IJLTER - Efficacy of GeoGebra for Linear Functions

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Vol.19 No.9

International Journal of Learning, Teaching and Educational Research
(IJLTER)
Vol. 19, No. 9 (September 2020)
Print version: 1694-2493
Online version: 1694-2116
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VOLUME 19 NUMBER 9 September 2020
Table of Contents
The Efficacy of GeoGebra-Assisted Instruction on Students’ Drawing and Interpretations of Linear Functions .....1
Ugorji Iheanachor Ogbonnaya and Melody Mushipe
Demystifying Perceptual Learning Style Preferences of Vietnamese University Freshmen in English Academic
Achievement.......................................................................................................................................................................... 15
Tuan Van Vu and Dinh Ngoc Tran
School Heads of Departments’ Roles in Advancing Science and Mathematics through the Distributed Leadership
Framework............................................................................................................................................................................. 39
Paul Nwati Munje, Maria Tsakeni and Loyiso C. Jita
Transformation of Geospatial Technology Knowledge in Pre-service and Experienced Geography Teachers as
Pedagogical Tools in the Technological-Pedagogical-Content Knowledge Framework............................................. 58
Purwanto ., Sugeng Utaya, Budi Handoyo and Syamsul Bachri
Transformation of the Educational Ecosystem in the Singularity Environment.......................................................... 77
Kateryna Andriushchenko, Vita Kovtun, Oleksandra Cherniaieva, Nadiia Datsii, Olena Aleinikova and Anatolii Mykolaiets
Guide Pedagogical Students to Design and Organize Experience-based Learning Activities in Schools ................ 99
Thi Hang Nguyen, Huu Quan Nguyen and Hoang Mau Chu
Novice Teachers’ Challenges and Coping Strategies in Qatari Government Schools ............................................... 118
Shaikha R. AL-Naimi, Michael H. Romanowski and Xiangyun Du
Didactic Aspects of Teachers’ Training for Differentiated Instruction in Modern School Practice in Ukraine...... 143
Nellia Nychkalo, Larysa Lukianova, Natalya Bidyuk, Vitaliy Tretko and Kateryna Skyba
Does Being Gritty Mean Being College-Ready? Investigating the Link between Grit and College Readiness
among Filipino K-12 Graduates........................................................................................................................................ 160
Febe Marl G. Paat, Antonio I. Tamayao, Rudolf T. Vecaldo, Maria T. Mamba, Jay Emmanuel L. Asuncion and Editha S.
Pagulayan
Analysis of the Efficiency of China’s Distance Economic Education in Force Majeure Circumstances.................. 175
Kseniia V. Tsytsiura and Ganna M. Romanova

Conditioning Factors in the Integration of Technology in the Teaching of Portuguese Non-Native Language: A
Post-COVID 19 Reflection for the Current Training of Teachers ................................................................................. 196
Joana Carvalho, Inmaculada Sánchez Casado and Sixto Cubo Delgado
The Impact of a Mosque-Based Islamic Education to Young British Muslim Professionals..................................... 220
Nader Al-Refai
Applying Agile Learning to Teaching English for Specific Purposes.......................................................................... 238
Liudmyla Lazorenko and Oksana Krasnenko
Teaching History in Ways C21st Students Learn – A Design-Based Research Perspective...................................... 259
Dorothy Kyagaba Sebbowa and Dick Ng'ambi
Analysis of Engineering Accreditation Process and Outcomes: Lessons Learned for Successful First Time
Application .......................................................................................................................................................................... 281
Tahar Ayadat and Andi Asiz
The Attitudes of Tertiary Level Students Towards Cooperative Learning Strategies in Afghan EFL Context...... 301
Rahmatullah Katawazai and Aminabibi Saidalvi
The Value of Competence-based Assessment in Pre-service Teacher Training ......................................................... 320
Mamsi Ethel Khuzwayo
Cognitive E-Tools for Diagnosing the State of Medical Knowledge in Students Enrolled for a Second Time in an
Anatomy Course.................................................................................................................................................................341
Guadalupe Elizabeth Morales-Martinez, Alberto Manuel Ángeles-Castellanos, Víctor Hugo Ibarra-Ramírez and Magaly
Iveth Mancera-Rangel
The Development of Writing Module on Enhancing the Writing Skills of Omani General Foundation Program
Students................................................................................................................................................................................ 363
Moustafa Mohamed Abdelmohsen, Rohaya Abdullah and Yasir Azam
COVID-19 and Online Learning: A SWOT Analysis of Users’ Perspectives on Learning Management System of
University of Education, Winneba, Ghana...................................................................................................................... 382
Dandy George Dampson, Richardson Addai-Mununkum, Stephen Kwakye Apau and Joseph Bentil

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©2020 The authors and IJLTER.ORG. All rights reserved.
Vol. 19, No. 9, pp. 1-14, September 2020
https://doi.org/10.26803/ijlter.19.9.1
The Efficacy of GeoGebra-Assisted Instruction on
Students’ Drawing and Interpretations of Linear
Functions
Ugorji Iheanachor Ogbonnaya
University of Pretoria, Pretoria, South Africa
https://orcid.org/0000-0002-6243-5953
Melody Mushipe
University of South Africa, Pretoria, South Africa
https://orcid.org/0000-0003-4005-898X
Abstract. The purpose of this study was to explore the effectiveness of
GeoGebra assisted instruction on students’ achievement in drawing
graphs of linear functions and interpretation of the representations of
linear functions. These aspects of linear functions tend to pose a
challenge to many students. The non-equivalent control group pre-test-
post-test quasi-experimental research design was used in the study. The
sample was 94 Grade 9 students from three secondary schools in a
province in South Africa. Two schools formed the control groups and
one school was the experimental group. Data were collected using
achievement tests. The tests results were analysed using inferential
statistics (Kruskal-Wallis and Mann-Whitney U comparison tests) at 0.05
level of significance. Statistically significant differences were found
between the groups with respect to drawing and interpretation of linear
functions graphs with the experimental group obtaining the highest
mean scores. The findings suggest that GeoGebra assisted instruction
might be a way of enhancing students’ ability to draw the graphs of
linear functions and analyse and interpret the representations of linear
functions.
Keywords: Drawing graphs; Geogebra; interpreting graphs; linear
functions; technology
1. Introduction
In mathematics, “a linear function is a function 𝑓 on the real numbers that is
given by 𝑓(𝑥) = 𝑎x + 𝑏, where 𝑎, 𝑏 are real numbers and 𝑎 ≠ 0” (Marsigit et al.,
2011, In Wijayanti, 2018, p. 475). Linear functions can be represented graphically
with straight lines (Laridon et al., 2004). Functions are very critical in
mathematics education; they are fundamental topics in school mathematics.
They are applied in many branches of mathematics and other subjects. Various

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aspects of functions are utilised in real life as a basis of decision-making. For
example, in an everyday economic situation, a function may be used to
understand how the cost of fuelling a car is related to the quantity of fuel added
or how the distance travelled is related to the quantity of petrol used. Today,
most statistical data in the media (depicting a relationship between two or more
variables) are presented in tables and graphs of which the knowledge of linear
functions will help one to make sense of most of the statistical information. Fair
grasp of statistical information is a necessary and valuable skill for the socio-
economic wellbeing of an individual and a society at large in the 21st century.
Hence, the learning of linear functions is important because it “provides
students with their first experience of identifying and interpreting the
relationship between two dependent variables” (Pierce, 2005, p.81). According to
Pierce, this experience is a significant point of transition in the students’
mathematical development.
In the South African school curriculum, linear functions is formally taught in the
Grades 7-9. In the Grade 9 mathematics curriculum, students are to “draw linear
graphs from given equations and determine the equations of functions from
given linear graphs”. Besides, students are expected to “analyse and interpret
linear functions with special focus on the x-intercept and y-intercept, and
gradient” (Department of Basic Education, 2011, p.26). The representations and
interpretations of linear functions seem to be challenging for many students.
One possible reason for the students’ challenge on this topic could be their
inability to relate the various representations of the function.
To support students’ learning of some mathematical concepts, many researchers
advocate the integration of technology with the teaching of those concepts.
Similarly, the South African school curriculum supports the use of available
technologies in the teaching of mathematics (Department of Basic Education,
2011).
This study explored the efficacy of GeoGebra assisted instruction on Grade 9
students’ drawing of linear functions graphs and interpretations of the
representations of linear functions using a non-equivalent control group pre-
test-post-test quasi-experimental design (Cohen, Manion & Morrison, 2011),
with a sample of 94 Grade 9 students from three secondary schools in a province
of South Africa. The background of the study is presented, followed by the
research methodology, the findings, discussion of the findings, and the
Conclusion and recommendations.
1.1 Background
Historically, the use of various forms of technologies (teaching aids and
manipulative) has been part of education. In the past few decades, development
in technology has significantly influenced teaching and learning (Akcay, 2017;
Mueller, Wood, Willoughby, Ross & Specht, 2008). There is strong evidence in
the literature that technology combined with an appropriate teaching approach
supports the learning of many school subjects. In mathematics education, the use
of technology in teaching could date back to the use of the Abacus. In the recent
years, Information, communication and technology (ICT) is found to support the

3
teaching and learning of mathematical concepts by enabling the visualisation of
some of these concepts and thereby making learning meaningful and joyful to
students (Ogbonnaya, 2010; Thambi & Eu, 2012).
The integration of technology (e.g. GeoGebra) with mathematics teaching agrees
with the constructivist theory of learning that learning is an active process;
people learn through exploration and active participation in the learning process
(Slavin & Davis, 2006). The integration of technology with teaching and learning
mathematics could enable students’ active engagement with the learning as they
strive to make sense of mathematical concepts using technology. Dynamic
geometry software technologies, such as GeoGebra, stimulates students to
develop their mathematical argumentation by making conjectures and explore
the outcome of their conjectures (Disbudak & Akyuz, 2019). Exploration with
this technological tool leads to reflection and knowledge construction in line
with the constructivist perspective of learning.
Many research studies in mathematics education have found the integration of
some ICT tools effective in supporting the teaching and learning of some
mathematical concepts (Bester & Brand, 2013; Ogbonnaya & Mji, 2012; Bray &
Tangney, 2017).
1.2 Research purpose
This study explored the effectiveness of GeoGebra assisted instruction on Grade
9 students’ learning achievement in linear functions. The research questions
addressed are: does GeoGebra assisted linear functions instruction make a
statistically significant difference in Grade 9 students’ learning achievement in
(i) drawing of linear functions graphs? and (ii) interpreting linear functions?
To help answer the research questions the following two hypotheses were tested
at 0.05 level of significance: GeoGebra assisted linear functions instruction does
not significantly affect Grade 9 students’ learning achievement in (i) drawing of
linear functions graphs, and (ii) interpreting of linear functions.
1.3 GeoGebra
GeoGebra is an interactive mathematics software created by Markus
Hohenwarter in 2002. GeoGebra brings together the features of computer
algebra systems and dynamic geometry software (Hohenwarter & Jones, 2007).
It is user friendly and multilingual in its menu and commands
(https://www.geogebra.org). Zengin, Furkanb and Kutluca (2012) noted that
“GeoGebra is a dynamic learning environment that enables its users to create
mathematical objects and interact with them. GeoGebra users, … can model
mathematical concepts and the relationships between them” (p. 184). GeoGebra
can be used to carry out statistical analysis. Users can create statistical graphs,
test hypotheses and simulate real-life situations (Phan-Yamad & Man, 2018). It
can be downloaded for free from the internet. GeoGebra makes it possible for
“dynamically linked multiple representations for mathematical objects”
(Hohenwarter & Lavicza, 2009, p.3) in one window as shown in the graphical
and algebraic representations of the lines y= -1.5x+20 and y=x+6 in Figure 1. This
feature makes GeoGebra a powerful tool for learning most mathematics topics.

4
Figure 1: Example of GeoGebra window
Many studies have explored the effectiveness of GeoGebra in teaching some
mathematical topics at different levels of education (Aydos, 2015; Granberg &
Olsson, 2015; Takači, Stankov & Milanovic, 2015; Wassie & Zergaw, 2018). Most
of the studies reported a positive effect of Geogebra on students learning. For
example, Arbain and Shukor (2015) studied the impact of GeoGebra on
secondary school students’ achievement in solving statistics problems in
Malaysia. The study found that GeoGebra positively impacted on the students’
learning achievement in statistics.
In a quasi-experimental with university students’ study in Jordan, Alkhateeb
and Al-Duwairi (2019) explored the effects of GeoGebra on the students’
learning achievement in geometry. The results show that GeoGebra had a
positive effect on the students’ achievement. Pjanić and Lidan (2015) studied the
influence of GeoGebra on pre-service mathematics teachers’ content knowledge
of the area of a trapezium, in a university in Turkey. The result of their study
showed that the use of GeoGebra applet had a positive effect on the pre-service
teachers’ knowledge of trapezium.
In Pakistan, Khalil, Farooq, Çakıroğlu, Khalil and Khan (2018), studied the effect
of GeoGebra aided instructions on the mathematical achievement of Grade 12
students in Analytic Geometry. The researchers compared the learning
achievement of (i) high achievers in the GeoGebra aided instruction class and
high achievers in the non-GeoGebra aided instruction class, (ii) low achievers in
the GeoGebra aided instruction class, and low achievers in the non-GeoGebra
aided instruction class. The results showed that in both the high and low
achievers’ categories, the students in the GeoGebra aided instruction class
significantly achieved better than the students in the non-GeoGebra aided
instruction class. Besides, the students of GeoGebra aided instruction class had
low standard deviation indicating that the GeoGebra instructions positively
affected all the students in the class. In a similar research study, Seloraji and Eu
(2017) found that Geogebra integrated teaching enhanced students’ performance
in geometry in Malaysia.

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The effect of GeoGebra on the mathematics learning of underprivileged students
with low mathematical ability was explored by Amam, Fatimah, Hartono and
Effendi (2017) in Indonesia. The mathematics topic of the study was
trigonometry. The study showed that GeoGebra positively impacted the
students’ mathematics learning achievement and motivation to learn
mathematics.
In South Africa, Pfeiffer (2017) found that GeoGebra enhanced pre-degree
students’ understanding of functions and other mathematics topics. Mthethwa
(2015) explored the effect of GeoGebra on students’ learning of Euclidean
geometry in some secondary schools in South Africa. The study found that the
students enjoyed learning with GeoGebra and found the GeoGebra assisted
learning motivating. Similarly, Godebo (2018) studied Grade 11 students’
experiences and perceptions on GeoGebra in learning Euclidean geometry in
some secondary schools in South Africa. The researcher found that GeoGebra
enhanced students’ understanding of Euclidean Geometry.
Some other studies (e.g. Bulut, Akçakın, Kaya, & Akçakın, 2016; Jelatu, Sariyasa,
& Ardana, 2018; Mustafa, 2015; Zulnaidi, Oktavika & Hidayat, 2020) show that
GeoGebra is effective in enhancing students’ learning of some mathematical
concepts. On the contrary, a few studies found reported that GeoGebra did not
have any significant effect on students’ learning of some mathematical concepts
when compared with the pencil and paper method. For example, Masri, Hiong,
Tajudin, Zamzana and Shah (2016) in a study on the effects of GeoGebra
integrated Teaching on Malaysian Secondary school students’ performance of
Circle III topic did not find any significant effect of teaching the GeoGebra.
In all, the literature discussed in this section, show strong evidence of the
positive effect of GeoGebra on students’ learning of many mathematics topics.
Equally, the findings from the literature suggest that GeoGebra could have a
significant effect on students’ linear functions learning achievement in the South
African context.
2. Research Methodology
2.1 Research design and sample
The study used a non-equivalent control group pre-test-post-test quasi-
experimental design. Non-equivalence control group quasi-experimental design
is a between-subjects design in which the experimental and control groups are
not equated by randomisation (Cohen, Manion & Morrison, 2011). The students
who participated had not been randomly assigned to the classes; instead, intact
classes were used to avoid disruption of classes.
The participants were 94 Grade 9 students from three underperforming schools.
The schools are in rural communities in the same geographical area in a
Province in South Africa. The students are from poor socio-economic
backgrounds hence they do not pay school fees and they are provided with free
meals at school by the government. The schools were purposively selected
because of their record of persistent poor achievement in mathematics over the
years. The sample comprised 31 students (15 girls and 16 boys) from school A,

6
35 students (16 girls and 19 boys) from school B, and 28 students (16 girls and 12
boys) from school C. Schools A and C were the control groups while School B
was the experimental group. School B was chosen as the experimental group
because it had some computers that were donated to the school by an
organisation. The computers were not used for teaching before the time of the
study. The two control groups were used to ensure that the effects of any
confounding variables are minimised because “two control groups can yield
consistent and unbiased estimates of bounds on the treatment effect when
conventional adjustments fail” (Rosenbaum, 1987, p.297).
2.2 Data collection instrument
The instrument used for data collection was a linear functions achievement test.
The test consisted of five questions with sub-questions that examined students’
knowledge of drawing and interpreting linear functions. For example, draw the
graph y = 2x – 1 explored the students’ ability to draw linear functions graphs.
What is the y-intercept of y = 2x – 3? explored the students’ ability to interpret a
representation of a linear functions. The test questions were developed by three
mathematics teachers with over 5 years of teaching experience. The test served
as the pre-test and the post-test.
The test was checked and validated by 2 mathematics education specialists
(called mathematics subject advisers in the Department of Education). The
validator adjudged the questions relevant for the study and at the appropriate
cognitive levels. The reliability of the test was ascertained using data from a trial
study conducted in another school. The reliability of the test was calculated
using the Kuder-Richardson (KR-10) formula (McMillan & Schumacher, 2013).
An alpha value of 0.72 was obtained. This value indicates that the test was
reliable (Fraenkel & Wallen, 2009).
2.3 Interventions
The teaching in all the groups followed 10 one-hour lessons designated for
teaching the topic. The lessons were taught by the teachers in their schools. The
teachers used the Department of Basic Education worksheets in teaching the
topic. The worksheets were issued to the teachers during cluster meetings where
teachers in an area meet and plan lessons together. The teachers were all
professionally qualified mathematics teachers and have had a minimum of 5
years of teaching experience. They have all been given basic training on
GeoGebra by the curriculum adviser before this study.
The teaching in the control groups involved the traditional teacher explanations,
followed by some examples on the chalkboard and giving of exercises.
GeoGebra was not introduced to these students either before or during the
intervention. In the experimental group, GeoGebra was used to teach the
lessons. The students were introduced to GeoGebra in the first lesson. During
the lessons, the teacher introduced the lesson, used GeoGebra to explain some of
the concepts, and gave exercise to the students to work through using GeoGebra
while the teacher monitors the students and helped them when they needed
help or further explanations. The lessons in all the schools were taught
following the lesson schedule provided by the Provincial Department of Basic

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Education. Hence, the same contents were covered in all the schools over the
same period according to the lesson plan. After the data collection, the teachers
in the control group schools were encouraged to introduce GeoGebra to their
students.
2.4 Data analysis
Inferential statistics were used for data analyses. The tests scores were tested for
normality using the Shapiro-Wilk test to establish whether the data were
normally distributed and thus determine whether a parametric or non-
parametric test should be carried out on the data. The results of the test of
normality for both tests showed that the scores were not normally distributed
(p<0.05). Hence, non-parametric tests (namely Kruskal-Wallis [KW] and Mann-
Whitney U [MWU] comparison tests) were conducted.
2.5 Ethical considerations
Permission was obtained from the provincial education authority and the
management of the schools before the commencement of the study. Also,
informed consent was obtained from participants in writing before the study
commenced. To ensure the confidentiality of the participants and the schools,
the names of the schools and students are not mentioned anywhere in reporting
the research.
3. Findings
The summary of the tests results is presented in Table 1. The pre-test mean
scores were 1.52, 2.11, and 1.61 for groups A, B, and C respectively. The overall
post-test mean scores were 17.74, 48.49, and 18.43 for groups A, B, and C
respectively. Besides, the groups’ post-test mean scores were 8.12, 42.65, and 6.08
in drawing linear functions graphs, and 29.03, 57.51, and 32.92 in the
interpretation of linear functions, for groups A, B and C respectively.
Table 1: Descriptive statistics of the results of the tests
Group N Min Max Mean Std. D
Pre-test
(General)
A 31 0.00 7.00 1.52 1.59
B 35 0.00 8.00 2.11 1.81
C 28 0.00 7.00 1.61 1.64
Post-test
(General)
A 31 2.00 50.00 17.74 10.80
B 35 16.00 74.00 48.49 15.01
C 28 6.00 50.00 18.43 11.13
Post-test
Drawing graph
A 31 0.00 55.56 8.12 15.95
B 35 0.00 81.48 42.65 24.21
C 28 0.00 40.74 6.08 10.49
Post-test
Interpretation
of linear
functions
A 31 0.00 65.22 29.03 15.16
B 35 30.43 82.61 57.51 15.10
C 28 13.04 60.87 32.92 13.97
To test for any statistically significant differences in the groups’ tests scores, a
non-parametric inferential statistics test namely the Kruskal-Wallis (KW) test
was conducted. The non-parametric inferential statistics test was used because

8
the students’ tests scores in the three groups were not found to be normally
distributed. The result of the KW test of the groups’ pre-test scores is shown in
Table 2.
Table 2: Result of the Kruskal-Wallis test of the pre-test scores
Rank Test statistics
Group N Mean rank
A 31 43.00 Kruskal-Wallis H 3.339
B 35 53.84 df 2
C 28 44.55 Asymp. Sig. .188
Total 94
The KW test result shows that there was no statistically significant difference
between any two groups (H(2) = 3.339, p>0.05) in the pre-test. Based on this, one
might say that mean pre-test scores of the students in all the groups were
similar. Hence, the three groups were of comparable ability in drawing and
interpreting linear functions before the treatment.
The descriptive statistics of the post-test results (Table 1) show that group B (the
experimental group) had the highest mean score among the three groups in the
post-test (in general and in drawing and interpreting linear functions). The
interest of this paper was on the effectiveness of GeoGebra on the students’
drawing linear functions graphs and interpreting linear functions. Accordingly,
further analyses of the post-test results were carried out.
3.1 Drawing linear graphs
The KW test result of the groups in drawing graphs of linear functions (Table 3)
shows mean ranks of 34.18, 70.20, and 33.88 for groups A, B, and C respectively.
Table 3: KW Test result - Drawing Linear Functions Graphs
Rank Test statistics
School N Mean rank
B 35 70.20 df 2
C 28 33.88 Asymp. Sig. .000
Total 94
The KW test result (H(2) = 43.07, p<0.001), shows that a statistically significant
difference exits between the mean ranks of at least two groups in drawing linear
functions graphs. Therefore, a post-hoc analysis (MWU test) was run to check
where the differences existed in groups.
MWU test descriptive statistics (Table 4) show that in all cases, the mean rank of
group B (the experimental group) was higher than the mean ranks of Groups A
and C (the control groups) in drawing linear functions graphs.

9
Table 4: The MWU test result - drawing Linear Functions Graphs
Ranks Test statistics
Group N Mean
rank
Sum of
ranks
A 31 29.74 922.00 Mann-Whitney U 426.000
C 28 30.29 848.00 Wilcoxon W 922.000
Total 59 Z -.146
Asymp. Sig. (1-tailed) .884
A 31 20.44 633.50 Mann-Whitney U 137.500
B 35 45.07 1577.50 Wilcoxon W 633.500
Total 66 Z -5.375
C 28 18.09 506.50 Mann-Whitney U 100.500
B 35 43.13 1509.50 Wilcoxon W 506.500
Total 63 Z -5.511
The test Statistics between Groups A and C (the control groups) show that no
statistically significant difference existed between their achievements scores (U =
426, p > 0.05). However, the test Statistics between Groups A and B shows that
the achievement of Group B was statistically significantly higher than the
achievement of Group A (U = 138, p < 0.05, r = .66). Similarly, the test Statistics
between Groups B and C shows that the achievement of Group B was
statistically significantly higher than the achievement of Group C (U = 101, p <
0.05, r = .69). Based on these, the hypothesis that GeoGebra assisted linear
functions instruction does not significantly affect Grade 9 students’ learning
achievement in the drawing of linear functions graphs was rejected. Hence, it
was concluded that GeoGebra assisted linear functions instruction significantly
affected the Grade 9 students’ learning achievement in drawing of linear
functions graphs. Moreover, the effect sizes (0.66 and 0.69) indicate that the
differences between the experimental group and the control groups were large
(Cohen 1988).
3.2 Interpreting linear functions
The KW test of the groups’ achievement scores on the interpretation of the linear
functions (Table 5) shows mean ranks of 31.87, 70.66, and 36.62 for groups A, B,
and C respectively.
Table 5: KW Test result - Interpreting Linear Functions
Rank statistics Test statistics
School N Mean rank
B 35 70.66 df 2
C 28 36.23 Asymp. Sig. .000
Total 94
The KW test statistics provide very strong evidence of a difference between the
mean rank of at least two groups in the interpretation of linear functions (H(2) =
40.91, p<.05). To ascertain where the differences existed in groups, a post-hoc

10
analysis using the MWU test was carried out. The result (Table 6) shows that
group B (GeoGebra group) achieved above each of the non- GeoGebra groups.
Table 6: MWU Test result - in Interpreting Linear Functions
Ranks Test statistics
Group N Mean
rank
Sum of
ranks
A 31 28.24 875.50 Mann-Whitney U 379.500
C 28 31.95 894.50 Wilcoxon W 875.500
Total 59 Z -.833
A 31 19.29 598.00 Mann-Whitney U 102.000
B 35 46.09 1613.00 Wilcoxon W 598.000
Total 66 Z -5.678
C 28 18.79 526.00 Mann-Whitney U 120.000
B 35 42.57 1490.00 Wilcoxon W 526.000
Total 63 Z -5.141
The test Statistics between the control groups (A and C) show that no
statistically significant difference existed between their achievements scores (U =
379.5, p > 0.05). Nevertheless, the test Statistics between Groups A and B show
that the achievement of Group B was statistically significantly higher than the
achievement of Group A ((U = 102, p < 0.05, r = .70). Equally, the test Statistics
between Groups B and C shows that the achievement of Group B was
statistically significantly higher than the achievement of Group C (U = 120, p <
0.05, r = .65). Based on these results, the hypothesis that GeoGebra assisted linear
functions instruction does not significantly affect Grade 9 students’ learning
achievement in interpreting linear functions was rejected. GeoGebra assisted
linear functions instruction significantly affected the Grade 9 students’ learning
achievement in interpreting of linear functions. The effect sizes of 0.65 and 0.70
indicate that the differences between the Geogebra group and the control groups
were large.
4. Discussion
This study explored the effectiveness of GeoGebra assisted instruction on Grade
9 students’ learning achievement in drawing and interpreting linear graphs. The
results showed that the students taught via GeoGebra assisted instruction,
significantly achievement better than the control groups students in drawing
and interpreting linear functions. The result appears to corroborate the findings
of several previous studies (e.g. Kushwaha, Chaurasia & Singhal, 2014; Seloraji
& Eu, 2017; Praveen & Leong, 2013; Rahman & Puteh, 2017). In particular, the
finding of this study agrees with the findings of some other research studies in
South Africa (for example, Godebo, 2018; Pfeiffer, 2017;), that GeoGebra has a
significant positive effect on students’ learning achievement in some
mathematics concepts.
The positive effect of GeoGebra on students learning achievement found in this
study could be because the interactive nature of GeoGebra (Hohenwarter &

11
Jones, 2007) enabled the students in the GeoGebra assisted instruction to
thoroughly explore and grasp linear functions better than the students in the
control groups. Moreover, GeoGebra makes it easy for one to accurately draw
graphs. Correctly drawn graphs enhance visualisation, understanding, and
interpretation. Zulnaidi, Oktavika and Hidayat (2020) noted that “GeoGebra can
illustrate mathematical concepts and procedures well through visuals and
graphs, which considerably aid students in mastering and understanding
concepts and procedures pertaining to functions” (p.1). In contrast, drawing
graphs manually is prone to error and makes it difficult for one to understand
and interpret the graphs accurately. So, accurately drawing of the graphs using
GeoGebra could have helped the students in the GeoGebra assisted class to learn
better than their counterparts did not learn using Geogebra.
Another factor that the findings of this study might be attributed to is the
younger generations’ love for technology (Bester & Brand, 2013). In all
possibility, students in the experimental group might have enjoyed their
learning of linear function more than the students in the control groups.
Students’ enjoyment of technology-assisted instructions has been observed in
other studies to lead to more student engagement with the subject content and
consequently higher achievement outcomes (Mthethwa, 2015, Ogbonnaya, 2010;
Thambi & Eu, 2012).
5. Conclusion and Recommendations
The study found that GeoGebra assisted instruction had significantly affected
9th Graders learning achievement in linear graphs and interpretations of linear
functions. The findings suggest that GeoGebra assisted mathematics instruction
has the potential to enhance students’ achievement in linear functions. Hence,
GeoGebra assisted mathematics instruction might contribute to improved
students’ mathematics learning and consequently the technological and socio-
economic development of the country. We, therefore, recommend more research
studies on the efficacy of technology-assisted instruction on students’ learning of
linear functions and other mathematics concepts.
The study adds to the evidence suggesting that the use of technology, and in
particular GeoGebra, in teaching some topics in mathematics might result in
higher levels of student achievement than the traditional ‘chalk-and-talk’
method. We recommend that teachers explore the effectiveness of integrating
GeoGebra and other information and communication technologies with their
teaching of mathematical topics in general.
We also recommend that the Department of Basic Education and all other
stakeholders in mathematics education in the country should encourage teachers
to integrate GeoGebra in mathematics teaching. When teachers begin to use
GeoGebra in teaching it will likely encourage students to learn mathematics by
themselves. The concomitant effect would be improved student learning as
desired by the Government and all stakeholders in mathematics education in the
country.

12
Many schools in the country do not have ICT facilities to enable the use of
GeoGebra or any computer-based technology in teaching. Hence, we
recommend the provision of ICT facilities in all the schools in the country so that
teachers and students will be able to use Geogebra for mathematics teaching and
learning.
Furthermore, we recommend that mathematics teachers be offered the relevant
professional development workshops to acquaint them with the affordances of
GeoGebra for mathematics teaching. This will likely enhance their knowledge
and dispositions towards the use of GeoGebra in teaching.
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Vol. 19, No. 9, pp. 15-38, September 2020
https://doi.org/10.26803/ijlter.19.9.2
Demystifying Perceptual Learning Style
Preferences of Vietnamese University Freshmen
in English Academic Achievement
Tuan Van Vu
Hanoi Law University
87 Nguyen Chi Thanh street, Dong Da district, Ha Noi city, Vietnam
https://orcid.org/0000-0002-3066-7338
Dinh Ngoc Tran
Hanoi Law University
87 Nguyen Chi Thanh street, Dong Da district, Ha Noi city, Vietnam
https://orcid.org/0000-0003-4868-4758
Abstract. Learning styles play an important role in teaching and learning,
especially in second language acquisition. This study aims to investigate
the perceptual language learning style preference of 385 first-year
university students in Vietnam. Adapting Reid’s (1984) learning style
questionnaire is used as a data gathering tool in which it was responded
and retrieved via students’ emails incorporated with Google form. The
results revealed that freshmen were active learners since they mostly
belonged to 4 major learning styles, namely Tactile, Auditory, Group, and
Kinesthetic learners, and 2 minor learning styles, i.e. Visual and
Individual learners. In addition, the study did not find the differences
between gender as well as major and non-major English students in
comparison with learning styles. Besides, freshmen’s English academic
achievement was highly influenced by their learning styles. The research
findings contribute resourceful references to the formation of
stakeholders’ policies on English language teaching and learning,
teachers of English, and future studies.
Keywords: Major learning style; Minor learning style; Model; Second
language acquisition
1. Introduction
In the educational setting, different learners have their own ways to acquire the
second languages (L2), and the issue of learning style preferences (hereafter, LSP)
has been investigated in a number of studies until now. Some learning style
models have been proposed and widely acknowledged since 1970s (Dunn &
Dunn, 1978; Kolb, 1985; Reid, 1984; Fleming, 2001). These researchers categorized

16
LSP into some specific styles. Since then, many studies have been carried out to
find out the congruency between LSP and English language achievement (Afshara
et al., 2015; Al-zayed, 2017; Gohar & Sadeghi, 2014; Tabatabaeia & Mashayekhi,
2013; Wilson, 2012). Other studies have investigated the influence of LSP on the
academic achievement (Almigbal, 2015; Bhattacharyya & Sarip, 2014; Khanum,
2014; Yee et al., 2015), and other influential fields as well.
Raising awareness about learners’ LSP plays an important role in acquiring the
second language (Sadeghiet al., 2012). Thus, motivating language learners is
pertinent in the current language teaching and its associated learning
environments. Besides, language teachers commonly blame their learners’
academic weaknesses and/or poor performance for the learners’ cognitive
measures (i.e. intelligence and mental abilities), poor vocabulary knowledge,
inability to listen well or reading disabilities, etc. while studying learners’
individual differences have been paid little attention. In practice, different
variables have caused many debates on how to assess learner’s failure or success
in academic performance (Furnham & Monsen, 2009). Oxford (1989) claims that
language learning styles and strategies are the most essential variables which
strongly affect learners’ performance in a second language. Language learning
styles are considered as a valid psychological construct according to the notion
which is put forward in a research in educational settings by Sim et al. (1989).
Moreover, language learning styles are also one of the most important
determinants of educational achievement. Obviously, some learners can still gain
simple knowledge even if there is a mismatch between the learning materials and
their learning styles, but they can learn better and faster if their learning resources
are in accordance with their learning style strengths (Stevenson & Dunn, 2001).
Therefore, getting to know students’ LSP helps teachers either design suitable
learning materials to meet their students’ demands, who possess different stylistic
preferences or improve students’ learning strategies.
With reference to the related studies, many studies have been conducted to
investigate the influence of LSP towards the academic performance (Almigbal,
2015; Bogamuwa, 2017; Magdalena, 2015; Wilson, 2012; Ajideh et al., 2018), gender
differences (Bidabadi & Yamat, 2010; Dobson, 2010; Choudhary et al., 2011;
Sarabi-Asiabar et al., 2014; Shuib & Azizan, 2015), English language achievement
(Afshara et al., 2015; Al-zayed, 2017; Gohar & Sadeghi, 2014; Komlosi, 2018; Moo
& Eamoraphan, 2018; Santos, 2017), and teaching instructions (Gilakjani, 2012;
Hallin, 2014; Khaki et al., 2015; Olivosa et al., 2016). Given the role of cultural
background, the findings of some researches (Wu, 2010; Sywelem et al., 2012)
indicate different frequencies of learning style categories which are employed by
learners in ESL or EFL contexts.
As the matter of fact, most learners have not thought about their learning style
preferences, which are considered as a vital role in determining an individual’s
preferred way of learning. Though in Vietnam, English has gradually grown and
expanded since the period from 1986 to the present (Hoang, 2010), studies on
learners’ learning style preferences have not been paid much attention and are
kept marginalized. In other words, very few studies have been carried out to

17
identify Vietnamese students’ English learning style preferences. This study
attempts to investigate Vietnamese students’ English learning style preferences at
the tertiary level using Reid’s perceptual learning style model taking into
consideration the following questions;
1. What are Vietnamese freshmen’s English learning style preferences?
2. What is the correlation between learning style preferences and gender?
3. What is the hypothesis that there is no relationship between language
learning styles and the English language achievement?
2. Literature review
2.1. Definition of language learning style preferences
Teaching methodology has undergone the shift from teacher-centered teaching
approach to the state-of-art learner-centered teaching one which focuses on the
role of learners in second language acquisition. Up to the present, the definition
of language learning styles has attracted educational experts’ and researchers’
attention. In simple term, a learning style, also referred as cognitive style or
cognitive strategy is a particular way of learning preferred by a learner. Different
learners have their own ways in learning, and an activity which is accomplished
by learners whose learning style prefers a visual mode of learning, may not be
helpful or successful with a learner who favours auditory or kinesthetic modes of
learning. Therefore, it is teacher’s responsibility to recognize different learning
styles among their learners because differences in learning styles are accounted
for the way learners approach learning tasks, and the success of those tasks
(Richards& Schmidt, 2014).
The definition of language learning styles dates back to the late 1970s.
Remarkably, Reid (1987) defines perceptual learning styles or interchangeably
learning styles as the differences that learners use one or more senses to
understand, organize, and retain experience. In another definition proposed by
Dunn (1990), learning styles are defined as the way in which individuals begin to
concentrate on, process, internalize, and retain new information. Kolb (1985)
defines learning style as the generalized differences in learning orientation, so
learning is regarded as the process whereby knowledge is accumulated through
the transformation of experiences. Gregorc (1979) defines learning styles as
“distinctive and observable behaviors that provide clues about the mediation
abilities of individuals and how their minds relate to the world and, therefore,
how they learn” (Gregorc, 1979, p. 19). Meanwhile, Fleming (2001) defines
learning style as “an individual’s characteristics and preferred ways of gathering,
organizing, and thinking about information. VARK is in the category of
instructional preference because it deals with perceptual modes. It is focused on
the different ways that we take in and give out information” (Fleming, 2001, p. 1).
2.2. Classification of language learning style models
Different researchers share the similarities and dissimilarities in terms of the
classification of language learning styles to some extent. Among the
classifications, some language learning style models such as Reid (1995), Dunn
and Dunn (1978/1992), Fleming (2001), Kolb (1985), Gregorc (1979), Felder and
Silverman (1988) are widely recognized and accepted.

18
Reid’s perceptual learning styles (1995) were used as the back-up theory behind
the current study. In his view, learning styles are classified into three main
categories, namely personality learning styles, cognitive learning styles, and
sensory learning styles. Based on learner’s personality, personality learning styles
can be divided into some learning styles such as extrovert, introvert, sensing,
perception, thinking, feeling, judging, perceiving, ambiguity-tolerant, ambiguity-
intolerant, left-brained, and right-brained learners. Meanwhile, cognitive learning
styles can be further split into field-independent and field-dependent, analytic
and global, and reflective and impulsive. Finally, sensory learning styles can be
classified into three main classifications, particularly personality learning styles,
environmental learning styles, and perceptual learning styles. Basically,
perceptual learning style preferences refer to the perceptual channels which
students choose their own favoured ways of learning. According to Reid (1984),
perceptual learning style preferences are categorized into auditory (involved in
listening to lectures and radio recording), tactile (lab experiments, hand-on),
visual (reading and studying diagrams), group (group work, share-study group),
kinesthetic (relating to movement or physical activity), and individual learning
(studying on own).
Another popular learning style model is widely acknowledged by Gregorc (1979),
which focuses on phenomenological model. He asserts that individuals have
natural predispositions for learning together with four bipolar, continuous mind
qualities which function as mediators because individuals learn from and react to
the surroundings. The model Gregorc (1979) suggested is also called Gregorc Style
Delieator which includes four learning styles, namely concrete-sequential,
abstract sequential, abstract random, and concrete random.
In line with Gregorc’s (1979) learning style model, Kolb (1985) has a different
approach basing on the experimental learning theory (hereafter ELT). ELT
combines between a holistic model of the learning process and a multi-linear
model of adult development. Kolb (1985) explains the terminology “experiential”
for its intellectual source in the experimental work of Dewey’s philosophical
pragmatism, Piaget’s cognitive-developmental genetic epistemology, and
Lewin’s social psychology, which shape a unique perspective on development
and learning. Kolb’s (1985) ELT comprises of four basic learning styles, namely
diverger, assimilator, converger, and accommodator on a model with two
dimensions. Diverger refers to a strong imaginative ability, good judgement from
different perspectives, creativity, and good interpersonal skills. Meanwhile,
assimilators yield theoretical models, encourage inductive reasoning, and work
with abstract ideas. Converger, however, has a strong practical orientation,
promote deductive thinking, and seem unemotional. Finally, accommodators
involve in risk-taking activities, and dealing with problems intuitively.
Filder and Silverman (1988) introduced another learning/teaching style model
which was originated in the engineering sciences. This model describes that
individuals’ learning style preferences are included in five bipolar continua such
as the active-reflective, the sensing-intuitive, the verbal-visual, the sequential-
global, and the intuitive-deductive. In particular, active learners enjoy working in

19
groups while reflective learners need their own privacy to work individually to
save time considering carefully the task before doing it. Sensing learners prefer
data, figures, experimentation, and detailed work whereas intuitive learners
would like theories and ideas, they especially like creating innovative and new
initiatives. Verbal learners want to participate in discussions, to listen and express
their opinions, meanwhile visual learners enjoy perceiving symbols, words, flow
charts, or reading books. Finally, sequential learners like step-by-step procedures,
linear reasoning, and systematic solutions, global learners are, in contrast,
regarded as synthesizers and integrators, who like making intuitive discoveries
and connections to grasp the whole system.
Dunn and Dunn (1992) propose a learning style model called the productivity
environmental preference survey (PEPS). This model includes 5 learning style
stimuli and sub-elements within each stimulus, i.e. environmental with its
representative elements, namely temperature, room design, light, or sound;
sociological (individual learning, pairwork with either peers or teachers, or both);
physiological (chronological energy pattern, perceptual, mobility needs, and
intake while learning); and psychological processing (hemisphericity, global or
analytic, and impulsive or reflective). This model strengthens the role of
individuals to find out, synthesize, and retain new information.
Fleming (2001) develops a sensory model which is referred to VARK model,
standing for Visual, Aural, Read/write, and Kinesthetic. The four perceptual
modes also have the differences among them. Visual learners prefer the intuitive
representations, for example charts, flow charts, pictures, different spatial
arrangements, etc. Aural learners, however, are dynamic because they like
demonstrating themselves in actions such as topic discussion, group work, idea
exchanges, retelling stories, and so on. Meanwhile, read/write learners tend to
perceive receptiveness via textbooks, printed handouts, manuals, surfing the
internet, or taking notes. Finally, kinesthetic learners prefer extroverted activities
such as apprentice, laboratories, problem-solving, project-learning, field trips, or
hand-on experiences. Thus, VARK model describes the perceptual modes that
learners prefer using to give out information.
2.3. Learning style preference with academic performance
Learning involves developing various aspects of learners’ progress and
improvement in terms of self-efficacy, self-direction, self-regulation, self-control,
autonomy, and intrinsic motivation. Academic performance, which is regarded as
a directly observable indicator of learning, reflects the efficiency resulting from
the mobilization of cognitive and emotional-volitional resources of learners doing
certain task-based activities (Dobson, 2010; Yee et al., 2015; Hamdani, 2015;
Almigbal, 2015; Moo & Eamoraphan, 2018). Learners’ performances refer to the
level of obtained academic results, the qualititative and quantitative
improvements in academic involvements. That is, it can be possibly predicted and
explained students’ learning performance thanks to a certain degree of probability
such as known factors and ways that their effects are implemented (Magdalena,
2015). The prediction of students’ academic performance includes the anticipation
of certain results in learning. From pedagogical perspective, the success of

20
academic performance accounts for the application and implementation of
instructive actions (Hamdani, 2015; Khaki et al., 2015; Almigbal, 2015). Academic
performance depends on the application and pedagogical practices by teachers on
the interactions between strategies and educational goals (Ahmad, 2011; Komlosi,
2018; Muhtar, 2014; Li, 2012; Wong, 2015). Therefore, LSP plays an important role
as a predictor of academic achievement and academic success default.
2.4. Factors Affecting Language learning Style Preference
The following factors greatly affect language learning styles to a certain extent.
Different genders may have dissimilar views on LSP, learners’ cultural differences
might lead to different perceptions towards LSP. Moreover, LSP can be clearly
recognized in second language acquisition.
Gender
Many researchers have investigated the relationship between LSP and gender.
The hypothesis comes up with the assumption whether LSP is influenced by the
gender. The research finding (Vaseghi et al., 2012; Bhattacharyya & Sarip, 2014;
Alkooheji & Al-Hattami, 2018; Tawir & Mustapha, 2017; Sarabi-Asiabar, 2014)
indicates that gender differences in LSP actually exist among learners. According
to Manova, cited by Vaseghi et al. (2012), students would rather receive more peer
interaction than learn alone, and more kinesthetic activities. Congruent with
Sarabi-Asiabar et al. (2014), their finding showed that using single model learning
styles had a significant impact on gender in the way that female students would
like to use aural learning style while male students preferred using the kinesthetic
learning styles.
On the other hand, some research results (Ahmad, 2011; Bidabadi & Yamat, 2010;
Shuib & Arizan, 2015; Tae-young& Miso, 2018) indicated that gender is not
affected by learning style preferences. For example, Ahmad (2011) investigated
the role of gender towards the learning style preferences of 252 Low English
Proficiency students at a local tertiary school. The result revealed that there was
no influence of gender on students’ learning style preferences. In another research
conducted by Shuib and Azizan (2015) on learning style preferences among ESL
students in Univesiti-Sains Malaysia, the finding shared the same view with
Ahmad (2011) that students’ learning preferences were not affected by gender.
Cultural perspectives
Another factor which can influence LSP is learners’ cultural perspectives. Studies
(Santos, 2017; Khanum, 2014; Shih, et al., 2013) have proved that it is important to
get to know about the cultural perspectives in LSP. Investigating the English
language learning style of the higher secondary learners in Bangladesh, Khanum
(2014) stresses the importance of the cultural behavior in which he recommends.
that teachers should incorporate culture-related style differences into the learning
styles. Different cultural background may happen at a small educational setting
or in different educational environments, cultural background to a certain extent
influences language learners’ learning style preferences (Santos, 2017).
Furthermore, understanding cultural background could help learners avoid
cultural shocks in cross-cultural tele-communication exchanges (Shih et al., 2013).
Second Language Acquisition

21
There are a number of studies examining the relationship between learning styles
and second language acquisition. In the educational setting, the language
knowledge intake of different learners can be the same in first language
acquisition, however, when accumulating more languages, students could be
influenced by their motivation to study as they could prefer visual, auditory or
kinesthetic learning styles. On close investigation into English language learning,
the adaptation of teaching methodology and course design are very necessary,
this seems to be very challenging for teachers to adjust their teaching styles to
match different learners in a classroom (Wong, 2015; Olivosa et al., 2016; Tee et
al., 2015; Afshara et al., 2015; Komlosi, 2018; Khaki et al., 2015).
2.5. Reid’s Perceptual Learning Style Preference Questionnaire
Perceptual learning style preference questionnaire (PLSPQ) is likened to and used
as the main backbone of this study. This pilot study dealt with native English
speakers and ESL students. The second pilot study, which was revised and
improved in comparison with the first pilot one, was conducted with solely on
ESL students in 1990. The questionnaire includes two parts, particularly the first
part collects the interviewees’ demographic information, while the second part
explores the characteristics of learners based on 30 question items. These 30-
question items are divided into six types of learners: auditory, visual, kinesthetic,
tactile, group and individual learners. In reality, there are many different learning
style inventories introduced by many researchers. Take the learning style survey
(LSS) introduced by Cohen, Oxford and Chi (2009) for example, there are 110
questions which cover the learners’ perceptual and physical factors. Unlike the
LSS, Reid’s PLSPQ addresses learners’ perceptual preferences in second language
learning field. Renou (2011) claimed that Reid’s PLSPQ was the first well known
instrument to assess the learners’ perceptual learning style preferences and it has
been widely exploited in many other researches as well as this study.
3. Method
3.1. Research design
The study is basically designed to investigate the LSP of first-year students in
Vietnam. The research backed up the quantitative method, using descriptive
approach to give out the references for teaching and learning English at the
tertiary level in Vietnam. The contact with university administrators for
permission to carry out the survey questionnaire was initially done. Using
Cochran’s formula to determine the sample population, 385 participants were
chosen through stratified sampling method. The respondents were asked to
answer the questionnaire, including two parts, namely the demographic
information and 30-adapted Reid’s questionnaire items. The questionnaires, with
a supporting letter from the university administrators, were sent to the
participants through email attachment with the active link of Google form. The
freshmen were requested to return the questionnaires after one week and in the
case of a low response rate, another email served as a reminder would be sent to
participants. The collected data went through the data screening before it was
treated by IBM SPSS program for the purpose of data analysis in answering the
30-item question.

22
3.2. Research Instruments
The study was used Reid’s (1984) perceptual learning style questionnaire. The first
part, which was designed by the author, identified respondents’ demographic
information such as sex, residence, learning English duration, and their groups.
The second part adapted 30 items of Reid’s (1984) PLSPQ, which masked into 6
categories examining four perceptual (auditory, visual, tactile, and kinesthetic),
and two social (group, and individual) learning style preferences. The participants
were expected to indicate (1) strong disagreement, (2) disagreement, (3)
undecided choice, (4) agreement, and (5) strong agreement.
3.3. Participants
The respondents were selected as freshmen from 3 national and regional
universities in Vietnam, namely the north – Vietnam National University, Hanoi;
the central – Hue university; the south – Vietnam National University, Ho Chi
Minh city. As clear explanation in the instruction, the target sample population
was first-year students. Owing to unknown number of participants, Cochran’s
(1977) formula was used to determine the expected population, which was 385. In
terms of gender participating in the study, the fewer number was 166 female
students accounting for 43.1%, whereas the majorrity of male students was 219,
equivalent to 56.9%. When examining the freshmen’s residence, over half of them
camefrom rural areas with 195 first-year students or 50.6%, then 117 students,
making up 30.4% were from urban areas, and the rest population was from
moutainous areas with the least propostion of 19.0%, similar to 73 first-year
students. As for the length of English learning experience, most of them spent
fewer than 15 years studying English, namely 73% or 281 freshmen, then followed
by lower 15.3% or 59 students who had fewer than 20 years of English education,
and the least rank 11.7% or 45 learners had 10 years fewer acquiring English. On
investigating students’ groups, the majority of respondents was English non-
major students with the proportion of 87.8%, equivalent to 338 freshmen, whereas
12.2% or 47 English major students who did an intensive English course at their
universities participating in the study.
3.4. Procedures
Having prepared the research instrument tools properly, the researcher had initial
contacts with 3 national and regional university administrators to explain the
purpose of the study and the assistance needed from the schools, and to seek
permission for their students to participate the study in the second term of the
school year 2019-2020. Once permission was granted, the questionnaire was sent
to first-year students’ email addresses provided by the universities concerned.
The questionnaire, which was incorporated with the researcher’s instruction,
explained the objectives and relevance of the study, assured the anonymity, and
gave them the option of not participating in the study if they wished. The
respondents were requested to return the questionnaire after one week since the
date of email-shot. A thanking email was sent back to the respondents as the
confirmation of reception.
The researcher made a list of relevant questionnaires, then carried out the careful
data screening process using the stratified sampling method to get the targeted
number. Finally, the preset 385 samples were obtained, and the screened data

23
were encoded for the purpose of the data treatment. The researcher used IBM
SPSS program to analyze the questionnaire and the outputs of English proficiency
test as well.
3.5. Data Analysis
The quantitative data was analyzed using descriptive statistics. Specially,
frequency count, and percentage were employed to analyzed the demographic
information such as sex, residence, length of English acquisition, and students’
groups. Descriptive mean was treated to address 30 item PLSPQ to find out the
legend of preferences in terms of 6 categories; auditory, visual, tactile, kinesthetic,
social, and individual learning styles, together with determining Likert scales,
particularly (1.0-1.79) very low, (1.8-2.59) low, (2.6-3.39) neutral, (3.4-4.19) high,
and (4.2-5.0) very high. Independent-samples T Test was used to compare LSP
and gender differences, among major and non-major English students with regard
to LSP. One-way ANOVA was employed to test the correlation between LSP with
students’ English grade term to examine the relationship between LSP with first-
year students’ English academic achievement.
4. Results and discussion
Table 1 presents two sources of information. That is, the discription of 6 kinds of
learning style preferences, and the self-scoring intepretation.
Table 1: The interpretation of perceptual learning style preferences
N
Mean
Std.
Deviation
Weighted
mean
Self-scoring
Visual
I learn better by reading what the teacher writes on
the chalkboard.
385 3.23 .655
3.13 31
When I read instructions, I remember them better. 385 3.27 .669
I understand better when I read instructions. 385 3.19 .774
I learn better by reading than by listening to
someone.
385 2.84 .663
I learn more by reading textbooks than by listening
to lectures.
385 3.13 .664
Tactile
I learn more when I can make a model of
something.
385 3.94 .612
4.08 41
I learn more when I make something for a class
project.
385 4.12 .710
I learn better when I make drawings as I study. 385 4.27 .646
When I build something, I remember what I have
learned better.
385 4.19 .707
I enjoy making something for a class project. 385 3.90 .594
Auditory
When the teacher tells me the instructions I
understand better.
385 4.09 .622
3.76 38
When someone tells me how to do something in
class, I learn it better.
385 3.66 .740

24
I remember things I have heard in class better than
things I have read.
385 3.06 .730
I learn better in class when the teacher gives a
lecture.
385 4.12 .712
I learn better in class when I listen to someone. 385 3.89 .651
Group
I get more work done when I work with other. 385 3.72 .562
4.01 40
I learn more when I study with a group. 385 4.17 .617
In class, I learn best when I work with other. 385 3.89 .840
I enjoy working on an assignment with two or
three classmates.
385 4.14 .678
I prefer to study with other. 385 4.13 .621
Kinesthetic
I prefer to learn by doing something in class. 385 4.44 .605
4.20 42
When I do things in class, I learn better. 385 4.06 .655
I enjoy learning in class by doing experiments. 385 4.45 .713
I understand things better in class when I
participate in role-playing.
385 4.01 .727
I learn best in class when I can participate in
related activities.
385 4.05 .645
Individual
When I study alone, I remember things better. 385 2.30 .680
2.45 25
When I work alone, I learn better. 385 2.68 .677
In class, I work better when I work alone. 385 2.49 .700
I prefer working on projects by myself. 385 2.37 .684
I prefer to work by myself. 385 2.43 .574
Legend
1.0 – 1.79 very low 1.8 – 2.59 low 2.6 – 3.39 neutral
3.4 – 4.19 high 4.2 – 5.0 very high
As glimpsed from Table 1, first-year students preferred reading instructions by
themselves (M = 3.27%, SD = .669), succeeding this ranking, reading what the
teacher wrote on the chalkboard (M = 3.23%, SD = .655), then reading instructions
(M = 3.19%, SD = .774), surprisingly reading textbooks rather than listening
lectures (M = 3.13%, SD = .664). The lowest figure in this category was the reading
preference over listening to someone (M = 2.84%, SD = .663). In general, freshmen
kept neutral opinions on Visual Learning Preference as the weighted mean of this
group is 3.13, which reveals the fact that first-year students were unsure about
their visual learning preference. Besides, the small standard deviation indicates
that the respondents had slight differences in their viewpoints. Mean scores also
supported the trend that first-year students preferred their autonomies in learning
even though the weighted mean still belonged to the neutral scale accordingly.
Basing on these figures, teachers should allow their students to be independent in
their learning, schools and teachers should encourage their learners to actively
involve the task-based learning and teaching or practical works instead of
academic learning policies (Hamdani, 2015; Nge & Eamoraphan, 2020).
In view of Tactile learning style in Table 1, this style refers to the opportunity for
learners to do “hand-on” experiences with materials. The respondents developed
the skills of mind map via drawings in studying (M = 4.27%, SD = .646), freshmen
needed to construct something to recall and review the previous knowledge (M =
4.19%, SD = .707). During the process of building something again, it is a good
chance for them to exchange the knowledge, create something new, adjust the

25
models, and mobilize the whole understanding from theory to practice (Olivosa
at al., 2016; Svarcova & Jelinkova, 2016; Yee et al., 2015). Participants confessed
that they learnt more when involving in a class project (M = 4.12%, SD = .710).
Tactile learners showed preferences for physical involvement relating to class
activities (Magdalena, 2015; Dobson, 2010). Handling and building models are the
remarkable characteristics of Tactile learners. When asked about this issue, the
respondents remarked that they learnt better by making a model of something (M
= 3.94%). Tactile learners enjoyed making something for a class project (M = 3.90),
this means that they were creative and would like to cooperate with other class
members in terms of academic performances. In general, the participants had high
preferences for Tactile learning style (M = 4.08), which is in line with other
researches (Gilakjani, 2012; Marica et al., 2015; Santos, 2017).
When examining Auditory learning style, students showed high preferences as
Tactile and Visual learning styles with the weighted means of 3.76. In particular,
students confessed that their teachers’ lectures helped them learn better (M = 4.12,
SD = .712). Similarly, students supposed that they understood their teachers’
instructions better (M = 4.09), and in such a situation that someone talking
something in class enabled students to learn better (M = 3.89), which was clearly
seen from Table 1. When instructed or explained how to do something during
lessons, students could learn better (M = 3.66). However, students were unsure
about the ability to remember things better in comparison to what they read (M =
3.06). From the data displayed in Table 1, the respondents indicated that they had
no difficulty listening to teachers or classmates. Students believed that they could
study and remember better when they were given instructions, lectures or
something relating to the auditory means of communication. In other words,
auditory medium in class could help students learn better which shared the
similar findings in other studies (Alkooheji & Al-Hattami, 2018; Tae-Young &
Miso, 2018; Gohar & Sadeghi, 2014; Shih et al., 2013; Bidabadi & Yamat, 2010).
Teamwork plays an important role at work. In terms of educational setting, group
learning style is also necessary to be categorized and examined. As glimpsed from
Table 1, studying with a group brought more positive result for students, who
revealed that they could learn better (M = 4.17, SD = .617). Besides, students
confirmed that working on an assignment in a group of two or three classmates
encouraged them to do better (M = 4.14). This was somehow similar to the
preference of studying with other classmates (M = 4.13). Nowadays, work-share
is very common at workplace, so is learning. Students reckoned that they could
learn best when cooperating with other class members (M = 3.89, SD = .840). In
addition, freshmen asserted that they got more work done under the condition
that they worked with other companions (M = 3.72, SD = .562). For this respective,
first-year students did not have much differences in their viewpoints as the
standard deviation was small (SD = .562). On the whole, students had high
preferences for the group learning style with the weighted mean of 4.01. As
students highly prefer working and studying in groups, it is advisable for teachers
to design cooperative assignments and classroom activities for students to do their
best to learn more (Hallin, 2014; Khaki et al., 2015; Bhattacharyya & Sarip, 2014;
Tee et al., 2015; Wong, 2015).

26
Kinesthetic learning style concentrates on the classroom experiences through
actively participating in activities, problem-solving, field trips or role-playing in
the classroom. When examining Kinesthetic learning style, freshmen showed very
high preferences for it by calculating the weighted mean of 4.20, which was clearly
shown in Table 1. In more detail, doing experiments in class activated students
most (M = 4.45%, SD = .713). Followed this rank, doing something in class was
students’ favour (M = 4.44%). Freshmen confirmed that they learnt better by doing
things in class, which indicated that they wanted to be active learners (M = 4.06%).
This confirmation was supported by another viewpoint that they learnt best
through the involvement in related class activities (M = 4.05%, SD = .645). Besides,
students revealed that role-playing in class helped them understand things better
(M = 4.01%). The overall results of Kinesthetic learning style denote that students
were active learners, they really wanted to participate and experience related class
activities, students understood and accumulated the knowledge best. Therefore,
the necessity of changing curriculum or teaching methodology is necessary to
create active learning environments for students to do their utmost. Some research
findings (Singh et al., 2015; Mulalic, et al., 2009; Ahmad, 2011; Bhattacharyya &
Sarip, 2014) recognized that the adaptation of curriculum and teaching
methodology was needed to meet the demands of students.
Individual learning style stresses the important role of self-study individually.
This style confirms that learners understand new material best when learning it
alone. On investigating individual learning style, the results came out that
students showed low preferences for it as the weighted mean was 2.45, which was
clearly presented in Table 1. In particular, students did not agree that they could
learn better when working alone (M = 2.49%, SD = .677). Similarly, they disagreed
that they could work better in class in case of working alone (M = 2.49%).
Mentioning about working on projects alone, freshmen highly protested the
opinion that they prefer to work by themselves (M = 2.37%). In addition, students
claimed that they disliked working on their own (M = 2.43%), they also had a high
similarity of choices as the standard deviation was quite small (SD = .574). The
respondents had a low favor for the statement that they could remember better
when studying alone (M = 2.30%). In comparison with group learning style which
had a high weighted mean, this style had a low one. When taking this opposite
into careful consideration, the difference in preference between two styles is
relevant. This finding has not been found in any other studies, for example Wong
(2015), Lui (2017), Moo & Eamoraphan (2018), Bidabadi & Yamat (2010), Al-Zayed
(2017), Khmakhien (2012), Marica et al. (2015), and so on.
As the explanation adapted from the C.I.T.E learning style instrument, Reid’s
PLSPQ is categorized into 6 kinds, i.e. Visual, Tactile, Auditory, Group,
Kinesthetic, and Individual learning styles. The total conversion score of the
whole PLSPQ is classified into 3 group preferences, namely (38-50) major LSP, (25-
37) minor LSP, and (0-24) negligible use. Major preference denotes any learning
method coming natural, normal to the learners, while minor preference refers to
learning ways which learners can perform adequately to meet the demands of the
tasks. Negligible preference mentions any learning method that learners find it
difficult to study with, they consequently will not choose it spontaneously

27
(Psaltou-Joycey & Kantaridou, 2011). By comparing between the results from
Table 1 with 3 equivalent explanation preferences, the outcome goes that Visual
and Individual learning styles belong to minor preferences whereas Kinesthetic,
Group, Auditory, and Tactile learning preferences are grouped into major
preferences. Table 2 presents the correlation between LSP and gender differences
on the choice of language learning styles. The purpose of this comparison is to
investigate whether there was a difference between male and female students in
the choice of employing different language learning styles.
Table 2: The comparison between LSP and gender differences
Levene's
Test for
Equality of
Variances t-test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence
Interval of the
Difference
Lower Upper
Visual
Equal
variances
assumed
.056 .813 .874 383 .383 .138 .158 -.173 .449
Equal
variances
not
assumed
.870 349 .385 .138 .159 -.174 .451
Tactile
Equal
variances
assumed
.420 .517 .169 383 .866 .024 .143 -.258 .306
Equal
variances
not
assumed
.169 352 .866 .024 .144 -.259 .307
Auditory
Equal
variances
assumed
7.94 .005 -.67 383 .505 -.103 .154 -.405 .200
Equal
variances
not
assumed
-.65 323 .515 -.103 .157 -.412 .207
Group
Equal
variances
assumed
1.77 .185 -.44 383 .658 -.069 .155 -.374 .236
Equal
variances
not
assumed
-.45 369 .655 -.069 .153 -.370 .233
Kinesthetic
Equal
variances
assumed
1.70 .193 -1.4 383 .173 -.213 .156 -.521 .094
Equal
variances
not
assumed
-1.4 367 .169 -.213 .155 -.518 .091

28
Individual
Equal
variances
assumed
.016 .898 -.14 383 .891 -.023 .165 -.347 .302
Equal
variances
not
assumed
-.14 354 .891 -.023 .165 -.348 .303
As clearly seen from Table 2, the data reveal that the Sig. values of Levene’s test
for equality of variances of 6 learning styles are higher than the confidence level
of 95%, so the Sig. (2-tailed) values in the equal variances assumed would be used
to take into account. Obviously, the Sig. (2-tailed) values turns out to be higher
that the confidence level (.005), too. Based on these findings, the conclusion goes
that male and female freshmen did not have differences on the choice of learning
style preferences. This finding shares the similarity with other researches
(Bhattacharyya & Sarip, 2013; Shuib & Azizan, 2015; Bidabadi & Yamat, 2010; Tae-
Yong & Miso, 2018).
Table 3 contrasts the dissimilarity between major and non-major English students
on the choice of language learning styles. It is clearly presented in the Sig. values
of Levene’s test for equality of variances that the Sig. values of 6 language learning
styles are higher than the confidence level (0.05), which leads to the decision on
choosing the Sig. (2-tailed) values of the equal variances assumed. Similarly, the
Sig. (2-tailed) values of 6 learning styles get higher than the confidence level (0.05).
Therefore, from two sources of the data – Sig. and Sig. (2 tailed), it is concluded
that there was no difference between major and non-major English students in
terms of choosing language learning styles. This contrastive analysis has not been
popular in the field of LSP as few studies have been conducted on the comparison
among major and non-major English learners and language learning style
preferences.
Table 3: The comparison between major and non-major English students
Levene's
Test for
Equality
of
Variances t-test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence Interval
of the Difference
Lower Upper
Visual
Equal
variances
assumed
1.4 .24 -.11 383 .909 -.027 .239 -.498 .443
Equal
variances
not
assumed
-.10 57.3 .915 -.027 .256 -.540 .485
Tactil
e
Equal
variances
assumed
.356 .55 .78 383 .436 .169 .217 -.257 .595

29
Equal
variances
not
assumed
.75 58.7 .456 .169 .225 -.282 .620
Auditory
Equal
variances
assumed
.001 .98 .97 383 .333 .225 .232 -.232 .682
Equal
variances
not
assumed
.99 60.1 .329 .225 .229 -.232 .683
Group
Equal
variances
assumed
.165 .69 -1.1 383 .270 -.259 .234 -.720 .202
Equal
variances
not
assumed
-1.1 60.5 .262 -.259 .229 -.717 .199
Kinesthetic
Equal
variances
assumed
.097 .56 -.47 383 .637 -.112 .237 -.577 .354
Equal
variances
not
assumed
-.47 59.4 .640 -.112 .238 -.588 .364
Individual
Equal
variances
assumed
.453 .50 .58 383 .565 .144 .250 -.347 .635
Equal
variances
not
assumed
.61 61.6 .547 .144 .238 -.331 .619
A far as the relationship between LSP and student academic achievement is
concerned, the following data is obtained. Table 4 addresses the hypothesis that
there is no relationship between LSP and English academic achievement. As seen
in Table 4, Sig. values of 6 learning style are higher than the preset confidence
level (0.05). That means the results reject the hypothesis and denote that LSP, to a
certain extent, influences English academic achievement. The influence of LSP on
English academic achievement reflects the students’ preferences as they are
classified into major and minor learners as shown in Table 1. That is, freshmen are
active language learners, which might somehow affect English academic
achievement. Some researches (Fang-Mei, 2013; Khmakhien, 2012; Gohar &
Sadeghi, 2014; Tabatabaeia & Mashayekhi, 2013) have shared the similar results
as this study.

30
Table 4: The relationship between LSP and English grade term
ANOVA
Sum of
Squares df
Mean
Square F Sig.
Visual Between
Groups
6.12 6 1.02 .429 .860
Within
Groups
899.98 378 2.38
Total 906.10 384
Tactile Between
Groups
30.07 6 5.01 2.65 .016
Within
Groups
714.07 378 1.89
Total 744.14 384
Auditory Between
Groups
10.21 6 1.70 .760 .602
Within
Groups
845.79 378 2.24
Total 855.99 384
Group Between
Groups
16.84 6 2.81 1.24 .284
Within
Groups
853.91 378 2.26
Total 870.74 384
Kinesthetic Between
Groups
13.31 6 2.22 .960 .452
Within
Groups
873.67 378 2.31
Total 886.96 384
Individual Between
Groups
7.13 6 1.19 .458 .839
Within
Groups
980.14 378 2.59
Total 987.26 384
5. Pedagogical implications
It is important for teachers to understand students’ learning styles. Teachers are
advisable to change the curriculum or teaching styles to meet the students’
expectations. In hope to do so, teachers should carry out the survey to find out
students’ learning styles, thanks to the results of the survey, teachers will have
relevant pedagogical activities to help students do their best to achieve the highest
English learning outcome. Besides, first-year students can modify and adjust their
learning styles so that they can adapt themselves to meet the requirements of
instructions, contexts, tasks or related English learning activities. Table 5
summarizes the learning strategies (Oxford, 1990) and recommended teaching
activities which are in accordance with the styles they belong to.

31
Table 5: Proposed combination of language learning strategies and teaching activities
matching the learning style preferences.
Language learning strategies Teaching activities
Major/minor learning styles
Visual Memory: visualizing mental
images Cognitive: identifying
different colours
Metacognitive: making up goals
and objectives
Extensive reading, written
instructions, using outlines,
flash cards, TV, videos, internet
Hands-on Compensation: mimes and
gestures
Memory: using physical response
Social: cooperating with others
Making posters, collages,
activities that allow students to
move around, change groups
frequently, projects, CALL, role
playing, activities that make
authentic use of the language,
jigsaw.
Extroverted Social: cooperating with
peers/proficient users, asking for
clarification
Metacognitive: organise own
learning, seeking practice
opportunities (mainly out of
class)
Do not use affective strategies. Do
not favour solitary/concentrated
study.
More indirect strategies than
direct ones.
Discussions/debates, role
playing, cooperative tasks,
question-generating activities,
activities that make students act
physically.
Intuitive-
random
Memory: associating, elaborating
Compensation strategies:
guessing from context
Metacognitive: planning
Cognitive: analysing and
reasoning
Social strategies: asking questions
Affective: (limited use): lowering
anxiety, encouraging oneself
Brainstorming, naturalistic
input, applying rules to new
situations, synthesis of
information from randomly
selected sources, inference tasks,
tasks offering change and
variety, skip around a text
Concrete-
sequential
Cognitive: practising
Memory: imagery, employing
action, structured reviewing, rote
memorisation
Metacognitive: arranging and
planning
Activities with clear
instructions, synthesis of
information from carefully
selected sources, well-planned
homework, drawings,
kinesthetic input
Closure-
oriented
Memory: associating/elaborating,
structured reviewing
Metacognitive: arranging and
planning, evaluating, goal-setting
with deadlines, overviewing and
linking with previous material
Cognitive: practising (formal,
drill-like)
Social: asking for correction,
clarification
Activities that have a clear goal,
tasks that follow a predictable
sequence to get a sense of
organisation

32
Global Memory: semantic mapping,
grouping,
Cognitive: skimming,
summarising, analysing
contrastively
Compensation: guessing
Social: cultural understanding
Mind-maps, inductive tasks,
finding
similarities/differences/main
idea, open-ended questions,
extensive reading, discussions,
learning through experiential
tasks
Negligible learning styles
Auditory Memory: representing sound in
memory
Cognitive: note-taking from
auditory input
Social strategies: asking questions
Reading aloud, discussions,
group work, using songs, music
Open Cognitive: recombining,
analysing, getting the idea
quickly, practising naturalistically
Metacognitive: seeking practice
opportunities
Compensation: guessing
Social: cooperating
Affective: Using humour to lower
anxiety, rewarding oneself
Discovery learning, activities
involving risk taking,
entertainment, cooperation
Analytic Cognitive: scanning, practising,
analysing contrastively, reasoning
deductively
Metacognitive strategies:
centering one’s learning
Drawing flowcharts with
linkage of ideas, taking detailed
notes, deductive tasks,
dissecting vocabulary
(suffixes/prefixes), drilling
exercises
Introverted Metacognitive (generally
preferred): planning for a
language task, careful
organisation of learning,
Cognitive: analysing and
reasoning (formal strategies)
Affective/social (generally
rejected) Self-encouragement
Individual tasks/work,
cooperative tasks or pair work
with familiar/
trusted classmate in stress free
environment,
CALL
6. Limitations and Recommendations for Future Research
This study has not done a pilot study to see how effective the realization and
application of known learning styles of students in teaching and learning English.
The future research should undertake a quasi-experimental study to find out the
effects of recognizing students’ learning styles in reality. By the way, more
researches should be done with more students’ scales, not only limited to the three
national and regional universities. If possible, there should be researches
conducted to compare and contrast between students’ English learning
preferences and English teachers’ teaching styles.
7. Conclusion
This study aimed to identify the relationship between perceptual learning style
preferences of Vietnamese university freshmen with English academic
achievement. The pupose of the study is that learning styles are regarded as the

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