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European Scientific Journal October 2018 edition Vol.14, No.29 ISSN: 1857 – 7881 (Print) e - ISSN 1857- 7431
199
Influence of Attention-Deficit Hyperactivity Disorder
(ADHD) on Academic Achievement of Learners in
International Primary Schools in Mombasa (Kenya)
and Kampala (Uganda): A Comparative Study
Judith Biirah
Dr. Alice Anika
Prof. Richard Simon Zigler
Department of Educational Psychology and Special Needs,
Pwani University,Kilifi, Kenya
Doi:10.19044/esj.2018.v14n29p199 URL:http://dx.doi.org/10.19044/esj.2018.v14n29p199
Abstract
Attention deficit hyperactivity disorder (ADHD) is one of the most
prevalent disorders among school-going children. The aim of this study was
to compare academic achievement of learners with and without ADHD in
international primary schools of Mombasa (Kenya) and Kampala (Uganda).
A comparative study was conducted among 377 respondents using Attention
Deficit Hyperactivity Disorder Scale Questionnaire (SNAP-IV) which
assessed the three ADHD subtypes in form of a closed-ended questionnaire.
Results revealed that learners with ADHD had low overall academic
achievement compared to those without ADHD in Mombasa (p <.001) and
Kampala, (p <.001). Boys with ADHD in Mombasa had better grades than
girls (M = 168.51, SD = 32.50 vs. M = 160.00, SD = 39.07) while girls with
ADHD in Kampala had better grades than boys (M = 103.50, SD = 24.77 vs.
M = 93.45, SD = 24.71). Learners with ADHD Inattentive subtype were
greatly impaired compared to those with ADHD Hyperactive-Impulsive and
Combined subtypes in both cities. ADHD significantly predicted academic
achievement with higher variability in Kampala (55%) than Mombasa (10%).
Attention-deficit hyperactivity disorder has a negative impact on academic
achievement of learners with the condition. The study recommended
integration of ADHD screening in school health services to enable early
detection and management of the condition.
Keywords: Attention Deficit Hyperactivity Disorder, Academic
Achievement, Primary School Children, Mombasa (Kenya), Kampala
(Uganda)
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Introduction
Education is an imperative aspect for national development especially
in developing countries including Kenya and Uganda. However, the literacy
level in both countries is still low despite efforts directed towards its increase
through free primary and secondary education. In spite of these efforts, little
is done to identify and eliminate the impediments to learning, especially among
children. One of such impediments is attention-deficit hyperactivity disorder
(ADHD), which has been identified to affect 3–5% of primary school children
(American Psychiatric Association- APA, 2013).
In an inclusive classroom environment, learners diagnosed with ADHD
may find school to be a struggle for a variety of reasons. They may have
difficulties with organizational skills, study skills, time management skills, and
completion of class work and homework. These learners also may show
inability to work within a set of rules causing them problems dealing with
teachers and other learners in the school (Collins, 2016). According to Murphy
(2014), difficulties associated with ADHD may first become apparent at school
due to a mismatch between learners’ behavior and classroom expectations.
Review of Problem Situation
The prevalence of ADHD is globally on the increase and varies from
one country to another. Using clinic referred samples; Mpango, Kinyanda,
Rukundo, Levin, Gadow, and Patel (2017), Wamithi, Ochieng, Njenga, Akech,
and Macharia (2015), and Wamulugwa et al. (2017) established that this
condition exists among school going learners in Kenya and Uganda. However,
academic achievement of these learners has been reported elsewhere to be
more compromised to an extent that they perform below their intellectual
ability in math and reading with severe impairment in rising childhood
inattention trajectory (Breslau, Miller, Breslau, Bohnert, Lucia, & Schweitzer,
2009; Pingault et al., 2014).
In a literature review of 30 cross-sectional and four longitudinal studies
done by Tosto Momi, Asherson, and Malki (2015), studies pointed at
substantial evidence for a negative association between ADHD symptoms and
mathematical ability. This association was particularly higher for ADHD
inattentive subtype than ADHD hyperactive-impulsive subtype. Holmberg
(2012) also found that more learners with ADHD performed poorly in math
and English, had been retained in a grade, and did not qualify for upper
secondary than controls with a higher impairment in inattentiveness. These
findings support the conclusion that among a variety of childhood behavioural
problems, attention deficit- a major characteristic of ADHD is the principal
predictor of diminished achievement relative to expectations based on a
learner's cognitive ability implying that ADHD impairs learners’ academic
achievement.
European Scientific Journal October 2018 edition Vol.14, No.29 ISSN: 1857 – 7881 (Print) e - ISSN 1857- 7431
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Despite the fact that this disorder exists among school going
learners in Kenya and Uganda, no study investigated the effect of this
condition on academic achievement of learners in international primary
schools. The study at hand thus explored this effect with reference to
international primary schools in Mombasa (Kenya) and Kampala
(Uganda).
Study Objectives
1) To compare academic achievement of learners with ADHD and
those without ADHD
2) To compare academic achievement between boys and girls with
ADHD
3) To establish academic achievement of learners with different ADHD
subtypes
4) To determine the extent ADHD predicts academic achievement of
learners
Methodology
A comparative study was conducted to examine academic achievement
of learners with ADHD and those without ADHD in international schools of
Mombasa (Kenya) and Kampala (Uganda). Research permits were obtained
from Pwani University Ethics Remuneration Committee Board and Uganda
National Council for Science and Technology, Kampala.
Purposive sampling was used to select both cities as no similar study
had been carried out in the study areas. Stratified sampling was used to select
schools according to region to ensure proper representativeness. Cluster
sampling was used to select learners to avoid omitting learners who might have
the ADHD condition. A sample of 377 respondents (236 from Mombasa and
141 from Kampala) participated in the study.
Attention Deficit Hyperactivity Disorder Scale Questionnaire (SNAP -
IV) developed by Swanson, Nolan, and Pelham (1982) was used to measure
ADHD among learners. The instrument has 26 items composed of two areas
namely; attention deficit hyperactivity disorder (ADHD) and Oppositional
Defiant Disorder (ODD). The study adapted this instrument as only 18 items
out of 26 items were used because the last 8 Items measured Oppositional
Defiant Disorder (ODD) which was not part of the study. The instrument’s
Cronbach Alpha reliability from test-retest was reported at .984 and .976 for
Mombasa and Kampala respectively; similar to Bussing et al. (2008) from
teachers and parents at 0.94 and 0.97 respectively. End of Term Assessment
Results (ETAR) involved class teachers recording actual scores attained by
individual learners in math, science and English in the previous term from
school academic records and scores ranged from 0-100.
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Data Analysis
Data were analyzed using both descriptive and inferential statistics. An
independent samples t-test and One-way ANOVA were used to test the null
hypotheses at .05 alpha level. A model was further created to fit the prediction
of academic achievement among learners.
Results
Demographic Characteristics of Respondents
Table 1. Demographic Characteristics of Participants
Mombasa
Mombasa (%)
Kampala (%)
Sex
Male
122 (51.7)
62 (44.0)
Female
114 (48.3
79 (56.0)
Class
Year Five
111 (47.0)
70 (49.6)
Year Six
125 (53.0)
71 (50.4)
Majority of the participants in the study were girls with 51.2%
compared to boys with 48.8%. Schools in Mombasa had more boys than girls
while those in Kampala had more girls than boys. In addition, most of the
participants were from year six with 53.0% in Mombasa and 50.4% in
Kampala.
Comparison of Academic Achievement among Learners with and those
without ADHD
Table 2: Independent Samples t-test on Academic Achievement of Learners with and
without ADHD
Mombasa
t
df
Sig (2-tailed)
Total
3.49
234
.001
Math
3.35
234
.001
Science
2.97
234
.003
English
2.92
234
.004
Kampala
Total
8.57
139
.000
Math
7.36
139
.000
Science
8.25
139
.000
English
7.55
139
.000
An independent samples t test revealed a statistically significant
difference in academic achievement between learners with ADHD and those
without ADHD in Mombasa: overall achievement (p < .001), math (p < .001),
science (p = .003), and English (p = .004). Similarly, there was a statistically
significant difference in academic achievement between learners with ADHD
and those without ADHD in Kampala: overall achievement (p < .001), math (p
< .001), science (p < .001), and English (p < .001). This implied that learners
European Scientific Journal October 2018 edition Vol.14, No.29 ISSN: 1857 – 7881 (Print) e - ISSN 1857- 7431
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with ADHD in both cities were academically impaired than those without the
condition in overall achievement and core subjects.
Comparison of academic achievement between boys and girls with ADHD
Table 3: Descriptive Statistics Showing Mean Scores of Learners with ADHD by Sex
Mombasa
Subject
Sex
N
Mean
SD
Std Error
Total
Male
37
168.51
32.50
5.34
Female
18
160.00
39.07
9.21
Math
Male
37
54.59
15.33
2.52
Female
18
50.89
16.13
3.80
Science
Male
37
57.22
11.35
1.87
Female
18
53.11
16.33
3.85
English
Male
37
56.70
12.93
2.13
Female
18
56.00
14.03
3.31
Kampala
Total
Male
11
93.45
24.71
7.45
Female
10
103.50
24.77
8.15
Math
Male
11
31.91
8.41
2.54
Female
10
32.30
10.03
3.17
Science
Male
11
27.09
10.38
3.13
Female
10
34.80
12.87
4.07
English
Male
11
34.45
11.00
3.32
Female
10
36.40
10.33
3.27
Descriptive results in Table 3 revealed that boys with ADHD in schools
of Mombasa had higher scores than girls in overall academic achievement (M
= 168.51, SD = 32.50 vs. M = 160.00, SD = 39.07); math (M = 54.59, SD=
15.33 vs. M = 50.89, SD = 16.13); science (M = 57.22, SD= 11.35 vs. M =
53.11, SD = 16.33); and English (M = 57.22, SD= 11.35 vs. M = 53.11, SD =
16.33). On the other hand, girls with ADHD in schools of Kampala had higher
scores than boys in overall academic achievement (M = 103.50, SD = 24.77 vs.
M = 93.45, SD = 24.71); math (M = 32.30, SD = 10.03 vs. M = 31.91, SD =
8.41); science (M = 34.80, SD= 12.87 vs. M = 27.09, SD = 10.38); and English
(M = 36.40, SD= 10.33 vs. M = 34.45, SD = 11.00). This implied that girls with
ADHD in schools of Mombasa were more academically impaired than boys
while boys with ADHD in schools of Kampala were more academically
impaired than girls.
Academic Achievement of Learners with Different ADHD Subtypes
A one way ANOVA revealed a non-statistically significant difference
in overall academic achievement among learners with different ADHD
subtypes in schools of Mombasa (p = .511). However, learners with ADHD
Inattentive subtype were the most affected whereas those with ADHD
Hyperactive-Impulsive subtype were the least impaired. Correspondingly, the
test revealed a non-statistically significant difference in overall academic
achievement among learners with different ADHD subtypes in schools of
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Kampala (p = .425). Even when the test was non-significant, learners with
ADHD Inattentive subtype were the most affected while the least affected were
those with ADHD Hyperactive-Impulsive subtype. The test further revealed
non-statistically significant mean differences in core subjects in Mombasa:
math (p = .324), science (p = .757), and English (p = .260); and Kampala: math
(p = .190), science (p = .753), and English (p = .768). However, great
impairment was observed in those with ADHD Inattentive subtype. This
implied that learners with ADHD Inattentive subtype were more academically
disadvantaged than those with the other two ADHD subtypes in both samples.
Table 4: One-Way Analysis of Variance on Academic Achievement of Learners with
Different ADHD Subtypes
Mombas
a
Source
df
SS
MS
F
p
Total
Between groups
2
1653.84
826.92
.680
.511
Within groups
52
63193.07
1215.25
Total
54
64846.91
Math
Between groups
2
553.80
276.90
1.52
.324
Within groups
52
12499.19
240.37
Total
54
13052.98
Science
Between groups
2
100.03
50.01
.280
.757
Within groups
52
9274.08
178.35
Total
54
9374.11
English
Between groups
2
472.44
236.22
1.38
.260
Within groups
52
8893.27
171.02
Total
54
9365.71
Kampala
Total
Between groups
2
1143.03
571.52
.897
.425
Within groups
18
11468.78
637.15
Total
20
12611.81
Math
Between groups
2
272.03
136.02
1.83
.190
Within groups
18
1341.78
74.54
Total
20
1613.81
Science
Between groups
2
89.31
44.66
.288
.753
Within groups
18
2788.50
154.92
Total
20
2877.81
English
Between groups
2
63.34
31.67
.268
.768
Within groups
18
2127.61
118.20
Total
20
2190.95
Predictive Power of ADHD on Academic Achievement of Learners
The predictive model revealed that ADHD significantly predicted
academic achievement of learners in Mombasa: overall achievement (p < .001,
adjusted R2 =.101); math (p < .001, adjusted R2 = .090); science (p < .001,
adjusted R2 = .097); and English (p < .001, adjusted R2 = .084). Similarly, the
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model showed that ADHD significantly predicted academic achievement of
learners in Kampala: overall achievement (p < .001, adjusted R2 = .552); math
(p < .001, adjusted R2 = .454); science (p < .001, adjusted R2 = .492); and
English (p < .001, adjusted R2 = .484). This means that ADHD is a strong
negative predictor of academic achievement among learners. Again, ADHD
explained more variability in Kampala sample than Mombasa sample implying
that this condition put learners in Kampala schools at a more disadvantage than
those in Mombasa schools.
Table 5: Significance of ADHD in Prediction of Academic Achievement
Model
Unstandardized Coefficients
Standardized Coefficients
1
B
Std. Error
Beta
t
Sig.
Mombas
a
(Constant)
148.52
36.13
4.11
.000
Total
-31.14
6.91
-.296
-4.51
.000
Math
-10.79
2.86
-.254
-3.77
.000
Science
-9.89
2.73
-.243
-3.62
.000
English
-10.46
2.22
-.301
-4.72
.000
Kampala
(Constant)
243.89
23.30
10.47
.000
Total
-97.88
9.25
-.652
-10.58
.000
Math
-33.16
3.84
-.581
-8.64
.000
Science
-33.32
3.39
-.635
-9.82
.000
English
-31.39
3.40
-.605
-9.25
.000
Discussion
The study investigated the effect of ADHD on academic achievement
of learners in international schools of Mombasa and Kampala. The findings
were discussed systematically as per objectives. This study makes several
inputs to literature using a comparative approach.
Firstly, it provides more empirical evidence on the association between
ADHD and academic achievement. For instance this condition impaired
academic achievement of learners with ADHD in both samples regardless of
improved support services in these schools. Low academic achievement
among learners with ADHD has also been highlighted in previous studies (e.g.,
Afeti & Nyarko, 2017). However, Zenderski, Sciberras, Mensah and Hiscock
(2017) attribute poor achievement among learners with ADHD to low IQ while
Bojuweye et al. (2014) challenges this finding by claiming that learners with
ADHD have average or even above average IQ. In fact this was beyond the
scope of the current study which may thus require another study to be done.
Secondly, the study provides evidence on the relationship between
ADHD and academic achievement of boys and girls as girls with ADHD in
Mombasa were academically impaired than boys while boys with ADHD in
Kampala were academically disadvantaged than girls. Gender differences in
academic achievement among learners with ADHD have been reported in
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206
literature. The finding from Kampala sample corroborate results by Cuffe et
al. (2015) who found better academic achievement among females than males
with ADHD while results from Mombasa sample are in agreement with
Yoshimasu et al. (2011) who found higher impairment among females than
males in their longitudinal studies. However, Young, Sabbah, Young, Reiser,
and Richardson (2010) found low achievement for both genders. Thus, this
gender factor should not be ignored.
Thirdly, although all the three ADHD subtypes impaired academic
achievement of learners, greater impairment was recorded in those with ADHD
Inattentive subtype in both samples. Low academic achievement among
learners with ADHD Inattentive subtype might be explained by the fact that
these learners are hardly identified by teachers as impaired and thus no effort
is put forward to help them perform at the same level as their peers. This
signifies to teachers that they should take an extra effort to intervene and help
learners with ADHD Inattentive subtype and stop treating them under
“clowns” of disciplined learners. According to van der Kolk et al. (2015),
ADHD inattentive symptoms are associated with difficulties in organization
skills which lead to decreased self-efficacy and development of depressive
symptoms both of which influence the relation between ADHD and academic
performance. However, longitudinal studies support our finding as high early
childhood inattention was associated with lower teacher-rated academic
performance in reading, writing and math while ADHD Hyperactive-
Impulsive subtype was not a consistent predictor of educational outcomes
(Rabiner et al., 2016; Pingault et al., 2014; Salla et al., 2016). On the contrary,
Bendiksen et al. (2014) and DuPaul et al. (2014) found higher impairment in
ADHD Combined preschool Norwegian and U.S kindergarten through 12th
grade learners respectively. This point to the fact that intervention should target
at all the three ADHD subtypes as they all put learners with ADHD at an
academic disadvantage.
Fourthly, the predictive model showed that ADHD explained more
variability among learners in schools of Kampala compared to those in schools
of Mombasa. This calls upon the government of Uganda to implement the
policy of every school having a professional counselor as this may help
enhance academic achievement of these learners. Prediction of academic
achievement among learners with ADHD has been reported elsewhere. For
instance DuPaul et al. (2014) found that ADHD significantly predicted
academic impairment among learners with the condition. Even though the
current study revealed higher impairment in Kampala than Mombasa, both
cities should pay more attention to this condition as these learners study in an
inclusive learning environment with general teachers who may not have
special skills to handle them. Nonetheless, controversy has been built on
whether it’s the ADHD condition or IQ and executive functions or a
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combination of all the three that contribute to low academic achievement
among learners with ADHD. Thus, further studies need to investigate this link
in the African context.
Conclusion
In conclusion, ADHD significantly impaired overall academic
achievement of learners with the condition in an inclusive learning
environment compared to those without the condition; with more impairment
in those who had ADHD Inattentive subtype in both cities. The condition also
impaired achievement in core subjects (math, science and English). Gender
differences in impairment were also observed in the study where girls with
ADHD in Mombasa and boys with ADHD in Kampala were greatly
academically impaired. A prediction model revealed that ADHD explained
more variation in academic achievement among Kampala sample than
Mombasa sample. Therefore, these learners should be provided with the
necessary support to help enhance their performance.
Recommendations
The study noticed that learners with ADHD in inclusive learning
environments had low academic achievement. Therefore, the governments of
both countries should consider classifying ADHD under the category of special
needs so as to enhance their academic achievement especially through
implementation of Individualized Education Plan (IEP).
The study further established that learners with ADHD Inattentive
subtype were severely impaired than those with the other ADHD subtypes.
Therefore, it is imperative to integrate the screening of learners for ADHDinto
the school health services in order to enable earlydetection and management
of the condition.
The study found that girls with ADHD were academically impaired
than boys in Mombasa while boys with ADHD were academically
disadvantaged than girls in Kampala. Therefore, this gender factor requires
another study to be done.
The predictive model showed that ADHD explained more variability
among learners in schools of Kampala compared to those in schools of
Mombasa. This calls upon the government of Uganda to implement the policy
of every school having a professional counselor who can help these learners
with ADHD be able to control their behaviours especially amidst academic
tasks.
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