Assessing Attitudes Toward Science, Technology,
Engineering, and Mathematics (STEM) for Enhancing Creativity in Secondary
Education
Evaluación de actitudes hacia la ciencia,
tecnología, ingeniería y matemáticas (STEM) para fomentar la creatividad en la
educación secundaria
Dr. Mujib
Mujib. Associate
Professor. Universitas Islam Negeri Raden
Intan Lampung. Indonesia
Dr. Mardiyah
Mardiyah. Associate Professor. Universitas
Islam Negeri Raden Intan Lampung. Indonesia
Recibido: 2024/08/24
Revisado 2024/09/25
Aceptado:
:2024/11/02 Online First: 2024/11/20 Publicado: 2025/01/07
Cómo citar este artículo:
Mujib,
M., & Mardiyah, M. (2025). Evaluación de actitudes hacia la ciencia,
tecnología, ingeniería y matemáticas (STEM) para fomentar la creatividad en la
educación secundaria [Assessing Attitudes Toward Science, Technology,
Engineering, and Mathematics (STEM) for Enhancing Creativity in Secondary
Education]. Pixel-Bit. Revista De Medios Y Educación, 72, 39–69. https://doi.org/10.12795/pixelbit.109760
ABSTRACT
RESUMEN
Las actitudes de los
estudiantes hacia asignaturas como ciencia, tecnología, ingeniería y
matemáticas (STEM) desempeñan un papel crucial en el proceso de aprendizaje del
siglo XXI. Aumentar el número de estudiantes que eligen carreras en STEM se ha
reconocido como importante. En consecuencia, mejorar el aprendizaje y la
participación de los estudiantes en las asignaturas STEM, así como fomentar
actitudes positivas hacia STEM, se ha convertido en un objetivo principal para
la educación STEM en K-12. Sin embargo, medir tales actitudes en un contexto de
aprendizaje sigue siendo un desafío significativo. Esta investigación ha tenido
como objetivo desarrollar una herramienta de evaluación completa y válida para
evaluar las actitudes de los estudiantes hacia STEM en un contexto de
aprendizaje, con el fin de mejorar su creatividad. La muestra para esta
investigación ha consistido en 311 estudiantes de secundaria con una media de
edad de 12,83 ± 1,04 años. La validez de la estructura de cuatro factores del
modelo ha sido evaluada utilizando un análisis factorial confirmatorio. Los
valores de fiabilidad para los cuatro factores han oscilado entre .73 y .94 con
Alfa de Cronbach, mientras que los de fiabilidad compuesta han oscilado entre
.97 y .97. La relación entre las variables en las actitudes hacia el
instrumento STEM ha identificado varios coeficientes de ruta y tamaños de
efecto, indicando fuertes correlaciones entre las variables de actitud STEM. El
análisis ha revelado diferencias significativas según el nivel de grado,
mostrando que los estudiantes de noveno grado han presentado un rendimiento
mejor o al menos competitivo en la mayoría de las disciplinas. Se ha encontrado
que este cuestionario es un instrumento viable para evaluar las actitudes STEM
de los estudiantes de secundaria. Estos hallazgos tienen importantes
implicaciones para las estrategias de educación STEM, enfatizando la necesidad
de enfoques sostenidos y enfocados en experiencias de aprendizaje profundo para
todos los estudiantes, independientemente del género.
KEYWORDS· PALABRAS CLAVES
Student attitudes; Creativity; Secondary education;
Attitude assessment; STEM education; Self-efficacy; Confirmatory factor
analysis
Actitudes estudiantiles; Creatividad; Educación
secundaria; Evaluación de actitudes; Educación STEM; Autoeficacia; Análisis
factorial confirmatorio;
1. Introduction
Students' attitudes towards subjects such as science,
engineering, and mathematics (STEM) play an important role in the 21st century
learning process. The importance of increasing the number of Indonesian
students pursuing careers in STEM has been widely recognised and documented (Rusmana et al.,
2021). Employment
projections for occupation groups from 2020 to 2030 indicate significant growth
in several STEM fields. Data scientists and mathematical science occupations
are expected to increase by 31.4%, statisticians by 35.4%, and physical
therapist assistants by 35.4%. In engineering, solar photovoltaic installers
are projected to grow by 52.1%, and wind turbine service technicians by 68.2% (Dubina et al., 2021). Therefore,
enhancing students' learning and engagement in STEM subjects, as well as
fostering positive attitudes toward STEM, has become a primary objective for
K-12 STEM education in Indonesia.
Creativity is also a crucial component of STEM
education, as it encourages innovative thinking and problem-solving skills
essential for tackling real-world challenges (Siew & Ambo,
2018). Previous research
has shown that positive attitudes towards these subjects can increase learning
motivation, student engagement in the learning process, and overall academic
achievement (Sölpük, 2017). Moreover,
incorporating design thinking into STEM education has enhanced children's
creativity and problem-solving abilities (Yalçın &
Erden, 2021). However, measuring
such attitudes in a learning context remains a significant challenge.
Therefore, it is important to develop effective assessment tools to assess
students' attitudes towards STEM to make the classroom learning process more
efficient and effective.
The development of an attitude towards STEM instrument
is important as it allows for a more holistic evaluation of students' attitudes
towards science, technology, engineering, and mathematics. With a good
evaluation tool in place, educators can understand students' preferences,
inclinations, and perceptions toward these subjects. In addition, the
development of an instrument for attitudes towards STEM is important to track
changes in students' attitudes over time. This allows for measuring the
effectiveness of learning programmes that focus on STEM concepts.
Previous research has highlighted various aspects of
students' attitudes towards STEM. Some of these aspects include interest in
learning, self-confidence, perceived value of the subject, as well as the
desire to be active in learning (Edwards et al.,
2023; Kong & Mohd Matore, 2022; Macun & Cemalettin, 2022; Temel, 2023). It is important to
be able to measure exactly these aspects when designing a comprehensive
assessment tool. In addition, an effective assessment tool should also be able
to provide valuable information to educators in understanding the level of
student attitudes toward the subject. Previous research emphasises that these
types of assessment tools should provide measurable, valid and reliable
information for teachers in adjusting their teaching methods (Guàrdia et al.,
2023).
However, designing an assessment tool to measure
students' attitudes towards STEM is not an easy task. Recently, various
instruments have been created to measure student attitudes toward the four STEM
fields collectively. However, these instruments lack items that address
integrated STEM education, which emphasises the fusion of all four subject
areas (e.g., (Antonietti et al.,
2023; Benek & Akcay, 2019; Wahono & Chang, 2019; Wicaksono & Korom,
2023)). For example, Antonietti et al.
(2023) developed the ICAP
Technology scale to measure how technology is integrated into learning
activities in the German context. The results showed that the four developed
scales were reliable, valid, and had a positive relationship on each scale.
Furthermore, Wicaksono & Korom
(2023) developed an
instrument to measure attitudes toward science with a sample of students in
higher education. The results showed that the instrument has good psychometric
properties and can be relied upon, the value of good fit based on the Rasch
model can also be relied upon. Although the study was in the context of
Indonesia, the sample was in the context of higher education. Research provides
insight into the development of evaluation measurement tools, but is limited to
the scope of the sample, such as elementary schools, higher education, and
western contexts. This provides a good opportunity to develop STEM evaluation
tools in the context of secondary school students.
Therefore, this research aims to develop a
comprehensive and valid assessment tool to evaluate students' attitudes toward
STEM in a learning context on enhancing students creativity. The research
addresses the following questions:
·
Are the instruments to measure attitudes toward STEM
reliable and valid?
·
What is the relationship between variables in
attitudes toward the STEM instrument?
·
Are there differences in students' attitudes toward
STEM based on sample backgrounds, such as gender and grade?
1.1.STEM education
The development of the STEM attitude questionnaire is
based on learning theory and cognitive psychology, involving the concept of
self-efficacy, as explained by Bandura (1969). The theory of
self-efficacy posits that an individual's belief in their own abilities
influences their behaviour, motivation, and achievement. In the context of
attitudes toward STEM, self-efficacy plays a crucial role in shaping students'
perceptions of their ability to master STEM subjects (Luo et al., 2021). When developing the
STEM attitude instrument, the concept of self-efficacy becomes relevant because
it affects how confident students feel about tackling STEM lessons (DeCoito &
Myszkal, 2018). Students with high
self-efficacy in STEM tend to have more positive attitudes towards these
subjects (Blotnicky et al.,
2018), feel more capable
of mastering the material (Cervone et al.,
2020), and are more
motivated to learn (Kryshko et al.,
2022). Building upon the
foundational concept of self-efficacy in STEM attitude assessment, it's crucial
to recognize its multifaceted nature and far-reaching implications.
Self-efficacy in STEM is often domain-specific, varying across disciplines (Thompson et al.,
2024) and significantly
influencing students' career aspirations (Rosenzweig &
Chen, 2023). It intersects with
important factors such as gender, diversity, and cultural background (Ogodo, 2023; Sparks
et al., 2023), necessitating a
nuanced approach in questionnaire design. The concept is closely tied to growth
mindset, persistence, and resilience in STEM learning (Höhne et al., 2024), as well as being
shaped by past experiences and social support systems (Akiri & Dori,
2022).
Bandura (1997) theory of
self-efficacy, also provides insight into how students' perceptions of success
and failure in the context of STEM can shape their attitudes toward these
subjects (Van Aalderen-Smeets
& Walma Van Der Molen, 2018). If students feel
capable of overcoming difficulties and challenges in STEM learning (Wilson, 2021), they are likely to
have a more positive attitude toward these subjects (X. Wang, 2013). These theories view
attitudes as mental constructs that influence an individual's perception and
behaviour. The theoretical foundation includes learning concepts that emphasise
the interaction between environmental factors and personality in shaping one's
attitude toward STEM.
Students' attitudes toward STEM are a primary focus in
the development of this assessment tool. According to research by Osborne dkk. (2003), attitudes encompass
aspects such as positive or negative feelings toward STEM, perceived value of
STEM, and interest in activities related to this field. Their research shows
that a positive attitude toward STEM is closely related to intrinsic motivation
in learning and student participation in the learning process.
Many studies link positive attitudes toward STEM with
academic success and career interest in science and technology fields (Durakovic, 2022;
Göktepe Körpeoğlu & Göktepe Yıldız, 2023; Óturai et al.,
2023). The STEM attitude
questionnaire allows for the identification of key variables that influence
students' interest in these sciences. It also helps researchers develop more
effective and engaging learning strategies for students. Additionally, the STEM
attitude questionnaire is an important instrument to evaluate the effectiveness
of STEM curricula. By obtaining information about students' attitudes toward
these subjects, educators can adjust teaching methods and curriculum content to
be more relevant and engaging for students.
Previous research shows that students with positive
attitudes towards STEM tend to have a higher interest in pursuing careers in
these fields (Chiu & Li, 2023;
Ozulku & Kloser, 2023; Xu & Lastrapes, 2022), motivation (Dökme et al., 2022), emotional (Koul et al., 2023). Therefore, the STEM
attitude questionnaire helps identify factors that encourage students' interest
in continuing their education in STEM fields at higher levels.
1.2. STEM innovation in
secondary education
STEM learning at the secondary education level
requires a holistic and integrated approach to teaching STEM concepts to
students (English, 2016). According to Asigigan & Samur
(2021), an effective STEM
learning approach should promote problem solving, critical thinking, and the
application of theoretical concepts in real world contexts. This helps develop
students' skills in creative thinking (Suherman &
Vidákovich, 2024), collaboration (Chen et al., 2019), and solving complex
problems (Tan et al., 2023), all of which
contribute to positive attitudes toward STEM (Steinberg &
Diekman, 2017). Furthermore, the
evaluation of STEM learning at the secondary education level requires effective
tools that can assess students' comprehension and application of STEM concepts (Saxton et al., 2014). These tools should
not only measure academic achievement but also gauge students' abilities to
innovate, analyse data, and apply scientific principles in practical settings.
Such assessments are crucial for ensuring that students are prepared to meet the
challenges of today's technological and scientific advancements.
Motivation to learn is a crucial factor in measuring
student responses to STEM education. According to Eccles & Wigfield
(2002), learning motivation
encompasses students' intrinsic and extrinsic desires to achieve academic goals
and personal development in the context of STEM learning. Intrinsic motivation
is closely related to students' interest in STEM fields, whereas extrinsic
motivation can be influenced by external factors such as rewards or praise from
others. Incorporating motivational strategies in STEM education can foster a
more positive attitude among students towards these subjects. Additionally, the
integration of STEM disciplines, supported by technology and mathematics, can
enhance student achievement across all scientific fields (Farida et al., 2024;
Komarudin & Suherman, 2024; Nguyen et al., 2020).
Recent research has highlighted the importance of
technological integration and innovative pedagogical approaches in enhancing
STEM education at the secondary level. The use of augmented reality (AR) and
virtual reality (VR) technologies in STEM classrooms has shown promising
results in increasing student engagement and conceptual understanding (T. Lee et al., 2022). Moreover,
project-based learning (PBL) approaches in STEM education have been found to
significantly improve students' problem-solving skills and attitudes towards
STEM subjects (AlAli, 2024). The incorporation
of computational thinking into STEM curricula has also gained traction, with
studies showing its positive impact on students' analytical skills and
future-ready competencies (H.-Y. Lee et al.,
2023). Additionally, the
development of STEM identity among secondary school students has been
identified as a crucial factor in their long-term engagement with STEM fields,
emphasizing the need for culturally responsive STEM education that resonates
with diverse student populations (Xie & Ferguson,
2024).
1.3. Assessment Tools for
Measuring STEM
Over the past five decades, the development of
measurement tools to evaluate STEM education has evolved significantly (Okulu &
Oguz-Unver, 2021). The need to assess
various aspects of STEM education has led to the creation of numerous
assessment tools, each aiming to measure different dimensions such as
knowledge, skills, attitudes, and self-efficacy among students.
The early efforts to develop STEM assessment tools
focused primarily on evaluating the outcomes of cognitive learning. Traditional
tests and quizzes were the primary methods used to measure students'
understanding of scientific concepts and principles. During the 1970s and
1980s, standardised tests such as the SAT and ACT included sections to assess
mathematical and scientific reasoning, providing a broad but limited measure of
STEM education outcomes (Clarke et al., 2000). In the late
twentieth and early twentieth centuries, the focus shifted towards creating
more integrated and comprehensive assessment tools that could evaluate multiple
dimensions of STEM education simultaneously. Instruments such as the Student
Attitudes toward STEM Survey (S-STEM) (Unfried et al.,
2015), and the STEM Semantics Survey (Knezek &
Christensen, 2008) were developed to
provide a more holistic view of students' experiences and attitudes toward STEM
subjects. The S-STEM survey, developed by Unfried et al. (2015), included scales for
science, technology, engineering, and mathematics, as well as skills for the
21st century. This tool was designed to measure students' self-efficacy,
interest, and perceived value of STEM subjects. The STEM Semantics Survey (Knezek &
Christensen, 2008) also sought to
assess students' attitudes toward STEM by assessing their feelings and beliefs
about the subjects. Furthermore, research conducted by Suprapto (2016) focused on
developing attitudes toward STEM. However, these instruments were specifically
designed to measure attitudes toward individual STEM fields.
Research by Wan et al. (2022) developed and
validated a multi-dimensional scale to measure students' experiences in STEM
project-based learning (PBL). The scale includes four key dimensions:
engagement in learning, collaboration, creativity, and real-world relevance,
with strong reliability and validity (Cronbach’s alpha .75 to .89). While the
scale is a valuable tool for assessing STEM PBL, its limitations include a
narrow sample size and geographic scope, potentially affecting
generalizability. It also doesn't account for external factors like teacher
support or curriculum variations, suggesting a need for broader studies to
address these gaps. At the same time Wicaksono & Korom
(2023) developed and
validated a questionnaire to assess attitudes towards science among science
teacher candidates and engineering students in Indonesia. The questionnaire
focused on dimensions such as interest in science, perceived relevance, and
self-efficacy, showing high internal consistency (Cronbach's alpha .80 to .92).
However, the study's limitations include a focus on specific science student
populations in Indonesia, which may limit its applicability to other contexts
or fields. Additionally, other subjects (i.e., math, engineering, technology)
that could influence attitudes toward student were not fully explored,
indicating a need for further research. In other words, S.-P. Tsai et al.
(2023) created and
initially validated a scale aimed at assessing middle school students'
attitudes toward STEM learning. Results demonstrated strong internal
consistency and confirmed the four-factor structure through factor analysis.
However, limitations include the narrow focus on a specific group of middle
school students, potentially limiting the generalizability of the results.
Additional research and validation in diverse cultural settings and age groups
are needed to expand its use.
Additionally, the integration of advanced psychometric
techniques and statistical methods has improved the reliability and validity of
STEM assessment tools. Item response theory (IRT) and factor analysis are
commonly used to refine and validate these instruments, ensuring that they
accurately measure the intended constructs. Given the limitations of existing
STEM attitude measurement tools, particularly in terms of generalizability and
applicability across different cultural and educational contexts, there is a
clear need for instruments specifically designed for the Indonesian context.
Additionally, considering the importance of addressing local educational needs
and the evolving STEM landscape, developing a contextually relevant assessment
tool would provide educators and policymakers with valuable insights to enhance
STEM engagement and outcomes in Indonesia.
2. Methodhology
2.1. Participants
The sample for this research consisted of 311
secondary school students aged 11 to 14 years (Mage = 12.83; SD
= 1.04). Most of the participants were women (80.1%). The students were
randomly selected from 19 different secondary schools in Bandar Lampung,
Indonesia, and completed an online questionnaire that took an average of 15
minutes to complete. The study was approved by the Institutional Review Board
of Universitas Negeri Raden Intan Lampung, Indonesia, adhering to the ethical
guidelines set by the institution. Detailed demographic information about the
participants is presented in Table 1.
Table 1
The characteristics of the participants
2.1. Instruments
In this investigation, the instruments developed by Unfried et al. (2015) were examined
through four scales: science (8 items), technology/engineering (9 items),
mathematics (4 items), and skills of the 21st century (11 items). Students were
asked to indicate their agreement with each statement using a 5-point Likert
scale ranging from 1 (strongly disagree) to 5 (strongly agree). Additionally,
the students provided demographic information including age, gender, grade,
school location, and place of residence.
2.2. Procedure
The original questionnaire was initially created in
English. Since the students in our sample spoke Indonesian primarily as their
native language, with English their second language, it was necessary to
translate the questionnaire into Indonesian. This ensured that all participants
could understand the content, thus improving the validity of the instrument
through accurate translation. The translation was performed by a team
consisting of a Ph.D. holder from the UK, a Ph.D. candidate from Ireland, and a
Ph.D. candidate from Japan, all of whom had extensive expertise in science,
mathematics, engineering, and linguistics. The newly translated versions were
meticulously reviewed, compared, and critiqued. Minor adjustments in word
choice were made to clarify any ambiguous points. Subsequently, a trial version
of the Indonesian questionnaire was emailed to field experts for review. These
experts assessed the validity of the questions and the general content,
suggesting specific words and phrases to ensure clarity and comprehension.
2.3. Data Analysis
In data analysis, the researchers will employ SPSS
version 29, Winstep version 4.0, and R software. SPSS will be used to examine
descriptive statistics such as mean, median, and standard deviation, providing
an overview of the data distribution. Confirmatory factor analysis (CFA) will
be performed to assess the fit of the model within the measurement model (Jomnonkwao &
Ratanavaraha, 2016). CFA follows fit
indices to evaluate model adequacy, including the Comparative Fit Index (CFI),
Tucker–Lewis Index (TLI), Goodness-of-Fit Index (GFI), Root Mean Square Error
of Approximation (RMSEA), Standardised Root Mean Square Residual (SRMR) and the
Kaiser-Meyer-Olkin (KMO) index (Kline, 2015). The cutoff values
for each parameter are CFI > 0.90; TLI > 0.90; RMSEA < 0.08; and SRMR
< 0.06 (Boone et al., 2014;
Hu & Bentler, 1999). Additionally, a
principal component analysis was conducted, and items with values lower than
.30 were excluded from further consideration. Several items that fell below
this threshold were removed from the database. This aligns with the recommended
threshold value of .40 suggested by experts in social science research (Straub et al., 2004).
Furthermore, chi-square statistics, including degrees
of freedom and p-values, will be mathematically represented. According to Kline (2015), the statistics of
the chi-square test are highly sensitive to sample size, with statistically
significant chi-square values found more frequently in larger samples. The
study will also perform reliability and validity analyses of the instrument.
The construct reliability will be assessed using Cronbach's alpha, composite
reliability (rho_c), and average variance extracted (AVE). Discriminant
validity will be evaluated using the HTMT0.90 ratio of correlations
among four factors. Furthermore, the R software will be used to analyse the
performance of the respondents regarding the STEM attitude instrument,
employing pirate plot violine (Phillips, 2017).
3. Results
3.1. CFA
CFA was used to confirm the latent factors in the
measurement model, indicating that all the latent factors performed well and
achieved the GoF (Goodness of Fit) index. Following the recommendations of Chuah et al. (2016), we conducted an
analysis for construct reliability and discriminant validity. To assess the fit
of the model, we created a CFA diagram in the measurement model using the
pattern matrix builder plugin from Gaskin & Lim
(2016). In this structural
model, the one-headed arrows indicate the hypothesised one-way direction in the
structured model, while the two-headed arrows indicate correlations between two
variables in the structured model. Latent variables (e.g., questionnaire factors)
are represented by ovals, while observed variables (e.g., questionnaire items)
are represented by rectangles. The small circles on the graph represent the
measurement errors associated with each observed indicator.
In this study, we found that the loading factors did
not meet the threshold criteria (Straub et al., 2004). Therefore, we
removed four items with loading factor values below 0.30. These included the
statements MA1 ((-) Mathematics is my worst subject), MA3 ((-) Mathematics is
difficult for me), MA4 (I am the type of student who performs well in
mathematics), and MA5 ((-) I can handle most subjects well, but I cannot do
mathematics well). We analyse the report using modification indices and
covariances with items in the same factor that had values greater than .30 to
achieve outstanding results and improve the fit of the model in the CFA. A more
accurate model fit was obtained (χ^2 = 1094.076; χ^2/df = 457; p
< .001; CFI =0.906; TLI = .898; RMSEA = .067; and SRMR = .055). The CFA
diagram and modification indices are shown in Figure 1, and the factor loading
values are shown in Table 2.
Table 2
Factor loading of
the items
|
No. Item |
Items |
21st Century |
Technology/ Engineering |
Math |
Science |
|
SK1 |
I am confident that
I can help others accomplish a goal. |
.78 |
|
|
|
|
SK2 |
I am confident
I can encourage others to do their best |
.84 |
|
|
|
|
SK3 |
I am confident
I can produce high quality work |
.85 |
|
|
|
|
SK4 |
I am confident I
can respect the differences of my peers |
.85 |
|
|
|
|
SK5 |
I am confident
I can help my peers |
.86 |
|
|
|
|
SK6 |
I am confident
I can include others’ perspectives when making decisions |
.79 |
|
|
|
|
SK7 |
I am confident I
can make changes when things do not go as planned |
.77 |
|
|
|
|
SK8 |
I am confident
I can set my own learning goals |
.83 |
|
|
|
|
SK9 |
I am confident
I can manage my time wisely when working on my own |
.86 |
|
|
|
|
SK10 |
When I have many
assignments, I can choose which ones need to be done first |
.82 |
|
|
|
|
SK11 |
I am confident
I can work well with students from different background |
.82 |
|
|
|
|
EN1 |
I like to
imagine creating new products |
.74 |
|||
|
EN2 |
If I learn
engineering, then I can improve things that people use every day |
.74 |
|||
|
EN3 |
I am good at
building and fixing things |
.70 |
|||
|
EN4 |
I am interested
in what makes machines work |
.60 |
|||
|
EN5 |
Designing
products or structures will be important for my future work |
.77 |
|||
|
EN6 |
I am curious
about how electronics work |
.72 |
|||
|
EN7 |
I would like to
use creativity and innovation in my future work |
.80 |
|||
|
EN8 |
Knowing how to
use math and science together will allow me to invent useful things |
.73 |
|||
|
EN9 |
I believe I can
be successful in a career in engineering |
.69 |
|||
|
MA2 |
I would
consider choosing a career that uses math |
.39 |
|||
|
MA6 |
I am sure I
could do advanced work in math |
.70 |
|||
|
MA7 |
can get good
grades in math |
.78 |
|||
|
MA8 |
I am good at
math |
.56 |
|||
|
SC1 |
I am sure of
myself when I do science |
.68 |
|||
|
SC2 |
I would
consider a career in science |
.69 |
|||
|
SC3 |
I expect to use
science when I get out of school |
.67 |
|||
|
SC4 |
Knowing science
will help me earn a living |
.69 |
|||
|
SC5 |
I will need
science for my future work |
.66 |
|||
|
SC6 |
I know I can do
well in science Science will be important to me in my life’s work |
.79 |
|||
|
SC7 |
(-) I can
handle most subjects well, but I cannot do a good job with science |
.31 |
|||
|
SC8 |
I am sure I
could do advanced work in science |
.77 |
3.2. Construct reliability
We utilized construct reliability to assess the
internal consistency and convergent validity of the items. The results of the
construct reliability are detailed in Table 3.
Table 3
Construct
reliability of the scales
|
Cronbach's alpha |
Composite reliability (rho_c) |
(AVE) |
|
|
21st-Century |
.94 |
.97 |
.78 |
|
Technology/Engineering |
.91 |
.94 |
.64 |
|
Math |
.73 |
.87 |
.64 |
|
Science |
.89 |
.94 |
.68 |
Table 3 presents the construction reliability of the
measured items using Cronbach's alpha, composite reliability, and AVE across
four main domains: 21st century skills, engineering, mathematics, and science.
The analysis results indicate that the 21st century skills domain has a
Cronbach alpha of .94, a composite reliability of .97, and an AVE of .78. The
engineering domain shows a Cronbach alpha of .91, a composite reliability of
.94, and an AVE of .64. The mathematics domain has a Cronbach alpha of .73, a
composite reliability of .87, and an AVE of .64. Finally, the science domain
demonstrates a Cronbach alpha of .89, a composite reliability of .94, and an
AVE of .68. Based on these results, it can be concluded that all domains
possess adequate construct reliability, with Cronbach's alpha values above .70,
indicating good internal consistency, and composite reliability and AVE values
that demonstrate sufficient convergent validity for each construct.
Figure 1
Model CFA
Validity
This study used discriminant validity. The
discriminant validity test was performed to assess whether the latent factors
are distinct from each other at the empirical level, as shown in Table 4.
Table 4
HTMT0.90 Ratio of the Four Correlations Factors
|
21st Century |
Technology/Engineering |
Math |
Science |
|
|
21st Century |
||||
|
Technology/Engineering |
.81 |
|||
|
Math |
.64 |
.67 |
||
|
Science |
.57 |
.73 |
.79 |
In this study, the results in Table 4 indicated
acceptable discriminant validity among the factors. The HTMT ratios between
21st century skills and technology / engineering, maths, and science were .81,
.64, and .57, respectively. Similarly, Technology/Engineering showed HTMT
ratios of .67 with Maths and .73 with Science. Lastly, the HTMT ratio between
mathematics and science was .79. These values are below the threshold of .90,
demonstrating that each factor is distinct and not highly correlated with the others,
thus confirming the discriminant validity of the constructs. This analysis
ensures that the measurement model accurately captures the unique aspects of
each factor, enhancing the credibility and reliability of the study's findings.
3.3. The relationship between
variables in attitudes toward the STEM instrument
The relationships between variables in the STEM
attitudes instrument can be seen in Figure 1. The values of the path
coefficient (
) between variables
vary. The coefficient between attitudes toward mathematics and science is = 0.735. The coefficients between attitudes
toward mathematics and engineering / technology and mathematics and skills of
the 21st century are = 0.655 and = 0.828, respectively. Furthermore, the
coefficient between attitudes toward science and engineering/technology is = 0.722, and between science and skills of the
21st century is = 0.561. Lastly, the coefficient between
attitudes towards engineering / technology and skills of the 21st century is = 0.826.
To analyse the scale scores of all components of the
STEM attitudes questionnaire, we compared the mean scores of the four latent
factors using an independent sample t-test. Effect sizes were also determined
according to Cohen's d. The effect size criteria include the following
categories: negligible (0-0.19), small (0.2-0.49), medium (0.5-0.79), and large
(> 0.8) (Cohen, 1992). This study found
that the variables mathematics (t(309) = 0.408, p > 0.05, Cohen's d =
0.49), science (t(309) = -0.869, p > 0.05, Cohen's d = 0.66),
engineering (t(309) = 0.970, p > 0.05, Cohen's d = 0.77), and
21st-century skills (t(309) = 1.026, p > 0.05, Cohen's d = 0.93)
demonstrated varying degrees of effect sizes.
3.4. Students' differences in
attitudes toward STEM due to gender and grade level
This study examined how the students' abilities to
complete the STEM attitudes questionnaire varied based on background factors,
specifically gender and grade level, as shown in Figure 2.
Figure 2
Pirate plot based
on gender and grade for all variables
Regarding gender, we found that Wilks' Lambda was
greater than 0.05, indicating that there were no significant differences among
the four variables. For the maths variable, men had a mean score of 3.196 with
an SD of 0.06, while women had a mean score of 3.16 with an SD of 0.03. In the
science variable, men had a mean score of 3.14 with an SD of 0.08, compared to
women who had a mean score of 3.18 with an SD of 0.04. For the engineering
variable, males had a mean score of 3.35 with an SD of 0.09, while females had
a mean score of 3.30 with an SD of 0.04. Lastly, for the skills variable of the
21st century, men had a mean score of 3.57 with an SD of 0.11, while women had
a mean score of 3.44 with an SD of 0.05.
In terms of subjects, we found that Wilks' Lambda was
0.492. For the mathematics variable, the F value was 11.350,7; p
< 0.001. For the science variable, the F value was 6462,9; p <
0.001. The engineering/technology variable had an F value of 5228,6; p
< 0.001, and the 21st century skills variable had an F value of
3968,2; p < 0.001.
The study also compared the descriptive statistics for
grades 7, 8, and 9 for all observed variables: Mathematics, Science, Technology
/ Engineering and 21st century skills. For the Mathematics variable, the
average score for grade 7 was 3.16 with an SD of 0.46, grade 8 had an average
score of 3.20 with an SD of 0.50, and grade 9 had an average score of 3.14 with
an SD of 0.54. In the science variable, grade 7 had an average score of 3.14
with an SD of 0.64, grade 8 had an average score of 3.16 with an SD of 0.68,
and grade 9 had an average score of 3.287 with an SD of 0.70. For the
Technology/Engineering variable, grade 7 had an average score of 3.28 with an
SD of 0.78, grade 8 had an average score of 3.31 with an SD of 0.74, and grade
9 had an average score of 3.38 with an SD of 0.79. Lastly, for the 21st century
skills variable, grade 7 had an average score of 3.39 with an SD of 0.81, grade
8 had an average score of 3.51 with an SD of 0.95, and grade 9 had an average
score of 3.57 with an SD of 0.84.
This analysis indicates variations in mean scores and
standard deviations between grades 7, 8, and 9 in each discipline, with
differences reflecting consistent patterns or higher variability depending on
the discipline. Overall, the analysis suggests that grade 9 students generally
performed better or at least compared to most disciplines.
4. Dicussions
This study developed and validated a STEM attitude
questionnaire. Research focused on assessing the reliability of statement items
through confirmatory factor analysis (CFA). Our goal was to determine whether
the statement items could be classified as suitable items based on their
conceptual meaning. The results indicated that CFA provided data consistent
with the model fit guidelines. However, several statements did not meet the
statistical or fit criteria of the CFA model, primarily due to factor loadings
below 0.3. For example, on the mathematics attitude scale, four statements were
deemed unsuitable based on CFA results. Statements such as "Mathematics is
my worst subject" and "Mathematics is difficult for me," which
are negatively worded items, showed low factor loadings. This was influenced by
the fact that for many students, mathematics is perceived as a challenging
subject, leading them to often strongly agree (score 5) with such statements.
This study underscores the importance of evaluating the construct of each
statement item between meaning and statistical data to achieve more
comprehensive results. According to Cheung dkk. (2023), CFA is an effective
method for validating theoretical constructs by testing relationships between
latent variables and measurable indicators. However, the study also emphasises
the critical role of understanding the context of the student and the interpretation
of the statement items in assessing the reliability and validity of the
evaluation instruments. Therefore, in developing evaluation instruments, a
thorough analysis should be performed not only based on statistical data but
also considering the meaning and context of each statement. This ensures that
the developed instrument accurately measures the intended construction reliably
(Farida et al., 2022;
Suherman & Vidákovich, 2022).
This study reinforces previous findings on the
importance of validation to ensure the reliability and validity of evaluation
instruments. According to Kline (2015), high path
coefficients indicate significant relationships between latent variables and
their indicators, supporting that the theoretical constructs are empirically
sound. Furthermore, large effect sizes indicate that STEM attitude variables
have significant impacts within this research context. Cohen (1992) states that effect
size provides information about the magnitude of the relationship or impact of
one variable on another, crucial for interpreting research findings. In this
context, effect sizes ranging from .49 to .93 suggest that STEM attitudes substantially
contribute to the research model. With positive and strong path coefficient and
effect size results, this study confirms that the developed STEM attitude
evaluation instrument has good validity and reliability in accurately measuring
student attitudes. Therefore, this instrument can be used in further research
to evaluate and improve STEM learning in schools.
Furthermore, the study explored how the ability of the
student to complete the STEM attitude questionnaire is influenced by background
factors, particularly gender and grade level. This analysis aimed to identify
differences in STEM attitudes based on these variables, providing crucial
information for educators and policymakers in designing more inclusive and
effective learning strategies.
The study did not find significant differences based
on gender in all observed variables. Mathematics, Science,
Engineering/Technology, and 21st Century Skills. Mean values and standard
deviations between men and women also indicated a relatively close similarity
in each variable. This finding aligns with research by N. Wang et al. (2023), showing that gender
differences in attitudes and performance towards STEM often prove insignificant
when considering other factors, such as intrinsic motivation and environmental
support. Therefore, this study confirms that STEM attitudes among students do
not differ significantly between males and females, indicating equal potential
in this field. The lack of significant gender differences observed in the
current study's variables (Mathematics, Science, Engineering/Technology, and
21st Century Skills) suggests that efforts to promote gender equality in STEM
education may be bearing fruit. However, it's important to note that while
attitudes and skills may be similar, other factors can still influence career
choices and persistence in STEM fields. For instance, Zając et al.
(2024) found that despite
similar abilities, women were more likely to opt out of certain STEM fields due
to perceived lack of work-life balance and concerns about workplace culture.
This indicates that addressing systemic issues in STEM industries remains crucial
for achieving true gender parity. Moreover, intersectionality plays a vital
role in understanding STEM participation. Sendze (2023) demonstrated that
women of color face unique challenges in STEM fields, highlighting the need for
more nuanced approaches to promoting diversity and inclusion.
The analysis revealed significant differences
according to grade level. Mean scores and standard deviations between grades 7,
8, and 9 exhibited intriguing variations in each discipline. Overall, grade 9
showed better or at least competitive performance in most disciplines. This
result is consistent with previous findings by Balta et al. (2023), suggesting that
STEM attitudes may change with increasing grade levels, where richer learning
experiences and deeper engagement in STEM activities can strengthen positive
attitudes toward the discipline. Improved performance and more positive
attitudes among grade 9 students may reflect increased exposure to and
understanding of STEM content over time. As students advance through their
educational journey, they encounter more complex concepts and real-world
applications that foster critical thinking and problem-solving skills (Supriadi et al.,
2024; Tuong et al., 2023).This maturation
process not only improves their academic performance but also cultivates a more
profound appreciation for the relevance and importance of STEM fields.
Furthermore, this trend raises important questions about curriculum design and
instructional strategies at lower grade levels. If earlier exposure and
engagement in STEM subjects can lead to better attitudes and performance in
higher grades, educational stakeholders should consider how to enhance STEM
education in grades 7 and 8. Implementing hands-on projects, collaborative
learning opportunities, and real-life problem-solving scenarios could help
younger students develop a stronger foundation and interest in STEM subjects (Ammar et al., 2024;
Huang et al., 2022; Nikolopoulou, 2023).
Although no significant differences were found based
on sex, notable differences were observed based on grade level, indicating that
longer learning experiences and deeper participation in STEM can improve
positive attitudes toward this discipline. These findings have important
implications for STEM education strategies, emphasising the need for sustained
and focused approaches to deep learning experiences for all students,
regardless of gender.
The results indicate a correlation with previous
research on the influence of STEM education on students' skills and academic
achievements. This suggests that implementing STEM learning and assessment is
important in preparing a competent generation that meets the demands of the
global era (Abina et al., 2024). Students' attitudes
toward STEM play a crucial role in determining their willingness to learn STEM
subjects and pursue a career in STEM (Maltese & Tai,
2011).
5. Limitations and
future research
This study has several limitations that must be noted.
First, the research sample was limited to high school students in Lampung,
Indonesia; hence, the results may not be generalizable to the entire student
population in Indonesia or other regions. While this research has the potential
to assess students' attitudes toward STEM on a global scale, it is crucial to
recognize that gender and grade level can significantly influence students'
perceptions and experiences with STEM education. For instance, male and female
students may have different interests, confidence levels, and barriers in
engaging with STEM subjects, which could affect their attitudes. Similarly,
students in different grades may experience varying levels of exposure to STEM
content, impacting their overall perceptions and enthusiasm for these fields.
Therefore, further studies are needed to test whether these findings apply in
various geographical and cultural contexts, utilizing diverse samples that
encompass different gender representations and grade levels. This broader
approach will enhance the understanding of how to effectively foster positive
attitudes toward STEM among students worldwide. Second, this research used a
quantitative design that provides objective data, but did not integrate
qualitative methods that could offer deeper insights into the reasons behind
students' attitudes toward STEM. Interviews or focus group discussions could
improve understanding of the factors that influence student attitudes.
Furthermore, the study focused on gender and
grade-level variables without considering other factors such as socioeconomic
background, family support, and prior learning experiences, which could also
influence students' STEM attitudes. Despite validation through CFA, some
statement items did not meet the suitability criteria, indicating that the
instrument used requires refinement. Further validation with a larger and more
diverse population is necessary to ensure the reliability and validity of the
evaluation instrument.
To address these limitations, future research is
recommended to expand the sample geographically and demographically. Research
should include samples from various regions (i.e., in Indonesia, Asian, Europe,
and USA) and consider diverse demographic backgrounds to obtain a more
comprehensive picture of student attitudes toward STEM. In addition, employing
mixed methods that combine quantitative and qualitative approaches can provide
deeper insights into student attitudes. For example, in-depth interviews and
focus group discussions can uncover factors that may not be detected through
surveys alone.
Future research should also consider other factors,
such as socioeconomic background, family support, and teaching quality, that
may influence students' STEM attitudes. This can provide a more complete and
detailed understanding. Continuous development and validation of the evaluation
instruments are also necessary. Involving subject matter experts and education
practitioners in the instrument development process can improve the accuracy
and relevance of the statement items.
Conducting longitudinal research can also help to
understand how attitudes towards STEM develop over time and what factors
contribute to these changes. This can provide valuable information for the
development of sustainable STEM curricula and learning strategies. Finally,
considering the recommendations of Tsai et al. (2023), future research
should incorporate comprehensive analyses of demographic, psychological, and
environmental factors that can influence students' STEM attitudes.
By considering these limitations and implementing
recommendations for future research, it is expected that a deeper and more
comprehensive understanding of student attitudes toward STEM and influencing
factors can be achieved.
6. Conclusion
In conclusion, this study successfully developed and
validated a STEM attitude questionnaire using CFA to assess item reliability.
The primary objective was to categorise the statement items based on their
conceptual meaning. While CFA generally supported model fit guidelines, several
items, particularly negatively worded ones in the mathematics scale, did not
meet statistical criteria due to low factor loadings.
The study underscores the importance of aligning the
conceptual meaning of statement items with statistical data for comprehensive
results. CFA effectively validates theoretical constructs by testing
relationships between latent variables and measurable indicators. However,
understanding how students interpret the statements is crucial for evaluating
the reliability and validity of the instrument. Therefore, the integration of
statistical data and contextual insights during instrument development is essential
for accurate measurement.
Additionally, the study identified various path
coefficients and effect sizes between variables, indicating strong correlations
between the STEM attitude variables. High path coefficients suggest substantial
relationships between latent variables and their indicators, supported by
significant effect sizes that clarify the magnitude of these relationships.
Based on these robust findings, the study affirms the validity and reliability
of the STEM attitude evaluation instrument in accurately measuring student attitudes.
This instrument can serve as a valuable tool for further research aimed at
enhancing STEM learning in educational settings. Additionally, the study
explored how background factors, such as gender and grade level, influence
students' responses to the STEM attitude questionnaire. Although no significant
gender differences were found, notable variations based on grade level suggest
that longer exposure and greater participation in STEM activities positively
impact student attitudes, aligning with previous research.
These insights have important implications for STEM
education strategies, emphasising the need for inclusive approaches that
promote deep learning experiences for all students, regardless of gender or
grade level. Designing specific educational interventions using instruments
that measure attitudes toward STEM can effectively identify students'
perceptions and areas of improvement. For instance, implementing targeted
programs based on the results from attitude assessments can help address
specific misconceptions and foster positive attitudes. Additionally, using
instruments to monitor changes in attitudes over time can inform curriculum
adjustments and teaching strategies, ensuring that interventions remain
relevant and impactful. By leveraging these tools, educators can create
tailored initiatives that nurture a positive environment for all students in
STEM education. Ultimately, such strategies can significantly enhance student
engagement and achievement in STEM disciplines, contributing to a more skilled
and diverse workforce.
Author´s Contribution
Mujib Mujib: Writing - Original Draft Supervision,
Funding acquisition, Formal analysis, Methodology, and Original Draft; Mardiyah
Mardiyah, Writing – review & editing, Conceptualization, Writing - Editing,
and Visualization.
Financing
The study reported was funded
by the Research and Community Service Department (LP2M) at Universitas Islam Negeri
Raden Intan Lampung, Indonesia
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