This study assessed the impact of technology integration and motivation on teaching and learning statistics in selected public universities in Ghana. A survey was conducted with 200 faculty and students from 4 universities. The results found that integrating technology and increasing motivation greatly improved students' academic performance in statistics. Technology allows for more interactive learning and access to information. While technology integration benefits education, some challenges remain like inadequate training and unreliable infrastructure. Overall, the study concluded that technology integration, when combined with motivation, can significantly enhance how statistics is taught and learned.

1. AKENTEN APPIAH- MENKA UNIVERSITY OF SKILL TRAINING AND
ENTREPRENEURIAL DEVELOPMENT
THE IMPACT OF TECHNOLOGY INTEGRATION AND MOTIVATION IN
TEACHING AND LEARNING OF STATISTICS IN SOME SELECTED PUBLIC
UNIVERSITIES IN GHANA
Prince Duah Mensah
Department of Information Technology, AAMUSTED
Stephen Tweneboah Koduah
Department of Information Technology, AAMUSTED
Benyin
Department of Information Technology, AAMUSTED
Caleb Yeboah
Department of Information Technology, AAMUSTED
Recommended Citation: Mensah, P. D., Stephen, T. K., Benyin, Caleb. (2023). Improving
Academic Performance of Students in Statistics through Technology Integration and Motivation
in some selected Public Universities in Ghana.

2. THE IMPACT OF TECHNOLOGY INTEGRATION AND MOTIVATION IN
TEACHING AND LEARNING OF STATISTICS IN SOME SELECTED PUBLIC
UNIVERSITIES IN GHANA
Prince Duah Mensah, Stephen Tweneboah Koduah, Benyin, Caleb
Department of Information Technology, Akenten Appiah- Menka University of Skill
Training and Entrepreneurial Development, Kumasi 1277, Ashanti, Ghana.
Abstract
Integrating Technology in teaching and learning in universities will ensure collaborative learning,
incorporate different style of learning, saves time and effort of faculty members and learners,
improves communication and access to information, widens the scope of distance education and
helps learners to acquire critical, analytical and creative skills. The Government of Ghana through
the Ministry of Education seems to be making progress in integrating technology into the teaching
and learning process in tertiary institutions. However, the increasingly vital role of data, especially
big data, in many applications, presents the field of statistics with unparalleled challenges and
many students have phobia for working with data. This study therefore aims to assess the impact
of technology integration and motivation in teaching and learning of statistics in some selected
public universites. A survey questionnaire was distributed randomly to a total of 200 faculty
members and undergraduate students from some selected Public Universities in Ghana, including
Kwane Nkrumah University of Science and Technology, Kumasi Technical University, Akenten
Appiah- Menka University of Skill Training and Entrepreneurial Development and University of
Education, Winneba. The data for this mixed research method were analyzed quantitatively using
IBM SPSS and AMOS (Version 23) software and qualitatively using the responses from
respondents through an inductive and deductive process. The outcome indicates great
improvement in students’ academic performance in statistics through the integration of technology
and motivation in teaching and learning. For future studies, there is the need to consider integrating
Technology in all subjects at the tertiary level of education.
Keywords: Technology Integration, Academic Performance, Statistics, Motivation

3. 1. INTRODUCTION
The impact of technology on modern
societies cannot be underestimated. The
spread, adoption and use of technology has
changed many societies in the world
(Ahiatrogah and Barfi, 2016). Studies across
the globe have shown that the introduction of
technologies have not only changed the
socio- cultural nature of traditional societies
but have also improved the politico-
economic performance of modern economies
(Buabeng- Andoh, 2012; Koc- Bakir, 2010).
According to Koc and Bakir (2010), the
educational sector continues to remain one of
the main beneficiaries of technology. Over
the years, there has been a growing demand
for ICT to be fully used by teachers to
improve teaching and learning processes that
will go a long way to enhance students’
academic performance (Ahiatrogah and
Barfi, 2016).
For instance, Papaioannon and
Charalambous (2011) observed that the
integration of technology in schools
encourages students, stimulates their
interests, increase their self- esteem and
confidence. Technology including the use of
ICT allows for greater interactively among
students and teachers, enhances critical
thinking skills and increases students’ ability
to understand what they have been taught in
class.
Many countries are steadily integrating
technology in their education system (Bell,
2011). Literature survey have shown that
some countries such as Nigeria, Kenya and
South Africa have positive associations
between the integration of technology in
teaching in teaching and learning and
improvement in the general academic
performance of students (Bhasin, 2012;
Farrell, 2007). This observed positive
relationship which exists between the
integration of technology and academic
performance becomes possible when
effective policies are made towards
technology integration in education.
The situation is not different in Ghana. There
is a rising prevalence of technology in almost
all spheres of the academic setting (Agyei,
2014). Literature survey in ICT integration in
teaching has shown that it can improve the
quality of education. Teaching and learning
has been made much easier with the use of
technology in schools (Buabeng-Andoh and
Yidana, 2015). A recent study by Amedeker
(2020), showed that the use of technology in
teaching has trained students who are
committed and are able to engage themselves
actively in their own learning; students who
develop new ideas by collaborating with
other students all over the world. However,
there is a growing apprehension about the
literature gap as far as integration of
technology in education in Ghana is
concerned. This study therefore aims to
assess the impact of technology integration
and motivation in teaching and learning of
statistics in some selected public universities
in Ghana.

4. 1.2Statement of the Problem
In today’s information age, technology has
become an indispensable tool which is
making a tremendous impact on the lives of
people worldwide. This effect is most
significant in education. The computer has
become a motivating tool for teaching and
learning in schools (World Bank, 1999). It
provides instantaneous accessibility to
information, which is why its presence in the
classroom is so important. Smart phones,
computers and tablets are already element of
everyday life for students and teachers alike.
In Ghana, many schools have access to
computers courtesy of the various
governments’ spear- headed initiatives,
developing partners, Non- Governmental
Organisations (NGO) and individual
stakeholders of these schools (Shiboko,
2015). Additionally, the Government of
Ghana and its partners have been trying to
provide teachers as well as students with free
laptops aimed at improving teaching and
learning. The situation is not different at the
various Universities in Ghana. For instance,
Kwame Nkrumah University of Science and
Technology has setup the KNUST SONSOL
Project which is the University’s initiative to
support one needy student with one laptop
and also the University of Ghana Legon has
setup one student one laptop initiative geared
toward assisting underprivileged students to
have a laptop. All these universities are
implementing technology into teaching and
learning in order create pathways for
differentiated instruction to meet the unique
needs of students as individual learners
within a broader classroom climate.
However, the increasingly vital role of data,
especially big data, in many applications,
presents the field of statistics with
unparalleled challenges and some students
have phobia for working with data. This is a
real big issue among such students and
rampant among students who are not
numerically or quantitatively sound. They
prefer working with words rather than with
numerical figures. It becomes an
insurmountable problem when confronted
with large numeric measurements. Such
students frequently prefer to drop such
lecture notes, handouts or textbooks. You
hear such students saying “I do not like all the
ugly figures, I prefer verbal notes”. It has
therefore become imperative to assess the
impact of technology integration and
motivation in teaching and learning of
statistics in some selected public universities
in Ghana.
2. LITERATURE SURVEY
The literature survey of this study
encompasses systematic identification,
location and analysis of documents
containing information related to the research
problem. It involves theoretical framework
underpinning this study, definition of
Technology Integration, Concept of
Technology and Education Technology. It
further looks at the integration of technology
into teaching and learning.
2.2 Concept of Technology
The word technology comes from the Greek
word technologia; a combination of techne,
meaning ‘craft’ and logia meaning ‘saying’.
Therefore, technology can be defined as the

5. application of a craft. Technology refers to
methods, systems and devices which are the
result of scientific knowledge being used for
practical purposes.
Some of the types of technology include,
Information Technology which refers to
hardware and software tools used to store
information. Communication Technology
which is used to transmit information or data
from one place to another. Construction
Technology which deals with advanced
methods and equipment for building
structures. Assistive Technology used by
people with disabilities to accomplish
specific tasks. Medical Technology which is
used to extend and improve human life.
2.3 Educational Technology
Technology in Education refers to the
application of scientific knowledge in using
media and equipment such as video and audio
cassette recorder, video cassette, player or
recorder, radio, television, telephone and
computer to improve the teaching and
learning process. Some instructional
materials include chalkboard, flannel boards,
models and overhead projectors.
The Association of Educational
Communication and Technology (AECT)
(1979) defined educational technology as a
field involved in the facilitation of human
learning through the systematic
identification, development, organization
and utilization of a full range of learning
resources and through the management of
these resources. It is a complex, integrated
process involving people, procedures, ideas,
devices and organization for analyzing
problems and devising, implementing,
evaluating and managing solutions to those
problems involved in all aspects of human
learning.
2.4 Integrating Technology in Teaching
and Learning
Integrating Technology into teaching and
learning refers to the use of technology to
enhance the student learning experience. It
involves how teachers use technology to
carry out activities more effectively and also
develop students’ thinking skills. Integrating
different types of technology in the teaching
and learning process, including a virtual
classroom creates learners who are actively
engaged with learning objectives (Hew and
Brush, 2007).
2.5 Impact of Technology Integration in
Teaching and Learning
In the first place, integrating technology in
teaching and learning has improved the
classroom environment and permits teachers
and their learners to interact as human beings.
Furthermore, integrating technology in
teaching and learning helps to preserve and
share knowledge over long distances. For
instance, we can preserve knowledge on
pendrives (CDs and floppy disks), audio and
video cassettes and share content with others
at long distances via the internet.

6. Moreover, technology integration through
the use of internet has unlocked the world of
opportunities for students. The ideas and
information that were once out of the reach of
learners are easily gained by a single click on
internet and with this, the learners of all ages
can connect, share and learn on global basis.
Again, integrating technology into teaching
and learning helps to promote problem
solving skills. It contributes significantly to
student participation in teaching and learning
2.6 Limitations to Integrating Technology
in Teaching and Learning
i. Inadequate training: This new method
of teaching and learning has shortage
of teachers and personnel, and
therefore special training is needed
for the existing ones so as to ensure
effective integration of classroom
technology. Training will enable
teachers to use software programmes,
media and equipment to stimulate
students to improve upon their skills.
ii. Malfunctions of Computer:
Computer in education depends
largely on electricity and without it, it
cannot function. Irregular supply
power is great problem to the use of
computer in the classroom.
iii. Capital Intensive: Integrating
technology in teaching and learning is
capital intensive. It requires a lot of
money and only few schools can
afford the equipment.
Figure 1 presents the conceptual framework
of the study.
3. METHODOLOGY
This section presents the research approach
and sample size determination to critically
study the teaching and learning of statistics in
some selected public universities in Ghana.
The Public Universities selected were:
Kwame Nkrumah University of Science and
Technology (KNUST), Kumasi Technical
University (KTU), Akenten Appiah- Menka
University of Skill Training and
Entrepreneurial Development
(AAMUSTED) and University of Education,
Winneba (UEW).
3.2 Research Approach
This study adopted the mixed research
approach to gather and analyse relevant data;
which involves both quantitative and
qualitative approaches. The approach
demands that the setting of the research is
described and explained.
The quantitative approach was used to
measure and understand the extent of impact

7. of technology and learning of statistics in
some selected public universities in Ghana.
This was deemed relevant because it sought
to assess the relationship between technology
integration in teaching and learning of
statistics as independent variables and
student academic performance as the
dependent variable.
3.3 Sample Size
Kothari (2004) defines sampling as the
process of obtaining data about an entire
population by examining only a part of it. The
purpose of sampling is to make
generalizations about the population. In
obtaining the population, interviews were
first held with Faculty members of the said
public universities, including faculty
members and lecturers in Ghana. The
researcher classified the Heads of
Departments and lecturers according to
their respective selected public
universities in Ghana. Subsequently,
students were also obtained. In all, a total
of one hundred and sixty (160) students
were obtained. Most of these students
were interviewed for the data. The
distribution is presented in Table 3.1.
Table 3.1: Summary of Sample Size
Distribution
Source: Author’s Construct, July 2023
Table 3.1 indicates that there are one
hundred and sixty (160) students in the four
selected public universities in Ghana.
Approximately, 25% representing 40
students were found in each of the selected
public university: Kwame Nkrumah
University of Science and Technology
(KNUST), Kumasi Technical University
(KTU), Akenten Appiah- Menka University
of Skill Training and Entrepreneurial
Development (AAMUSTED) and
University of Education Winneba (UEW).
Furthermore, the table indicates 61.25%
representing 98 male students and
38.75% representing 62 female students
in the selected public universities in
Ghana.

8. 3.4 Research Instrument
This research used one dependent variable
(Academic Performance in Statistics- ACH),
and two independent varibles (Statistics
Learning Motivation- MOT and Technology
Integration- TECH; Statistics). These five
constructs were all responded to on a Likert
scale weighted 1= Stronly Disagree, to 5=
Strongly Agree.
The study was also piloted tested, where
ambiguous statements were rewarded. The
Cronbach’s alpha scores for the constructs
were calculated after the pilot study data was
generated. The Cronbach’s alpha scores for
the constructs were calculated after the pilot
study data was generated. The alpha score for
Statistics learning motivation (MOT) was
0.655, that of technology integration in
teachng and learning was 0.701 and that of
academic performance in statistics was
0.704. The poor factor loadings for some of
the constructs was because of the ambiguity
in some of the statements (measurement
items), which were identified through the
pilot study, and corrected before the final
questionnaire was used for the main data
collection.
3.5 Validity and Reliability
This study runs Confirmatory Factor
Analysis (CFA) using Amos (version 23)
software and was adopted due its many
advantages, as identified by Lahey et al.
(2012). CFA allow for comparison of
competing models, integrating data limitation
in the analysis.
The results of CFA are indicated in Table 3.2.
As part of the CFA procedure, observed
variables with poor factor loading (less than
0.5), were deleted from further analysis.
After the CFA process, all measurement
items were retained for academic
performance in Statistics (ACH). Two (2)
each observed variable were however deleted
from technology integration in teaching and
learning (TECH) and Statistics learning
motivation (MOT).
Cronbach’s alpha (CA) was also run to assess
the internal consistency of the observed
variables. The CA was calculated using SPSS
(v.23), using the retained items. Reliability of
the observed variables is said to be achieved
when the CA score is at least 0.7. From the
analysis presented in Table 3.2, it could be
ascertained that the CA for all latent variables
were higher than 0.7, which indicates internal
consistency had been achieved. Technology
Integration had CA score of 0.856, Statistics
learning motivation had a CA score of 0.871
and academic Performance in statistics also
had an alpha score of 0.872.
Average Variance Extracted (AVE) was
calculated, to assess the convergent validity
of the observed variables. Convergent
validity assesses how well the measurement
items in the new scale, correlate with other
measurement items on the same construct
(Trochim & Donnelly, 2001). Based on the
Fornell and Larcker (1981) criteria, the least
of AVE must be 0.5, while that of composite

9. reliability (CR) must also be 0.7, to conclude
that convergent validity was achieved among
the observed variables. The results indicate
that 0.909 was the least (MOT), while 0.916
was the least CR (ACHIEV), suggesting this
study achieved convergence validity.
As suggested by Hair et al. (2010), CMIN/DF
should be less than 3, CFI and TLI should be
at least 0.9, RMR and RMSEA should be at
least 0.8, while P-close should also be greater
than 0.05.
Table 3.2: Confirmatory Factor Analysis (CFA)
Model Fit Indices: CMIN = 58.886; DF = 51; CMIN/DF = 1.155; TLI = 0.990;
CFI = 0.992; GFI = 0.904; RMR = 0.037; RMSEA = 0.042; p-Close =.590
Factor Loading
Technology Integration (TECH): CA = 0.856; CR = 0.944; AVE = 0.737;
TECH3 0.924
TECH4 0.873
TECH5 0.875
TECH6 0.826
TECH7 0.882
TECH8 0.824
Statistics Learning Motivation (MOT): CA = 0.871; CR = 0.909; AVE = 0.769;
MOT2 0.848
MOT3 0.828
MOT4 0.951
Academic Performance in Statistics (ACH): CA = 0.872; CR = 0.916; AVE= 0.78;
ACH1 0.855
ACH3 0.924
ACH4 0.878
CFI=Comparative Fit Index; CMIN/DF=Chi-Square/Degree of Freedom; RMR=Root Mean Square Residual;
RMSEA=Root Mean Square Error of Approximation; TLI=Tukey-Lewis Index
CMIN measures the minimum discrepancy in
the model; RMR and RMSEA represent
absolute fit indices, by assessing the
deviation of a hypothesized model from a
perfect model; while CFI and TLI represent
incremental fit indices, by comparing how
well the hypothesized model fits the baseline
model (assessing the worst fit) (Xia & Yang,
2019). The cutoff values for both CFI and
TLI are based on normal-theory maximum

10. likelihood with continuous data. P-close is
also expected to be statistically insignificant
at 5% (greater than 0.05). P-close represents
the p-value for testing the null hypothesis that
the population RMSEA is no greater than
0.05. These were all achieved as presented in
Table 3.2.
The descriptive statistics and discriminant
validity are presented in Table 3.3. From the
analysis presented, technology integration in
teaching and learning had the highest
composite reliability (CR) of 0.944 whilst
academic performance in Statistics had the
least composite reliability (CR)= 0.916.
Academic performance had the highest
Average Variance Extracted (AVE) of 0.785
whilst Technology Integration had the least
of 0.785. Since the constructs were measured
on a 5-point Likert scale of 1=Strongly
Disagree to 5=Strongly Agree, the highest
possible mean score is 5. The mean scores as
presented in Table 3.3 were therefore high for
all the five variables studied.
There are number of approaches in assessing
discriminant validity, but this current study
adopts an approach of measuring the square-
root of AVEs (√AVEs) against the inter-
correlation coefficients, as done by past
studies such as Bamfo et al. (2018). While
convergent validity measures the extent to
which measurement items on the same
construct correlate with each other,
discriminant validity measures the extent to
which measurement items are uncorrelated
with measurement items on different
constructs (Trochim and Donnelly, 2001).
The discriminant validity scores were
generated along with the CFA output, using
the plugin tool available in Amos (v.23). The
discriminant validity is said to be achieved
when the least √AVE is greater than the
largest correlation coefficient (Arthur et al.,
2021).
Table 3.3: Descriptive and Discriminant Validity Analysis
Variable CR AVE MSV Max(H) TECH MOT ACH
TECH 0.944 0.737 0.694 0.945 0.859
MOT 0.909 0.769 0.630 0.934 0.787*** 0.877
ACH 0.916 0.785 0.694 0.923 0.833*** 0.794*** 0.886
Note: √AVE are bold and underlined **p-value significant at 1% (0.01)
Discriminant validity was therefore achieved in the dataset.

11. 4. RESULTS
Structural Equation Modelling (SEM) was
run in Amos (v.23) to assess the various paths
hypothesized in the study. Bias-Corrected
(BC) percentile method of bootstrapping was
used, with a huge bootstrap sample, and 95%
confidence level. Just like the CFA, the
structural model as presented in Table 4.1,
which also met the various fit indices as
proposed by Hair et al. (2010). Figure 4.1
also presents the structural model (in
diagrammatic form) for the study. Results on
the hypothesized paths indicate that Statistics
learning motivation had a direct positive
effect on performance in Statistics (β = 0.335;
C. R.= 2.965). That is, about 93.7%
improvement in students’ performance in
statistics, when students are fully motivated.
H1: Motivation has a direct positive effect on
performance in statistics, was thus supported.
Technology integration had a direct positive
effect on students’ performance in statistics
(β = 0.547; C. R.= 4.260). Technology
integration has a direct positive effect on
statistics learning motivation (β =0.853; C.
R.= 7.332). That is, total integration of
technology and motivation in teaching and
learning of statistics increased students’
performance in mathematics by about 18.5%.
Table 4.1: Direct paths
Direct Effect Estimate S.E. C.R. P-value
TECH→MOT 0.853 0.116 7.332 ***
TECH→ACH 0.547 0.128 4.260 ***
MOT→ACH 0.335 0.113 2.965 .003
Bias-corrected (BC) percentile method; 5,000 Bootstrap samples; 95% Confidence level; **p-value
significant at 1% (0.01)

12. Figure 4.1: Structural Path Analysis
The study also assessed the mediating effect
of statistics learning motivation. It states
statistics learning motivation mediates the
relationship between technology integration
in teaching and learning and students’
academic performance in statistics. Since
technology integration had direct effect on
performance in Statistics, it was concluded
that statistics learning motivation played a
partial mediating role in this relationship.
5. DISCUSSION
Among the students from the various
Universities sampled for the study, it was
ascertained that statistics learning motivation
had a significant effect on the academic
performance of students in statistics of these
undergraduate students. Past studies on
statistics learning motivation have largely
focused on basic and secondary level
education, with very little attention on the
tertiary. This study therefore contributes to
literature on mathematics learning
motivation at the tertiary level. For example,
García et al. (2016) assessed the effect of
affective-motivational variables on
performance in mathematics in upper
elementary levels. Results suggested that,
affective-motivational variables accounted
for 21.3% of the variance in performance in
mathematics of these upper elementary
students. The study found a significant
difference in affective motivational variables
for both high and low performance in
mathematics.

13. Findings also revealed that the effect of
technology integration in teaching and
learning on the academic performance of
students in statistics is not only direct, but
partially mediated by statistics learning
motivation. Ryan and Deci (2017) considered
technology integration as an energetic
resource generating the needed fuel to persist
and successfully accomplish tasks, while
Smith et al. (2012) indicated that technology
integration in teaching and learning ignites
statistics learning motivation. It is thus clear
that technology integration in teaching and
learning motivate students in learning
statistics.
Among other things, Zhang and Wang (2020)
found out that statistics learning motivation
had a direct and positive effect on students’
academic performance in statistics. Tosto et
al. (2016) also found that learning motivation
had a significant effect on academic
performance in statistics. This implies that
technology integration in teaching and
learning affects learning motivation in
statistics, whiles statistics learning
motivation subsequently affects the academic
performance of tertiary students. Therefore,
although the effect of technology integration
in teaching and learning on students’
academic performance in statistics could be
direct, this effect could also be explained
through the mediating effect of statistics
learning motivation.
6. CONCLUSION
The study concluded that technology
integration in teaching and learning and
statistics learning motivation had significant
positive effects on the academic
performance in statistics among tertiary
students. The effects of technology
integration in teaching and learning was also
partially mediated by statistics learning
motivation.
6.2 RECOMMENDATION
Based on the various findings, I will
recommend the technology integration in
teaching and learning of all subjects to
Management of Higher Institutions,
especially to tertiary education. This is
largely because technology integration in
teaching and learning had a significant direct
effect and was also mediated to stimulate
the academic performance of students in
statistics.

14. REFERENCES
Abreh, M. K., Owusu, K. A., &
Amedahe, F. K. (2018). Trends in performance of
WASSCE candidates in the science and
mathematics in Ghana: Perceived contributing
factors and the way forward. Journal of
Education, 198(1), 113-123. https://doi.org/
10.1177/0022057418800950
Adas, M. (2015). Machines as the
Measure of Men: Science, Technology, and
Ideologies of Western Dominance. Ithaca:
Cornell University Press
Ahiatrogah, P. D., & Barfi, K. A.
(2016). The attitude and competence level of
basic school teachers in the teaching of ICT in
Cape Coast Metropolis. Proceedings of INCEDI
2016 conference, August 29–31, 2016, Accra
Ghana, 446–454
Amedeker, M. K. (2020). Changing
Educational Policies: Implications for ICT
Integration in Science Instruction and
performance of students in Ghanaian Senior High
Schools. Educational Technologies
(ICEduTech), 72-78
Arthur, Y. D., Asiedu-Addo, S., &
Assuah, C. (2017). Teacher-student variables as
predictor of students’ interest in mathematics:
The use of stepwise multiple linear regression
analysis. Asian Research Journal of Mathematics,
4(3), 1-11. https://doi.org/
10.9734/ARJOM/2017/33544
Awe, O. O., Crandell, I., and Vance, E.
A. (2015). Building statistics capacity in Nigeria
through the LISA 2020 Program. In Proceedings
of the International Statistical Institute’s 60th
World Statistics Congress, Rio de Janeiro, Brazil
Bell, F. (2011). Connectivism: Its place
in theory–informed research and innovation in
technology-enabled learning. The International
Review of Research in Open and Distance
Learning, 12(3), 98–118.
Buabeng-Andoh, C. (2012). Factors
influencing Teachers' Adoption and Integration
of Information Technology into teaching. A
review of the literature. International Journal of
Education and Development Using Information
and Communication Technology, 8(1) 136-155
Buabeng-Andoh, C. (2017). An
investigation of educational use of information
and communication technology from the
perspectives of Ghanaian students. International
Journal of Information and Communication
Technology Education, 13(3), 40–52.
Buabeng-Andoh, C., & Yidana, I.
(2015). Teachers ICT usage in second cycle
institutions in Ghana; a qualitative study.
International Journal of Education and
Development Using Information and
Communication Technology, 11(2),104-112
Farrell, G. (2007). Survey of ICT in
Education in Uganda. Washington, DC:
infoDev/World Bank.
García, T., Rodríguez, C., Betts, L.,
Areces, D., & González-Castro, P. (2016). How
affective motivational variables and approaches
to learning predict performance in mathematics in

15. upper elementary levels. Learning and Individual
Differences, 49, 25-31. https://doi.org/10.1016/
j.lindif.2016.05.021
Gyamfi, S. A. (2017). Pre-service
teacher’s attitude towards information and
communication technology usage: A Ghanaian
survey. International Journal of Education and
Development using ICT, 13(1), 52.
Gyamfi, S. A., & Gyaase, P. O. (2017).
Virtualization of university education: The
impact of ICT-mediated learning environment on
students’ performance. International Journal of
E-Services and Mobile Applications, 9(4), 24–40.
Koc, M., & Bakir, N. (2010). A needs
assessment survey to investigate preservice
teachers’ knowledge, experiences and
perceptions about preparation to using
educational technologies. The Turkish Online
Journal of Educational Technology, 9(1), 13–22.
Kothari, C. R., & Gang, W. (2014).
Research methodology; methods and techniques.
New Delhi: New Age International Publishers
Ltd.
Papoioannon, P., & Charalambous, K.
(2011). Principals’ attitudes towards ICT and
their perceptions about the factors that facilitate
or inhibit ICT integration in primary schools of
Cyprus. Journal of Information Technology
Education, 10(1), 349–369.
Rogers, E. M. (2003). Diffusion of
innovations (5th ed.). New York: Free Press.
Sarfo, F. K., Amankwah, S. K., Oti-
Agyen, P., & Yidana, I. (2016). Information and
communication technology access and use and
competency level among second-cycle school
teachers in Ghana. Journal of Media and
Communication Studies, 8(5), 43.
Shiboko, M. (2015). The impact of
modeling technology integration on pre-service
teachers’ technology confidence. Journal of
Computing in Higher Education, 17(2), 34–45.
Xia, Y., & Yang, Y. (2019). RMSEA,
CFI, and TLI in structural equation modeling
with ordered categorical data: The story they tell
depends on the estimation methods. Behavior
Research Methods, 51(1), 409-428.
https://doi.org/10.3758/s13428- 018-1055-2
Yusuf, M. O., & Yusuf, M. (2017).
Using ICT in enhancing learning in elementary
statistics. 12th International Conference of
Information, 12–13 December, Kuala Lumpur,
312–321.
Zhang, D., & Wang, C. (2020). The
relationship between mathematics interest and
performance in mathematics: mediating roles of
self-efficacy and mathematics anxiety.
International Journal of Educational Research,
104, 101648. https://doi.org/
10.1016/j.ijer.2020.101648
Zhou, D., Liu, J., & Liu, J. (2020). The
effect of problematic Internet use on performance
in mathematics: The mediating role of self-
efficacy and the moderating role of teacher-
student relationships. Children and Youth
Services Review, 118, 105372.
https://doi.org/10.1016/j.childyouth. 2020.10537