely included. Supply-side information can be obtained from education
administrative statistics collected by local governments from schools, such as Education Monitoring
Information System (EMIS), but in most cases, it is extremely difficult to merge household data andschool administrative data for analysis. In addition to household surveys, national statistics offices
also conduct national censuses, albeit less frequently conducted, and the number of variables foundin the national census is much smaller.

2.2. Studies Using Household Survey Data in Education
Household surveys or national censuses are a good source for the estimation of access to education,
numbers of out-of-school children, and/or dropout students. There is also a bulk of literature of
empirical studies using household surveys to examine how demand-side factors influence children’s
school participation, education attainment or dropouts in developing countries as illustrated in Figure
1. These kinds of analyses can help identify individual and household factors associated with schoolparticipation and education attainment or shed some light on educational gaps between subgroupsof populations. Household factors commonly found to have influences on children’s schoolinginclude parental education, socioeconomic status, and residence location (Connelly & Zheng, 2003;
Mani, Hoddinott, & Strauss, 2013; Sánchez & Singh, 2018; Tansel, 2002). Studies on the effect of
migration on the schooling outcomes of left-behind children have produced mixed results as
migration is inextricably linked with remittances that can off-set the absence of migrant household
members (Iwasawa, Inada, & Fukui, 2014; Mansuri, 2006; Rapoport & Docquier, 2006). Although
the correlations found between labor status and schooling outcomes are rather controversial, longhour child labor or child work seems to do more harm than good to the human capital accumulationof the children (Edmonds, 2007; Guarcello, Lyon, & Valdivia, 2015).
Since household surveys normally also collect information of household income andexpenditure, we can also conduct a benefit incidence analysis to examine the distribution of public
education spending across the population to assess inequality issues by assuming that education
expenditure per student of each level is the same (Lassibille & Tan, 2007). For instance, using Ghana

2

Living Standards Survey (GLSS5) 2005/06, Gaddah, Munro, and Quartey (2015) found that the poorin Ghana greatly benefit from the increase in public spending, in particular in the pre-school andprimary school levels. The Mincer equation, using wage earnings as a function of schooling andworking experience, is widely adopted in the estimation of the rates of return to education(Psacharopoulos, 1994; Psacharopoulos & Patrinos, 2018). The cross-country study in 1994 revealed
that return to primary education was very high, but the study using newer datasets in 2018 foundthat return to higher education has become higher recently. Information about hour wages, education
backgrounds, and working experiences are commonly available in household surveys or labor
surveys for such a rate of return analysis.
Figure 1. Analysis Framework of Household Factors and Educational OutcomesChild Characteristics

Household Characteristics











Age
Gender
Employment status

Household composition
Parent or head education
Parent occupations
Household wealth
Location of residence

Education Outcomes

School participation
Educational attainment
Drop-out or out of school

Source: Created by the author based on UIS (2004)

Although information on government’s educational interventions is scarce in household surveys,
some interventions, such as government’s scholarships and school feeding programs, can be foundin some household surveys. Canton and Blom (2004) examined the relationship between a student
loan program and university enrollment using a probit model with Mexico’s national household
survey data. While it is easy to investigate relationships between public interventions andeducational outcomes using cross-sectional household surveys, it is much more challenging tomeasure the impact (causality) of the programs due to self-selection issues. In other words,
characteristics of public program beneficiaries are normally different from non-participants.
Randomized Controlled Trial (RCT), to be discussed later, is now the gold standard to evaluate the3program impacts. Nevertheless, there are some techniques using econometric models, such as
instrument variables (IV) and propensity score matching (PSM), to capture the impact of suchprogram interventions (Angrist & Pischke, 2008). For example, Nakata (2013) uses the IV methodwith data from the Vietnamese Household Living Standard Survey (VHLSS), in an attempt tomeasure the effect of government’s financial assistance programs on access to higher education
among upper secondary graduates from poor households.

2.2. Potential Data Sources
Some well-known large-scale international household surveys include the Demographic and Health
Survey (DHS), the Multiple Indicator Cluster Survey (MICS), and the Living Standards
Measurement Study (LSMS). Countries conducting these household surveys are listed in Table A.1
in the Appendix.

-

The Demographic and Health Survey (DHS): Started in the 1980s, DHS has a bigcollection of more than 400 household surveys collected from over 90 countries, providing
good sources of information about health, nutrition, demography, socioeconomic status, andeducation. Due to its large sample size, in the range of 5,000 to 30,000 households, it canbe represented by both the national and sub-national levels. Recent surveys also provided
the GPS data of the sampled locations. Different from other surveys, DHS has detailed
information on women aged 15-49 years old. DHS is supported by ICF International and the
U.S. Agency for International Development (USAID).1-

The Multiple Indicator Cluster Survey (MICS): Although MISCS was only started in
1995, it has expanded rapidly in a short period of fewer than three decades. As of 2019,
UNICEF has conducted 326 surveys in more than 116 countries around the world, mainly
in developing countries. It has become an important source of internationally comparable
data to produce indicators on women and children to track the agreed targets of theMillennium Development Goals (MDGs) and Sustainable Development Goals (SDGs).
Variables related to topics such as maternal and child health, education, child mortality, andchild protection can be found in the MICS survey.2

12

https://www.dhsprogram.com/data/
https://mics.unicef.org/surveys

4-

The Living Standards Measurement Study (LSMS): Initiated in 1980 aiming to improve
the quality of household data, the World Bank has worked in collaboration with local
government statistical offices in developing countries to collect household data and, at thesame time, built up the capacities of the national statistical officials. Key indicators include
consumption, income, savings, human capital, and anthropometrics. To date, 37 countries
have participated in the LSMS, and 121surveys are listed in the LSMS collection of theWorld Bank’s microdata library.3 The sample size of the LSMS is relatively small, around2,000 to 5,000 households, as the World Bank puts more effort into the quality of the data.
Although it is nationally representative, in most cases, it is not designed to represent the subnational level. In the LSMS surveys, households are asked to report their expenditure on the
education of their members enrolled in formal education systems from pre-primary school
to post-secondary school; however, it does not cover technical and vocational education
training (TVET) and non-formal education (UIS, 2017).

Besides the above-mentioned large-scale household surveys, there are smaller-scale household
surveys, including Integrated Household Survey (IHS), Core Welfare Indicators Questionnaire
surveys (CWIQ), Household Income and Expenditure Survey (HIES), and Rand Family Life
Surveys (FLS) as well as country-specific national household surveys. However, these survey data
sets, in particular, the country-specific household surveys, are rather difficult to obtain. The
International Household Survey Network (IHSN) has collections of 6,986 surveys as of the end of2019 from most countries in the world.4 Although the data sets are not available directly from thewebsite, information on how and from whom those data sets are shared on their online database.
IPUMS-International houses the largest collection of publicly available census samples andregistered users can download sub-sets of the samples of those national censuses for free.5 However,the number of variables is much less abundant in comparison to the household survey.

3. Student Learning Assessments
Educational attainment used to be measured by years of schooling or grade completion, andeducational intervention traditionally emphasized expanding access to education, but a growing
number of studies now suggest that it is not the number of years students stay in school, but what
they actually learn in that period that matter the most (Hanushek & Woessmann, 2011; Pritchett,3

https://microdata.worldbank.org/index.php/catalog/lsmst
http://catalog.ihsn.org/index.php/catalog
5
https://international.ipums.org/international/
45

2013). UNESCO (2013) alerted, in its annual Global Monitoring Report, that a large number ofchildren and young people leave the education system without the competencies they need to leadproductive and healthy lives. The World Bank (2018) dedicated three chapters of its annual flagshipreport, the World Development Report 2018, talking about the learning issue. In response to thislearning crisis, effective learning outcomes are now included as indicators in SDG 4 to ensure
learning quality for all. Learning assessments can be designed and implemented internally withinclassrooms or externally through standardized national, regional or international assessments.
However, this paper only covers the later standardized learning assessments.
There are critiques that overemphasize assessment, normally in developed countries, whichfails to prioritize actual learning and that, in some cases, teachers and schools even engage incheating to increase their students’ performance (Fausset, 2014; Jacob, 2005). In contrast, in many
developing countries, there is no reliable information to measure and monitor whether programinterventions or polices actually translate into learning (World Bank, 2018). Although UNESCO(2015) reports that the number of countries conducting national learning assessments increased from
12 in 1990 to 101 in 2013, the percentage of countries with reliable data to track their progress
toward SDG 4 regarding learning is still very low (see Figure 2). Less than half of the countries in
Asia and the Pacific region have reliable data on learning outcome indicators to monitor theirprogress toward SDG 4, meaning that there are many countries still have no means to measure or
evaluate student performance and effectiveness of national education programs.
Figure 2. Percentage of Countries with Reliable Learning Assessment Data at Primary andSecondary Schools

Source: World Bank (2018): 17
6

Well-designed learning assessments can be used to inform policymakers of the disparities among
gender, socio-economic, region and other characteristics and to reveal education system
performance trends. Besides student performance (test scores), learning assessments also include
additional questionnaires to gather information related to the characteristics of students, families,
teachers, schools, and communities. Most of the large-scale international assessments are designed
to offer a wide range of variables such as student learning environment at home and school, family
background, socio-economic status, and school resources (Cresswell, Schwantner & Waters, 2015).
In addition to the school-based learning assessments, there are household-based assessments
conducted by Non-Government Organizations (NGOs), such as the Annual Status of Education
Reports (ASER) in India and Pakistan, and UWEZO in Kenya, Tanzania, and Uganda. Different
from school-based assessments, these assessments are able to include out-of-school children in theirsamples by visiting households directly.

3.1. Studies Using Learning Assessment Data
The large-scale learning assessments at regional and international levels are designed for various
purposes, but oftentimes they are to evaluate the quality of the education system, to ensure equity
among sub-groups of students, and to identify possible interventions to improve the learning
outcomes and equity (Tobin et al., 2015). International assessments, using the same standardized
tests across participating countries, allow researchers to make comparisons between the countries
and track the progress of the countries that participated in the same assessment on several occasions(Crawfurd et al., 2019). Studies using learning assessment data can help educators and policymakers
make more informed decisions and guide them in formulating new policies or improving the existingones (Raudonyte, 2019).
Numerous studies have tried to identify the factors–family background, home input, teacher
quality or school resources–that explain the gaps in student performance (Ammermüller et al., 2005;
Fuchs & Wößmann, 2008; Glewwe et al., 2011; Hanushek & Woessmann, 2011). For instance,
Ammermüller et al. (2005) examined the TIMSS data of seven countries in Eastern Europe andconcluded that student backgrounds (such as gender, age, and immigrant backgrounds) have stronger
effects on students’ learning achievement than school resources do. Nevertheless, a systematic
review conducted by Glewwe et al. (2011) on the effects of school resources on student’s learningin developing countries indicated that school infrastructure, for instance, good quality of roofs and7

walls, tables and chairs, school libraries, teachers’ subject knowledge, and teacher in-service training,
have positive effects on students’ learning outcomes.6
A large share of education’s recurrent budget is spent on teacher salaries, meaning that
reducing or increasing class size (number of students per class) significantly influences the educationbudget. Yet, it seems that small class size does not always result in good learning outcomes. Basedon TIMSS’s learning assessment data of 11 countries, Wößmann and West (2006) found that insome countries small class size does not lead to better student performance in mathematics andscience test scores. Studies using SEACMEQ II, the regional student assessment in Sub-Sahara
Africa, revealed that streaming students by ability level lead to wider performance gaps. Based on
the results, Seychelles decided to revise the national policy to terminate the student streaming at all
education levels (Leste, 2005).

3.2. Potential Data Sources
International and regional learning assessment includes not only students’ test scores but also
information related to the characteristics of students, parents, teachers, and schools. As mentioned
earlier, more and more countries are interested in measuring the students’ learning outcomes. Below
are some potential sources of learning assessment datasets that are publicly available for free. A listof other learning assessments can be found in Table A.2 in the Appendix.

-

Programme for International Student Assessment (PISA): Conducted every three years
since 2000, PISA was developed by the OECD to assess the knowledge and skills of 15year-old students in reading, mathematics, and science. The first assessment was joined by
43 countries, mostly OECD member countries and other developed countries. In 2018, morethan 600,000 students from 79 countries participated in the seventh PISA including new
countries from Belarus, Bosnia and Herzegovina, Brunei Darussalam, Morocco, thePhilippines, Saudi Arabia, and Ukraine. Although PISA has expanded to include morecountries from lower-middle income countries, none of the participants are from lowincome countries (Crawfurd et al., 2019). There are also countries that previously joined theassessment but later dropped out. The PISA data sets are available for download from its
website at https://www.oecd.org/pisa/data/. To increase the participation from middleincome and low-income countries, OECD launched the PISA for Development (PISA-D)

6

79 studies and papers were selected from 9,000 studies after several screening processes for the reviews. Not
all, but a large proportion of the reviewed studies used assessments tests.

8

initiative in 2014. By 2017, eight countries (Bhutan, Ecuador, Honduras, Paraguay, Zambia,
Cambodia, Guatemala, Panama, and Senegal) signed the participation agreement with theOECD and seven of them (except Bhutan and Panama) took part in the pilot assessments.

-

Trends in International Mathematics and Science Study (TIMSS) and Progress inInternational Reading Literacy Study (PIRLS): Both TIMSS and PIRLS are directed by
the International Association for the Evaluation of Educational Achievement (IEA) (Altinok,
Angrist, & Patrinos, 2018). Started in 1995, TIMSS is held every four years to assess fourthand eighth-grade students in mathematics and science. 580,000 students from 57 countries
and 7 benchmarking entities participated in the sixth TMISS in 2015. Six years after TIMSS
was introduced, PIRLS was conducted for the first time in 2001 to assess the reading abilityof fourth-grade students. 61 countries and entities joined the fourth PIRLS in 2016. Veryfew countries from Latin America and Africa participated in these assessments. TIMSS andPIRLS datasets can be downloaded from https://timssandpirls.bc.edu/databaseslanding.html.

-

Southern and Eastern Africa Consortium for Monitoring Educational Quality(SACMEQ): In addition to international learning assessments, there are also regional
assessments conducted in specific regions including SACMEQ in Southern and Eastern
Africa, LLECE in Latin America, PILNA in Pacific Island, SEA-PLM in Southeast Asia,
and PASEC in Francophone countries. Compared to international assessments, they areorganized less regularly. Although most African countries do not participate in international
students’ learning assessments, many African countries join the regional assessment,
SACMEQ. First conducted between the period of 1995 and 1999, so far three rounds of
SECMEQ have been carried out to assess sixth-grade students in reading, mathematics, andsciences. The 15 participating countries of the third SACMEQ 2006-2011 are Botswana,
Kenya, Lesotho, Mauritius, Malawi, Mozambique, Namibia, Seychelles, South Africa,
Swaziland, Tanzania, Tanzania, Uganda, Zambia, and Zimbabwe. Registered users canmake a request for the SECMEQ dataset through its website.7

7

http://www.sacmeq.org/?q=seacmeq-data

9

4. Field Experiment Data (Randomized Controlled Trials)
The 2019 Nobel Prize in economics was awarded to Abhijit Banerjee, Esther Duflo and Michael
Kremer for their innovative approach using field experiments, also known as Randomized Control
Trial (RCT), to answer global development issues from agriculture, micro-finance to education. RCT
is considered as the most rigorous approach in assessing the impacts of specific programs orinterventions in development studies including in the field of education. It had been challenging to
identify reliable causal impacts of a program due to the selection bias issue. However, this selection
bias problem can be solved by randomly assigning individuals or groups of individuals into
treatment and control groups (Duflo, Glennerster, & Kremer, 2007). The rise of evidence-based
studies has helped governments identify interventions that can produce desired results or outcomes.
The United Kingdom even created a research team, called the Nudge Unit, to conduct various field
experiments to provide evidence-based results to the government to design new public policies(Thaler & Sunstein, 2008).
A range of educational interventions has been made to ensure that no one is left behind andthat students in schools can acquire certain knowledge and skills. However, not all education
programs are effective in producing the expected outcomes and some are just too expensive.
Randomized evaluation has been used to identify which programs are effective so that governments
can make better-informed decisions on education investments. As the field experiment approach has
gained more popularity, there is an increasing number of evidence-based studies using it in education
research. Connolly, Keenan, and Urbanska (2018) conducted a systematic review of education
literature using the RCT method and found that more than 1,000 unique RCTs were implemented
during the period of 1980 and 2016 around the world. More than two-thirds of those experiments
happened in the last ten years after 2007. Interestingly, most of the field experiments in education
are conducted in developing countries. International Initiative for Impact Evaluation (3ie), an
International NGO promoting evidence-based development policies around the world, conducted asystematic review of studies on the impact of education interventions on access to education andlearning outcomes in 52 lower- and middle-income countries and identified 238 experimental or
quasi-experimental studies assessing 216 education interventions implemented between the periodof 1990 and 2015 (Snilstveit et al., 2015).

4.1. Studies Using Experiment Data
The 3ie’s systematic review categorized interventions into children, household, teacher, school andsystem levels and found that the most popular intervention is cash transfer (49 interventions) at the10household level (see Table 1). Normally, cash transfers are provided to a child’s parent (mother ismore preferable to father) with some conditions attached, i.e, the child needs to maintain a minimum
attendance rate and/or not work and focus on learning. The review found that cash transfer has strong
and positive impacts on school participation, but has little or no effect on student’s learning(Snilstveit et al., 2015). The most common educational access outcomes include enrollment,
attendance, and dropout, while the major learning outcome indicators are language, mathematics
and art test scores. It seems that interventions aiming at addressing non-cognitive knowledge andskills are very scarce. Among the reviewed studies, a score of papers is related to pedagogicalintervention at school level to improve students’ learning outcomes by adapting or introducing newclassroom practices, curriculum or training school teachers on how to use new teaching and learningmaterials. The meta-analysis suggested that most of the pedagogical interventions have positiveeffects on student test scores and help lower student dropout rates.
Table 1. List of Educational Interventions Reviewed by 3ie
Intervention Level
Child Level

Household Level
Teacher Level

School Level

System Level

Intervention

No. of Studies

School-Based Health

16

School Feeding

16

Merit-Based Scholarships

11

Providing Information

4

Reducing/Eliminating Fees

9

Cash Transfer

49

Teacher Hiring

8

Teacher Incentives

10

Teacher Training

1

Diagnostic Feedback

2Computer Assisted Learning

18

Pedagogy

22

Extra Time

3

New Schools and Infrastructure

7

Providing Materials

4

Remedial Education

4

Grade Retention

1

Tracking

2

School-Based Management

14

Community-Based Monitoring

11

Public-Private Partnerships

13

Source: Snilstveit et al. (2015).

11

4.2. Potential Data Sources
Datasets of field experiment surveys, designed to evaluate specific programs or interventions, were
not widely available for public scrutiny. However, an increasing number of researchers and research
institutes have committed to research transparency and have started to provide unrestricted accessto their datasets for other researchers to reuse or to ensure that their results are reproducible.
Dataverse, developed by Harvard's Institute for Quantitative Social Science (IQSS), is an opensource web application that allows researchers, journals, publishers and research institutes aroundthe world to house, share, organize and archive their project data.
3ie has shared more than 100 impact evaluation datasets on Dataverse.8 An advanced searchof datasets with the subject: “social science” and keyword: “education” or “school” or “student” or“learning,” yield 9 results of impact evaluation datasets available for download. In most cases, dofiles also come along with the database for other researchers to replicate and verify the publishedfindings. Recently in late 2019, Innovations for Poverty Action (IPA) and the Abdul Latif Jameel
Poverty Action Lab (J-PAL) jointly created Datahub for Field Experiments in Economics and Public
Policy (DFEEP) and started housing their project databases on the Dataverse Project.9 At the timeof writing, 149 datasets (at least 14 are education-related) from the two institutes are available fordownload along with the statistical codes and related documents.
Another good source to obtain the RCT dataset is the World Bank’s microdata library.10 A
large proportion of the World Bank’s development projects consists of an impact evaluation
component; however, it seems not all the impact evaluation databases are shared with the public on
the website. Currently, only 158 datasets are accessible.

5. Predicting Student’s Success with Educational Data Mining
Empirical studies in the field of education, more or less, have tried to identify factors or programinterventions that can improve students’ learning, expand education access or narrow the gaps
between sub-groups of the population. In other words, it attempts to understand what and why it
happened. The advancement of technology and computational power have motivated researchers toapply computer science in educational settings. Educational data mining, an emerging
interdisciplinary research field of computer science, statistics, and education, is trying to better
understand and predict students’ success or failure and/or to identify at-risk students in the future8

https://dataverse.harvard.edu/dataverse/3ie
https://dataverse.harvard.edu/dataverse/DFEEP
10
https://microdata.worldbank.org/index.php/catalog/impact_evaluation
912

using data mining and machine learning techniques (Romero & Ventura, 2013). It is designed toautomatically detect meaningful patterns from a large amount of data, for instance, information
related to student backgrounds or learning activities. Asif et al. , (2017) used the educational data
mining approach to identify poor undergraduate students in Pakistan, so that schools can provide
timely warnings and advice. The commonly used methods for prediction include classification,
regression, clustering, and feature selection (Bakhshinategh et al., 2018). In Educational Data
Mining’s educational knowledge discovery process, the decision tree algorithm is one of the mostwidely used methods due to its accuracy and ease in interpretation (Romero et al., 2013).
A Google Scholar’s search using the key term of “educational data mining,” suggests thatthe number of studies on this topic has steadily increased in the last ten years. We found no
publication related to educational data mining in 2004, but Google Scholar’s search produced morethan 3,000 results published in 2017. As a result of the growing interest in educational data mining,
the Journal of Educational Data Mining was established in 2009 and two years later in 2011, theInternational Educational Data Mining Society was founded.
Figure 3. Google Scholar Search Results Using "Educational Data Mining"
3500
3000
2500
2000
1500
1000
500
0
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Source: Created by the author based on Google Scholar search results

However, a majority of these studies are conducted by researchers in the field of computerscience rather than by researchers in the field of education development. This is probably due to thefact that researchers in the field of education development are not trained and not familiar with themachine-learning algorithms.
13

6. Conclusion
Thanks to global initiatives from international organizations such as the World Bank, UNICEF,
OECD and UNESCO, more and more data related to education are publicly available for researchers
and academics. Yet, in many countries, particularly in developing countries, openly accessible data
are still limited. Data mentioned in the paper are mainly collected by international organizations;however, there are many more education data collected by national statistics, ministries of education,schools, or through specific projects. Unfortunately, most of these data are locally stored andrestricted for internal use only. Nevertheless, there have also been efforts from universities andresearch institutes to make open-source data more accessible to researchers. This data sharing allows
other researchers to replicate the analysis and promote transparency in social science research.
Educational Data Mining can be a potential tool in education research in the near future
although currently most studies using this approach are done by researchers in the field of computerscience without deep knowledge of education development issues. Since the amount of data has
increased rapidly in recent years, there should be more collaboration between researchers of bothfields using big data in identifying education issues and its solutions.

Acknowledgment
The author is deeply grateful to Prof. Takashi Kamihigashi and Prof. Koji Yamazaki for their ideas
and suggestions at the initial stage of the study. The author is also would like to express his thanks
to his colleagues at RIEB and GISCS for their comments on the draft.

14

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Annex
Table A1. List of Countries Conducting DHS, MISC or LSMS
RegionSub-Saharan Africa

Country
AngolaBenin
Botswana
Burkina Faso
BurundiCameroon
Cape VerdeCentral African Republic
Chad
Comoros
Democratic Republic of the Congo
Cote d'Ivoire
Djibouti
Equatorial Guinea
Eritrea
Ethiopia
Gabon
GambiaGhana
GuineaGuinea-Bissau
Kenya
LesothoLiberia
Madagascar
MalawiMali
Mauritania
Mozambique
Namibia
Niger
Nigeria
Rwanda
Sao Tome and Principe
SenegalSierra Leone
SomaliaSouth Africa
South Sudan
Sudan
Swaziland
Tanzania

18

DHS

✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓

MISC

LSMS

✓
✓
✓
✓
✓
✓

✓

✓
✓
✓
✓
✓
✓
✓

✓

✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓

✓

✓
✓
✓
✓
✓
✓
✓

✓
✓

✓
✓
✓
✓

✓

✓

✓
✓

✓
✓
✓
✓
✓

✓

RegionCountry
Togo
UgandaZambiaZimbabwe

Northern Africa

Algeria
Egypt
Libya
MoroccoTunisia

DHS

MISC

✓
✓
✓
✓

✓

✓

Central Asia

Kazakhstan
Kyrgyzstan
Tajikistan
Turkmenistan
Uzbekistan

Eastern Asia

China
Korea, Democratic People's Republic of
Mongolia

LSMS
✓

✓
✓
✓
✓
✓

✓
✓

✓

✓
✓
✓
✓
✓

✓
✓
✓
✓
✓

✓
✓
✓

✓
✓
✓

✓

South-East Asia

Southern Asia

Western Asia

Cambodia
Indonesia
Lao People's Democratic Republic
MyanmarPhilippines
Thailand
Timor-Leste
Vietnam

✓
✓
✓
✓
✓
✓
✓
✓

Afghanistan
Bangladesh
BhutanIndia
IranMaldives
Nepal
Pakistan
Sri Lanka✓✓

Armenia
Azerbaijan
BahrainGeorgia
Iraq

✓
✓

✓
✓
✓
✓
✓

19

✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓

✓
✓
✓
✓

✓
✓

✓

✓
✓

✓
✓

✓

RegionCountry
JordanLebanonOman
Qatar
State of Palestine
Syrian Arab RepublicTurkeyWest Bank/Gaza
Yemen

Latin America
and the Caribbean

Oceania

Argentina
Barbados
BelizeBolivia
BrazilColombia
Costa Rica
Cuba
Dominican Republic
EcuadorEl Salvador
Guatemala
GuyanaHaiti
Honduras
Jamaica
MexicoNicaragua
PanamaParaguay
Peru
Saint Lucia
Suriname
Trinidad and Tobago
Turks and Caicos Islands
Uruguay
Venezuela

DHS

MISC

✓

✓
✓
✓

✓
✓
✓

✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓

✓

✓
✓
✓
✓
✓
✓
✓

✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓

✓
✓

20

✓
✓

✓
✓
✓
✓

✓
✓
✓
✓
✓
✓
✓
✓
✓

Fiji
Kiribati
Nauru
Papua New Guinea
Samoa
Tonga
TuvaluVanuatu

LSMS

✓
✓
✓
✓

RegionCountry

DHS

MISC

LSMS

Eastern Europe

Albania
BelarusBosnia and Herzegovina
Bulgaria
Croatia
KosovoMoldova
Montenegro
North Macedonia
SerbiaUkraineYugoslavia

✓

✓
✓
✓

✓

✓

✓

Sources: Created by the author based on databases from DHS, MICS, and LSMS.

21

✓
✓
✓
✓
✓
✓
✓
✓

✓
✓
✓
✓
✓

Table A2. List of Selected Learning Assessments
Assessment

Year

Organization

Locations

Subjects

International (School-based)
PISA

OECD

International

R, M, SPISA-D

2000, 2003, 2006, 2009,2012, 2015, 2018
2018

OECD

R, M, S

PIRLS

2001, 2006, 2011, 2016

IEA

Low-and Middle
Income Countries
International

TIMSS

IEA

International

M, S

EGRA

1995, 1999, 2003, 2007,2011, 2015
Since 2007

RTI and USAID

International

R

EGMA

Since 2009

RTI and USAID

International

M

SEACMEQ
Consortium
CONFEMEN

LLECE

1995-1999, 2000-2004,
2006-2011, 2012-204
1993-2011, 2002-2012,2014
1997, 2006, 2013

Southern and Eastern R, M
AfricaFrancophone SubR, M
Saharan Africa
Latin America
R, M, S, W

PILNA

2012

Pacific Island

L, N

SEA-PLM

2019

EQAP and
UNESCOSEAMEO and
UNICEFSoutheast Asia

R, M, W, GC

R

Regional (School-based)
SACMEQPASECUNESCOHousehold-based
ASER

Annually since 2005

PrathamIndia, Pakistan

R, N

UWEZO

Annually since 2009

Twaweza

L, N

PIAAC

OECD

STEP

2011-2012, 2014-2015,2017
2011, 2012, 2014

Kenya, Tanzania,
UgandaInternational

R

LAMP

Since 2003

UNESCO, UIS

Low-and Middle
Income Countries
Low-and Middle
Income Countries

World Bank

L, N, PS

L, N

- Note: R=Reading, M=Mathematics, S=Science, W=Writing, L=Literacy, N=Numeracy, GC=Global
Citizenship, PS=Problem Solving- Source: Created by the author based on Altinok et al., (2018), Cresswell, Schwantner, & Waters(2015), Raudonyte (2019), and Organizers’ websites.

22