www.ijcrsee.com
1
Retnawati H. et al. (2024). Exploring inuential factors and conditions shaping statistical literacy among undergraduate students
in mathematics education, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
12(1),1-17.
Heri Retnawati
1,2*
, Kana Hidayati
1,2
, Ezi Apino
3
, Ibnu Ra
1
, Munaya Nikma Rosyada
1
Original scientic paper
Received: November 27, 2023.
Revised: February 24, 2024.
Accepted: March 06, 2024.
UDC:
378.147:51(669)
37.011.2-057.875(669)
10.23947/2334-8496-2024-12-1-1-17
© 2024 by the authors. This article is an open access article distributed under the terms and conditions of the
Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
*
Corresponding author: heri_retnawati@uny.ac.id
Exploring Inuential Factors and Conditions Shaping Statistical Literacy
Among Undergraduate Students in Mathematics Education
1
Department of Mathematics Education, Faculty of Mathematics and Natural Sciences, Universitas Negeri Yogyakarta,
Special Region of Yogyakarta 55281, Indonesia,
e-mail: heri_retnawati@uny.ac.id, kana@uny.ac.id, ibnura789@gmail.com, munayanikma38@gmail.com
2
Master’s and Doctoral Programmes in Educational Research and Evaluation, Graduate School, Universitas Negeri
Yogyakarta, Special Region of Yogyakarta 55281, Indonesia
3
Doctoral Programme in Educational Research and Evaluation, Graduate School, Universitas Negeri Yogyakarta,
Special Region of Yogyakarta 55281, Indonesia, e-mail: eziapino.2021@student.uny.ac.id
Abstract: Statistical literacy (hereafter SL) has been considered an important learning outcome in statistics learning in higher
education, yet studies that focus on investigating the factors and conditions that inuence students’ SL, especially mathematics
education students, are still limited. This study seeks to uncover the factors and conditions that signicantly contribute to the SL
of mathematics education students. This survey study involved 1,287 mathematics education students from 21 higher education
institutions in Indonesia. Linear regression analysis involving four predictor variables (i.e., gender, status of higher education institution,
laptop ownership, and research preference) was performed to determine the variables that contributed signicantly in predicting SL
achievement. The results revealed that gender, higher education institution’s status, and laptop ownership contributed signicantly, but
research preference was not signicant in predicting mathematics education students’ SL. Furthermore, laptop ownership was found
to have the highest contribution in predicting mathematics education students’ SL. All ndings and their implications are discussed.
Keywords: gender, laptop ownership, mathematics education, research preference, statistical literacy, status of
higher education institution.
Introduction
Given that the world is developing so rapidly and complexly, students need to be facilitated to
master fundamental skills to deal with daily life and various complex challenges and build character
(intrapersonal and interpersonal) needed in dealing with dynamic environments. These fundamental
skills are associated with the term ‘literacy’ whose meaning has expanded beyond simply referring to
the skills of reading and writing effectively in a variety of contexts (Pilgrim and Martinez, 2013; Watson
and Callingham, 2003). In addition to the expansion of meaning, literacy has also developed in terms of
types based on the combination of literacy and a particular specialised discipline or field (Gal, 2002) to
keep up with the challenges of an increasingly complex world. Among the types of literacy developed in
the last two decades, such as mathematical literacy, ICT literacy, and cultural and civic literacy, we are
more interested in exploring SL further. Although SL is considered as one of the new literacies needed
along with the development of ICT which has led to the availability of large data and the rapid and wide
distribution of data in the last decade, the term SL has in fact been introduced since 1979 by Haack. SL
was introduced by Haack (1979a, 1979b) as a form of his concern for statistics which was positioned
more as a research tool than as a language. As a result, statistics learning is only focused on facilitating
students to remember formulas and the use of these formulas without understanding the meaning, so
that students do not experience progress after participating in statistics learning, where they still cannot
better understand the statistics they encounter in the media. Thus, this SL implicitly leads to positioning
statistics as a language and is interpretive rather than calculative so that students who have adequate
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Retnawati H. et al. (2024). Exploring inuential factors and conditions shaping statistical literacy among undergraduate students
in mathematics education, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
12(1),1-17.
statistical literacy competence can apply the statistical principles they learn to the statistics they encounter
in everyday life (Haack, 1979b).
SL then continues to evolve and receive increasing attention from researchers so that various
meanings of SL have been offered – no consensus has been reached on the meaning of SL. Of the various
narrow and broad meanings of SL, SL is seen as a key competency that refers to the ability to interpret,
critically evaluate, and communicate statistical data, statistical information, or arguments contained in
various forms of media in the context of everyday life (Gal, 2002; Kurnia et al., 2023; Schield, 2010;
Sharma, 2017; Wallman, 1993). The prerequisite for a person to have behaviours that reflect statistical
literacy is that the person masters the knowledge or understanding and fundamental competencies that
include the symbols, concepts, terms, and language of statistics and mathematics (Gal, 2003; Rumsey,
2002) and is able to apply them (Gal, 2002). Given the significance of SL, it makes sense that existing
studies (e.g., Carvalho and Solomon, 2012; Chick & Pierce, 2012, 2013; Gal, 2002; Kurnia et al., 2023;
Sharma, 2017) have widely recognised that being statistically literate is not only essential for students, but
also for everyone as individuals or as part of society, both in professional and social life. The importance
of being statistically literate is again inseparable from the demands of the development of information
technology that allows us to obtain, process and disseminate data and information for various purposes,
such as making decisions and policies (Chick and Pierce, 2012, 2013; Gal, 2002; Sharma, 2017).
Given the importance of students and adults being statistically literate, a number of efforts to
promote statistical literacy have been made by previous studies ranging from secondary school to higher
education (Aksoy and Bostan, 2021; Forgasz et al., 2022; Yotongyos et al., 2015). Such efforts can
certainly be based on aspects or components of SL, the results of studies that focus on investigating the
achievements of SL so as to detect which aspects need more attention, and the results of studies that focus
on investigating the factors that directly or indirectly influence SL. A number of studies focusing on the
latter have successfully identified factors that potentially influence (Aizikovitsh-Udi and Kuntze, 2014; Aziz
and Rosli, 2021; Carmichael et al., 2009; MacFeely et al., 2017) and that affect (Lukman and Wahyudin,
2020; Pamungkas and Khaerunnisa, 2020; Sproesser et al., 2014) SL. Based on literature reviews,
systematic reviews, and interview studies that researchers have conducted, they suggest critical thinking
(Aizikovitsh-Udi and Kuntze, 2014), student demographics, learning environment, student attitudes, basic
knowledge and skills in statistics (Aziz and Rosli, 2021), interest or attitude towards statistics (Carmichael
et al., 2009), and learning support including the application of specific learning models and the use of
technology (Aziz and Rosli, 2021; MacFeely et al., 2017) as factors considered vital in influencing SL.
From the empirical studies we have identified, some of the factors that influence students’ SL
are socio-economic status, general cognitive abilities, and knowledge of specific content in mathematics
especially those related to probability and functional reasoning (Sproesser et al., 2014). From their findings,
Sproesser et al. (2014) recommended future studies to investigate the extent to which students’ socio-
economic status can explain the diversity of their SL. In another empirical study conducted by Lukman
and Wahyudin (2020) which focused on investigating the factors influencing the SL of undergraduate
students in terms of aspects or components of SL, fundamental knowledge in mathematics and statistics
and adequate language skills acted as these factors. Prior knowledge and mathematical self-esteem
have also been reported to be two factors that influence students’ SL (Pamungkas and Khaerunnisa,
2020). Another study indicated that grade level affects the SL of high school students (Kurnia et al., 2023);
although there were also studies (e.g., Yolcu, 2014) that reported the insufficient influence of grade level
on students’ SL.
When it comes to SL, we struggled to find sufficient studies that reveal the factors that influence and
cause the differences in SL achievement. This is in contrast to when it comes to literacy achievement in
mathematics – one of the focuses in the OECD’s Programme for International Student Assessment (PISA)
– and achievement in statistics. Extensive empirical studies have identified various factors that influence
students’ mathematical literacy achievement. The PISA 2018 results have suggested that immigrant
background, gender, and the socio-economic status of students’ families and schools have been factors
that influence students’ achievement in mathematical literacy across countries (OECD, 2019). Meanwhile,
Wang et al. (2023) through a systematic literature review of 156 empirical studies have successfully
identified 135 factors that contribute to influencing student achievement in mathematical literacy in PISA,
where these factors are then categorised into five levels: individual student as level 1, household context
as level 2, school community as level 3, education system as level 4, and macro society as level 5. One of
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Retnawati H. et al. (2024). Exploring inuential factors and conditions shaping statistical literacy among undergraduate students
in mathematics education, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
12(1),1-17.
the results revealed from the systematic study of Wang et al. (2023) at level 1 is that although the influence
of gender on students’ mathematical literacy achievement has been extensively investigated, the results
in this regard are still mixed in a number of countries. Mixed results were also found on the factor of
students’ experience in using ICT. At level 2, the study conducted by Wang et al. (2023) indicates that
the factor of ICT availability at home has received relatively little attention by previous studies compared
to other factors at level 2. Previous studies show mixed results regarding the effect of ICT availability at
home on students’ mathematical literacy – some show a positive effect, while others show a negative or
insignificant effect. Furthermore, among the three factors most investigated by previous studies at level 3,
the effect of school type (i.e., private or public) on students’ mathematical literacy is the most diverse than
socio-economic status (SES) composition and school location (Wang et al., 2023).
It has been mentioned earlier the importance of mastering two things, namely fundamental knowledge
in statistics and statistical reasoning, in order to be statistically literate. Both of these can be reflected
by students’ achievements in statistics, where one of the factors that affect students’ achievements in
statistics is their perception or attitude towards statistics (Emmioğlu and Capa-Aydin, 2012; Ncube and
Moroke, 2015; Ramirez et al., 2012). In addition, perceptions or attitudes towards statistics can also
influence undergraduate students in determining the preference of the research method or approach they
will choose, whether it is more quantitative or qualitative (Dani and Al Quraan, 2023). When students have
negative perceptions or attitudes towards statistics due to various factors including the view that statistics
overemphasises numerical and technical activities in their experience in dealing with mathematics and
statistics during high school, they tend to avoid quantitative research (Dani and Al Quraan, 2023) and
prefer qualitative research. Their aversion to quantitative research may also reflect their reluctance to
engage in learning related to statistics, so it could possibly hinder their opportunity to develop their SL.
However, how much students’ preference for research methods or approaches contributes to their SL is
still under-investigated. Therefore, in this study, we endeavour to shed light on the extent to which research
method or approach preferences contribute to students’ SL, including how the potential of gender, higher
education institution’s status, and laptop ownership to explain students’ SL. In detail, this study focused
on answering the following two research questions (RQs).
RQ1: How is the SL profile of undergraduate students in mathematics education programme in
terms of gender, higher education institution’s status, laptop ownership, and research preference?
RQ2: What factors and conditions significantly predict the SL of undergraduate students in
mathematics education programme?
Statistical Literacy and Statistical Literacy of Undergraduate Students
It cannot be denied that SL is considered important to have in the midst of the massive distribution
of data and information containing statistics which brings its own opportunities and challenges. Various
meanings of SL have been offered by the literature, where the diversity concerns what competencies build
SL and what content needs to be the focus in SL. By Wallman (1993), SL is interpreted as a competency
built by cognitive abilities in the form of understanding and critically evaluating statistical results in
people’s daily lives and the ability to appreciate the role of statistical thinking to solve problems and make
decisions in various contexts of daily life. It is implied that to be statistically literate which is demonstrated
through the ability to critically evaluate, one must first have an adequate understanding of the underlying
concepts or ideas associated with these statistical results. The basic idea of grouping the skills that
build SL into cognitive and affective elements is also offered by Gal (2002), where according to him, SL
is a competence composed of skills that support each other which can be grouped into knowledge and
dispositional elements. The skills in the knowledge element include general literacy skills – the ability to
read and interpret data, information, or readings in various forms and contexts and communicate what has
been read and interpreted clearly, basic knowledge of statistics and mathematics, basic knowledge related
to various contexts in everyday life, and critical thinking skills (Gal, 2002). Meanwhile, the dispositional
elements of SL include attitudes and beliefs to engage in statistical thinking for various purposes and
having the willingness to think critically about information that contains statistics despite having no formal
learning experience in statistics or mathematics (Gal, 2002).
The idea that Wallman puts forward that places an important role on understanding basic statistical
concepts as the foundation of SL is in line with some of what Watson (1997), Sharma (2017), and Gonda et
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Retnawati H. et al. (2024). Exploring inuential factors and conditions shaping statistical literacy among undergraduate students
in mathematics education, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
12(1),1-17.
al. (2022) stated in presenting the meaning of SL. According to them, SL in addition to being built by higher
order cognitive skills (i.e., interpreting, making predictions, thinking critically) is also built by basic skills
or lower order cognitive skills consisting of understanding basic concepts, symbols, and terminology in
numeracy, mathematics, probability, and statistics and general literacy skills (i.e., reading, understanding
what is read, and communicating what is understood from reading). The meaning of SL in more detail
is conveyed by Watson (2006), where the meaning of SL does not only emphasise on the components
of the skills that build SL but also on the components of the topic, context, motivation, and task form
that encourage someone to demonstrate their SL. Based on the components of SL that Watson (2006)
provides and the interaction between these components, SL refers to the mathematical or statistical skills
and literacies that a person demonstrates when faced with a task whose context relates to variation,
data collection including sampling, presentation of data in various representations, averaging, chance,
and inference. The task requires the person to choose the best among a number of options or provide a
range of alternative possible solutions. The meaning of SL that Watson (2006) put forward aligns with that
of Gonda et al. (2022) mentioned that some of the skills that make up SL, especially the basic skills, are
working with data which includes organising data, presenting data into various representations, and using
these various data representations for specific purposes.
Based on the meanings of SL that we have found in the literature, we agree with those mentioned
by Kurnia et al. (2023) that SL includes the skills involved when one provides data or information for
others to use or receives data from others to use for a specific purpose. The skills involved in producing
data include the skills to ask questions (e.g., what to investigate, what problems to answer, and what is
needed), collect data, analyse data, and interpret the results of the data analysis. Meanwhile, the skills
involved when one uses or receives data may include describing the data, organising the data so that one
can determine what is important, analysing the data, interpreting or evaluating the data, and critiquing
the data received. These skills are ultimately inseparable from the skill of communicating what has been
gained from working with data. Furthermore, all the skills we have mentioned are again based on an
understanding of context, representation, and basic knowledge of statistics and mathematics (Gal, 2002;
Gonda et al., 2022; Watson, 1997, 2006). An understanding of the basic knowledge of statistics required
to be statistically literate includes the rationale and techniques or methods of data collection, descriptive
statistics, data presentation and interpretation, basic notions of probability, and statistical inference. Thus,
based on the meaning of SL that we described based on the literature, we conclude that SL is the skill of
applying an understanding of basic ideas about statistics (and probability) supported by an understanding
of basic ideas in mathematics, interpreting statistical data and information, communicating statistical
data and information in various forms effectively, and critically evaluating the results of data analysis or
inference in various contexts and basic topics in statistics (and probability).
Previous studies have attempted to investigate the extent of students’ SL at the secondary school
level (Aksoy and Bostan, 2021; Kurnia et al., 2023; Utomo, 2021) to higher education (Forgasz et al.,
2022; Hassan et al., 2020; Lukman & Wahyudin, 2020; Setiawan and Sukoco, 2021; Yotongyos et al.,
2015). When it comes to undergraduate students, we found limited studies exploring the extent of their
SL attainment. One of the studies that focused on undergraduate students was a study conducted by
Setiawan and Sukoco (2021) with the aim of uncovering the SL of 39 first-year undergraduate students in
a statistics study programme at a public higher education institution in terms of their skills in describing and
visualising data. This study reported that the SL level of students in general could already be categorised
as high in terms of skills in describing data, while in terms of skills in visualising data, the SL level of
students was still in the medium category.
In another study (i.e., Hassan et al., 2020) involving 360 undergraduate students in their eighth
semester from programmes in applied science and social science disciplines, it was revealed that their SL
in terms of statistical symbols, central tendency, descriptive and inferential statistics, and elements of data
analysis in SPSS programme were overall at a low level. Although the study descriptively showed that
students from applied science programmes had better SL than those from social science programmes,
there was no statistically sufficient evidence that the achievements of students from the two disciplines
were different or that one was superior to the other. In addition, involving 114 undergraduate students
from mathematics and mathematics education programmes and using the SL framework proposed by
Gal (2002), Lukman and Wahyudin (2020) found that students’ SL was already at a satisfactory level and
found that the SL achievement of students who had taken elementary statistics was significantly better
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Retnawati H. et al. (2024). Exploring inuential factors and conditions shaping statistical literacy among undergraduate students
in mathematics education, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
12(1),1-17.
than those who had not. By also using the SL concept that Gal (2002) proposed which divides SL into
knowledge and dispositional components, Yotongyos et al. (2015) reported that the overall SL of 103
undergraduate students in the faculty of education at a public higher education institution was moderate.
Potential Factors and Condition Contributing to Statistical Literacy
Many previous studies have attempted to investigate the factors that might influence student
learning outcomes in primary to higher education and that might lead to differences in outcomes between
one group and another. In science and mathematics, including statistics, gender – the biological difference
of being male or female (El Refae et al., 2021) – has been recognised as one such factor (Meinck and
Brese, 2019; Wang et al., 2023). Student learning outcomes in terms of gender show mixed results (Yolcu,
2014), both in small-scale and large-scale assessments. Some studies demonstrate that male students
significantly outperform female students, but others show the opposite (Meinck and Brese, 2019; OECD,
2019; Wang et al., 2023). However, there are also studies that do not show sufficient evidence that the
achievement of one group is superior to the other in terms of gender (Arroyo-Barrigüete et al., 2023).
Studies focusing on literacy in mathematics – part of SL – of students aged around 15 years report that
male students perform better than female students (OECD, 2019), although this is not uniform across
countries in the world as reported in the study by Wang et al. (2023). Nevertheless, some studies that
directly focus on SL leading to students’ skills in understanding, interpreting and communicating data
show that female students outperform male students (Risqi and Ekawati, 2020; Yolcu, 2014). Another
study conducted by Kurnia et al. (2023) showed that there was no significant difference in students’ SL
achievement in terms of gender. Based on the results of these studies, it can be said that the influence
of gender on students’ SL achievement and differences in students’ SL achievement in terms of gender
still need to be explored further considering that there is still an opportunity to obtain different results
depending on various contexts, including research subjects.
It is believed that the status of a school or higher education institution, public or private, also has
an influence on student achievement. The main distinction between public and private higher education
institutions is in terms of funding sources. This distinction is considered to contribute to differences in
the provision of learning opportunities and facilities to students, which in turn may lead to differences in
student achievement. Previous studies, however, have reported inconsistent results on the consequences
of differences in the status of higher education institutions on student outcomes as reported by Wang et
al. (2023) who focused on literacy in mathematics. A number of studies reported a significant difference in
students’ literacy achievement in terms of school status, where students from private schools outperforming
those from public schools (Cheema, 2015, 2016). However, it is still unclear how the SL achievement of
students from public higher education institutions differs from those from private.
The use of technology in learning has been widely shown by previous studies (e.g., Kristanto, 2018;
Liestari and Muhardis, 2021; Rusilowati et al., 2022) to support students’ learning process, which in turn
can promote learning achievement. The availability of access to technology such as laptops or computers
allows students to be able to gain the potential offered by the technology when it is used optimally,
which can develop problem-solving skills, communication, and conduct research through searching for
important information relevant to the research being carried out (Kposowa and Valdez, 2013). When
students have widespread access to technological devices such as laptops, they are likely to have a
better chance than those without laptops of using their time flexibly to optimise their learning, such as
doing homework, conducting research, and reading study materials (Reisdorf et al., 2020). The presence
of technological advances that occur today has the consequence that the use of technology in learning
is inevitable, especially in higher education, where the use of laptops is considered an integral part of
the learning culture in the classroom. On the one hand, this phenomenon is certainly an opportunity
that should be utilised, but on the other hand, by Reisdorf et al. (2020) it is considered to bring its own
challenges for those who do not have a laptop. It is clear that the ownership of technological devices such
as laptops will have an influence on students’ learning and their learning achievement, especially when
the role of laptops has become an integral part of learning.
In mathematics and statistics learning, ownership of a laptop can also lead to differences to a
varied degree in learning opportunities that students receive and in students’ learning achievement. A
number of studies (e.g., Kposowa and Valdez, 2013; Papadakis et al., 2016) have suggested that when
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Retnawati H. et al. (2024). Exploring inuential factors and conditions shaping statistical literacy among undergraduate students
in mathematics education, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
12(1),1-17.
technological devices such as laptops are used appropriately, including with teacher guidance, to conduct
mathematical or statistical exploration activities through applications available on laptops and access any
relevant information that students learn using the internet, it will provide greater opportunities for students
to be able to more easily understand concepts that are abstract in nature than when they do not use laptops.
Other studies (e.g., Wittwer and Senkbeil, 2008) have also implicitly demonstrated the effect of home
computer use on students’ mathematics achievement. In contrast to most studies that show a large effect
of home computer use on students’ academic performance at school such as mathematics achievement,
Wittwer and Senkbeil’s study (2008) shows the frequency of computer use and how it is used by students
has little impact on their mathematics achievement. Such mixed results are influenced by a variety of
factors such as the context of the country in which the study was conducted and the educational level of
the students involved in the study (Wang et al., 2023). Wittwer and Senkbeil’s study (2008) suggests that
the computers students own can have a greater impact on their mathematics achievement depending on
the students’ skills in using the computers and the problem-solving and higher-order thinking activities that
teachers facilitate in the classroom using the computers or laptops. To date it remains unknown whether
laptop ownership can lead to differences in undergraduate students’ SL achievement and to what extent
laptop ownership supports undergraduate students’ SL.
Lastly, one’s research preferences (i.e., qualitative and quantitative) clearly influence one’s
development as a researcher, such as motivation to conduct specific research method and attitude
towards research (Gonulal, 2018; Nenty, 2009). Researchers who embrace a more quantitative research
orientation may wish to develop themselves in areas related to statistical methods, and engage in more
quantitatively orientated research (Gonulal, 2018). Quantitatively-orientated students are likely to take
more statistics courses and do more self-training in statistics (Gonulal, 2018). On the other hand, those
who have tended to avoid their orientation towards quantitative research, or tended to be orientated
towards qualitative research methods, it is possible that they have difficulties in learning quantitative
or empirical research methods (Nenty, 2009). In this regard, because learning quantitative or empirical
research methods can be associated with learning statistics, there are indications that those who are
more orientated towards qualitative research or have a negative view of quantitative research tend to
struggle more in learning statistics. This implicitly leads to the hypothesis that there will be differences in
students’ SL achievement, where those who favour quantitative research are likely to perform better in
statistics than those who favour qualitative research.
Materials and Methods
Design of the Study
This survey study (Check and Schutt, 2012) focused on uncovering the factors and conditions
that are thought to influence mathematics education students’ SL attainment. A total of four predictor
variables have been selected to test their contribution in predicting the SL of mathematics education
students. The four predictor variables are gender, higher education institution’s status, laptop ownership,
and research preference. In order to capture information related to students’ SL achievement and the
predictor variables that influence it, we administered a survey to mathematics education students who
were taking or had taken Elementary Statistics course or its equivalent course. Although the survey could
be accessed online, the administration of the survey was still carried out in the classroom or computer
laboratory directly supervised by the survey supervisor. This is to ensure that the responses obtained are
accurate and truly represent the actual condition of the participants.
Participants
A total of 1,287 undergraduate students in their first to tenth semesters from mathematics education
programmes participated in completing the SL survey in this study. They were taking or had taken
Elementary Statistics, Introduction to Statistics, Educational Statistics, or equivalent course. Participants
were spread across 21 higher education institutions (university, college, or institute) selected through
convenience sampling technique (Edgar and Manz, 2017). This technique was chosen because it is
relatively cheap, less time-consuming, and simple (Stratton, 2021). In addition, this sampling technique
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Retnawati H. et al. (2024). Exploring inuential factors and conditions shaping statistical literacy among undergraduate students
in mathematics education, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
12(1),1-17.
was also used with the consideration that the results of this study could contribute to developing potential
hypotheses or references for further studies that are more rigorous (Stratton, 2021).
Table 1. Description of participants (n = 1,287)
Variable n (%)
Gender:
Male 227 (17.6)
Female 1060 (82.4)
Higher education institution’s status:
Public 1053 (81.8)
Private 234 (18.2)
Laptop ownership:
Yes 1011 (78.6)
No 276 (21.4)
Research preference:
Quantitative 867 (67.4)
Qualitative 420 (32.6)
The institutions were divided into two status categories, namely public (n = 15) and private (n = 6).
In addition, the institutions were also divided into three area categories based on time zones in Indonesia,
namely western area (n = 11), central area (n = 7), and eastern area (n = 3). The higher education
institutions involved in this study were mostly in the western area because most higher education
institutions in Indonesia are located in the western area. The participants in this study were mostly female
students and came from public higher education institutions. Table 1 presents description in detail of the
participants involved in this study.
Instrument and Data Collection
We developed an online survey to capture information related to SL of mathematics education
students and the factors and conditions that influence it. The online survey consisted of two parts. The
first part captured information related to students’ higher education institution where the student studies,
gender, laptop ownership, and research preference. The second part was a test used to measure the SL
of mathematics education students. In this study, the test to measure SL was developed based on the
aspects and their respective descriptions that were derived based on the results of the literature review
(e.g., Gal, 2002; Gonda et al., 2022; Sharma, 2017; Watson, 2006) (see Table 2). Besides considering the
aspects and indicators in Table 2, the SL test also focused on the core content of elementary statistics.
We used four basic statistical contents in the SL test, namely sampling techniques and probability, data
presentation (i.e., tables, diagrams, and graphs), descriptive statistics (i.e., measures of central tendency
and dispersion), and inferential statistics (i.e., t-test, correlation, regression, and analysis of variance).
Thus, a total of 20 multiple-choice items with four options were used to measure the SL achievement of
mathematics education students. Before being used for data collection, the SL test was first sent to three
reviewers for collecting feedback. They were experts in statistics education, statistics, and educational
measurement. They were asked to provide suggestions and assessments of the correctness of the
substance and quality of the SL test items. All feedback from reviewers was accommodated to improve
the quality of SL test. The test reliability estimate obtained Cronbach’s α = .612. Although the reliability
coefficient is not very high, the test is reliable enough (Reynolds et al., 2010; Rudner and Schafer, 2002;
Taber, 2018) or it can be regarded as having moderate reliability (Taber, 2018) to measure the SL of
mathematics education students.
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Retnawati H. et al. (2024). Exploring inuential factors and conditions shaping statistical literacy among undergraduate students
in mathematics education, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
12(1),1-17.
Table 2. Indicators of SL and their descriptions
Aspect Description Item
Application of statistics
concepts (Application)
Able to apply basic concepts of statistics in various contexts
Understand the use of various simple statistical symbols
1, 6, 11,
16
Interpretation of statistical
data and information
(Interpretation)
Able to read and interpret tables, graphs and charts accurately
Identify trends, patterns, and outliers in data
Draw accurate conclusions from data and hypothesis testing results
2, 3, 7, 8,
12, 13,
17, 18
Communication skills
(Communication)
Able to effectively communicate important statistical ndings to an audience
Able to present information using visual aids to enhance understanding
4, 9, 14,
19
Critical evaluation
(Evaluation)
Able to assess the credibility and reliability of statistical claims
Able to make effective decisions based on data
Recognise data limitations and potential biases
5, 10, 15,
20
We used Zoho Survey (https://www.zoho.com/id/survey/) to distribute the online survey to
mathematics education students. Data collection through the online survey was conducted during October
2023. We recruited mathematics education lecturers at the higher education institutions selected for the
sample to administer and supervise the survey at their institutions. To ensure that data collection was
conducted in an honest manner, we invited all recruited lecturers to attend a technical briefing on the
rules for conducting the survey. The recruited lecturers were given the responsibility to conduct data
collection in their respective institutions honestly. Students completed the survey by accessing the survey
link and password shared by our recruited lecturers. Students could access the survey via their laptops
or smartphones. In order for the survey to run smoothly, we required that the survey be completed in
the classroom and guided and supervised directly by the lecturers we have recruited. Completing the
survey could also be done in the computer labs owned by each higher education institutions that we have
selected as a sample. Students were expected to take about 5 minutes to complete their personal data on
the survey. Meanwhile, the time provided to answer the SL test is a maximum of 45 minutes. To anticipate
students answering the SL test carelessly, we set a minimum time to complete the SL test. Students could
not submit their answers if they have not passed 25 minutes. In taking the SL test, students were not
allowed to collaborate with other students and use a web browser other than to access the survey and
take the SL test. In addition, students were not provided with a piece of paper to do calculations because
in the SL test students did not need to do any calculations.
Data Analysis
After the data collection process, we conducted data verification to remove duplicate responses
and incomplete responses. Data on gender, the higher education institution where the student studies,
laptop ownership, and research preference were binary coded. For gender, code 1 for male and code 0
for female. For the higher education institution where the student studies, code 1 was given if the student
was from a public higher education institution and code 0 for a student from a private higher education
institution. For laptop ownership, code 1 was given for students who own a laptop and code 0 for students
who do not own a laptop. Meanwhile, for the research preference, code 1 was assigned for students who
chose quantitative and code 0 was assigned for students who chose qualitative. These binary codes were
used to describe the SL achievement of mathematics education students in terms of each code.
We assigned a score of 1 for the correct answer on each SL test item. Thus, respondents will
obtain a maximum score of 20 if they can answer all SL test items correctly. A score of 0 is given for each
item answered incorrectly and there is no penalty (point deduction) for each incorrect answer. The total
score of each respondent was then used to describe the SL profile of mathematics education students in
general and based on the four aspects of SL (i.e., application, interpretation, communication, and critical
evaluation). The SL scores were then cross-tabulated with the variables of gender (male vs. female),
status of higher education institution (public vs. private), laptop ownership (yes vs. no), and research
preference (quantitative vs. qualitative). This cross tabulation was conducted to compare students’ SL
score achievement in terms of the category of each variable. An independent sample t-test procedure was
conducted to test the significance of differences in mean SL scores in terms of gender, higher education
institution’s status, laptop ownership, and research preference. The t-test was conducted by first providing
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Retnawati H. et al. (2024). Exploring inuential factors and conditions shaping statistical literacy among undergraduate students
in mathematics education, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
12(1),1-17.
evidence that the statistical literacy score data was normally distributed (skewness = 0.012, SE
skewness
=
0.068, kurtosis = –0.258, SE
kurtosis
= 0.136) and evidence that there was no significant difference in the
variance of statistical literacy scores between the two groups by gender (F = 0.959, p = .328), higher
education institution’s status (F = 0.683, p = .409), laptop ownership (F = 0.219, p = .64), and research
preference (F = 0.699, p = .403). Lastly, we used linear regression analysis to investigate the variables that
contributed significantly in predicting the SL level of mathematics education students. We conducted this
linear regression analysis on the basis that the assumption of collinearity has been confirmed in regard
to gender (VIF = 1.01, Tolerance = 0.995), status of institution (VIF = 1.03, Tolerance = 0.974), laptop
ownership (VIF = 1.03, Tolerance = 0.971), and research preference (VIF = 1.00, Tolerance = 0.996) and
autocorrelation was not a concern (autocorrelation = 0.123, Durbin-Watson = 1.75). All statistical tests
used a significance level of 5%.
Results
A total of 1,287 mathematics education students participated in this study. First, we report the SL
profile of mathematics education students in general and by gender, higher education institution’s status,
laptop ownership, and research preference. Afterwards, we report the results of regression analysis to
reveal the variables that significantly contribute to predicting the SL of mathematics education students.
SL Profile of Mathematics Education Students
Table 3 presents descriptive statistics of SL of mathematics education students in general and based
on SL aspects. In general, the mean score of SL of mathematics education students is not satisfactory.
Based on four aspects of SL, the mean score of the interpretation is the highest, while the mean score
of the critical evaluation is the lowest. Furthermore, when it comes to the variance of students’ SL scores
based on the aspects of SL, students’ scores on the interpretation aspect were the most varied compared
to the other three aspects.
Table 3. Descriptive statistics of SL of mathematics education students (n = 1,287)
Aspect of SL M (SD) Min. Max.
Application 1.70 (0.96) 0 4
Interpretation 3.96 (1.48) 0 8
Communication 1.63 (0.87) 0 4
Evaluation 1.47 (1.02) 0 4
Score of SL 8.76 (2.70) 0 17
SL Profile of Mathematics Education Students Based on Gender
The SL profile of mathematics education students in terms of gender (male vs. female) is summarised
in Table 4. The results presented in Table 4 demonstrate that both male and female students performed
the best in the interpretation aspect compared to the other three aspects. In addition, male students
outperformed female students in every aspect of SL and the overall SL. Furthermore, through inferential
analysis, the current study revealed that the mean score of SL of male and female students differed
significantly (t(1285) = –3.913, p < .01), where SL scores of male students were higher than female
students. On the application aspect, the mean score of male and female students was not significantly
different (t(1285) = –0.512, p = .608). In the interpretation aspect, the mean score of male students is
significantly higher than female students (t(1285) = –2.361, p = .018). On the communication aspect, there
was no difference in mean score of SL between male and female students (t(1285) = –1.746, p = .081).
On the critical evaluation aspect, this study found that the mean score of male students was higher than
female students (t(1285) = –4.944, p < .01).
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Retnawati H. et al. (2024). Exploring inuential factors and conditions shaping statistical literacy among undergraduate students
in mathematics education, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
12(1),1-17.
Table 4. Descriptive statistics of SL of mathematics education students based on gender
Aspect of SL
Male (n = 227) Female (n = 1060)
M (SD) Min. Max. M (SD) Min. Max.
Application 1.73 (0.93) 0 4 1.70 (0.97) 0 4
Interpretation 4.17 (1.54) 0 8 3.92 (1.46) 0 8
Communication 1.72 (0.86) 0 4 1.61 (0.87) 0 4
Evaluation 1.78 (1.06) 0 4 1.41 (1.00) 0 4
Score of SL 9.40 (2.84) 0 17 8.63 (2.65) 0 16
Statistical Literacy Profile Based on Status of Higher Education Institution
The SL profile of mathematics education students based on the higher education institution’s status
(public vs. private) is summarised in Table 5 indicating that students from both public and private higher
education institutions performed best in interpretation and performed poorest in evaluation. Students
from public higher education institutions in general out-performed those from private higher education
institutions. The performance of students from private higher education institutions was slightly better than
those from public higher education institutions, although in the other three aspects of SL those from public
institutions were superior.
Table 5. Descriptive statistics of SL of mathematics education students based on higher education
institution’s status
Aspect of SL
Public (n = 1053) Private (n = 234)
M (SD) Min. Max. M (SD) Min. Max.
Application 1.70 (0.97) 0 4 1.69 (0.94) 0 4
Interpretation 4.02 (1.46) 0 8 3.67 (1.52) 0 7
Communication 1.62 (0.86) 0 4 1.64 (0.88) 0 4
Evaluation 1.52 (1.03) 0 4 1.28 (0.94) 0 4
Score of SL 8.87 (2.71) 2 17 8.27 (2.59) 0 15
This study revealed that the mean SL scores of students from public and private higher education
institutions were significantly different (t(1285) = 3.091, p < .01), where the SL scores of students from
public higher education institutions were higher than private higher education institutions. In the application
aspect, the mean score of students from public and private higher education institutions is not significantly
different (t(1285) = 0.239, p = .811). In the interpretation aspect, the mean score of students from public
higher education institutions is significantly higher than private higher education institutions (t(1285) =
3.391, p < .01). On the communication aspect, there was no difference in mean score between students
from public and private higher education institutions (t(1285) = –0.190, p = .850). In the critical evaluation
aspect, this study found that the mean score of students from public higher education institutions was
significantly higher than private institutions (t(1285) = 3.190, p < .01).
Statistical Literacy Profile Based on Laptop Ownership
The SL profile of mathematics education students based on laptop ownership (yes vs. no) is
summarised in Table 6. Table 6 indicates that ownership of a laptop offers more opportunities to perform
better in SL, including in every aspect of SL, than those who do not own a laptop.
Table 6. Descriptive statistics of SL of mathematics education students based on laptop ownership
Aspect of SL
Has a laptop (n = 1011) Has no laptop (n = 276)
M (SD) Min. Max. M (SD) Min. Max.
Application 1.74 (0.95) 0 4 1.56 (0.10) 0 4
Interpretation 4.10 (1.46) 0 8 3.46 (1.45) 0 7
Communication 1.64 (0.85) 0 4 1.58 (0.91) 0 4
Evaluation 1.56 (1.02) 0 4 1.14 (0.95) 0 3
Score of SL 9.04 (2.64) 2 17 7.75 (2.65) 0 14
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Retnawati H. et al. (2024). Exploring inuential factors and conditions shaping statistical literacy among undergraduate students
in mathematics education, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
12(1),1-17.
This study also found that the mean score of SL of students who own a laptop is significantly
different from students who do not own a laptop (t(1285) = 7.219, p < .01). In terms of application, the
mean score of students who have laptops is significantly higher than students who do not have laptops
(t(1285) = 2.812, p < .01). In terms of interpretation, the mean score of students who have laptops is also
significantly higher than students who do not have laptops (t(1285) = 6.413, p < .01). However, in terms of
communication, the mean score of students who own and do not own a laptop was found not significantly
different (t(1285) = 1.024, p = .306). While in terms of communication aspect, the mean score of students
who own a laptop is significantly higher than students who do not own a laptop (t(1285) = 6.125, p < .01).
Statistical Literacy Profile Based on Research Preference
The SL profile of mathematics education students based on research preference (quantitative vs.
qualitative) is summarised in Table 7. Table 7 reports that those who favour quantitative research tend
to have better SL performance, both overall and by aspects of SL, than those who favour qualitative
research.
Table 7. Descriptive statistics of SL mathematics education students based on research preference
Aspect of SL
Quantitative (n = 1011) Qualitative (n = 276)
M (SD) Min. Max. M (SD) Min. Max.
Application 1.71 (0.95) 0 4 1.68 (0.98) 0 4
Interpretation 4.02 (1.48) 0 8 3.85 (1.47) 0 7
Communication 1.63 (0.84) 0 4 1.61 (0.91) 0 4
Evaluation 1.48 (1.02) 0 4 1.45 (1.02) 0 3
Score of SL 8.85 (2.68) 2 17 8.60 (2.73) 0 14
In this study, the mean score of SL of students who favour quantitative research was found to be not
significantly different from that of students who favour qualitative research (t(1285) = 1.547, p = .122). The
study also revealed that there was no significant difference in mean score between students who preferred
quantitative and qualitative research in terms of application (t(1285) = 0.476, p = .634), interpretation
(t(1285) = 1.933, p = .054), communication (t(1285) = 0.436, p = .663), and critical evaluation (t(1285) =
0.470, p = .639).
Factors and Conditions Affecting Statistical Literacy
In this study, we used four predictors to determine the factors and conditions that influence the
SL of mathematics education students. We binary coded each predictor to perform linear regression
analysis. The first stage of regression analysis involved all predictors. Simultaneously, all four predictors
significantly contributed to the SL achievement of mathematics education students (F (4, 1282) = 20.042,
p < .001), but these four predictors only had a coefficient of determination R
2
= 0.059. This indicates
that the four predictors used can only explain about 5.9% of the variation in the statistical literacy scores
of mathematics education students. Table 8 presents the coefficients of the regression equation and
the contribution of each predictor. Gender, higher education institution’s status, and laptop ownership
each contributed significantly in predicting the SL achievement of mathematics education students, but
research preference was found not to contribute significantly.
Table 8. Regression coefficient involving all predictors
Predictors (Reference)
Unstandardized Coefcients Standardized Coefcients
t p
B SE β
(Constant) 7.104 0.237 29.926 .000
Gender (Male) 0.878 0.192 0.124 4.573 .000
Institution’s status (Public) 0.440 0.192 0.063 2.292 .022
Laptop ownership (Yes) 1.276 0.181 0.194 7.067 .000
Research preference (Quantitative) 0.213 0.156 0.037 1.365 .173
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Retnawati H. et al. (2024). Exploring inuential factors and conditions shaping statistical literacy among undergraduate students
in mathematics education, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
12(1),1-17.
We then eliminated the insignificant predictor (i.e., research preference) from the regression model
and re-analysed, and obtained the results as presented in Table 9. The three predictors simultaneously
still contributed significantly in predicting the SL achievement of mathematics education students (F(3,
1283) = 20.084, p < .001). However, the coefficient of determination of these three predictors is R
2
= 0.057,
meaning that they only explain about 5.7% of the variation in the SL scores of mathematics education
students, while the rest (i.e., more than 90%) may be explained by other predictors. In addition, the three
predictors, namely gender, higher education institution’s status, and laptop ownership, each still made a
significant contribution in predicting the SL achievement of mathematics education students. Although the
contribution of these three predictors simultaneously only explains about 5.7% of SL achievement, laptop
ownership has the highest contribution in predicting the SL achievement.
Table 9. Regression coefficient after removing insignificant predictors
Predictors
Unstandardized Coefcients Standardized Coefcients
t p
B SE β
(Constant)
7.247 0.213 33.997 .000
Gender (Male)
0.877 0.192 0.124 4.563 .000
Institution’s status (Public)
0.428 0.192 0.061 2.233 .026
Laptop ownership (Yes)
1.289 0.180 0.196 7.148 .000
Discussions
This survey study sought to uncover how gender, higher education institution status, laptop
ownership, and research preferences affect the SL attainment of mathematics education students. Firstly,
our study found that there is a significant difference in the SL attainment of mathematics education
students in terms of gender. This finding is consistent with the findings of previous studies (Cheema, 2015,
2016), but different from the findings of other studies (Kurnia et al., 2023; McLauchlan and Schonlau,
2016). This gender predictor was found to contribute significantly in predicting the SL achievement of
mathematics education students. Based on our findings, males were predicted to have higher SL than
females. This indicates that the current statistics learning curriculum tends to favour male students. This
has an impact on the SL gap between men and women. Cheema (2015, 2016) explains that the existence
of a literacy gap in terms of gender indicates that the education process has not been able to serve male
and female students fairly. Referring to this argument, we believe that this also occurs in the learning
process of statistics in mathematics education study programmes. Thus, this finding also encourages
higher education institutions, especially mathematics education study programmes, to adjust the statistics
learning curriculum in order to fairly develop the SL potential of both male and female students.
In terms of higher education institution’s status, this study revealed that the SL achievement
of students from public and private higher education institutions differed significantly. This finding is
consistent with the findings of previous studies (Cheema, 2015, 2016). This indicates that there is a
gap in students’ SL in terms of higher education institution’s status. Thus, this reveals the fact that there
is still a gap in the statistics lecture process between public and private universities. In addition, the
predictor higher education institution’s status was found to have a significant contribution in predicting
the SL achievement of mathematics education students. Our study reveals that students from public
higher education institutions are predicted to have better SL than students from private higher education
institutions. This finding encourages private higher education institutions to pay more attention to the
quality of statistics learning in their institutions. Cheema (2016) asserts that learning in private educational
institutions is more varied than public, where those with high economic status can obtain higher quality
learning. Thus, it can be understood that students in private higher education institutions have not been
thoroughly provided with equal services in learning statistics. This is strongly suspected to contribute to the
differences in SL of mathematics education students. In addition, Cheema (2015) revealed that there may
be three things that cause differences in student literacy between public and private institutions, namely
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Retnawati H. et al. (2024). Exploring inuential factors and conditions shaping statistical literacy among undergraduate students
in mathematics education, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
12(1),1-17.
the ability to recruit qualified educators, differences in curriculum, and the availability of facilities, and
infrastructure to support learning. We believe that these three factors are plausible causes of differences
in SL of students from public and private higher education institutions.
In this study, we use the variable of laptop ownership to describe the economic status of students.
Students who own a laptop represent middle to upper economic status, whereas students who do not
own a laptop represent middle to lower economic status. Our findings revealed that the SL attainment of
mathematics education students was also found to be significantly different between students who owned
and did not own laptops. Unsurprisingly, this finding is consistent with several previous studies (Cheema,
2015, 2016) which revealed that there is a literacy gap in terms of students’ economic status. The
predictor of laptop ownership was also found to contribute significantly in predicting the SL achievement
of mathematics education students. We found that students who own a laptop are predicted to have
higher SL achievement than students who do not own a laptop. As stated by Cheema (2016), learners
with socioeconomic status advantage can access higher quality education services. We believe that the
different opportunities to gain access to better education between students with high and low economic
status contribute to the development of SL of mathematics education students.
Unlike the other predictors, research preference (quantitative vs. qualitative) was found not
to contribute significantly in predicting the statistical literacy achievement of mathematics education
students. This finding is different from previous findings (Gonulal, 2018; Loewen et al., 2014). Although
Gonulal (2018) stated that one’s research orientation (i.e., qualitative or quantitative) will influence one’s
development as a researcher, or vice versa, this study did not find any differences in students’ SL in terms
of their chosen research orientation. Gonulal (2018) explained that researchers who adopt a quantitative
research orientation tend to take more statistics courses, but our study did not find evidence that this
had an impact on the SL achievement of mathematics education students. It should be noted that the
studies of Loewen et al. (2014) and Gonulal (2018) were conducted in the context of Second Language
Acquisition (SLA) so the sample characteristics in these two studies are clearly different from our study.
Mathematics education students, although preferring qualitative research, are still required to take
statistics courses, both at the basic and advanced levels, as offered by the study programme curriculum.
Whereas students of non-statistics and non-mathematics study programmes, there is no such obligation.
This is what we believe to be the cause of the absence of differences in SL achievement of mathematics
education students in terms of their research choices.
Overall, this study managed to reveal three predictors that significantly influenced the statistical
literacy achievement of mathematics education students, namely gender, higher education institution’s
status, and laptop ownership. However, these three predictors only explained about 5.7% of SL
achievement. The contribution of these three predictors in predicting the SL achievement of mathematics
education students is relatively small. This means that these three predictors are not the main predictors
in predicting SL of mathematics education students. Although the contribution of these three predictors is
small, these three predictors are still useful for predicting the SL achievement of mathematics education
students. Compared to the other two predictors, laptop ownership has the largest contribution in predicting
SL achievement. Without ignoring the contribution of the other two predictors, it seems that the issue of
socio-economic status gap needs to get more serious attention to overcome the gap in SL achievement
of mathematics education students. We present some recommendations in this regard in the implications
section.
Implications for Practices and Policies
The gap in SL achievement in terms of gender, higher education institution’s status, and laptop
ownership indicates that the statistics learning curriculum in the mathematics education study programme
has not been able to serve the SL development needs of all students. Reorganising the statistics
learning curriculum is needed to overcome, at least reduce the gap. The existence of a statistics learning
curriculum that is fair, equal, and able to facilitate students from all economic groups is believed to be
able to provide new hope for the SL development of mathematics education students. The facility gap as
one of the triggers of the SL gap is the responsibility of the authorities to provide equitable distribution
of learning support facilities (for example, computer laboratories, statistics applications or software, and
other statistics learning resources) for public and private institutions. For statistics educators, they are
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Retnawati H. et al. (2024). Exploring inuential factors and conditions shaping statistical literacy among undergraduate students
in mathematics education, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
12(1),1-17.
expected to be able to design statistics learning and assessment that is fair for males and females and
able to facilitate the learning needs of students from economically disadvantaged groups. Although the
use of technology is inseparable in learning statistics (Austerschmidt et al., 2022; Koparan, 2019; Lloyd
and Robertson, 2012; Sosa et al., 2011), it needs to be considered so that the use of technology does not
become a problem for students from low economic backgrounds.
Limitations and Future Directions
The main limitation of this study lies in the non-random sampling technique. Although the number
of respondents involved in this study is quite large and we have strived to consider the distribution of
higher education institutions based on the coverage of regions in Indonesia, the generalisation of the
research findings cannot fully reach all members of the population. Thus, in the future we encourage
other researchers to conduct studies on this topic by applying random techniques in selecting research
samples. In addition, we believe that the characteristics of mathematics education students between
countries may differ. Therefore, we encourage studies in this area to also involve samples from various
countries. The existence of new studies in this area involving a wider population is expected to be used
as a consideration in making policies related to improving the SL achievement of mathematics education
students. In addition, considering that the three predictor variables in this study can only explain a small
part of the SL of mathematics education students, future studies are advised to explore other factors
that have the potential to further explain students’ SL. Given the study conducted by Kurnia et al. (2023)
and the recent study by Wang et al. (2023) which showed that grade level has an impact on high school
students’ attainment, while the study by Yolcu (2014) suggested no significant impact of grade level on
students’ SL, what year a student is in university or college could be considered by future studies as one
of these other factors.
Conclusions
This study offers results that contribute to efforts to promote SL in prospective mathematics
teachers through the disclosure of factors that potentially affect SL achievement which include gender,
higher education institution’s status, and laptop ownership. Differences in gender, higher education
institution’s status, and laptop ownership cause a gap in SL achievement. Although the contribution of
these three factors in predicting SL is small, these three factors are important to consider in designing
curriculum and policies to improve the SL of mathematics education students. This study encourages the
need to reorganise the statistics education curriculum so that it can facilitate all student characteristics
in developing their SL. Based on the findings of this study, we also encourage other researchers to
investigate other factors that are thought to contribute significantly in predicting the SL achievement of
mathematics education students.
Acknowledgements
The authors gratefully acknowledge the research funding from the Directorate of Research,
Technology and Community Service, Directorate General of Higher Education, Research and Technology,
Ministry of Education, Culture, Research and Technology, the Republic of Indonesia, fiscal year 2023,
through scheme of Regular Fundamental Research [grant number: 146/E5/PG.02.00.PL/2023 and
T/13.12/UN34.9/PT.01.03/2023].
Conflict of interests
The authors declare no conflict of interest. The funders had no role in the design of the study; in the
collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the
results.
Author Contributions
Conceptualization, H.R. and K.H.; methodology, H.R. and E.A.; software, E.A., I.R. and M.N.R;
validation, H.R. and K.H.; formal analysis, E.A. and I.R.; investigation, E.A., I.R. and M.N.R.; resources,
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Retnawati H. et al. (2024). Exploring inuential factors and conditions shaping statistical literacy among undergraduate students
in mathematics education, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
12(1),1-17.
H.R. and K.H.; data curation, E.A., I.R. and M.N.R.; writing—original draft preparation, E.A. and I.R.;
writing—review and editing, H.R. and K.H.; visualization, I.R.; supervision, H.R.; project administration,
M.N.R.; funding acquisition, H.R. and K.H. All authors have read and agreed to the published version of
the manuscript.
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