Does computer-aided instruction improvechildren’s cognitive and non-cognitive skills?
: Evidence from Cambodia
Hirotake Ito∗

Keiko Kasai †Makiko Nakamuro‡

December 5, 2018

Abstract
This paper examines the causal effect of computer-aided instruction(CAI) on children’s cognitive and non-cognitive skills. Closely working
with the Cambodian government, we ran clustered-randomized controlled
trial at five elementary schools near Phnom Penn for three months. Students
were randomly assigned into 20 treatment classes which were allowed to use
an app based on CAI instead of regular math classes during the intervention,or 20 control classes. Our empirical results drawn from these experimentssuggest that average treatment effect on cognitive skills measured by several types of math achievement tests and IQ tests is positive and statisticallysignificant. The effect size is significantly large, especially as comparedwith prior literature examined in developing countries: the estimated coefficients on student achievements are 0.56-0.67 standard deviations and IQ
scores 0.70 standard deviations even after controlling for demographic factors. Furthermore, it is found that that CAI-based app can raise the students’subjective expectation to go to college in the future. However, there is no
significant effect on non-cognitive skills, such as motivation and self-esteem.

Keywords: CAI, cluster-randomized controlled trial, non-cognitive skills∗

Graduate School of Media and Governance, Keio University, 5322 Endo, Fujisawa-shi, Kanagawa 252-0882 Japan, Email: itouhrtk@keio.jp.
†
Faculty of Policy Management, Keio University, 5322 Endo, Fujisawa-shi, Kanagawa 2520882 Japan, Email: s15230kk@sfc.keio.ac.jp.
‡
Faculty of Policy Management, Keio University, 5322 Endo, Fujisawa-shi, Kanagawa 2520882 Japan, Email: makikon@sfc.keio.ac.jp.

11Introduction

The World Bank stated in its flagship report about ”learning crisis” (World Bank,2017): a large fraction of students in developing countries missed the opportunity
to acquire even foundational skills at school, e.g., how numbers work, which is
required when buying and selling goods in market, handling household budgets,or doing transactions with back and other financial institutions, etc.
While lower-income countries are rapidly expanding the primary enrollmentsfor past decades, many of them face substantial obstacles to keep away from this”learning crisis”. First of all, the expansion of primary enrollments has kept higher
pace than the expansion of school inputs, such as teachers and other school resources. It is concerned that a decline in per-capita input available leads to low
quality of primary education. Secondly, hiring high-quality of teachers has beendifficult in many countries because teachers are paid less than other comparably
qualified professionals, particularly in urban areas. Thirdly, the huge gap between
high- and low-achieving students brings teachers into more difficult situation toadjust their instruction in response to class composition.
Advanced technologies are able to offer promising ways to help with thesesituation in developing countries: although just simply installing computers toclassrooms does not change students’ learning, as shown in Barrera-Osorio and
Linden (2009), well designed computer-assisted learning (CAL) may help students to access high-quality of instructions and materials even under the severeteacher shortage and learn at their own pace. However, despite the great expectation, the evidence on the effect of CAL is yet mixed. A computer-assisted learning
in India was confirmed to vastly improve student performance, especially for the
initially lowest achieving students (Linden, 2008), while ”One Laptop Per Child
programs” in Peru and Uruguay had no impact on student reading or math achievements (Cristia et al., 2012; De Melo, Machado and Miranda, 2014).
This research is designed to rigorously estimate the causal impact of the CAI
on students’ cognitive and non-cognitive skills, closely working with the government of the Kingdom of Cambodia, Japan International Cooperation Agency(JICA), and Hanamaru-lab, one of the Japanese private companies, which provides the newly developed CAI apps, called ”Think!Think!”. The primary objective of ”Think!Think!” is to foster the foundational math skills for elementary
school students.
To examine the effect of ”Think!Think!”, we ran clustered randomizedcontrolled trial and intervened approximately 1,500 students from G1 thoroughG4 at five public elementary schools near Phnom Penh during May though Au2

gust, 2018. Because each school has two classes in each grade, students are randomly assigned either into 20 treatment classes which are using ”Think!Think!”
instead of taking regular math class during the three-months intervention, or 20
control classes.
Our empirical results drawn from this experiments suggest that average treatment effects on cognitive skills measured by several types of math achievement
tests and IQ tests are positive and statistically significant. The effect size is significantly large, especially as compared with prior literature examined in developing
countries: the preferred point estimates on student achievements are 0.56-0.67
standard deviations and IQ scores 0.70 standard deviations even after controllingfor prior score at the baseline-survey, gender, grade, birth-of-month, parental education and schools’ time-invariant characteristics. Furthermore, it is found thatthat CAI-based app can raise the students’ subjective expectation to go to college
in the future. However, there is no significant effect on non-cognitive skills, suchas motivation and self-esteem.
The remainder of this paper proceeds as follows: Section I provides the literature review. Section II explained the research design, data and empirical specifications. Section III presents the main results on cognitive and non-cognitive
skills. Section IV provides heterogenous effects in terms of students’ characteristics. Section V concludes and discusses policy implications.

2

Literature Review

Literature indicated that investments in computers at school is divided into two
broad areas: (i) investments in Information and Communication technology (ICT)
and (ii) Computer-Aided Instruction (hereafter, CAI). Because several studies
showed that CAI programs appeared to have strong and positive effects in developing countries, CAI is becoming more broadly accepted for recent years. Because ”Think!Think!” that we use in our experiment is very based on CAI, wesummarize on the extent to which studies focus on CAI programs.
Rouse and Krueger (2004) had a RCT experiment with the study app ”Fast For
Word” for third grade to sixth grade students in the US. From their experiment,
they didn t see statistically significant improvement in reading skills. Banerjee
et al. (2007) had a RCT experiment in fourth grade math classes in India. The
study app was not used as an alternative for regular classes, but was instead used
as a supplemental product. The experiment showed statistically significant positive effects in math. Barrow, Markman and Rouse (2009) did a RCT experiment3

using the math study app ”I Can Learn” instead of normal math classes for middle
school students and high students for two years. The experiment showed statistically significant positive effects in math. Carrillo, Onofa and Ponce (2011) had
a RCT experiment in Ecuador for two years. In the experiment, third, fourth andfifth grade students used the study app ”Mas Tecnologia” instead of normal mathclasses. The results showed that the app improved students’ math scores in astatistically significant fashion, but it didn’t affect language scores.
Mo et al. (2014) they recognized statistically significant positive effects inmath. Throughout this past research, we will try to estimate the effect which the
CAI class has had in short terms, such as three months, as well as the effects of its
substitute.
In sum, the majority of evaluation are designed as classroom-level cluster randomized controlled trial.
In the field of economics, investments in computers, the Internet, software andother technologies is placed in the context of education production function. As
Bulman and Fairlie (2016) pointed out, these investments are regarded as one of
the inputs, which may offset other inputs: time spent using the computer in class
offsets time spent for traditional instruction. In addition, spending extra budgets
to the computer offset budgets for other educational activities.
The evidence from more recent economic studies is mixed and suggests thatthe characteristics of the intervention are the key. Studies in this area differ significantly in the extent to which CAI is a substitute or a supplement to traditional
instruction. Interestingly, the positive effects appears to be stronger in developing
countries.

3

Methodology and Data

3.1 BackgroundOur study targets five public elementary schools around 10 km radius near Phnom Penh. Because these schools did not receive any aids or assistance fromother development agencies during the period of our intervention, it is certainly
ruled out the confounding factors from other external interventions. A majorityof households around the schools are engaged in farming and fishing as income
generating activities. Only small fraction of parents completed tertiary education.
The location of these five schools are illustrated the below.
Figure1.
4Figure 1: intervention schools

5

3.2

Baseline Survey

Prior to the intervention, we conducted the baseline survey in class during May
21 to May 25, 2018, with a full cooperation of teachers and staffs. The baseline
survey included two sets of 40-minute achievement tests for G3 and G4 students
respectively, 40-minute IQ tests for all students and approximately 20-minute surveys both for all students and parents.
In order to measure students’ cognitive skills, the two sets of achievement
tests were derived from the past exam of National Assessment Test (NAT) originally administered by Cambodia’s Ministry of Education, Youth and Sports forG3 students and Trends in International Mathematics and Science Study (TIMSS)
originally administered by The International Association for the Evaluation of
Educational Achievement (IEA) for G4 students. Unfortunately, because there
are any accessible standardized tests to measure the cognitive skills for younger
students, we did not administer the achievement tests for G1 and G2 students.
Instead, we administered two sets of age-appropriate IQ tests implemented at
the baseline survey. One of the IQ test is called ”new Tanaka-B type intelligencetest” (Tanaka, Okamoto and Tanaka (2003)), which has long been widely used inJapan and Asian countries to measure young children’s cognitive skills. G3 andG4 students took more advanced version of test, which is slightly different fromthe test that G1 and G2 students took. Both of these Tanaka-B type intelligencetests were not only translated into the local language but also modified to appropriately describe the local situations (e.g.,illustrations of banknote, food, and people,
etc).
The other intelligence test is Goodenough Draw-a-Man (DAM) test (Goodenough, 1926). In this test, students were asked to complete drawings of a whole
person(s) on a piece of paper for 10 to 15 minutes. The several examples of children’s drawings collected at our baseline survey in Figure 4. Although this test
has been criticized on the validity as a measurement of intelligence, more recent
research suggested that this DAM scale is still effective to screen the lower range
of intelligence for 5-12 years old children(Scott, 1981).
The survey for all G1-G4 students asked them to answer their demographic
information, such as gender, grade, birth-of-month, hours of studying at homeand subjective likelihood to go to college in the future, etc. Furthermore, the survey included a set of questionnaires to measure the non-cognitive skills, such as
Rosenberg self-esteem scale (Rosenberg, 1965) and internal and external motivation for study (Sakurai and Takano, 1985). The survey for all parents asked toanswer their socio-economic status, such as parents’ educational backgrounds.
6

After three-month intervention, the end-line survey was then conducted between Aug 16 to Aug 25. We again administered the same sets of achievement
tests, IQ tests, the questionnaires for students, especially focusing only on timevarying variables, such as the willingness to go to college and time spent studying
at home.

3.3 Education app: ”Think!Think”
The app called ”Think!Think!” that we used in our intervention is originally developed by Hanamaru-lab. Ltd, taking an full advantage of its long-time experiences accumulated through running a great numbers of cramming schools forschool-aged children nationwide in Japan. The objective of this app is to foster
the foundational math skills for elementary school students. More specifically,
this app incorporates an adaptive learning with its own original computer algorithm and automatically provides a large numbers of math problems, materials,
and instructions in response to the proficiency level of each individual students attheir own pace.

Figure 2: Sample problem
To be more comfortably used by elementary school students in Cambodia,
7

Think!Think! was slightly modified to meet Cambodian curriculum standards
and translated into the local language, Khmer. The tablet-PCs were allocated individually, while students were involved in ”Think!Think!” in class. Moreover, CAI
does not often require the additional teaching staff to manage a class. In our intervention, we placed three staffs who did not have any teaching experiences prior to
the intervention to helped students on the technical matter and time management.

3.4 Cluster-Randomized Controlled Trial
Nevertheless to say, if we allow students to access CAI-based app,”Think!Think!”, based on their own preference, students who often use the app
probably perform better to begin with. Students who have sought to access higher
quality of education, including the exposure to new technology, are not as enthusiastic to study, on average, as those who never did. Random assignment of theaccess to CAI-based app solves this selection bias.
In our experiments, approximately 1,500 from G1 though G4 students at five
public elementary schools are supposedly assigned either into treatment group
who are using ”Think!Think! instead of regular math class, or control group.
Students in treatment classes used ”Think!Think” for approximately 30 minutes
everyday. Peer effect may be one of the major threat to the internal validity of thisexperiment and the interactions among students may violate the stable unit treatment value assumption (SUTVA). To avoid this situation, besides the fact thatcluster-randomized controlled trial (hereafter, cluster-RCT) is more common in
education as suggested in literature, we randomized students within intact classrooms, rather than individual students within them.1
Because each school has two classes at each grade, We thus randomly assigned one of the classes at each grade and each school into treatment, or control
otherwise (See Figure 3 for the cluster-randomization). This setting made us tocreate 20 treatment classes (with 807 students) and 20 control classes (with 829
students) across five schools. However, there is still a potential concern that students in treatment classes would talk to their friends in other control classes at thesame school about what they have learned. To reduce the risk of spillovers, weare not allowed treatment students to access ”Think!Think!” out of classes. They
are neither allowed to bring the tablet PCs to their own place of residence.
1 However, as pointed out by (Imbens, and Wooldridge, 2009), it is technically difficult toseparate out the direct effect of the intervention on individual from the indirect effect of the peeron the individual.

8The intervention is implemented during the period between May 25 and Aug
15. Despite the short-run intervention, it seems that students are very organizedand enthusiastic to do ”Think”Think” for this three months.

4

Econometric specification and Results

4.1 Econometric specification
To identify the causal effect of using ”Think!Think!”, we run regression analysis
with difference-in-difference approach. Our equation of interest is:
Yi, j,t = α + β Ti, j,t + γ Yi, j,t−1 + Xi, j,t δ + εi, j,twhere Yi, j,t is outcome variables of individual student i, school j at time t.
Ti, j,t in equation (1) is the exogenous variation in the access to CAI and the key
independent variable of interest. Xi, j,t is the vector of controls, while εi, j,t is theidiosyncratic error term.
The crucial identifying assumption in this empirical model is that the relationship between the exposure to CAI-based app and students’ unobserved ability isorthogonal to the error term, conditional on the controls. Under this assumption,
the estimate of β in equation (1) can be interpretable as the causal impact of theintroduction to CAI-based app on outcomes.

4.2

Variables Defined

As shown in Table1, the outcome variables of interest denoted by Yi, j,t is defined
as follows: both achievement tests are converted to T-scores with mean 50 andstandard deviation 10. Table1 shows a balance check performed for the baseline survey. It is found that there is no statistically significant difference in NAT
between G3 students assigned into treatment and control classes, although G4 students in treatment classes performed much better in TIMSS than ones in control
classes.
The other outcome variables are defined as IQ test scores: The results of
Tanaka-B type IQ test and Draw-a-Man test are converted into Mental Age (measured as level of intellectual performance, MA) and are then defined IQ scores as
MA over chronological age (biological age, CA) multiplied by 100. According to
the descriptive statistics, a mean of Tanaka-B type IQ score is 78.612 with 13.451
standard deviation and a mean of DAM type IQ score is 0.692 with 0.207. While9there doesn’t exist statistically significant difference in Tanaka-B type IQ score
but is in DAM type score.
As other set of outcome variables, the measures of non-cognitive skills arecoded as the mean of a set of questionnaires specific to self-esteem and motivation.
The self-esteem scale is slightly higher for treatment students, while motivation
scale is balanced between two-group students. The willingness to go to college ismeasured on a 3-point scale (1=not likely through 3 very likely) based on student
subjective expectations. The original questionnaire of the hours spent studying at
home is the response category, ranging from one (=not at all) through six (=more
than four hours). We set the minimum to zero and maximum to four and thentook the median value for categories between two (=less than 30 minutes) and five(=two to three hours).
The key independent variable of interest denoted by Ti, j,t is a dummy variable
coded as one if students are assigned into treatment class, zero otherwise.
As controls denoted by xi, j,t , the demographic variables, such as gender, age,and parental educational backgrounds, are substantially equal between treatment
and control students. It should be noted that the variable on parental education
represents the highest level of education of either one of the parents.
Even though the observable characteristics are balanced between two groups,
it is the fact that the several outcome variables,such as the achievement score forG4 students, DAM type IQ score, and self-esteem scale, are not substantially comparable at the baseline survey. Because the lack of balance can occur by chanceeven when randomization is carried out correctly and the chance of achievingbalance when we randomize at the group level is increasing as the sample size
increases, we may not worry too much if we see the imbalance in four out of 15
variables. However, even though schools randomized the change in class composition every year, it may still happen to make imbalance between treatment andcontrol groups due to drop-out or absence at the day of the baseline survey. We
thus control for the demographic variables we use for the balance check in orderto make as much as possible ”pure” comparison.
It may be possible that the average treatment effects depends on particular
students’ sub-groups of interest. For example, if boys are more familiar withthe computer related equipment, the effect may be stronger for boys than girls.
This kind of heterogeneous effect must be very important for policy makers inconsidering how to tailor the needs of particular subgroups. We will discuss thispoint in section 4.

10

ALL
Treatment (A) Control (B) Difference (A)-(B)49.22750.00050.765
Achievement Test (NAT, G3)(10.000)(9.573)(10.375)
1.538
51.92150.00048.068
Achievement Test (TIMSS, G4) (10.000)(9.213)(10.407)-3.853***78.79578.61278.432IQ Test (Tanaka-B)(13.451)(13.131)(13.777)
0.363
0.692
0.678
0.705
IQ Test (Draw-a-man)(0.207)(0.206)(0.207)0.027**
2.794
2.762
2.726
Self-esteem(0.549)(0.596)(0.502)0.068**
0.656
0.652
0.660
Motivation(0.142)(0.150)(0.133)
0.008
2.410
2.342
2.467
Willingness to go to college(0.771)(0.809)(0.734)0.125***
114.858
112.384
117.056
Hours of studying at home(121.313)(115.812)(126.032)
4.672
0.519
0.525
0.530
Gender (male=1, woman=0)(0.500)(0.499)(0.500)-0.011
8.485
8.501
8.470
Age
(1.553)(1.573)(1.535)-0.031Highest parental education
0.017
0.012
0.021
College or Graduate school(0.129)(0.110)(0.145)
0.009
0.340
0.353
0.328
High school(0.474)(0.478)(0.470)-0.026
0.222
0.218
0.227
Junior high school(0.416)(0.413)(0.419)
0.009
0.164
0.160
0.167
Elementary school(0.370)(0.367)(0.374)
0.007
0.001
0.002
0.000
no education(ref)(0.035)(0.049)(0.000)-0.002Birth of Month
0.219
0.238
0.228
Jan. - Mar.
(0.420)(0.414)(0.426)
0.019
0.240
0.258
0.223
Apr.-Jun.
(0.427)(0.438)(0.417)-0.035*
0.243
0.251
0.236
Jur.-Sep.
(0.429)(0.434)(0.425)-0.014
0.264
0.254
0.273
Oct.-Dec.
(0.441)(0.436)(0.446)0.019
Note: ***, **, and * represent 0.1%, 1%, and5% significance level, respectively.

Table 1: Descriptive statistics and balance test

11

4.3

Results

Effect on cognitive skill
Our analysis begins with the estimated effect of CAI on student achievements. The coefficients estimated by OLS are reported in Table 2 with the theheteroskedasticity-robust standard errors and clustering at the school level. Our
primary interest is presented in the first raw in Table, the estimated coefficient of
the access to ”Think!Think” on NAT for G3 and TIMSS for G4.
Basically, Model I contols only for the prior achievement score at the baseline survey, and Model II controls basic demographic controls, such as gender,
grade, birth-of-month, parental education and school time-invariant fixed effects,
in addition to the prior test score.
The results clearly show that the estimated coefficients on standardized testscores has a positive and statistically significant at a 0.1 percent level (Table 2).
The estimated coefficients for the sample of G3 students indicate the exogenous
exposure to CAI app raises average test scores by about 0.67 if expressed in unitsof standard deviations by grade.
Adding demographic controls in Model II neither change the magnitude ofcoefficients across specifications nor improve the precision of our estimates by
explaining the variation in test scores. Once We includes the interaction term
and tests whether to exist the heterogenous effects in terms of gender, grade, andparental education, we find small point estimates on nearly all interaction terms,
and difference among these coefficients do not support the hypothesis of significant heterogenous effects on test scores. The results will be provided upon request.
The results drawn from our empirical evidence is consistent with expectations
for the sample of G4 sample (Table 2): The access to CAI app improves standardized test score by 0.56 standard deviations per three-month exposure. Adding
controls tends to increase the point estimates and decrease the standard errors ofthese estimates. By the same token, we do not find any significant and heterogenous effects on test scores in terms of gender, grade and parental education.
Looking at the results in Table 3, the estimated coefficient on the Tanaka-B
type IQ score is positive and statistically significant at 0.1 percent level. We calculate the effect size on the IQ score from Model I of Table 2 and obtain a coefficientof 0.70 standard deviation (=9.415/13.451). The estimated coefficient remains
constant after controlling for demographic characteristics in Model II. However,the coefficient of the DAM score, regardless of specifications, are not statisticallysignificant. Taken as a whole, the magnitude in cognitive skills appears to be very12

large, as compared with evidence suggested by previous literature that intervened
for at least one year.
By using kernel density estimation, we obtain the probability density function
both of achievement test scores and IQ scores to compare in score distributions
between before and after the three-month intervention (Figure 3, 4 and 5, respectively). Although the difference in the DAM score for the entire sample, andeven interaction term with grade, are not statistically significant, younger students
seemed to be slightly improved.
Effect on non-cognitive skills
We then repeated the above protocol with a set of non-cognitive skills as outcomes. Unlike the results of cognitive skills, we do not find any significant effectfor non-cognitive skills, measured by motivation and self-esteem (Table 4). However, it is clear that the estimated coefficient on willingness to go to school ispositive and statistically significant at a 0.1 percent level, indicating students who
used CAI app during the class are more likely to believe that they would takemore advanced education in the future. The coefficient remains constant after
controlling for demographic characteristics in Model II and suggests that the heterogenous effects in terms of gender, grade and parental education does not exist.
Although the estimated coefficient do not support the hypothesis of a positive effect of CAI-based app on non-cognitive skills, the estimated probability density
function suggest the sign of slight change in younger grade.
Dependent Variable National Asssessment
TIMSS
Model1Model2Model1Model26.771*** 6.791*** 5.642*** 5.847***(0.633)(1.029)(1.102)Treatment(0.579)Baseline Score✓✓✓✓
Control✓✓
Observations
331
285
314
278
Adjusted R2
0.718
0.702
0.215
0.212
Note: ***, **, and * represent 0.1%, 1%, and 5% significance level,
respectively.
Table 2: Effect of treatment: cognitive skills

13

Dependent Variable

IQ

Draw A Man
Model1Model2 Model1
Model29.415*** 9.186*** 0.003-0.004Treatment(0.715)(0.753)(0.008)(0.009)Baseline Score✓✓✓✓
Control✓✓
Observations
1226
1042
1060
898
Adjusted R2
0.461
0.489
0.255
0.288
Note: ***, **, and * represent 0.1%, 1%, and 5% significance level,
respectively.
Table 3: Effect of treatment: IQ
Dependent Variable

Motivation
Self Esteem
Model1 Model2 Model1
Model2-0.0001 0.0001
0.008
0.005
Treatment(0.008) (0.008) (0.026)(0.028)Baseline Score✓✓✓✓
Control✓✓
Observations
876
743
1016
866
Adjusted R2
0.297
0.361
0.057
0.121
Note: ***, **, and * represent 0.1%, 1%, and 5% significance level,
respectively.
Table 4: Effect of treatment: non-cognitive skills
Dependent Variable Study Time(minutes) Willigness to go to college
Model1Model2Model1Model2-11.982-12.860
0.135***
0.133**
Treatment(7.366)(7.946)(0.049)(0.053)Baseline Score✓✓✓✓
Control✓✓
Observations
1247
1031
920
790
Adjusted R2
0.021
0.048
0.040
0.034
Note: ***, **, and * represent 0.1%, 1%, and 5% significance level,
respectively.
Table 5: Effect of treatment: study input
14

5Conclusion

This paper examines the causal effect of computer-aided instruction (CAI) on
children’s cognitive and non-cognitive skills. Closely working with the Cambodian government, we ran clustered-randomized controlled trial at five elementary
schools near Phnom Penh for three months. Students were randomly assigned
into 20 treatment classes which were allowed to use apps based on CAI insteadof regular math class during the intervention, or 20 control classes. Our empirical results drawn from this experiments suggest that average treatment effect on
cognitive skills measured by several types of math achievement tests and IQ tests
is positive and statistically significant. The effect size is significantly large, especially as compared with prior literature examined in developing countries: theestimated coefficients on student achievements are 0.56-0.67 standard deviations
and IQ scores 0.70 standard deviations even after controlling for demographic
factors. Furthermore, it is found that that CAI-based app can raise the students’subjective expectation to go to college in the future. However, there is no significant effect on non-cognitive skills, such as motivation and self-esteem.

References
Banerjee, Abhijit V, Shawn Cole, Esther Duflo, and Leigh Linden. 2007.
“Remedying education: Evidence from two randomized experiments in India.”
The Quarterly Journal of Economics, 122(3): 1235–1264.
Barrera-Osorio, Felipe, and Leigh L Linden. 2009. “The use and misuse ofcomputers in education: Evidence from a randomized controlled trial of a language arts program.” Cambridge, MA: Abdul Latif Jameel Poverty Action Lab(JPAL). www. leighlinden. com/Barrera-Linden, 20.
Barrow, Lisa, Lisa Markman, and Cecilia Elena Rouse. 2009. “Technology’s
edge: The educational benefits of computer-aided instruction.” American Economic Journal: Economic Policy, 1(1): 52–74.
Bulman, George, and Robert W Fairlie. 2016. “Technology and education:
Computers, software, and the internet.” In Handbook of the Economics of Education. Vol. 5, 239–280. Elsevier.

15

Carrillo, Paul E, Mercedes Onofa, and Juan Ponce. 2011. “Information technology and student achievement: Evidence from a randomized experiment in
Ecuador.”
Cristia, Julian, Pablo Ibarrarán, Santiago Cueto, Ana Santiago, and Eugenio
Severı́n. 2012. “Technology and child development: Evidence from the one
laptop per child program.”
De Melo, Gioia, Alina Machado, and Alfonso Miranda. 2014. “The impact ofa one laptop per child program on learning: Evidence from Uruguay.”
Goodenough, Florence Laura. 1926. “Measurement of intelligence by drawings.”
Imbens, Guido W., , and Jeffrey M. Wooldridge. 2009. “Recent developmentsin the econometrics of program evaluation.” 47: 5–86.
Linden, Leigh L. 2008. Complement or substitute?: The effect of technology on
student achievement in India. InfoDev Working Paper, Columbia University.
Mo, Di, Linxiu Zhang, Renfu Luo, Qinghe Qu, Weiming Huang, Jiafu
Wang, Yajie Qiao, Matthew Boswell, and Scott Rozelle. 2014. “Integrating computer-assisted learning into a regular curriculum: evidence from a randomised experiment in rural schools in Shaanxi.” Journal of development effectiveness, 6(3): 300–323.
Rosenberg, Morris. 1965. Society and the adolescent self-image. Princeton university press.
Rouse, Cecilia Elena, and Alan B Krueger. 2004. “Putting computerized instruction to the test: a randomized evaluation of a scientifically based reading
program.” Economics of Education Review, 23(4): 323–338.
Sakurai, Shigeo, and Seijun Takano. 1985. “A new self-report scale of intrinsic
versus extrinsic motivation toward: learning for children.” Tsukuba psychological research, 7: 43–54.
Scott, Linda. H. 1981. “Measuring intelligence with the Goodenough-Harrisdrawing test.” 89: 483.
Tanaka, Kanichi, Kenroku Okamoto, and Hidehiko Tanaka. 2003. The New
Tanaka B Intelligence Scale. Kaneko shobo.
16

World Bank. 2017. World Development Report 2018: Learning to Realize Education’s Promise. The World Bank.

A

Effect of Treatment: Estimated PDF function
National Assessment

TIMSS

Figure 3: National assessment score and TIMSS

17

IQ(End-line)
G1

G2
0.030

Control
Treatment

0.025

Control
Treatment

0.025
Estimated Prob

Estimated Prob

0.020
0.015
0.010
0.005
0.000

0.020
0.015
0.010
0.005

40

60

80

100

120

0.000

140

40

60

80

G3
0.040

0.035

Control
Treatment

0.035

140

100

120

140

0.025
Estimated Prob

Estimated Prob

120

Control
Treatment

0.030

0.030
0.025
0.020
0.015

0.020
0.015
0.010

0.010

0.005

0.005
0.000

100

G4

40

60

80

100

120

0.000

140

40

60

80

Figure 4: IQ
Draw-a-Man(End-line)
G1

2.5

G2
Control
Treatment

Control
Treatment

2.5

2.0
Estimated Prob

Estimated Prob

2.0
1.5
1.0
0.5
0.0
0.0

1.5
1.0
0.5

0.2

0.4

0.6

0.8

1.0

1.2

0.0
0.0

1.4

0.2

0.4

0.6

G3

2.5

0.8

1.0

1.2

1.4

G4
Control
Treatment

Control
Treatment

3.0

2.0
Estimated Prob

Estimated Prob

2.5
1.5
1.0

2.0
1.5
1.0

0.5
0.0
0.0

0.5
0.2

0.4

0.6

0.8

1.0

1.2

0.0
0.0

1.4

0.2

0.4

Figure 5: Draw a man test
18

0.6

0.8

1.0

1.2

1.4

Motivation (End-line)
G1

G2
Control
Treatment

0.10

Control
Treatment

0.08

Estimated Prob

Estimated Prob

0.08
0.06
0.04

5

10

15

20

25

0.00

30

10

15

20

25

30

20

25

30

G4

Control
Treatment

Control
Treatment

0.10

0.08

0.08
Estimated Prob

Estimated Prob

5

G3

0.10

0.06
0.04
0.02
0.00

0.04

0.02

0.02
0.00

0.06

0.06
0.04
0.02

5

10

15

20

25

0.00

30

5

10

15

Figure 6: Motivation
Self-esteem(End-line)
G1

G2
Control
Treatment

0.040

Estimated Prob

0.030
Estimated Prob

Control
Treatment

0.05

0.035

0.025
0.020
0.015

0.04
0.03
0.02

0.010
0.01

0.005
0.000

10

0

10

20

30

40

0.00

50

10

0

10

G3

20

30

40

50

G4

0.08

Control
Treatment

0.07

Control
Treatment

0.08

Estimated Prob

Estimated Prob

0.06
0.05
0.04
0.03
0.02

0.06

0.04

0.02

0.01
0.00

10

0

10

20

30

40

50

0.00

10

0

Figure 7: Self-Esteem
19

10

20

30

40

50