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IJLTER.ORG Vol 19 No 9 September 2020

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We are very happy to publish this issue of the International Journal of Learning, Teaching and Educational Research. The International Journal of Learning, Teaching and Educational Research is a peer-reviewed open-access journal committed to publishing high-quality articles in the field of education. Submissions may include full-length articles, case studies and innovative solutions to problems faced by students, educators and directors of educational organisations. To learn more about this journal, please visit the website http://www.ijlter.org. We are grateful to the editor-in-chief, members of the Editorial Board and the reviewers for accepting only high quality articles in this issue. We seize this opportunity to thank them for their great collaboration. The Editorial Board is composed of renowned people from across the world. Each paper is reviewed by at least two blind reviewers. We will endeavour to ensure the reputation and quality of this journal with this issue.

IJLTER.ORG Vol 19 No 9 September 2020

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International Journal
of
Learning, Teaching
And
Educational Research
p-ISSN:
1694-2493
e-ISSN:
1694-2116
IJLTER.ORG
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
IJLTER
International Journal of Learning, Teaching and Educational Research (IJLTER)
Vol. 19, No. 9
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part
of the material is concerned, specifically those of translation, reprinting, re-use of illustrations,
broadcasting, reproduction by photocopying machines or similar means, and storage in data banks.
Society for Research and Knowledge Management
International Journal of Learning, Teaching and Educational Research
The International Journal of Learning, Teaching and Educational
Research is a peer-reviewed open-access journal which has been
established for the dissemination of state-of-the-art knowledge in the
fields of learning, teaching and educational research.
Aims and Objectives
The main objective of this journal is to provide a platform for educators,
teachers, trainers, academicians, scientists and researchers from over the
world to present the results of their research activities in the following
fields: innovative methodologies in learning, teaching and assessment;
multimedia in digital learning; e-learning; m-learning; e-education;
knowledge management; infrastructure support for online learning;
virtual learning environments; open education; ICT and education;
digital classrooms; blended learning; social networks and education; e-
tutoring: learning management systems; educational portals, classroom
management issues, educational case studies, etc.
Indexing and Abstracting
The International Journal of Learning, Teaching and Educational
Research is indexed in Scopus since 2018. The Journal is also indexed in
Google Scholar and CNKI. All articles published in IJLTER are assigned
a unique DOI number.
Foreword
We are very happy to publish this issue of the International Journal of
Learning, Teaching and Educational Research.
The International Journal of Learning, Teaching and Educational
Research is a peer-reviewed open-access journal committed to
publishing high-quality articles in the field of education. Submissions
may include full-length articles, case studies and innovative solutions to
problems faced by students, educators and directors of educational
organisations. To learn more about this journal, please visit the website
http://www.ijlter.org.
We are grateful to the editor-in-chief, members of the Editorial Board
and the reviewers for accepting only high quality articles in this issue.
We seize this opportunity to thank them for their great collaboration.
The Editorial Board is composed of renowned people from across the
world. Each paper is reviewed by at least two blind reviewers.
We will endeavour to ensure the reputation and quality of this journal
with this issue.
Editors of the September 2020 Issue
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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IJLTER.ORG Vol 19 No 9 September 2020

  • 1. International Journal of Learning, Teaching And Educational Research p-ISSN: 1694-2493 e-ISSN: 1694-2116 IJLTER.ORG Vol.19 No.9
  • 2. International Journal of Learning, Teaching and Educational Research (IJLTER) Vol. 19, No. 9 (September 2020) Print version: 1694-2493 Online version: 1694-2116 IJLTER International Journal of Learning, Teaching and Educational Research (IJLTER) Vol. 19, No. 9 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically those of translation, reprinting, re-use of illustrations, broadcasting, reproduction by photocopying machines or similar means, and storage in data banks. Society for Research and Knowledge Management
  • 3. International Journal of Learning, Teaching and Educational Research The International Journal of Learning, Teaching and Educational Research is a peer-reviewed open-access journal which has been established for the dissemination of state-of-the-art knowledge in the fields of learning, teaching and educational research. Aims and Objectives The main objective of this journal is to provide a platform for educators, teachers, trainers, academicians, scientists and researchers from over the world to present the results of their research activities in the following fields: innovative methodologies in learning, teaching and assessment; multimedia in digital learning; e-learning; m-learning; e-education; knowledge management; infrastructure support for online learning; virtual learning environments; open education; ICT and education; digital classrooms; blended learning; social networks and education; e- tutoring: learning management systems; educational portals, classroom management issues, educational case studies, etc. Indexing and Abstracting The International Journal of Learning, Teaching and Educational Research is indexed in Scopus since 2018. The Journal is also indexed in Google Scholar and CNKI. All articles published in IJLTER are assigned a unique DOI number.
  • 4. Foreword We are very happy to publish this issue of the International Journal of Learning, Teaching and Educational Research. The International Journal of Learning, Teaching and Educational Research is a peer-reviewed open-access journal committed to publishing high-quality articles in the field of education. Submissions may include full-length articles, case studies and innovative solutions to problems faced by students, educators and directors of educational organisations. To learn more about this journal, please visit the website http://www.ijlter.org. We are grateful to the editor-in-chief, members of the Editorial Board and the reviewers for accepting only high quality articles in this issue. We seize this opportunity to thank them for their great collaboration. The Editorial Board is composed of renowned people from across the world. Each paper is reviewed by at least two blind reviewers. We will endeavour to ensure the reputation and quality of this journal with this issue. Editors of the September 2020 Issue
  • 5. 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
  • 6. 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
  • 7. 1 ©2020 The authors and IJLTER.ORG. All rights reserved. International Journal of Learning, Teaching and Educational Research 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
  • 8. 2 ©2020 The authors and IJLTER.ORG. All rights reserved. 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
  • 9. 3 ©2020 The authors and IJLTER.ORG. All rights reserved. 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.
  • 10. 4 ©2020 The authors and IJLTER.ORG. All rights reserved. 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.
  • 11. 5 ©2020 The authors and IJLTER.ORG. All rights reserved. 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,
  • 12. 6 ©2020 The authors and IJLTER.ORG. All rights reserved. 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
  • 13. 7 ©2020 The authors and IJLTER.ORG. All rights reserved. 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
  • 14. 8 ©2020 The authors and IJLTER.ORG. All rights reserved. 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 A 31 34.18 Kruskal-Wallis H 43.072 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.
  • 15. 9 ©2020 The authors and IJLTER.ORG. All rights reserved. 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 Asymp. Sig. (1-tailed) .000 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 Asymp. Sig. (1-tailed) .000 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 A 31 31.53 Kruskal-Wallis H 40.909 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
  • 16. 10 ©2020 The authors and IJLTER.ORG. All rights reserved. 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 Asymp. Sig. (1-tailed) .405 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 Asymp. Sig. (1-tailed) .000 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 Asymp. Sig. (1-tailed) .000 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 &
  • 17. 11 ©2020 The authors and IJLTER.ORG. All rights reserved. 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.
  • 18. 12 ©2020 The authors and IJLTER.ORG. All rights reserved. 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. 6. References Akcay, A. O. (2017). Instructional Technologies and Pre-Service Mathematics Teachers’ Selection of Technology. Journal of Education and Practice, 8(7), 163–173. Alkhateeb, M. A., & Al-Duwairi, A. M. (2019). The Effect of Using Mobile Applications (GeoGebra and Sketchpad) on the Students’ Achievement. International Electronic Journal of Mathematics Education, 14(3), 523-533. https://doi.org/10.29333/iejme/5754 Amam, A., Fatimah, A. T., Hartono, W., & Effendi, A. (2017). Mathematical Understanding of the Underprivileged Students through GeoGebra. Journal of Physics: Conf. Series, 895 012007, 1-2. https://doi.org/10.1088/1742- 6596/895/1/012007 Arbain, N., & Shukor, N. A (2015). The effects of GeoGebra on students’ achievement. Procedia - Social and Behavioral Sciences, 172, 208–214. https://doi.org/10.1016/j.sbspro.2015.01.356 Aydos, M. (2015). The impact of teaching mathematics with GeoGebra on the conceptual understanding of limits and continuity: the case of Turkish gifted and talented students (Master’s thesis). İhsan Doğramacı Bilkent University, Ankara, Turkey. Bester, G., & Brand, L. (2013). The effect of using technology on learner attention and achievement in the classroom. South African Journal of Education, 33(2), 1-15. Bray, A., & Tangney, B. (2017). Technology usage in mathematics education research—A systematic review of recent trends. Computers and Education, 114, 255–273. https://doi.org/10.1016/j.compedu.2017.07.004 Bulut, M., Akçakın, H. U., Kaya, G., & Akçakın, V. (2016). The effects of GeoGebra on third grade primary students’ academic achievement in fractions. International Electronic Journal of Mathematics Education, 11(2), 347-355. https://doi.org/10.12973/iser.2016.2109a Cohen, J. (1988). Statistical power analysis for the behavioral sciences. New York: Routledge Academic. Cohen, L., Manion, L., & Morrison, K. (2011). Research Methods in Education (7th ed.) New York: Routledge Department of Basic Education. (2011). Curriculum Assessment Policy Statement Grades 7-9. Pretoria: Government Printer. Disbudak, O., & Akyuz, D. (2019). The Comparative Effects of Concrete Manipulatives and Dynamic Software on the Geometry Achievement of Fifth-Grade Students. International Journal of Technology in Mathematics Education, 26 (1), 3-20. https://doi.org/10.1564/tme_v26.1.01 Fraenkel, J. R., & Wallen, N. E. (2009). How to Design Evaluate Research in Education (7th ed). New York: McGrawHill Companies.
  • 19. 13 ©2020 The authors and IJLTER.ORG. All rights reserved. Godebo, G. H. (2018). Application of GeoGebra on Euclidean geometry in rural high schools: Grade 11 learners (Master’s dissertation). University of Zululand, South Africa. Granberg, C., & Olsson, J. (2015). ICT-supported problem solving and collaborative creative reasoning: Exploring linear functions using dynamic mathematics software. Journal of Mathematical Behavior, 37, 48–62. https://doi.org/10.1016/j.jmathb.2014.11.001 Hohenwarter, M., & Jones, K. (2007). Ways of linking geometry and algebra: The case of GeoGebr. Proceedings of British Society for Research into Learning Mathematics, 27 (3), 126-131. Hohenwarter, M., & Lavicza, Z. (2009). The strength of the community: how GeoGebra can inspire technology integration in mathematics teaching. MSOR Connections, 9(2), 3-5. Jelatu, S., Sariyasa, & Ardana, I. M. (2018). Effect of GeoGebra-Aided REACT Strategy on Understanding of Geometry Concepts. International Journal of Instruction, 11(4), 325-336. https://doi.org/10.12973/iji.2018.11421a Khalil, M., Farooq, R. A., Çakıroğlu, E., Khalil, U., & Khan, D. M. (2018). The Development of Mathematical Achievement in Analytic Geometry of Grade-12 Students through GeoGebra Activities. Eurasia Journal of Mathematics, Science and Technology Education, 14(4), 1453–1463. https://doi.org/10.29333/ejmste/83681 Laridon, P., Barnes, H., Kitto, A., Myburg, M., Pike, M., Scheiber, J., Sigabi M., & Wilson, H. (2004). Classroom mathematics: Grade 10 learners’ book. Sandton: Heinemann. Masri, R., Hiong, T. S., Tajudin, N. M., Zamzana, Z. Z., & Shah, R. L. Z. (2016). The effects of using GeoGebra teaching strategy in Malaysian secondary schools: A case study from Sibu, Sarawak. Malaysian Journal of Society and Space, 12(7), 13–25. McMillan, J., & Schumacher, S. (2013). Research in education: Evidence-based inquiry, New York: Pearson. Mthethwa, M. Z. (2015). Application of GeoGebra on Euclidean geometry in rural high schools: Grade 11 learners (Master’s dissertation). University of Zululand, South Africa. Mueller, J., Wood, E., Willoughby, T., Ross, C., & Specht, J. (2008). Identifying discriminating variables between teachers who fully integrate computers and teachers with limited integration. Computers & Education, 51(4), 1523–1537. Mustafa, A. (2015). The impact of teaching mathematics with GeoGebra on the conceptual understanding of limits and continuity: The case of Turkish gifted and talented students, (Master’s dissertation). İhsan Doğramacı Bilkent University, Turkey. Ogbonnaya, U. I. (2010). Improving the teaching and learning of parabolic functions by the use of information and communication technology. African Journal of Research in Mathematics, Science and Technology Education, 14(1), 49-60. Ogbonnaya, U. I., & Mji, A. (2012). Enhancing students’ learning of hyperbolic functions by the use of information and communication technology. Proceedings of 5th Annual International Conference on Education and New Learning Technologies (Edulearn), Barcelona, Spain, 5619-5216. Pfeiffer, C. (2017). A study of the development of mathematical knowledge in a GeoGebra focused learning environment (Doctoral thesis). Stellenbosch University, Stellenbosch. Pierce, R. (2005). Linear functions and the triple influence of teaching on the development of students’ algebraic expectations, Proceedings of the 19th Conference of the International Group for the Psychology of Mathematics Education, Melbourne, Australia, 4, 81-88. Pjanić, K., & Lidan, E. (2015). One Usage of Geogebra in Enhancing Pre-service Mathematics Teachers’ Content Knowledge. Turkish Journal of Computer and Mathematics Education, 6(1), 18-30. https://doi.org/10.16949/turcomat.78085
  • 20. 14 ©2020 The authors and IJLTER.ORG. All rights reserved. Phan-Yamad, T., & Man, S. W. (2018). Teaching statistics with GeoGebra. North American GeoGebra Journal, 7(1), 14-24 Praveen, S., & Leong, K. (2013). Effectiveness of using GeoGebra on students’ understanding of circles. The Malaysian Online Journal of Educational Technology, 1(4), 1-11. Rahman, M. H. A., & Puteh, M. (2017). Learning trigonometry using GeoGebra learning module: Are underachiever pupils motivated? Sains humanika, 9(1-2), 39-42. https://doi.org/10.11113/sh.v9n1-2.1095 Rosenbaum, P. R. (1987). The role of a second control group in an observational study. Statistical Science, 2(3), 292-306. Kushwaha, R. C., Chaurasia, P. K., & Singhal, A. (2014). Impact on students’ achievement in teaching mathematics using GeoGebra, Proceedings of IEEE Sixth International Conference on Technology for Education, Amrita University, 34–137. Richardson, V. (2003). Constructivist pedagogy. Teachers College Record, 105(9), 1623-1640. Slavin, R. E., & Davis N. (2006). Educational Psychology: Theory and Practice. East Montpelier: Johnson State College. Seloraji, P., & Eu, L. K. (2017). Students’ performance in geometrical reflection using GeoGebra. Malaysian Online Journal of Educational Technology, 5(1), 65–77. https://doi.org/10.17220/mojet Takači, D., Stankov, G., & Milanovic, I. (2015). Efficiency of learning environment using GeoGebra when calculus contents are learned in collaborative groups. Computers and Education, 82, 421–431. https://doi.org/10.1016/j.compedu.2014.12.002 Takači, D., & Vukobratović, R. (2011). On the role of GeoGebra in examining functions. Proceedings of the International GeoGebra Conference for Southeast Europe, Novi Sad-Serbia, 53-60. Thambi, N., & Eu, L. K. (2012). Effect of students’ achievement in fractions using GeoGebra. SAINSAB, 16, 97-106. Wassie, Y. A., & Zergaw, G. A. (2018). Capabilities and Contributions of the Dynamic Math Software, GeoGebra—A review. North American GeoGebra Journal, 7(1), 68- 86. Wijayanti, D. (2018). Two notions of ‘linear function’ in lower secondary school and missed opportunities for students’ first meeting with functions. The Mathematics Enthusiast, 15(3), 467-481. Zengin, Y., Furkanb, H., & Kutluca, T. (2012). The effect of dynamic mathematics software GeoGebra on student achievement in teaching of trigonometry. Procedia - Social and Behavioral Sciences, 31, 183–187. https://doi.org/10.1016/j.sbspro.2011.12.038 Zulnaidi, H., Oktavika, E., & Hidayat, R. (2020). Effect of use of GeoGebra on achievement of high school mathematics students. Education and Information Technologies, 25(1), 51–72. https://doi.org/10.1007/s10639-019-09899-y
  • 21. 15 ©2020 The authors and IJLTER.ORG. All rights reserved. International Journal of Learning, Teaching and Educational Research 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
  • 22. 16 ©2020 The authors and IJLTER.ORG. All rights reserved. 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
  • 23. 17 ©2020 The authors and IJLTER.ORG. All rights reserved. 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.
  • 24. 18 ©2020 The authors and IJLTER.ORG. All rights reserved. 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
  • 25. 19 ©2020 The authors and IJLTER.ORG. All rights reserved. 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
  • 26. 20 ©2020 The authors and IJLTER.ORG. All rights reserved. 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
  • 27. 21 ©2020 The authors and IJLTER.ORG. All rights reserved. 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.
  • 28. 22 ©2020 The authors and IJLTER.ORG. All rights reserved. 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
  • 29. 23 ©2020 The authors and IJLTER.ORG. All rights reserved. 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
  • 30. 24 ©2020 The authors and IJLTER.ORG. All rights reserved. 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
  • 31. 25 ©2020 The authors and IJLTER.ORG. All rights reserved. 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).
  • 32. 26 ©2020 The authors and IJLTER.ORG. All rights reserved. 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
  • 33. 27 ©2020 The authors and IJLTER.ORG. All rights reserved. (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
  • 34. 28 ©2020 The authors and IJLTER.ORG. All rights reserved. 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
  • 35. 29 ©2020 The authors and IJLTER.ORG. All rights reserved. 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.
  • 36. 30 ©2020 The authors and IJLTER.ORG. All rights reserved. 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.
  • 37. 31 ©2020 The authors and IJLTER.ORG. All rights reserved. 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
  • 38. 32 ©2020 The authors and IJLTER.ORG. All rights reserved. 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