An Assessment of Technological Pedagogical
Content Knowledge (TPACK) among Pre-service Teachers: A Rasch Model Measurement
Una evaluación del conocimiento del
contenido pedagógico tecnológico (TPACK) entre profesores en formación: una
medición del modelo de Rasch
Dr. Komarudin
Komarudin. Lecture Professor. Universitas Islam
Negeri Raden Fatah Palembang. Indonesia
Dr. Suherman
Suherman. Lecturer and Researcher. University
of Szeged, Hungary. Universitas
Islam Negeri Raden Intan Lampung. Indonesia
Recibido:
2024/01/07 Revisado 2024/01/31 Aceptado: :2024/06/29 Online First: 2024/07/08 Publicado: 2024/09/01
Cómo citar este artículo:
Komarudin, K.,
& Suherman, S. (2024). Evaluación del
conocimiento tecnológico pedagógico del contenido (TPACK) entre los profesores
en formación: modelo de medición Rasch [An Assessment of
Technological Pedagogical
Content Knowledge (TPACK) among
Pre-service Teachers: A Rasch Model Measurement].
Pixel-Bit. Revista De Medios Y Educación,
71, 59–82. https://doi.org/10.12795/pixelbit.107599
ABSTRACT
The increasing importance of integrating technology
into educational environments has underscored the importance of Technological Pedagogical
Content Knowledge (TPACK) for effective teaching in the 21st century. However,
many pre-service teachers face challenges in proficiently accessing and utilising new technologies in their teaching practices. The
existing literature lacks a thorough examination of the empirical aspects of
TPACK instruments and their applicability across various educational settings
and levels, particularly in non-Western contexts. This research aimed to
evaluate and compare TPACK among pre-service teachers in Indonesia. A diverse
sample of 405 Indonesian pre-service teachers from different disciplines
participated by completing an online TPACK questionnaire. Confirmatory factor
analysis and the Rasch model were used to validate the questionnaire,
demonstrating a well-fitting model consistent with its theoretical framework
and a satisfactory fit for individuals and items. Evaluation of TPACK among
pre-service elementary, preschool and mathematics education teachers revealed
superior performance by pre-service elementary school teachers. The robust
psychometric properties make it suitable for exploring TPACK. This research
lays the groundwork for further investigation of the empirical dimensions of
TPACK in diverse educational contexts.
RESUMEN
Currently, the integration of
technology in 21st-century education is becoming increasingly important;
however, many pre-service teachers face difficulties in accessing and utilizing
new technologies in their teaching practices. Despite the growing importance of
technology, the existing literature still lacks empirical examination of TPACK
instruments and their application in various educational contexts, particularly
in non-Western countries such as Indonesia. This study involved 405 Indonesian
pre-service teachers from various disciplines who completed an online TPACK
questionnaire. Confirmatory factor analysis and the Rasch model used to
validate this questionnaire indicated that the model aligns with the
theoretical framework and is suitable for individuals and items. The evaluation
results showed that pre-service elementary school teachers exhibited superior
TPACK performance compared to pre-service early childhood education and
mathematics teachers. The strong psychometric properties of this instrument make
it suitable for further exploration of TPACK in various educational contexts.
This research lays the groundwork for further investigation into the empirical
dimensions of TPACK in diverse educational settings.
PALABRAS CLAVES· KEYWORDS
Maestros en formación; Conocimiento
tecnológico pedagógico del contenido (TPACK); cuestionario de validación;
modelo rasch; Educación
tecnológica;
1. Introduction
The integration of technology into educational
settings has become increasingly important. Technological Pedagogical Content
Knowledge (TPACK) has emerged as a crucial component of effective teaching in
the 21st century. TPACK refers to teachers' ability to integrate technology
into their teaching to enhance learning outcomes (Roussinos & Jimoyiannis,
2019). The TPACK
framework has been widely adopted as a guide to understanding and developing
technological and pedagogical knowledge among teachers. Recent digital teaching
competence frameworks, such as DigCompEdu, further
reinforce the importance of digital competence in education (Haşlaman et al., 2024). These frameworks
provide detailed guidelines and standards for educators to effectively use
digital tools and resources, ensuring that technology integration is
pedagogically sound and contextually relevant. The synergy between the TPACK
model and frameworks such as DigCompEdu highlights
the need for a comprehensive approach to teacher training, focussing not only
on the use of technology, but also on its pedagogical application to foster
enhanced learning experiences (Redecker &
Punie, 2017). In other words,
digital integration in learning activities, much faster and more accessible way (Guillén-Gámez et al., 2024; Komarudin et
al., 2024).
The first two decades of the 21st century have seen
significant changes in preservice teacher education, particularly with the
increasing availability of technology in classrooms (Almazroa & Alotaibi, 2023). However, many
pre-service teachers face obstacles to accessing and effectively using new
technologies in their teaching in Indonesia. These challenges include limited
access to technological resources, inadequate training in technology
integration, and a lack of institutional support for technological initiatives.
Many teachers struggle to effectively incorporate technology into their
classrooms (Abedi et al.,
2024; Bolyard et al., 2024; Bray & Tangney, 2017; Park & Scanlon, 2024). Studies have
identified the lack of technological and pedagogical content knowledge as a
significant barrier for teachers to use technology in teaching (Ardiç & Isleyen, 2017; Kind, 2009; Stoilescu,
2015), highlighting the
need for tools to assess technological knowledge (Smith & Zelkowski, 2023). Despite
participating in technological professional development, teachers often fail to
integrate available technology into classroom instruction (Fütterer et al., 2023; Lawless & Pellegrino, 2007).
Recognising this challenge is essential as it
emphasises the critical importance of TPACK among pre-service teachers.
Equipping future teachers with TPACK enables them to effectively integrate
technology into their teaching practices and enhances student learning
experiences (Elmaadaway & Abouelenein,
2023). By synthesising
technological expertise with pedagogical and content knowledge, pre-service
teachers are better prepared to navigate the complexities of modern education,
fostering innovation and equipping students with the skills necessary for
success in the digital age.
Self-report measures have been developed to assess
teachers' confidence levels and perceptions about the effectiveness of
technology in educational settings. Previous research has underscored the
importance of TPACK in various educational settings, demonstrating its ability
to enhance teaching practices and student learning outcomes. Koh (2019) and Baran et al.
(2019) highlighted the
positive impact of TPACK on teachers' instructional strategies and their
confidence in integrating technology into classrooms. Zelkowski et al. (2013) developed and
validated an instrument to measure the TPACK of secondary mathematics
pre-service teachers in the United States, finding the construct reliable and
valid. However, they noted that pre-service teachers struggled to discern
self-report domains, such as Pedagogical Content Knowledge (PCK), Technological
Content Knowledge (TCK) and Technological Pedagogical Knowledge (TPK). Furthermore,
a study by Ong &
Annamalai (2024) focused on the
development of skills from the 21st century TPACK to create a model stage of
ICT tasks for communication, collaboration, critical thinking and creative
thinking. Their research found that TPACK-21st century skills were missing,
while content knowledge and pedagogical content knowledge were emphasised in
the planned curriculum. This research contributes to the larger effort to
enhance pre-service mathematics teachers for effective technology integration.
Another study by Smith & Zelkowski (2023) validated a TPACK
questionnaire instrument for middle- and high-school mathematics teachers in
the United States, originally developed in Australia. The research, which
involved a comparable national sample in the US, revealed differences in the
factor structure of the Australian instrument within the US context. The
finding led to the creation of a new validated instrument, TPACK-M-US, tailored
for US pre-service secondary mathematics teachers. The study provided three
sources of evidence for the validity of the instrument and discussed its
appropriate uses and interpretations, emphasising the importance of validation
research in educational settings. However, a limitation was that all data were
self-reported, which could lead to an overestimation or an underestimation of
TPACK by US participants.
Furthermore, Li et al. (2023) created and
validated a TPACK scale for secondary mathematics teachers in China. The
results demonstrated strong reliability and validity, making the scale a robust
tool to assess TPACK and guide professional development and technology
integration policies within Chinese mathematics education. Additionally, Sofyan et al.
(2023) validated the
TPACK instrument for the evaluation of elementary school teachers in Indonesia.
Their research found that the items were valid and reliable to evaluate teacher
TPACK and Internet use. However, the study was limited to focussing on the
level of TPACK in classroom settings. Furthermore, Martin et al.
(2024) developed and
validated a self-audit survey for primary school pre-service teachers, which
was also found to be valid and reliable. The limitation of their research was
that the instrument needed to include items related to technological changes,
especially the rapid evolution of artificial intelligence.
However, the existing literature lacks a comprehensive
exploration of the empirical attributes of TPACK and its application across a
wide range of educational settings and levels, particularly in non-Western
contexts. Challenges, such as limited access to technological resources,
inadequate training in technology integration, and insufficient institutional
support for technology-related initiatives, exacerbate this gap. Most studies
have mainly focused on Western countries, leaving a gap in our understanding of
how TPACK operates in diverse cultural and educational environments, such as
Indonesia. Furthermore, limited research examines how TPACK levels vary between
different disciplines, including elementary, pre-school, and mathematics
education pre-service teachers. Another significant gap arises from the
reliance on self-reported measures to evaluate TPACK, raising concerns about
potential response bias and its impact on the precision and consistency of
research findings.
Furthermore, we aimed to refine and validate robust
assessment tools that can accurately measure the TPACK levels of pre-service
teachers. This effort contributes to optimising teacher education curricula and
better-preparing teachers for the digital demands of contemporary classrooms.
By achieving these objectives, this research aimed to offer valuable information
on the effective measurement and enhancement of TPACK, thus supporting the
advancement of teacher education and the seamless integration of technology
into teaching practices across diverse educational settings.
1.1 Technological
Pedagogical Content Knowledge
The concept of Technological Pedagogical Content
Knowledge or TPACK serves as a framework for understanding and describing the
types of knowledge a teacher needs to effectively practice pedagogy and improve
concept understanding by integrating technology into the learning environment.
Fundamentally, TPACK revolves around the relationship between subject matter,
technology, and pedagogy (Elas et al.,
2019; Irmak & Yilmaz Tüzün, 2019; Nordin et al., 2013;
Reyes Jr et al., 2017). The interaction among these three components has the
strength and appeal to foster active learning focused on learners (Malik et al.,
2019). TPACK, one of
the most recognized theoretical frameworks, was developed by Mishra &
Koehler (2006) to ensure the
integration and representation among technology, pedagogy, and content components.
TPACK denotes the understanding that teachers need to effectively incorporate
technology into their teaching across various content areas (Luik et al.,
2024). This framework
highlights that effective technology integration requires a nuanced
understanding of the dynamic relationship between pedagogy, content, and
technology, ultimately aiming to enhance educational outcomes and foster more
engaging learning experiences. Mishra &
Koehler (2006) emphasise that
TPACK is not a universal skill applicable in the same way for every teacher,
but rather a form of knowledge that varies according to different curriculums
and teaching philosophies. They state that “quality teaching requires
developing a nuanced understanding of the complex relationships between
technology, content, and pedagogy and using this understanding to develop
appropriate, context-specific strategies and representations”. Thus, the
learning paradigm shifts from teacher-centred to learner-centred. Consequently,
the basic theory of TPACK empowers teachers to develop the skills needed to
make informed decisions about integrating technology into teaching, ensuring
that its use supports students' understanding of the subject matter.
However, it is crucial for teachers to integrate
technology with their content and pedagogical knowledge. A tangible example of
TPACK is when a mathematics teacher employs simulation software to assist
students in grasping abstract concepts. Through a combination of strong subject
knowledge, sophisticated pedagogical skills, and judicious use of technology,
learning becomes not only more engaging but also more effective. Thus, TPACK
emerges as the key to shaping a generation that is not only technologically skilled,
but also critical (Maskur et al., 2022), problem solving (Supriadi et al.,
2024), creative (Suherman &
Vidákovich, 2024) and prepared to
face the challenges ahead.
1.2 The Components of
Technological Pedagogical Content Knowledge
In the TPACK framework, there exists an interconnected
relationship between its constituent components, namely content knowledge (CK),
pedagogical knowledge (PK), and technological knowledge (TK). They overlap and
influence each other in the context of learning. A holistic understanding of
how these three dimensions relate and interact is crucial to support effective
learning processes. The following is a detailed description of the basic theory
of TPACK. Furthermore, the TPACK framework is illustrated in Figure 1 (Mishra &
Koehler, 2006).
Figure1
The Dimensions of
TPACK
Content Knowledge (CK) refers to knowledge of the
subject matter to be learned, as outlined in the curriculum. It encompasses
concepts, theories, ideas, frameworks, methods, and real-world applications (Flores-Castro et
al., 2024).
Pedagogical Knowledge (PK) encompasses in-depth
knowledge related to the theory and practice of teaching and learning, covering
goals, processes, learning methods, evaluation, strategies, and more. It
requires understanding cognitive, affective, and social, as well as developing
learning theories and their practical application (Saubern et al., 2020).
Technology Knowledge (TK) includes the technology
basics that support learning, such as software, animation programmes, Internet
access, molecular models, and virtual laboratories. Teachers must be proficient
in processing information and communicating with ICT in learning environments (Malik et al.,
2019).
Pedagogical Content Knowledge (PCK) involves the
interaction and intersection between pedagogy (P) and subject matter (C). PCK
is the ability to transform content or subject matter for teaching purposes,
including the learning process related to the subject matter and the assessment
system (Saubern et al., 2020). Technology
Content Knowledge (TCK) encompasses the relationship between technology and
subject matter, understanding how technology can support and influence other
components. It involves technological proficiency and subject matter domains (Mishra &
Koehler, 2006). Technology
Pedagogy Knowledge (TPK) integrates PK and TK, emphasising how technology can
be applied effectively in teaching. It requires an understanding of the
advantages and disadvantages of technology in the context of subject matter and
the learning process (Schmidt et al.,
2009).
Technological Pedagogical Content Knowledge (TPACK)
integrates PK, CK and TK, summarising a series of learning in which the ability
to master integrated technology is inseparable from its constituent components
(C), (P), and (K). TPACK requires multiple interactions and combinations among
components: subject matter, pedagogy, and technology (Mishra &
Koehler, 2006). Teachers need
the ability to effectively integrate technology into their teaching strategies
to align with the subject matter and the needs of students.
2. Methodology
2.1. Participants
The research enlisted 405 pre-service teachers from
various public and private universities. Among these participants, 52.8%
identified as female, while 46.7% identified as male, with an average age of Mage
= 19.58; SD = 1.006. Participants were selected from various districts and
villages, representing a spectrum of living environments, ages, majors, and
university types. The ethical clearance for the study was obtained from the
Institutional Review Board of Universitas Islam Negeri Fatah Palembang,
Indonesia, ensuring the adherence to the ethical guidelines. Before
participating, all individuals provided their informed consent. Further
demographic details of the participants are presented in Table 1.
Table 1
Characteristics of
the Participants
|
Characteristics |
n |
Frequency (%) |
|
Gender |
|
|
|
Female |
215 |
52.8 |
|
Male |
190 |
46.7 |
|
Age |
|
|
|
17 |
4 |
1.0 |
|
18 |
46 |
11.3 |
|
19 |
146 |
35.9 |
|
20 |
150 |
36.9 |
|
21 |
40 |
9.8 |
|
22 |
19 |
4.7 |
|
Type of universities |
|
|
|
Private |
267 |
65.6 |
|
Public |
138 |
33.9 |
|
Major |
|
|
|
PGMI |
181 |
44.5 |
|
PIAUD |
106 |
26.0 |
|
PSPM |
118 |
29.0 |
|
Living place |
|
|
|
City |
184 |
45.2 |
|
Suburb |
221 |
54.3 |
N = 405; Mage =
19.58; SD = 1.006; PGMI = Elementary teacher programme; PIAUD = Preschool
teacher programme; PSPM = Mathematics teacher programme
2.2. Instrument
The TPACK instrument developed by Schmidt et al.
(2009) served as the
foundation of this research. Adapted to the Indonesian context, the instrument
comprised seven dimensions. The first dimension addressed technology knowledge
and comprised five items. The second dimension focused on content knowledge,
encompassing 12 items. Pedagogical knowledge constituted the third dimension,
comprising seven items. The fourth dimension was related to the pedagogical
content knowledge, with 4 items. The fifth dimension was related to
technological content knowledge, featuring 4 items. The sixth dimension
addressed technological pedagogical knowledge with five items. Lastly, the
seventh dimension addressed technological pedagogical content knowledge with
eight items. The participants' responses were recorded using a five-point
Likert scale, ranging from strongly agree (5) to strongly disagree (1).
2.3. Data Analysis
Participants voluntarily completed the questionnaire
with confidential identification. To evaluate the validity of the
questionnaire, Confirmatory Factor Analysis (CFA) was used, using parameters
such as the Comparative Fit Index (CFI), the Tucker-Lewis Index (TLI), the Root
Mean Square Error of Approximation (RMSEA), the Standardised Root Mean Square
Residual (SRMR). The model fit criteria were established as CFI > .90, TLI
> .90, RMSEA < .08, and SRMR < .06 (Hu & Bentler,
1999).
Additionally, the Rasch analysis further assessed the validity
of the construct. This analysis evaluated the fit of individual items,
considering parameters such as fit and fit mean square (MNSQ), ranging from 0.5
to 1.5 (Boone et al.,
2014), as well as a
positive value of the point-measure correlation (PTMA). The Differential
Element Function (DIF) was also performed to identify potential bias toward
specific sample groups.
Furthermore, descriptive and comparative analyses were
conducted to profile students' TPACK and discern differences among teacher
groups. Ordinal student responses were converted to logit values derived from
Rasch analysis to estimate attitude levels, representing student performance in
different aspects of a single trait (Boone et al.,
2014). Data were
analysed using the SPSS version 29, SmartPLS version
4, and Winstep programmes.
3. Analysis and
results
3.1 The Validity of the
Instrument
Validity analysis evaluates the quality of the
questionnaire based on the theoretical model and the parameters of individual
items (see Table 2). The results of the Confirmatory Factor Analysis (CFA)
demonstrated satisfactory results for TPACK with seven latent variables: 
= 2051.845, df = 2.262, p < .001,
CFI = .92, TLI = .91, RMSEA = .05, and SRMR = .04. The factor loadings derived
from the CFA consistently ranged from .45 to .85, indicating the alignment of
the items with the explanation of the constructed variable (see Figure 2). It
should be noted that all the questionnaire items effectively captured the
dimensions of TPACK within each latent variable.
Table 2
The Item Validity
of TPACK Based on the CFA and Rasch Analysis
|
CFA Factor Loading |
Rasch Analysis |
|
|||||
|
Measure |
SE |
Infit MNSQ |
Outfit MNSQ |
PTMA |
|||
|
F1: Technology Knowledge (TK) |
|
||||||
|
TK2 |
.63 |
-.54 |
.09 |
1.33 |
1.39 |
.54 |
|
|
TK3 |
.66 |
-1.07 |
.09 |
1.23 |
1.24 |
.56 |
|
|
TK5 |
.69 |
-.17 |
.09 |
1.60 |
1.90 |
.53 |
|
|
TK6 |
.74 |
.44 |
.09 |
1.27 |
1.29 |
.62 |
|
|
TK7 |
.73 |
.15 |
.09 |
1.31 |
1.34 |
.61 |
|
|
F2: Content Knowledge (CK) |
|
||||||
|
CKL1 |
.73 |
.44 |
.09 |
.98 |
.98 |
.68 |
|
|
CKL2 |
.73 |
.65 |
.09 |
1.08 |
1.08 |
.68 |
|
|
CKL3 |
.77 |
.46 |
.09 |
.87 |
.86 |
.72 |
|
|
CKM1 |
.45 |
.57 |
.09 |
1.79 |
1.83 |
.48 |
|
|
CKM2 |
.59 |
.40 |
.09 |
1.31 |
1.36 |
.60 |
|
|
CKM3 |
.63 |
.47 |
.09 |
1.22 |
1.24 |
.63 |
|
|
CKS1 |
.68 |
.29 |
.09 |
1.10 |
1.09 |
.67 |
|
|
CKS2 |
.78 |
.52 |
.09 |
.83 |
.82 |
.74 |
|
|
CKS3 |
.71 |
.48 |
.09 |
.97 |
1.01 |
.67 |
|
|
CKT1 |
.70 |
-.35 |
.09 |
.94 |
.97 |
.66 |
|
|
CKT2 |
.70 |
-.28 |
.09 |
.90 |
.91 |
.66 |
|
|
CKT3 |
.69 |
.17 |
.09 |
1.07 |
1.05 |
.65 |
|
|
F3: Pedagogical Knowledge (PK) |
|
||||||
|
PK1 |
.80 |
-21 |
.09 |
.89 |
.88 |
.72 |
|
|
PK2 |
.77 |
-.36 |
.09 |
.84 |
.84 |
.70 |
|
|
PK3 |
.79 |
-.42 |
.09 |
.92 |
.90 |
.70 |
|
|
PK4 |
.75 |
-.39 |
.09 |
.99 |
.97 |
.67 |
|
|
PK5 |
.75 |
-.27 |
.09 |
1.07 |
1.07 |
.67 |
|
|
PK6 |
.82 |
.04 |
.09 |
.81 |
.80 |
.74 |
|
|
PK7 |
.80 |
-.08 |
.09 |
.82 |
.81 |
.73 |
|
|
F4: Pedagogical Content Knowledge (PCK) |
|
||||||
|
PCK1 |
.79 |
.04 |
.09 |
.76 |
.74 |
.74 |
|
|
PCK2 |
.81 |
.22 |
.09 |
.79 |
.78 |
.74 |
|
|
PCK3 |
.72 |
.18 |
.09 |
.97 |
.95 |
.68 |
|
|
PCK4 |
.81 |
.15 |
.09 |
.80 |
.78 |
.75 |
|
|
F5: Technological Content Knowledge (TCK) |
|
||||||
|
TCK1 |
.80 |
.01 |
.09 |
.88 |
.86 |
.72 |
|
|
TCK2 |
.77 |
.00 |
.09 |
.84 |
.84 |
.72 |
|
|
TCK3 |
.83 |
.05 |
.09 |
.88 |
.85 |
.75 |
|
|
TCK4 |
.75 |
.07 |
.09 |
1.00 |
.98 |
.70 |
|
|
F6: Technological Pedagogical Knowledge (TPK) |
|
||||||
|
TPK1 |
.77 |
-.20 |
.09 |
.88 |
.85 |
.72 |
|
|
TPK2 |
.85 |
-.36 |
.09 |
.79 |
.78 |
.74 |
|
|
TPK3 |
.71 |
-.66 |
.09 |
1.32 |
1.28 |
.61 |
|
|
TPK4 |
.76 |
-.38 |
.09 |
1.03 |
1.01 |
.67 |
|
|
TPK5 |
.85 |
-.20 |
.09 |
.74 |
.72 |
.74 |
|
|
F7: Technological Pedagogical
Content Knowledge (TPACK) |
|
||||||
|
TPACK1 |
.82 |
.16 |
.09 |
.81 |
.80 |
.74 |
|
|
TPACK2 |
.83 |
.07 |
.09 |
.77 |
.76 |
.76 |
|
|
TPACK3 |
.80 |
-.30 |
.09 |
.80 |
.80 |
.73 |
|
|
TPACK4 |
.78 |
-.41 |
.09 |
.90 |
.90 |
.71 |
|
|
TPACK5 |
.82 |
.19 |
.09 |
.86 |
.85 |
.74 |
|
|
TPACK6 |
.78 |
.15 |
.09 |
.93 |
.93 |
.72 |
|
|
TPACK7 |
.82 |
.05 |
.09 |
.83 |
.82 |
.76 |
|
|
TPACK8 |
.71 |
.23 |
.09 |
1.09 |
1.08 |
.69 |
|
Rasch analysis yielded favourable MNSQ values for
infit (Minfit = 0.98) and outfit (Moutfit = 1.02), indicating that the
questionnaire items effectively assess the TPACK of pre-service teachers.
However, for items TK5 and CKM1, the infit and outfit MNSQ values exceeded 1.5
(see Figure 3). These items were considered acceptable due to their positive
point-measure correlation. Therefore, removing these items from the
questionnaire would compromise the theoretical integrity of the measurement.
Regarding the parametric properties of the items, the
logit measure of the overall items indicated proximity to 0 (Mlogit = -0.03, SD = 0.36), suggesting that the
measured items were located at a moderate level (see Fig. 3). The
questionnaire's most challenging items were CKL2 and CKM1, with students
predominantly providing lower scores in their responses (logit measure = 0.65
and 0.57, respectively). On the contrary, the least challenging element was TK3
(logit measure = -1.07), where students consistently provided high confidence
scores.
Furthermore, Rasch's analysis evaluated
dimensionality, revealing that the average variance explained by the measure of
the TPACK variables exceeded the critical point (35%). The point indicated that
the questionnaire effectively measures only the dimension of TPACK.
Figure 2
CFA Model Fit
Figure 3
Wright Map of Items
Figure 4
DIF Analysis in Three
Different Pre-service Teachers Major
The DIF analysis, conducted through Rasch analysis,
aimed to assess the invariance of the questionnaire between groups, determining
whether specific items exhibited different behaviours between different groups.
In this research, we focused on measuring DIF between elementary school
pre-service teachers in their first year, pre-service preschool teachers in
their first year, and pre-service mathematics teachers in their first year.
The estimate for the DIF analysis was based on a
significant probability (p < 0.05) with a large size estimation
(≥0.64) (Boone et al.,
2014). A significant
result with a large size estimate indicated the presence of DIF in the item.
Conversely, a nonsignificant result with a low size estimation suggested no
DIF, while a significant result with a low size suggested negligible bias
towards different groups. The DIF analysis in the three pre-service teacher
groups produced non-significant results for each item in TPACK (p > 0.05)
(see Figure 4).
Taking into account the results of
both the CFA and the Rasch analysis, the TPACK framework demonstrated validity
and could accurately measure the knowledge and skills of the pre-service
teachers. Furthermore, the DIF analysis indicated that the questionnaire did
not exhibit bias toward any specific group of pre-service teachers. Given these
robust findings, TPACK is a suitable instrument for further assessment and
evaluation of pre-service teachers, providing valuable information on their
integration of technological, pedagogical, and content knowledge. These results
underscore the importance and effectiveness of TPACK in assessing pre-service
teachers' readiness to effectively integrate technology into their teaching
practices.
3.2. Reliability
Reliability analysis was performed to assess the
consistency of the participant's responses to the questionnaire. The criteria
for a reliable coefficient required a range value of (r > 0.7) for an
acceptable result (Wicaksono & Korom, 2023). The analysis
revealed a favourable outcome for the TPACK, indicating that the questionnaire
items consistently measured students' attitudes toward science (Table 3).
Table 3
The Reliability of
the TPACK Questionnaire
|
Factors |
Cronbach's Alpha |
Coefficient ω |
Person’s Reliability |
Item’s Reliability |
|
CK |
.97 |
.96 |
.92 |
.93 |
|
PCK |
.99 |
.94 |
.81 |
.85 |
|
PK |
.99 |
.96 |
.90 |
.90 |
|
TCK |
.98 |
.94 |
.84 |
.86 |
|
TK |
.92 |
.95 |
.81 |
.83 |
|
TPACK |
.99 |
.97 |
.91 |
.92 |
|
TPK |
.98 |
.95 |
.87 |
.88 |
|
Total |
.97 |
.98 |
.97 |
.94 |
The reliability measures for CK were notably high,
with a Cronbach Alpha of .97, Coefficient ω of .96, Persons' reliability
of .92, and the reliability of .93. Similarly, PCK exhibited even higher reliability,
boasting a Cronbach Alpha of 0.99, Coefficient ω of .94, Persons'
reliability of .81, and component reliability of .85. PK and TCK also
demonstrated robust reliability, highlighting the stability and internal
consistency of the questionnaire across these dimensions. Furthermore, the
reliability measures for TK, TPACK and TPK consistently show high values,
indicating the reliability of the questionnaire in assessing teachers'
technological proficiency and its integration with pedagogy and content
knowledge.
3.3. The Profile of
Pre-service Teachers’ Technological Pedagogical Content Knowledge
Figure 5 depicts the TPACK of the pre-service teachers
using a violin plot, offering a comprehensive comparison of various data points
within the data set. This graphical representation, similar to a box plot, is
specifically designed to showcase important statistical features, such as the
symmetry of distribution, central tendency, and dispersion of data points (Potter et al.,
2010). The violin plot
enhances the visualisation of TPACK performance, facilitating a more detailed
exploration of the dataset.
Analysis of logit values revealed notable findings
regarding differences between groups for various variables in the study.
Significant differences were observed in PCK, with F(2,
402) = 5.773, p < .001, indicating variations in logit values between
groups. Similarly, PK showed significant differences between the groups, with F(2, 402) = 7.925, p < .001. TCK also demonstrated
significance, as seen in F(2, 402) = 3.988, p
< .05. Conversely, the total score, TPACK, and TPK did not show significant
differences in logit values between the groups. Specifically, F(2, 402) = 0.405, p = 0.667 for Total, F(2, 402) = 1.267, p
= 0.283 for TPACK, and F(2, 402) = 0.062, p = 0.940 for TPK. These
findings provided insight into nuanced variations in logit values for different
aspects of teacher knowledge and competencies across different groups.
In addition, logit value measurements were performed
for three different programmes. In the PSPM programme, the Mlogit
was 2.18, with an SD of 2.76. Furthermore, the logit values in PIAUD and PGMI
were Mlogit = 2.28 (SD = 2.83) and Mlogit = 2.41 (SD = 2.72), respectively.
Figure 5
The Distribution
of TPACK Based on the Pre-service Teachers’ Levels
Note: (1 = PSPM; 2 = PIAUD; 3 = PGMI)
3.4. The Correlation between
the TPACK Variables
The researchers performed a TPACK correlation analysis
to examine the relationships between the TPACK variables (Figure 6). For
elementary pre-service teachers, the determination coefficient revealed that
R-squared (R2) for PCK is .637, indicating that independent
variables explain 63.7% of the variance in PCK. Similarly, TCK has an R2
of 0.679, suggesting that independent variables account for 67.9% of the
variance in TCK. The general TPACK variable shows a higher R2 of
.756, indicating that 75.6% of its variance is explained. Lastly, TPK has an R2
of .618, signifying that the independent variables explain 61.8% of the
variance in TPK. These R^2 values provide insights into the predictive power of
independent variables in each specific aspect of knowledge and skills.
Regarding preschool preservice teachers, PCK, TK, CK,
TPK, PK, and TCK explained the TPACK at 78% (R2 = .780). Similarly,
PCK was explained by CK and TK, accounting for 52.6% (R2 = .526) of
the variance. Furthermore, TCK was explained by CK and PK, approximately 58.6%
(R2 = .586). Then, TPK was explained by TK and PK, accounting for
65.9% (R2 = .659) of the variance.
For the pre-service mathematics teachers, five
variables (PCK, TK, TPK, PK, and TCK) explained TPACK at 83.2% (R2 =
.832). Similarly, PCK was explained by TK and CK, reaching approximately 61.5%
(R2 = .615). Furthermore, CK explained TCK at 55.5% (R2 =
.555), and TPK was explained by PK and TK, which account for 58.6% (R2
= .586) of the variance.
Figure 6
Correlations
between TPACK variables among variables
Note: (a) PGMI =
Elementary School Pre-service Teacher, (b) PIAUD = Preschool Pre-service
Teachers, (c) PSPM = Mathematics Pre-service Teacher
4. Discussion
This research aimed to modify and validate a modified
TPACK instrument for Indonesian pre-service teachers. Various statistical
procedures, including Confirmatory Factor Analysis (CFA) and Rasch analysis,
were employed to enhance the validity of the designed instrument. A 45-item
questionnaire was developed to assess pre-service teachers' TPACK levels, with
exploratory factor analysis revealing three variables. The high
Kaiser-Meyer-Olkin (KMO) value of .97 indicated the instrument's ability to
effectively distinguish the three latent factors. Rasch's analysis further
affirmed the effectiveness of the questionnaire. However, some items (TK5 and
CKM1) showed slightly elevated infit and outfit values, which were deemed
acceptable due to their positive correlation with the overall construct. Given
the alignment with the TPACK framework, these elements were retained as they
pertained to essential knowledge, concepts, theories, and practical
applications for pre-service teachers in their everyday contexts. Furthermore, this
perspective recognizes the TPACK theoretical framework (Mishra &
Koehler, 2006) and underscores
the necessity for teachers to effectively integrate technology into their
teaching across various content areas (Luik et al.,
2024). Furthermore, the
conceptual focus of these items emphasised the use of software, Internet
access, and virtual laboratories, underscoring the importance of teachers'
proficiency in information processing and effective communication through ICT
in their instructional practices and STEM education (Malik et al.,
2019; Suherman, 2018).
This research revealed the absence of Differential
Item Functioning (DIF), which is crucial to ensure unbiased measurements
between different groups. Previous research has highlighted the importance of
evaluating DIF to maintain fairness in evaluations and interventions. When
present, DIF may suggest potential biases in questionnaire items that could
impact the instrument's validity and reliability. Both statistical and
practical significance (effect size) in DIF analysis offers a comprehensive
understanding of how biases could influence measurement results (Boone et al.,
2014). The implications
of identifying DIF are significant, particularly in educational evaluations and
interventions, as changes to the questionnaire may be required to ensure
fairness across various teacher education programmes. Furthermore, identifying
specific items showing DIF can inform targeted intervention strategies or
curriculum reforms aimed at meeting the particular needs of each group of
pre-service teachers, thus enhancing the effectiveness of teacher training
programmes (Lautenbach &
Heyder, 2019).
The variance analysis conducted on the logit values
for different teacher groups has yielded valuable insights into the nuanced
distinctions within the elements of the TPACK framework (Castaño et al.,
2015; Kimmons et al., 2015). Elementary,
preschool, and mathematics pre-service teachers exhibited discernible
differences in their interaction with specific items related to TPACK. These
findings align with the existing literature, highlighting the diverse
interpretations and reactions to PCK items across the groups (Hill et al.,
2008). Such variations
likely reflect the unique instructional needs or perspectives inherent to each
group (König et al.,
2020). Significant
group-specific disparities were also observed in PK and TCK, indicating the
influence of different educational environments, teaching obligations or focus
areas of the respective programmes. However, aspects such as the overall TPACK
score and TPK did not demonstrate significant variance between groups,
suggesting a consensus on the understanding and applying broader TPACK
constructs (Hall et al.,
2020; Tondeur et al., 2020). These findings
underscore the importance of recognising subtle yet distinct differences in how
specialised pre-service teachers perceive and incorporate specific TPACK
stages. Customising teacher education programmes to better meet particular needs and overcome challenges specific to various
instructional domains and subjects is crucial.
When evaluating the effectiveness of the three
educational programmes, the analysis focused on comparing logit values,
indicative of the programme's ability to enhance certain competencies. It was
observed that the pre-service elementary school teachers’ teaching positions
tended to outperform the preschool and mathematics pre-service teachers in
terms of mean logit values. Several factors may contribute to this performance
distinction. First, the curriculum and training provided to elementary school
pre-service teachers may be more comprehensive, leading to a stronger
foundation in the assessed areas. It may reflect alignment of the curriculum
with assessment objectives or a more adept implementation of instructional
strategies that resonate with measured competencies. Second, the nature of
elementary education can offer broader exposure to diverse teaching contexts
and content areas, equipping pre-service teachers with a more versatile skill
set. The findings resonate with previous research highlighting the critical
role of curriculum coherence and instructional alignment in promoting TPACK
among educators (Koh, 2019; Mishra
& Koehler, 2006). Addressing
disparities in TPACK development through targeted professional development and
curriculum enhancements is crucial for preparing educators to meet the evolving
demands of digital-age learning environments.
In contrast, the specialisation required for preschool and mathematics pre-service teachers might limit
their exposure and, subsequently, their performance in the broad-based competencies
assessed by logit values. Additionally, it is plausible that elementary school
pre-service teachers' training programmes place greater emphasis on specific
skills and knowledge areas evaluated in the study, directly influencing
outcomes. Alternatively, assessment instruments may inherently favour
competencies developed in elementary school pre-service teachers, contributing
to observed performance discrepancies.
5. Limitations and
future research
Although this study offers valuable insights into
TPACK development among pre-service teachers in Indonesia, several limitations
should be acknowledged. First, the predominantly Indonesian sample from public
and private universities can restrict the generalisability of the findings to
other global contexts. Furthermore, despite efforts to ensure demographic diversity,
the potential bias inherent in self-reported data and responses on the Likert scale
could have influenced the precision of the TPACK proficiency assessments.
Additionally, although rigorous validation procedures were applied, certain
items exhibited slightly elevated fit statistics in Rasch analysis, potentially
affecting the instrument's reliability in specific contexts. Furthermore, the
cross-sectional design limits causal interpretations and longitudinal insights
into TPACK development over time or across different educational stages.
Moving forward, future research should consider
longitudinal studies to track TPACK development among pre-service teachers
across multiple years and educational stages. Comparative studies across
different countries or educational systems could provide information on
cultural and contextual influences on technology integration in education.
Qualitative research methods could complement quantitative findings by
exploring pre-service teachers' perceptions and strategies related to TPACK
development in-depth. Intervention studies are also needed to evaluate the
effectiveness of targeted professional development or curriculum enhancements
in enhancing TPACK competencies. Furthermore, exploring the function of the
differential elements in diverse demographic groups and validating the TPACK
instrument in diverse educational settings would improve its reliability and
applicability worldwide.
6. Conclusions
In conclusion, this study has meticulously examined
the validity and reliability of a modified TPACK instrument among Indonesian
pre-service teachers across diverse educational programs. Through rigorous
analysis of CFA and Rasch, the instrument has demonstrated robust psychometric
properties, confirming its suitability for assessing the integration of
technology, pedagogy, and content knowledge. The findings of the CFA
underscored the strong model fit of the instrument and the alignment of
questionnaire items with the underlying dimensions of TPACK, as evidenced by
satisfactory factor loadings across seven latent variables. Additionally, Rasch
analysis provided further validation by indicating effective item measurement
without significant DIF across different pre-service teacher groups, ensuring
unbiased assessments. Reliability analysis consistently showed high internal
consistency across all TPACK dimensions, reflecting the instrument's stability
in evaluating pre-service teachers' technological competencies. The study also
revealed nuanced differences in TPACK proficiency among pre-service teachers
specializing in elementary, preschool, and mathematics education, highlighting
specific strengths and areas for improvement within each group. The correlation
and variance analyses elucidated strong relationships between the TPACK
variables and identified key factors influencing the development of TPACK in
educational programmes. This research lays a solid groundwork for future
research to validate TPACK's empirical attributes across diverse educational
levels and contexts. Furthermore, the developed questionnaire holds promise for
research that investigates the factors influencing technological pedagogical
content knowledge.
Komarudin Komarudin:
Writing - Original Draft Supervision, Funding acquisition; Suherman Suherman,
Writing – review & editing, Conceptualization, Writing - Original Draft,
Formal analysis, Methodology, Editing, and Visualization.
Funding Agency
The Research and Community Service Department (LP2M)
funded the reported study at Universitas Islam Negeri Raden Fatah Palembang,
Indonesia.
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