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ISSN: 2311-1550
2022, Vol. 9, No. 3, pp. 528-544
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Relationships Between Cognitive Styles and Indigenous Students’
Mathematics Academic Outcomes
Murni Sianturi1, Riska Suliantin2 and Hariani Fitrianti3
1Department of Social Sciences, The University of New South Wales, NSW, Australia
2Muhammadiah Junior High School, Merauke, Indonesia
3Musamus University, Papua, Indonesia
Abstract: This article explores the link between cognitive styles and Indigenous students’ mathematics
academic outcomes. There were three different groups of Indigenous West Papuan students participating
in this study: 9 junior high school students, 12 senior high school students, and 46 university students.
Data were collected from the results of the group embedded figure test and the previous semester's scores
and analysed using a quantitative approach. In contrast to previous studies, the results indicated no
significant correlation between cognitive styles and mathematics academic outcomes for junior and senior
high school students. However, the different cognitive styles showed a significant contribution for
university students. As Indigenous students pursue a higher level of education, their cognitive styles
would influence their mathematics academic outcomes.
Keywords: Indigenous students, cognitive styles, mathematics academic outcomes, mathematics
learning achievement, gender.
Introduction
The contested discourses about the success of Indigenous students have continued to be discussed
among researchers (Chung et al., 2021; Harrison, 2011; Lowe, 2017; Sianturi et al., 2018).
Underperformance among Indigenous students has been found globally (Abdullah et al., 2013;
Burridge et al., 2012; Dhir, 2015; Chen, 2016; Jacob, 2017; Sianturi et al., 2018). For example, after
several years of implementing a range of improvement strategies in Australia, the percentage
attainment rates of Indigenous students in terms of literacy (Department of the Prime Minister and
Cabinet, 2018; Riley & Webster, 2016) and numeracy performance lag behind those of non-Indigenous
students (Department of the Prime Minister and Cabinet, 2018).
The literature also notes that the literacy and numeracy performances of Indigenous West Papuan
students are lower than non-Papuan students (BPS, 2020; Sianturi et al., 2018). According to the latest
report on schooling annual evaluation in 2019, junior high and senior high school students' average
scores on compulsory subjects were below the national average (Kementrian Pendidikan dan
Kebudayaan [Indonesian Ministry of Education and Culture] (Kemendikbud), 2019). Indigenous West
Papua students in Merauke Regency have the lowest scores in mathematics out of all the subjects
offered in schools (Kemendikbud, 2019). Based on a report from previous research, it was discovered
that West Papuan students in higher education had only a 43 out of 100 score on the basic
mathematical ability test (Sianturi & Suryani, 2017).
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The mathematics academic achievement gap between Indigenous and non-Indigenous students has
become a worldwide issue. The ongoing legacy of colonisation that persistently has impacts on
Western mainstream education, which numerically assesses the academic success of Indigenous from
colonial lenses, and the global neoliberal views that prioritise standardised scores, perpetuates placing
these students in a marginalised position (Rigney et al., 2020; Sianturi, Chiang, et al., 2022). Despite
the constant educational disadvantage faced by Indigenous students, there remains a limited
understanding of the source of the educational gap and of ways to improve Indigenous students'
outcomes and close the gap.
Although governments and schools have attempted to increase the students' learning achievement,
the attempts seem to achieve less. For example, the Indonesian government strives to enhance Papuan
students' academic success, making them equivalent to their Indonesian peers, however, what the
government has undertaken provides limited space for Papuan students to multiply their potential
(Rahmawati, 2004; Sianturi, Chiang, et al., 2022). Why? Because these efforts are more likely to
assimilate them into a community different from them (Sianturi, Chiang, et al., 2022). This community
is inaccessible and culturally divisive (Yunkaporta, 2009). If the focus of the government and teachers
is only on how to make the scores of Indigenous students equal to non-Indigenous students, then the
divide will never be closed. Only if all parties can see the uniqueness and distinctive characteristics of
Indigenous students and their needs, and set aside the intention of equating all students with a
standardised value, then the conceptualisations of knowledge and learning will be more inclusive
(Munroe et al., 2013; Perso, 2012). This would encourage teachers to focus on efforts to understand
students and dig deeper into their potential and the way they think and learn so that teachers will be
able to design lessons that are appropriate and accessible for them (Hogg & Volman, 2020; Sianturi et
al., 2018).
One of students’ characteristics seen as influencing the success of students' mathematical performance
that teachers might consider is cognitive style. Several studies have shown that there is a significant
correlation between the classification of students' cognitive styles on their mathematics learning
outcomes (Azlina et al., 2018; Jantan, 2014; Nur & Palobo, 2018). That is, the classification of certain
cognitive styles of students affects them in approaching mathematics, which leads to their success in
learning that subject. Considering the low performance of Indigenous West Papuan students in
mathematics, our study focused on identifying the relationship between their mathematics academic
outcomes, cognitive styles, and gender.
Literature Review
Cognitive Styles and Indigenous Students’ Ways of Learning
Cognitive styles refer to the terminology used to describe an individual’s unique way of receiving and
processing information (Umaru, 2013; Witkin et al., 1977). Individuals may proceed with cognition
differently, including attention, memory, awareness, understanding, perception, and reasoning
(Macneil, 1980; Umaru, 2013). Although cognitive styles are popular in psychological disciplines,
cognitive styles have become expansive in educational fields. A growing literature has shown the
relevance of cognitive styles to students’ learning process (Jantan, 2014; Singer et al., 2017; Umaru,
2013), in which they are considered a determining element of students’ uniqueness in perceiving and
managing information from the environment (Kozhevnikov, 2007; Singer et al., 2017).
530
Cognitive styles are distinct from cognitive ability, which is often measured by intelligence tests
(Umaru, 2013; Witkin et al., 1977). Although cognitive style and cognitive ability probably influence
learning practice performance, several cognitive styles may match some practices more than others
and vice versa (Kozhevnikov, 2007; Riding & Sadler-Smith, 1997; Singer et al., 2017; Witkin et al.,
1977). Cognitive styles reflect qualitative rather than quantitative differences between individuals in
their thinking processes (Riding & Sadler-Smith, 1997; Vega-Vaca & Hederich-Martínez, 2015; Witkin
et al., 1977). While cognitive ability emphasises students’ intellectual level more, cognitive style is
more about students’ ways of thinking, their steady mode of intellectual and perceptual functioning
(Umaru, 2013), and their attitudes toward interpreting situations and resolving matters in mind
(Desmita, 2012; Umaru, 2013). The students’ preferred method of organising information leads them
to develop knowledge (Witkin et al., 1977), enables combination in a cross-ability and personality, and
ultimately manifests it in several activities (Desmita, 2012).
Witkin et al. (1977) studied individuals' ways of thinking profoundly and discovered two
classifications of cognitive styles: field-independent (FI) and field-dependent (FD). The characteristics of a
person classified as FI are: 1) proceeding with information in part patterns and analytic ways and
having a tendency to one’s own self-defined purpose; 2) feeling challenged to learn and comprehend
social information and contents and, therefore, requiring certain assistance (Blanton, 2004; Witkin et
al., 1977); 3) developing inductive reasoning to interpret inputs (Saville-Troike, 2012) one’s own
structures in unstructured situations (Blanton, 2004; Witkin et al., 1977), and feeling efficient working
alone (Vega-Vaca & Hederich-Martínez, 2015; Volkova & Rusalov, 2016); 4) not being influenced by
criticism and more introverted (Vega-Vaca & Hederich-Martínez, 2015); 5) tackling problems without
explicit guidance and instructions (Witkin et al., 1977). Meanwhile, characteristics of a person
classified as FD are: 1) proceeding with information in a whole pattern and defined structures and
having a tendency to require reinforcement; 2) showing more interest in social contents and, therefore,
memorising social information well (Blanton, 2004; Witkin et al., 1977); 3) developing deductive
reasoning to interpret inputs (Saville-Troike, 2012), having difficulty with structured learning material
(Blanton, 2004; Witkin et al., 1977), and enjoying working in a team (Volkova & Rusalov, 2016); 4)
being influenced by criticism and more extroverted (Vega-Vaca & Hederich-Martínez, 2015); 5)
needing clear instruction mnemonics or metaphors to solve problems (Blanton, 2004).
Research in Indigenous education suggests recognising Indigenous children’s ways of learning
(Krakouer, 2015; Yunkaporta, 2009). How Indigenous students think is distinct from non-Indigenous
students. For instance, Harris (1984) asserted that Australian Aboriginal students have unique
elements of learning that are the same as other non-Aboriginal students have. While non-Aboriginal
pupils get used to school-based learning, Aboriginal children prefer informal learning. However,
many parties, even the teachers themselves, do not understand this difference, thus generalising the
learning approaches between Indigenous and non-Indigenous students (Hughes & More, 1997).
Yunkaporta (2009) continued similar research and found eight Indigenous ways of learning: 1)
Deconstruct/Reconstruct: scaffolding and framing, working from a whole pattern to parts; 2) Learning
Maps: explicitly visualising processes; 3) Community Links: embracing local perspectives and learning
from the community; 4) Symbols and Images: utilising images, metaphors, and symbols to understand
concepts; 5) Non-verbal: using intra-personal and kinaesthetic skills to think, organise information, and
531
learn; 6) Land Links: place-based learning, linking content to local land and environment; 7) Story
Sharing: approaching learning through narrative; 8) Non-linear: producing innovations and
understanding by thinking laterally or combining systems (pp. 46-50).
The concepts Yunkaporta (2009) discussed resonate with the FD classification criteria — there are
some commonalities. Evidence corroborates that some Indigenous groups (e.g., Native American and
Australian Aboriginal learners) tend to be more FD (Pewewardy, 2002; Rasmussen et al., 2004).
However, it should be noted that this prevalence is not general for all Indigenous peoples; even within
one group, the tendency to be FD cannot be generalised (Rasmussen et al., 2004). This is further
explained by Kozhevnikov (2007), that although cognitive styles tend to be identical and stable in each
individual, they are not consistent over time because changes may occur in line with responses to
specific environmental situations.
Cognitive Styles and Mathematics Learning
There is a link between students’ cognitive styles and their mathematical achievement (Jantan, 2014).
This positive correlation has drawn several researchers’ interest to its profound impacts on more
exceptional mathematical skills: creativity (Azlina et al., 2018; Singer et al., 2017), problem-solving
(Ademola, 2015; Faradillah et al., 2018), mathematical communication (Pratiwi et al., 2013) and
particular subject areas, such as geometry (Singer et al., 2017) and algebraic reasoning tasks
(Chrysostomou et al., 2013; Rosita, 2018). These studies’ findings underscored the importance of
delving in more substantial depth into how cognitive styles influence mathematics learning. Singer et
al. (2017) considered cognitive style a good predictor of students' mathematical creativity in a
comprehensive analysis. Some of these studies also provided recommendations for the idea of
dynamic balance in teaching mathematics by embracing students' cognitive styles (Chrysostomou et
al., 2013; Singer et al., 2017).
Cognitive Styles and Gender
The literature has recognised the relationship between gender and mathematics performance
(Goodchild & Grevholm, 2009; Lin et al., 2020; Munroe, 2016). There is a higher performance rate
among males in mathematics than among females. During the mathematics learning process,
compared to their male peers, girls tend to exhibit a less positive attitude towards mathematics
(Munroe, 2016). For questions in which they are uncertain of the answer, girls are less likely to take a
risk (Goodchild & Grevholm, 2009). The discrepancy also appears in ways they approach
mathematical problems. When determining the solutions process and presenting their results, boys
are more likely to use diagrams, while girls prefer to utilise traditional methods and narratives
(Munroe, 2016).
It is evident that there is a tendency for males to perform better on mathematics tests than females,
when it comes to learning mathematics, and according to Arnup et al. (2013), these discrepancies
might be partly explained by the differences in the cognitive styles of the individuals. Their study
involving 190 primary school pupils aged between 8 and 11 years reported the relationship between
cognitive styles, mathematics performance, and gender. Based on the results presented, there was a
difference in the way boys and girls perform mathematics based on their cognitive styles. It was found
that there was a significant interaction between gender and cognitive styles in the context of
532
mathematics learning. Compared to girls with the FI classification, boys with that classification
performed significantly better.
Moreover, Pathuddin et al. (2019) specifically studied the characteristics of male students with
different cognitive styles in solving mathematics problems. Boys with the FI cognitive style were
typically capable of reshaping and connecting new information to the knowledge they already
possessed and organised in resolving problems. Contrary, it is difficult for boys with the FD
classification to solve problems effectively as they feel challenged to organise their approach and
connect new information to their existing knowledge.
These previous studies focused more on the relations to mathematics performance, cognitive styles,
and gender among students in general. Although findings of studies that explored the prevalence of
Indigenous students’ cognitive classifications were presented in the previous discussion, those studies
did not examine the relationships to mathematics academic performance, cognitive styles, and gender.
Therefore, a study that identifies the relationships between mathematical performance, cognitive
styles, and gender among Indigenous students is considered necessary. Moreover, this is because
Indigenous students have different ways of learning from students in general.
Research Objectives and Questions
This research profoundly investigated the relationships between Indigenous students’ cognitive
styles, gender, and mathematics academic outcomes. This study is critical to help teachers develop
teaching strategies more relevant to Indigenous students' characteristics and uniqueness and is
accessible and inclusive for Indigenous students. This study adds to the literature on cognitive styles
and Indigenous students' mathematical skills. The following research questions (RQs) were used to
accomplish the objective of this study:
RQ1: Is there a significant association between gender and Indigenous students’ cognitive style
classification?
RQ2: Is there a significant relationship between cognitive styles and mathematics academic
outcomes among Indigenous West Papuan students?
Research Framework
This study was structured according to the research framework illustrated in Figure 1. In the present
study, the relationship between cognitive styles, genders, and mathematics academic outcomes was
examined among Indigenous West Papuan students who were enrolled in this study. There are three
main pieces of information required in order to identify such a relationship, including gender,
cognitive style classification, and mathematics academic outcomes. This study first examined whether
or not there is a relationship between gender and cognitive styles. We identified if Indigenous West
Papua students' gender affects their prevalence of cognitive style. The relationship between cognitive
style classification and mathematics academic outcomes was sequentially investigated. Through
correlation analysis, we intended to understand if the prevalence of the cognitive style of Indigenous
West Papua students affected their mathematics academic outcomes.
533
Figure 1: Research Framework
Methods
Research Methodology
This study examined the relationship between cognitive styles, genders, and mathematics academic
outcomes among Indigenous West Papuan students. In case-study research methods, researchers can
explore the data closely within the context of a particular situation or issue in order to assess its
relevance (Zainal, 2007). Typically, a small number of individuals, or limited geographical areas, are
chosen as the subjects of a case study (Creswell, 2014; Zainal, 2007). There is a weakness that a case
study may not have a broad application, due to the fact the statistical requirements may not be met
when analyzing small data sets. If that is the case, case studies could be limited in their application
(Davies & Beaumont, 2011). There is no one particular tradition of social scientific research that
inspired case studies. "Case study is defined by individual cases, not by the methods of inquiry used"
(Stake, 1994, 236). Depending on the nature of the case, it can be complex or simple (Stake, 1994). It is
also possible for case studies to be quantitative and/or qualitative. Using quantitative analysis, case
studies can include both empirical and analytical elements (Mills et al., 2010).
Because our study concentrated on a small group of Indigenous West Papuan students inhabit in the
Merauke area who had a specific case in terms of low mathematics performance, we considered our
study a simple case study using quantitative analysis, as we discussed in the previous section
regarding the low mathematics performance of Indigenous West Papuan students at different school
levels. Depending on each child's age and grade level, as well as the academic area in which the child
was studying, the role of cognitive style in relation to academic achievement could vary (Saracho,
1988). We were interested in identifying how the cognitive styles differ for each level. Research on
cognitive styles using GEFT is generally intended for individuals or groups of young people over ten
years old, particularly research that specifically discusses the area of mathematics. Therefore, in our
Relationship
between
Gender and
Cognitive Style
Relationship
between
Cognitive Style
and
Mathematics
Academic
Outcomes
Indigenous
West Papuan
Student
GEFT
Score
Cognitive
Style
Gender
Field
Independent
t
Field
Dependent
Mathematics
Academic Outcomes
534
study, we intended to examine these three different levels (i.e., junior and senior high schools, and
university).
Population and Sample
Although, in order to sample cases in a quantitative study it is necessary to have some understanding
of both the cases and the populations within which they are drawn, Thompson (1999) and Uprichard
(2013) asserted that we need to know what kinds of methods are most appropriate to deal with certain
cases. Some cases in quantitative studies employ non-probability (non-random) sampling for some
consideration (Vehovar et al., 2016). As a way of optimising response rates, non-probability sampling
is also introduced in quantitative research (Asiamah et al., 2022). Non-probability sampling methods
are often used by researchers for practical reasons. There are specific criteria that are used to select
non-probability samples, such as: practicality (data can be collected more easily and efficiently
through a sample); necessity (not all populations can be studied because they are too large or
inaccessible); cost-effectiveness (there is expense involved — equipment, travel, etc.) and a reduction
in participant enrollment manageability (using smaller datasets, the process of storing and conducting
statistical analyses is easier and more reliable) (Asiamah et al., 2022; Uprichard, 2013; Vehovar et al.,
2016). This sampling method will help particularly early career epistemologists who might have to
consider cost-effectiveness (since many of them might not receive funding or sponsorship), conduct
feasible studies and obtain an understanding of reporting their practices plausibly (Asiamah et al.,
2022). To bridge tensions between methodological and practical activities (Uprichard, 2013),
researchers must determine the limits of inference (Asiamah et al., 2022) and whether or not it is
intended to generalise population. As long as there is researcher awareness of the epistemological
perspectives they adopt, the door is opened to a set of other discussions.
Although this study employed a quantitative approach, some practical consideration was made to
select non-random sampling. As early-career researchers, cost-effectiveness, practicality, and necessity
were the most reasonable justification that we took into account. Therefore, it is less likely that we
could make statistically valid inferences about the population at large based on nonrandom selection
methods rather than based on a probability sample.
Our study focused on a group of Indigenous West Papuan students who lived in the Merauke District
and who had a specific case in terms of low mathematics performance. The number of Indigenous
Papuan people is decreasing compared to non-Indigenous, and, in general, Indigenous Papuans live
in remote areas, where access to them is quite difficult and costly. Therefore, we identified schools and
universities that we could reach, as well as classes and majors that have a fairly large proportion of
Indigenous Papuan students. We decided to select two schools and one department of a local
university. For confidentiality purposes, the names of the schools and university were anonymous.
After discussing with homeroom teachers at each school and the head of the Department of Primary
Teacher Education, we recruited the seventh grade for the junior high school, the eleventh grade for
the senior high school and the second- and third-year students for the university. All participants
were Indigenous West Papuans (n = 67), in which n = 9 were junior high school, n = 12 senior high
school, and n = 46 university students (Table 1).
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Table 1: Demography of Participants
Criteria
Junior High
School Senior High School University n
Male
Female
n
4
5
9
8
4
12
15
31
46
27
40
21
Tool Used
A group embedded figure test (GEFT) was given to gather information about students’ cognitive
styles. GEFT is a set of psychometric tests developed by Witkin et al. (1977) and commonly used to
classify individuals as FI or FD learners. GEFT takes 15 minutes to complete. It consists of three
subcategories: subcategory I (a part of training, three minutes to complete), which consists of seven
items; subcategories II and III (main parts, six minutes to complete for each), respectively, comprise
nine items − one point for each correct answer and zero for the wrong answer. The maximum total
score is 18, and the minimum is zero. If a GEFT taker gains a total score in the range of 0 to 9, he/she is
classified as FD, while in the range 10-18, he/she is classified as FI (Kamaruddin et al., 2004).
The GEFT is a standardised test and has been tested across a wide range of cultures to ensure its
reliability and validity. Since the GEFT was published, some scholars have tested and employed it,
and it shows a strong correlation ranging from .60 to .90 (Fyle, 2009; Jantan, 2014; Lis & Powers, 1979;
Panek et al., 1980; Witkin et al., 1971). This correlation remains strong even when it is translated in
Indonesian (Kamaruddin et al., 2004; Nugraha & Awalliyah, 2016; Puspananda & Suriyah, 2017) and
employed in the Merauke Regency (Nur & Palobo, 2018; Nur & Nurvitasari, 2017). Puspananda and
Suriyah (2017) in their research on factor analysis on the GEFT to measure cognitive styles found that
the GEFT instrument has one dominant dimension, namely, cognitive styles. The validity and
reliability show the internal consistency of the GEFT, and, therefore, GEFT is ready to use
(Puspananda & Suriyah, 2017). This consideration led our study to use a translated version of GEFT
(Kamaruddin et al., 2004; Nur & Nurvitasari, 2017) without testing it.
Data Collection and Analysis
There were two kinds of data gathered in this study, one was the information about students'
cognitive style classifications, and another was students’ mathematics learning achievements.
Students’ cognitive styles were collected from the GEFT results of each student. As this study is a
continuation of previous research (Sianturi, Suliantin, et. al., 2022), where the cognitive style
classifications of the junior and secondary high school students in that study were used for this study.
We distributed GEFT to university students (Appendix A).
Students’ mathematics academic outcomes for junior and secondary high school levels were gathered
from mathematics academic scores from the latest semester. Meanwhile, the mathematics academic
outcomes of university students were collected from their grades in their geometry and measurement
course. All scores here use percentage grading systems with the scale of 0-100.
We employed Fisher’s exact test value, independent t-test, and Nonparametric Correlation (Spearman
Rho) assisted by SPSS Statistic 26 to analyse the data (Creswell, 2014; Creswell & Guetterman, 2019).
Fisher’s exact test value was utilised to determine whether there is a significant association between
536
gender and students’ cognitive style classification (RQ1). Meanwhile, independent t-test, and
Nonparametric Correlation (Spearman Rho) were employed to identify whether there is a significant
relationship between cognitive styles and mathematics academic outcomes among Indigenous West
Papuan students (RQ2).
Ethical Clearance
Ethical approval was granted from the local ethics committees of the local university, the head of the
department, and the principals of each school. All participants provided informed consent. The names
of each school and university were kept anonymous for confidentiality purposes.
Results
Gender Preferences
As our data sample sizes were too small and data violated an assumption for Chi-Square tests, we
used exact Fisher’s exact test value to discover whether the number of FI and FD learners was
associated with the gender of the students. Table 2 shows that the p-value of 1.000 (for junior high
school and university students) and the p-value of .333 (for senior high school students) were greater
than .05. Thus, we concluded there was no significant association between gender and students’
cognitive style classifications (FI and FD).
Table 2: Cognitive Style Classifications and Gender
Seventh Grade Value df
Asymptotic
Significance (2-
sided)
Exact Sig. (2-
sided)
Exact Sig. (1-
sided)
Pearson Chi-Square .032a 1 .858
Continuity correction .000 1 1.000
Likelihood ratio .032 1 .858
Fisher’s exact Test 1.000 .722
Linear-by-linear Association .029 1 .866
N of Valid cases 9
Eleventh Grade
Pearson Chi-Square 2.182b 1 .140
Continuity correction .136 1 .712
Likelihood ratio 2.385 1 .122
Fisher’s exact Test .333 .333
Linear-by-linear Association 2.000 1 .157
N of Valid cases 12
University students
Pearson Chi-Square 1.012c 1 .314
Continuity correction .055 1 .814
Likelihood ratio 1.622 1 .203
Fisher’s exact Test 1.000 .499
Linear-by-linear Association .99 1 .320
N of Valid cases 12
a. 4 cells (100.0%) have expected count less than 5. The minimum expected count is .89.
b. 3 cells (75.0%) have expected count less than 5. The minimum expected count is .33.
c.
2 cells (50.0%) have expected count less than 5. The minimum expected count is .65.
537
Cognitive Styles and Learning Achievement
Table 3 provides the relationship between GEFT results and student mathematics academic outcomes.
For junior high school students, it shows p-values = .412 (p > .05) and Spearman correlation coefficient
(r) = .313 and for senior high school students, p-values = .181 (p > .05) and Spearman correlation
coefficient (r) = .414. Differently, for university students, p-values = .002 (p <. 01) and Spearman
correlation coefficient (r) = .438.
Table 3: Correlation between Mathematics Learning Achievement and GEFT Scores
Criteria Junior High School Senior High School University
Mean SD r p* Mean SD r p* Mean SD r p**
Learning
achievement 62.44 5.28 .313 .412 37.67 8.08 .414 .181
60.33
19.70
.438
.002
GIFT 4.33 4.12
3.33 2.77
1.72 3.17
n 9 12 46
*95% confidence level
**Significance at the .01 level
Meanwhile, students’ cognitive styles did not significantly affect either junior high school or senior
high school students’ learning outcomes; they significantly affected the learning outcomes of the
university students. Although the cognitive styles of junior high school and senior high school
students positively affected their learning outcomes, the correlation was small.
Table 4: The Effect of Cognitive Styles on Mathematics Academic Outcomes
Criteria
Junior High
School Senior High School University
F pa t p* F pa t p* F pa t p*
Math outcomes 2.815 .137 .768 .472 b b 2.609 .026 2.5222
.119
2.213 .032
FI/FD
a Levene’s test, variance is homogenous at 95% confidence level
b Levene's test is not computed because there are less than two non-empty groups.
*Significance at the .05 level
Prior to using independent t-test, we tested whether the data were normally distributed. The results of
normality test for each level showed p-values = .151 (p < .05), p-values = .200 (p < .05), p-values = .000
(p < .05), which means all data distributed normally and independent t-test could be applied
(Appendix B). The effect of cognitive style classifications on mathematics academic outcomes is
described in Table 4. It showed that p-values =.472 (p > .05), p-values =.026 (p < .05), and p-values
=.032 (p < .05), for each junior high school, senior high school, and university students, in order. The
differences in FI or FD classifications only positively influenced mathematics academic outcomes of
538
the senior high school and university students. If we compare the correlation value of junior high
school, senior high school, and university students and the description in Table 4, we discover that
students' cognitive styles provide greater influence as students age. Students with the FI classification
were more likely to have better learning outcomes than students with the FD classification, in line
with their ages. Table 5 shows that p-values = .014 (p <. 01) and Spearman correlation coefficient (r) =
.369. It shows that the relationship between the FD and mathematics outcomes of university students
was significantly correlated.
Table 5: Relationship between Field-Dependent and Mathematics Outcomes of University Students
Variable r p**
Math outcomes .369 .014
FD
**Significance at the .01 level
Discussion
In previous and recent research, it has been noted that cognitive styles have a positive connection with
many cognitive attributes (Umaru, 2013; Volkova & Rusalov, 2016) and mathematics (Chrysostomou
et al., 2013; Jantan, 2014; Rahman, 2013; Singer et al., 2017; Suranata et al., 2019). However, our
findings support a slightly different point of view. This study’s findings revealed that Indigenous
students’ cognitive styles, both junior high school and senior high school, did not significantly
correlate with students' mathematics learning outcomes. Although the correlation was positive, the
cognitive styles showed a small contribution to supporting Indigenous students in achieving their
learning outcomes. Meanwhile, higher education students' cognitive styles significantly influenced
their learning outcomes. However, it was seen that the positive correlation became more noticeable as
the students’ grade level increased. A small correlation for younger students was also discovered by
Jantan (2014), where cognitive styles were more likely to influence students’ outcomes as the child
matures.
This may occur because of the way teachers deliver mathematics material that is not relevant to the
students’ environment. Yunkaporta (2009) argues that educators often focus on linear viewpoints, a
part of Western-oriented pedagogy, within school practices, which further marginalises Indigenous
students by preventing them from reconstructing their own knowledge. The learning outcomes
collected in this study were analysed together with GEFT scores that were mathematics academic
outcomes measured according to standard scores. Given the small contribution of cognitive styles to
mathematics academic outcomes for younger students, this may be acceptable. A study involving
Papuan students aged 10-12 years by Sianturi et al. (2018) also found that the delivery of learning
materials for Papuan students was often monopolised by the dominant culture, making it difficult for
students to grasp mathematics learning. Furthermore, another study stated that the education
implemented in many schools in West Papua attempted to alienate Papuan students from their
environment and culture (Sianturi, Chiang, et al., 2022).
However, it should be noted that this needs to be studied with a qualitative approach. The aim is to
see how Indigenous students' ways of learning, culture, the environment, and even their gender,
affects Indigenous Papuans' cognitive styles prevalence and the ways students capture information
539
conveyed during the mathematics learning process. Previous studies that discovered a positive
correlation between cognitive styles and mathematical achievement involved students in general (e.g.,
Ademola, 2015; Azlina et al., 2018; Chrysostomou et al., 2013; Faradillah et al., 2018; Pratiwi et al.,
2013; Rosita, 2018; Singer et al., 2017). Thus, more similar studies involving Indigenous students’
mathematics learning are required. Topics about the relevance of Indigenous ways of learning and
cognitive styles to students' mathematical abilities, but not taken from standardised mathematics
outcomes, might be worth considering for further research.
Other than the insignificant differences between cognitive style and learning outcomes against
previous studies' results (Scholar & Abdurauf, 2015), our results also identified slight differences
according to gender. Our research results indicated no significant association between Indigenous
students' gender and cognitive style classification. In other words, no matter whether an Indigenous
West Papuan student was a boy or a girl, his or her cognitive style would not be affected. However,
we could not articulate this statement strongly because of the limited number of participants we
studied.
Conclusion and Recommendations
This study sought the relationship between cognitive styles and mathematics academic outcomes
among Indigenous West Papuan students. There is no significant correlation between the Indigenous
students’ cognitive styles and mathematics academic outcomes at the junior and senior high school
level. Indigenous students with different cognitive styles might show a small improvement in
accomplishing their learning outcomes. As Indigenous students pursue a higher level of education,
their cognitive styles will influence their mathematics academic outcomes.
The findings of this study provide recommendations for school practices and policymakers. Teachers
should recognise Indigenous students' cognitive styles when developing teaching instructions and
assessments. Knowing Indigenous students’ diverse characteristics and needs might help teachers
create more culturally appropriate lessons for students. As cognitive styles might influence students’
mathematics academic outcomes as their school level increase, teachers might consider their cognitive
classifications for each school level.
The results of the study should be viewed within the context of several limitations. Considering the
small number of participants and the fact that we employed non-probability sampling in this study, it
is not possible to generalise the results of this study. Thus, a similar procedure with a larger sample
size could be applied for future research with probability sampling. Studies using qualitative
approaches to find the relationship among Indigenous students' ways of learning, cognitive styles,
gender, culturally responsive approaches, and their impacts on mathematical performance might be
considered for future research.
Acknowledgements: We are thankful for all students who dedicated their time and energy to take part in this
research. We also express our gratitude to the homeroom teachers and principals of the junior and senior high
schools, and the head of the Department of Primary Teacher education who supported and facilitated this
research. The meaningful input and feedback from two anonymous peer-reviewers and colleagues that
improved this article is also highly appreciated.
540
Funding: There was no financial support received by the authors from any organisation for the work submitted
for publication.
Disclosure Statement: The authors reported no potential conflict of interest.
References
Abdullah, R. Bin, Mamat, W. H. W., Amir Zal, W. A., & Ibrahim, A. M. Bin. (2013). Teaching and learning
problems of the Orang Asli Education: Students’ perspective. Asian Social Science.
https://doi.org/10.5539/ass.v9n12p118
Ademola, K. (2015). Predicting academic success of junior secondary school students in Mathematics through
cognitive style and problem solving technique. Journal of Education and Practice, 6(4), 72-79.
Arnup, J. L., Murrihy, C., Roodenburg, J., & McLean, L. A. (2013). Cognitive style and gender differences in
children’s mathematics achievement. Educational Studies, 39(3), 355–368.
https://doi.org/10.1080/03055698.2013.767184
Asiamah, N., Mensah, H. K., & Oteng-Abayie, E. F. (2022). Non-probabilistic sampling in quantitative clinical
research: A typology and highlights for students and early career researchers. International Journal of Applied
Research on Public Health Management, 7(1), 1–18. https://doi.org/10.4018/IJARPHM.290379
Azlina, N., Amin, S. M., & Lukito, A. (2018). Creativity of field-dependent and field-independent students in
posing mathematical problems. Journal of Physics: Conference Series, 947(1), 1-6. https://doi.org/10.1088/1742-
6596/947/1/012031
Blanton, E. L. (2004). The influence of students’ cognitive style on a standardized reading test administered in three
different formats. The University of Central Florida Orlando.
Burridge, N., Whalan, F., & Vaughan, K. (2012). Indigenous education: A learning journey for teachers, schools
and communities. In Indigenous Education: A Learning Journey for Teachers, Schools and Communities.
https://doi.org/10.1007/978-94-6091-888-9
Chen, S. (2016). Dawning of hope: Practice of and reflections on indigenous teacher education in Taiwan. Policy
Futures in Education, 14(7), 943-955. https://doi.org/10.1177/1478210316656312
Chrysostomou, M., Pitta-pantazi, D., Tsingi, C., Cleanthous, E., & Christou, C. (2013). Examining number sense
and algebraic reasoning through cognitive styles. Educational Studies in Mathematics, 83, 205-223.
https://doi.org/10.1007/s10649-012-9448-0
Chung, C., Huan, H., Tseng, K., Jung, R., & Chen, C. (2021). Transformation from traditional schools to
alternative schools: Curriculum leadership of the principals of Taiwanese aborigines. Asia Pacific Education
Review, 22(1), 53-66. https://doi.org/10.1007/s12564-020-09663-9
Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). SAGE
Publications, Ltd.
Creswell, J. W., & Guetterman, T. C. (2019). Educational research: Planning, conducting, and evaluating quantitative
and qualitative research (6th ed.). Pearson.
Davies, M., & Beaumont, T. J. (2011). Case studies: Research methods. In Study Skills for International
Postgraduates (pp. 1-7). The University of Melbourne. https://doi.org/10.1007/978-0-230-34553-9
Department of the Prime Minister and Cabinet. (2018). Closing the gap: Prime Minister’s Report 2018.
https://www.pmc.gov.au/sites/default/files/reports/closing-the-gap-2018/employment.html
Desmita, O. (2012). Psikologi Perkembangan Peserta Didik [Developmental Psychology of Students]. PT. Rosdakarya.
Dhir, R. K. (2015). Indigenous peoples in the world of work in Asia and the Pacific. International Labour Organization.
www.ilo.org/publns
541
Faradillah, A., Hadi, W., & Tsurayya, A. (2018). Pre-service mathematics teachers ’ reasoning ability in solving
mathematical non-routine problem according to cognitive style. Journal of Physics: Conference Series, 948(1), 1–
6.
Fyle, C. O. (2009). The effects of Field Dependent/Independent style awareness on learning strategies and outcomes in an
instructional hypermedia module. Florida State University.
Goodchild, S., & Grevholm, B. (2009). An exploratory study of mathematics test results: What is the gender
effect? International Journal of Science and Mathematics Education, 7, 161-182. https://doi.org/10.1007/s10763-
007-9114-7
Harris, S. (1984). Aboriginal learning styles and formal schooling. The Aboriginal Child at School, 12(4), 3–23.
Harrison, N. (2011). Teaching and learning in Aboriginal education (2nd ed.). Oxford University Press.
Hogg, L., & Volman, M. (2020). A synthesis of funds of identity research: Purposes, tools , pedagogical
approaches, and outcomes. Review of Educational Research, 90(6), 862–895.
https://doi.org/10.3102/0034654320964205
Hughes, P., & More, A. J. (1997). Aboriginal ways of learning learning styles. Annual Conference of the Australian
Association for Research in Education, 1-21.
Jacob, M. M. (2017). Indigenous studies speaks to American sociology: The need for individual and social
transformations of Indigenous education in the USA. Social Sciences, 7(1), 1–10.
https://doi.org/10.3390/socsci7010001
Jantan, R. B. (2014). Relationship between students’ cognitive style (Field-Dependent and Field-Independent
cognitive styles) with their mathematic achievement in primary school. International Journal of Humanities
Social Sciences and Education (IJHSSE), 1(10), 88–93. www.arcjournals.org
Kamaruddin, M. I. Bin, Bakar, Z. B. A., Surif, J. Bin, & Li, W. S. S. (2004). Relationship between cognitive styles, levels
of cognitive thinking and chemistry achievement among form four science students. Universiti Teknologi Malaysia.
Kemendikbud. (2019). Laporan Hasil Ujian Nasional — Papua [National Examination Results Report - Papua].
https://bit.ly/3fbZsYr
Kozhevnikov, M. (2007). Styles in the context of modern psychology: Toward an integrated framework of
cognitive style. Psychological Bulletin, 133(3), 464–-481.
Krakouer, J. (2015). Literature review relating to the current context and discourse on Indigenous cultural
awareness in the teaching space: Critical pedagogies and improving Indigenous learning outcomes through
cultural responsiveness. Australian Council for Educational Research, November, 1–21.
https://research.acer.edu.au/indigenous_education/43/
Lin, A. Y., Wu, C., Chen, Z., Ku, P., Lin, Y., Wu, C., Chen, Z., & Ku, P. (2020). How gender pairings affect
collaborative problem solving in social-learning context. International Forum of Educational Technology &
Society, 23(4), 30-44. https://www.jstor.org/stable/26981742
Lis, D. J., & Powers, J. E. (1979). Reliability and validity of the Group Embedded Figures Test for a grade school
sample. Perceptual and Motor Skills, 48, 660–662. https://doi.org/10.2466/pms.1979.48.2.660
Lowe, K. (2017). Walanbaa warramildanha: The impact of authentic Aboriginal community and school
engagement on teachers’ professional knowledge. Australian Educational Researcher, 44(1), 35–54.
https://doi.org/10.1007/s13384-017-0229-8
Macneil, R. D. (1980). The relationship of cognitive style and instructional style to the learning performance of
undergraduate students. Journal of Educational Research, 73(6), 354-359.
https://doi.org/10.1080/00220671.1980.10885265
Mills, A. J., Durepos, G., & Wiebe, E. (2010). Quantitative analysis in case study. In Encyclopedia of Case Study
Research (pp. 761-764). SAGE Publications, Inc. https://doi.org/10.4135/9781412957397.n279
542
Munroe, E. A., Borden, L. L., Murray Orr, A., Toney, D., & Meader, J. (2013). Decolonizing Aboriginal education
in the 21st century. McGill Journal of Education, 48(2), 317–337. https://doi.org/10.7202/1020974ar
Munroe, K. L. (2016). Impact of open approach on students’ understanding of mathematical concepts: A gender
comparison. Graduate School for International Development and Cooperation, 22(2), 85–96.
https://www.semanticscholar.org/paper/Impact-of-Open-Approach-on-Students’-Understanding-
Munroe/a8fcb07b59eb2f7be012929c2d6ed346ff616dbc
Nugraha, M. G., & Awalliyah, S. (2016). Analisis gaya kognitif field dependent dan field independent terhadap
penguasaan konsep fisika siswa kelas VII [Field dependent and field independent cognitive style analysis on
the mastery of physics concepts for class VII students]. Prosiding Seminar Nasional Fisika (E-Journal) SNF2016,
V(Oktober), 71–76. https://doi.org/10.21009/0305010312
Nur, A. S., & Nurvitasari, E. (2017). Geometry skill analysis in problem solving reviewed from the difference of
cognitive style students junior high school. Journal of Educational Science and Technology, 3(3), 205–210.
https://doi.org/10.26858/est.v3i3.4130
Nur, Andi Saparudin, & Palobo, M. (2018). Profil kemampuan pemecahan masalah matematika siswa ditinjau
dari perbedaan gaya kognitif dan gender [Profile of students' mathematical problem solving abilities in
terms of differences in cognitive style and gender]. Kreano, Jurnal Matematika Kreatif-Inovatif, 9(2), 139-148.
http://journal.unnes.ac.id/nju/index.php/kreano%0AProfil
Panek, P. E., Funk, L. G., & Nelson, P. K. (1980). Reliability and validity of the Group Embedded Figures Test
across the life span. Perceptual and Motor Skills, 50, 117 1-1 174. https://doi.org/10.2466/pms.1980.50.3c.1171
Pathuddin, Budayasa, I. K., & Lukito, A. (2019). Metacognitive activity of male students: Difference field
independent-dependent cognitive style. Journal of Physics: Conference Series, 1218(1), 1-10.
https://doi.org/10.1088/1742-6596/1218/1/012025
Perso, T. F. (2012). Cultural responsiveness and school education with particular focus on Australia’s first peoples: A
review & synthesis of the literature. Menzies School of Health Research, Centre for Child Development and
Education, Darwin Northern Territory.
Pewewardy, C. (2002). Learning styles of American Indian/Alaska Native students: A review of the literature
and implications for practice. Journal of American Indian Education, 41(3), 22–56.
https://www.jstor.org/stable/24398583
Pratiwi, D. D., Sujadi, I., & Pangadi. (2013). Kemampuan komunikasi matematis dalam pemecahan masalah
matematika sesuai dengan gaya kognitif pada siswa kelas IX SMP Negeri 1 Surakarta tahun pelajaran
2012/2013 [Mathematical communication skills in solving math problems in accordance with the students’
cognitive styles at year 9 SMP Negeri 1 Surakarta academic year 2012/2013]. Jurnal Pembelajaran Matematika,
1(5), 525–538. https://jurnal.fkip.uns.ac.id/index.php/s2math/article/view/3525/2459
Puspananda, D. R., & Suriyah, P. (2017). Analisis faktor pada group embbeded figures test untuk mengukur
gaya kognitif [Factor analysis on group embedded figures test to measure cognitive styles]. Seminar
Matematika Dan Pendidikan Matematika UNY 2017, 225–230.
http://repository.ikippgribojonegoro.ac.id/id/eprint/1384%0A
Rahman, A. (2013). Pengajuan masalah matematika ditinjau dari gaya kognitif dan kategori informasi [The
submission of mathematical problems in terms of cognitive style and information categories]. Jurnal Ilmu
Pendidikan, 19(1), 244–251. https://doi.org/10.17977/JIP.V19I2.4220
Rahmawati, A. (2004). Papuan ethno-political conflict: causes, contexts , and policy implication. Naval Postgraduate
School.
Rasmussen, C., Sherman, J., & Baydala, L. (2004). Learning patterns and education of Aboriginal children: A
review of the literature. The Canadian Journal of Native Studies, 24(2), 317-342.
http://www3.brandonu.ca/cjns/24.2/cjnsv24no2_pg317-342.pdf
543
Riding, R. J., & Sadler-Smith, E. (1997). Cognitive style and learning strategies: Some implications for training
design. International Journal of Training and Development, 1(3), 199–208. https://doi.org/10.1111/1468-
2419.00020
Rigney, L., Garrett, R., Curry, M., & Macgill, B. (2020). Culturally responsive pedagogy and mathematics through
creative and body- based learning: Urban Aboriginal schooling. https://doi.org/10.1177/0013124519896861
Riley, T., & Webster, A. (2016). Principals as Literacy Leaders with Indigenous Communities (PALLIC) building
relationships: One school’s quest to raise Indigenous learners’ literacy. Teaching Education, 27(2), 136-155.
https://doi.org/10.1080/10476210.2015.1049261
Rosita, N. T. (2018). Analysis of algebraic reasoning ability of cognitive style perspectives on field-dependent
field-independent and gender. Journal of Physics: Conference Series, 983(1). https://doi.org/10.1088/1742-
6596/983/1/012153
Saracho, O. N. (1988). Cognitive Styles and Young Children’s Learning. Early Child Development and Care, 30(1-4),
213–220. https://doi.org/10.1080/0300443880300118
Saville-Troike, M. (2012). Introducing second language acquisition. Cambridge University Press.
Scholar, T. M., & Abdurauf, R. A. (2015). Cognitive styles Field Dependence / Independence and scientific
achievement of male and female students of Zamfara State College of Education Maru, Nigeria. Journal of
Education and Practice, 6(10), 58–64. https://files.eric.ed.gov/fulltext/EJ1081653.pdf
Sianturi, M., Chiang, C. L., & Hurit, A. A. (2018). Impact of a place-based education curriculum on Indigenous
teacher and students. International Journal of Instruction, 11(1), 311–328.
https://doi.org/10.12973/iji.2018.11122a
Sianturi, M., Chiang, C. L., & Sumarsono, A. (2022). The preservation of the identity and culture of the
Indigenous Lanninese people of West Papua, Indonesia, through education (Preservación de la identidad y
la cultura del pueblo Lani, comunidad indígena de la provincia Indonesia de Papúa Occidental, a través de
la educación). Culture and Education, 34(2), 424–465. https://doi.org/10.1080/11356405.2022.2034287
Sianturi, M., & Suryani, D. R. (2017). Performansi matematika dasar siswa Papua calon guru sekolah dasar [The
basic mathematical performance of Indigenous students in higher education]. In M. Palobo, A. K. Hidayat,
O. Dadi, & N. R. Marwa (Eds.), Inovasi Pendidikan dan Pembelajaran dalam Rangka Percepatan Pembangunan di
Daerah Pinggiran [Educational and Learning Innovation in the Context of Accelerating Development in Outlying
Areas] (pp. 178–190). FKIP Musamus University.
Sianturi, M., Suliantin, R., & Sianturi, I. A. J. (2022). Culturally relevant teaching: A case study on Indigenous
students’ cognitive styles and difficulties in processing information. [Manuscript submitted for publication].
Singer, F. M., Vouca, C., & Pelczer, I. (2017). Cognitive styles in posing geometry problems: Implications for
assessment of mathematical creativity. ZDM, 49(1), 37–52. https://doi.org/10.1007/s11858-016-0820-x
Stake, R. E. (1994). Case studies. In N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of Qualitative Research (pp. 236-
247). Sage Publications.
Suranata, K., Rangka, I. B., Ifdil, I., Ardi, Z., Dharsana, I. K., Suarni, N. K., & Gading, I. K. (2019). Exploring of
mathematics learning difficulties for students based on heterogeneous group and cognitive style in
elementary school. Journal of Physics: Conference Series, 1157, 1–6. https://doi.org/10.1088/1742-
6596/1157/3/032091
Thompson, C. (1999). If you could just provide me with a sample: Examining sampling in qualitative and
quantitative research papers. Evidence-Based Nursing, 2(3), 68–70.
Umaru, Y. (2013). Influence of reflective and impulsive cognitive styles on students’ achievement in mathematics
among senior secondary school students. Ife PsychologIA, 21(2), 123–128.
544
Uprichard, E. (2013). Sampling: Bridging probability and non-probability designs. International Journal of Social
Research Methodology, 16(1), 1–11. https://doi.org/10.1080/13645579.2011.633391
Vega-Vaca, M. L., & Hederich-Martínez, C. (2015). The impact of a cooperative learning program on the
academic achievement in mathematics and language in fourth grade students and its relation to cognitive
style. New Approaches in Educational Research, 4(2), 84–90. https://doi.org/10.7821/naer.2015.7.124
Vehovar, V., Toepoel, V., & Steinmetz, S. (2016). Non-probability sampling. In C. Wolf, D. Joye, T. W. Smith, &
Y. Fu (Eds.), The SAGE Handbook of Survey Methodology (pp. 329–345). SAGE.
Volkova, E. V., & Rusalov, V. M. (2016). Cognitive styles and personality. Personality and Individual Differences,
99, 266-271. https://doi.org/10.1016/j.paid.2016.04.097
Witkin, H. A., Moore, C. A., Goodenough, D. R., & Cox, P. (1977). Field dependent and field-independent
cognitive styles and their education implications. Review of Educational Research, 47(1), 1-64.
https://doi.org/10.3102/00346543047001001
Witkin, H., Oltman, P. K., & Raskin, E. (1971). Manual Embedded Figures Test, Children Embedded Figures Test, Grup
Embedded Figures Test. Consulting Psychology Press, Inc.
Yunkaporta, T. K. (2009). Aboriginal pedagogies at the cultural interface [James Cook University].
http://eprints.jcu.edu.au/10974/%0A
Zainal, Z. (2007). Case study as a research method. Jurnal Kemanusiaan, 9(June), 1-6.
https://core.ac.uk/download/pdf/11784113.pdf
Authors:
Murni Sianturi is an Indigenous researcher with 7 years’ experience working with Indigenous communities. She
is currently a Scientia PhD Candidate at the University of New South Wales. She has more intentionally focused
on issues influencing Indigenous West Papuan students, with a specific interest in mathematics learning,
identity, and culture. Specifically, building on her experiences as a mathematics teacher at all school levels and a
local university lecturer, her work is concerned with schooling practices and Indigenous students’ identities,
impacting their experiences, culture, identities, and learning achievements. Her current research project focuses
on using technology to facilitate partnerships between schools and Indigenous West Papuan parents.
(http://orcid.org/0000-0002-2898-7084). Email: m.sianturi@unsw.edu.au
Riska Sulantin is a mathematics teacher at Muhammadiah Junior High School, Merauke, Papua. Born and
raised in the remote area of West Papua, Riska has an interest in educating children living in rural areas. In
addition to her career as a teacher, she is also actively involved in community activities. Email:
rsuliantin@gmail.com
Hariani Fitrianti is a lecturer at Musamus University, Papua. Her research interests aim to explore students’
mathematical learning and statistical analysis in educational research. Email: harianifitrianti@unmus.ac.id
Cite this paper as: Sianturi, M., Sulantin, R., & Fitrianti, H. (2022). Relationships between cognitive styles and
Indigenous students’ mathematics academic outcomes. Journal of Learning for Development, 9(3), 528-544.