Universitas Muhammadiyah Malang, East Java, Indonesia

JPBI (Jurnal Pendidikan Biologi Indonesia)

p-ISSN 2442-3750, e-ISSN 2537-6204 // Vol. 5 No. 2 July 2019, pp. 185-196

10.22219/jpbi.v5i2.7818 http://ejournal.umm.ac.id/index.php/jpbi jpbi@umm.ac.id 185

Research Article

Moderated effect of teachers’ attitudes to the
contribution of technology competencies on TPACK
Hendra Yulisman a,1,*, Ari Widodo a,2, Riandi a,3, Cut Intan Evtia Nurina a,4
a Master's Program in Science Education, School of Postgraduates, Universitas Pendidikan Indonesia, Jl. Dr. Setiabudhi No.229,

Bandung City, West Java 40154 Indonesia
1 hendra.yulisman@fkip.unsyiah.ac.id*; 2 widodo@upi.edu; 3 rian@upi.edu; 4cutintanevtia@student.upi.edu

* Corresponding author

INTRODUCTION

Technology integration is a complex process that involves many interconnected factors, such as
environmental, technology, individuals, organizations, and pedagogy (Niederhauser & Lindstrom, 2018;
Sánchez, Marcos, González, & GuanLin, 2012). The success of technology integration cannot be seen only
from the availability of technology. However, it must be seen from the teachers’ skill and ability in choosing and
using technology effectively that appropriate with learning content and pedagogy (Bilici, Guzey, & Yamak,
2016). The appropriate technology integration model to describe such skill or ability is Technological
Pedagogical and Content Knowledge (TPACK) (Chai, Koh, Tsai, & Tan, 2011; Joo, Park, & Lim, 2018).

TPACK framework consists of the main components and integration components (a combination of the
main components). The main components consist of Content Knowledge (CK), Pedagogical Knowledge (PK),

A R T I C L E I N F O A B S T R A C T

Article history
Received March 06, 2019

Revised April 16, 2019

Accepted May 26, 2019

Published June 30, 2019

Technological Pedagogical and Content Knowledge (TPACK) can be seen as teacher’s
skills and abilities in choosing and using technology effectively. However, information
about the teacher’s attitudes contribution to technology competencies and science
teacher's TPACK is still limited. Therefore, this study aimed to analyze the relationship
between teacher's technology competencies and their TPACK as well as the effect of
teacher’s attitudes as a moderating effect on relation between these two variables. This
survey research used total sampling technique which involved 88 science teachers from
junior high school of Kota Banda Aceh. The instruments consisted of questionnaires
and multiple-choice test which were distributed directly to the teachers. The data
analysis was performed by using the Partial Least Square approach. The results
showed that technology competencies have a positive relation to TPACK; likewise, the
teachers’ attitudes in technology integration were able to strengthen the positive relation
between technology competency and science teacher’s TPACK. Therefore, teacher's
attitude is an important factor should be concerned in improving teacher's TPACK.

This is an open access article under the CC–BY-SA license

Keywords
Science teacher’s TPACK

Teacher’s attitudes

Technology competencies

How to cite: Yulisman, H., Widodo, A., Riandi, R., & Nurina, C. I. E. (2019). Moderated effect of teachers’ attitudes to the

contribution of technology competencies on TPACK. JPBI (Jurnal Pendidikan Biologi Indonesia), 5(2), 185-196. doi:
https://doi.org/10.22219/jpbi.v5i2.7818

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and Technological Knowledge (TK). Furthermore, the integration components consist of Pedagogical Content
Knowledge (PCK), Technological Content Knowledge (TCK), Pedagogical Technology Knowledge (TPK), and
Technological Pedagogical and Content Knowledge (TPACK) (Koehler, Mishra, & Cain, 2013; Valtonen,
Kukkonen, Kontkanen, Mäkitalo-Siegl, & Sointu, 2018; Valtonen et al., 2017). These seven components are
interrelated and cannot stand alone. Therefore, in order to have a good TPACK, a teacher must have the
ability to develop fluency and cognitive flexibility in each of the main components and relationships between
TPACK components. Finally, they can develop effective solutions in technology integration (Kimmons, 2015;
Koehler et al., 2013).

The TPACK model is suitable to be used to describe and study the technological integration capabilities of
teachers, especially teachers in Indonesia. The reason, several components of TPACK are part of the
Indonesian professional teacher competencies that listed in the Standards Academic Qualification and Teacher
Competencies in Indonesia. These components are pedagogical knowledge (related to pedagogical
competence) and content knowledge (related to professional competence) (Nofrion, Wijayanto, Wilis, & Novio,
2018). Furthermore, the TPACK model also consists of the PCK, which is the knowledge, experience, and skill
that the teacher acquires through teaching experience in the classroom, which is a potential indicator of the
teacher in implementing quality learning activities (Anwar, Rustaman, Widodo, & Redjeki, 2014; Widodo, 2017).

As stated earlier, the integration of technology is influenced by several factors, one of which is an individual
factor. Individual factor such as teachers’ attitudes in technology integration and teachers’ technology
competencies can influence technology integration (Buabeng-Andoh, 2012; Karaca, Can, & Yildirim, 2013;
Yerdelen-Damar, Boz, & Aydın-Günbatar, 2017). Attitude or the disposition of individuals to regulate thoughts,
feelings, and behaviors towards psychological objects (Birkollu, Yucesoy, Baglama, & Kanbul, 2017) is an
indicator of the strength of their beliefs about the object and the critical aspect of those beliefs (Niederhauser &
Lindstrom, 2018). Positive teacher attitudes are significant predictors of the success of the teaching process.
Therefore, if teachers have a positive attitude in technology integration, they can quickly adapt and integrate
technology in teaching-learning processes and practices (Birkollu et al., 2017; Dalal, Archambault, & Shelton,
2017; Huda, Yulisman, Nurina, Erni, & Abdullah, 2018; Yanti, Setiawan, Nurhabibah, & Yannuar, 2018).

Technology competence or the ability to handle various types of technology, both hardware, and software,
for various purposes of learning activities is another factor influencing teachers’ technology integration
(Yerdelen-Damar et al., 2017). The results of the study showed that teachers who have a negative or neutral
attitude towards the technology integration influenced by their knowledge and skills. They do not have sufficient
knowledge and technology skills to integrate or choose the right technology into their learning activities
(Baturay, Gökçearslan, & Ke, 2017; Buabeng-Andoh, 2012).

According to Buabeng-Andoh (2012), teachers’ positive attitude is a determinant in the thriving technology
integration in learning activities despite obstacles such as lack of hardware and software. Furthermore,
Kimmons and Hall (2018) states that teacher beliefs and values may have a stronger influence on the
integration of integration than their knowledge. That is, the teachers’ attitude can be a moderating effect on the
successful integration of technology in learning activities. The moderating effect is caused by a moderator
variable. The moderator variable is the secondary independent variable that chosen to determine whether the
variable affects, changes the strength or even the direction of the relationship between the two constructs
(Fraenkel, Wallen, & Hyun, 2012; Hair, Hult, Ringle, & Sarstedt, 2014).

In Indonesia, information about teachers’ attitudes concerning technology competencies and science
teachers’ TPACK is still limited and focuses on descriptions. The previous studies limited on describing the
competencies, experiences, and attitudes of teachers in technology integration (Huda et al., 2018), the
perceptions of the lecturers in technology integration through mobile learning (Yulisman, 2017), and the
teachers’ ICT level (Miskiah, Suryono, & Sudrajat, 2019). Furthermore, several studies on TPACK are also still
in the scope of describing and focusing on certain materials, such as investigating experience in integrating
technology and TPACK through training activities for science teachers (Agustin, Liliasari, Sinaga, &
Rochintaniawati, 2018) and English teachers (Drajati, Tan, Haryati, Rochsantiningsih, & Zainnuri, 2018),
knowing and analyzing the TPACK from Geography teachers (Nofrion et al., 2018) and English teachers
(Mahdum, 2015), describing the TPACK ability of pre-service Physics teachers (Supriyadi, Bahri, & Waremra,
2018), knowing the role of TPACK in designing learning (Sholihah & Yuliati, 2016), and describing teachers’
TPACK through animal physiology learning (Pusparini, Riandi, & Sriyati, 2017).

Based on these studies, it appears that there was a gap to be filled between technology competencies and
teachers’ attitudes in technology integration with science teachers' TPACK. These variables have a relationship
and influence each other. Also, there was no information about teachers’ attitude in technology integration as a
moderator variable on the relationship of technology competencies with TPACK. Therefore, by using the PLS
approach, this study aimed to analyze the relationship of teacher technology competencies with their TPACK

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and the effect of teachers’ attitudes as a moderating effect on the relations between technology competencies
and TPACK.

METHOD

Research design
This research is quantitative with survey research design. The data collection method used a cross-

sectional survey.

Research samples
This research used total sampling technique involved all junior high school science teachers from Kota

Banda Aceh. The teachers are from different educational backgrounds, namely Biology Education, Physics
Education, and Chemical Education. Therefore, learning activity is conducted based on the teachers’
background and learning material. For example, if there is a learning material related to biology, then teachers
who will teach that subject is the teacher from Biology Education.

The teachers who became the sample of this research was the teacher sent by Education and Culture
Office of Kota Banda Aceh. The total number of samples is 88 teachers (54 Biology teachers, 29 Physics
teachers, 2 Science teachers, 1 Chemistry teachers, and two teachers from other backgrounds). The teachers
are from 19 public junior high schools and nine private junior high schools. Furthermore, in order to obtain more
in-depth information about the state of TPACK and the ability to integrate technology, researchers interviewed
two teachers who are instructors, namely instructor of MGMP (teacher group) activities (Physics teacher, as
well as the chairman of one of the Science MGMP in Banda Aceh) and instructor of multimedia usage in Aceh
province (Biology teacher). This determination was based on their abilities, knowledge, and experience when
becoming training instructors. Thus, the answers they gave were able to describe the state of the Banda Aceh
science teachers.

Instruments

The definition of technology that used in the instruments of technology competencies and teacher attitudes
in technology integration refers to the definition of technology according to Magana and Marzano (2014)
namely electronic, digital, and multimedia devices that used to achieve goals effectively and efficiently.
Furthermore, the definition of technology that used in TPACK instruments refers to Koehler et al. (2013) and
Yerdelen-Damar (2017) stating that the word technology applies equally to analog and digital technologies or
new and old technologies. However, they emphasize that the technology that used should be new and digital
and have innate traits that require particular abilities in its use.

The data about teachers’ attitudes in technology integration were obtained using a Likert scale
questionnaire that consists of 19 questions. Each statement refers to five aspects, namely Strongly Agree,
Agree, Neutral, Disagree, Strongly Disagree (Sugiyono, 2016). The attitude in technology integration is
measured by using The Scale of Attitude Towards Technology developed by Birkollu et al. (2017) and Yavuz
(2005).

The data about technology competencies were obtained using a multiple-choice test that consists of 17
questions. The question indicators were developed based on the Technology Competency scale developed by
Karaca et al. (2013) and Yerdelen-Damar et al. (2017).

The science teachers’ TPACK data was measured using 45 multiple choice test questions. The question
indicators were made based on TPACK for 21st Century Skills instrument (TPACK-21) (Valtonen et al., 2018,
2017) and TPACK survey for Meaningful Learning (Chai et al., 2011; Deng, Chai, So, Qian, & Chen, 2017;
Koh, Chai, & Tsai, 2013). The development of this instrument was adjusted to science content and the
integration of technology in pedagogically meaningful ways that are by the twenty-first-century skills framework
(21st-century skills framework) (Valtonen et al., 2017). Therefore, this instrument is suitable to be used to
measure the TPACK of science teachers. Furthermore, this instrument observes the four capabilities needed in
the 21st century, namely the ability of communication, collaboration, critical thinking, and creative thinking
(Valtonen et al., 2017; Valtonen, Sointu, Mäkitalo-Siegl, & Kukkonen, 2015) and five meaningful learning
dimensions such as active learning, cooperative learning, constructive learning, intentional learning, and
authentic learning (Koh et al., 2013). In particular, indicators of the component of Content Knowledge (CK) and
Pedagogy Knowledge (PK) refer to the Standards of Academic Qualification and Teacher Competencies
(Standar Kualifikasi Akademik dan Kompetensi Guru) (Badan Standar Nasional Pendidikan, 2007).

Before being used, all of the three instruments were tested for validity and reliability on 55 teachers who
have the same characteristics as science teachers. They have experience in teaching Biology, Chemistry, or

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Physics in high school. The trial instruments were distributed via the internet by using Google Form. The
reliability test results showed that all of the three instruments have reliability requirements, namely 0.847 for
science teachers’ TPACK instrument, 0.622 for technology competency instrument, 0.775 for teachers’
attitudes in technology integration instrument. Based on the validity test, several items excluded, namely two
items from the science teachers’ TPACK instrument, three items from the technology competencies instrument,
and two items from the teachers’ attitude in technology integration instrument.

Instruments distribution and data analysis

The research instruments were directly distributed to the teachers through technology integration training
conducted in Faculty of Teacher Training and Education-Universitas Syiah Kuala. The training was initiated by
researchers who collaborated with Faculty of Teacher Training and Education-Universitas Syiah Kuala and
Education and Culture Office of Kota Banda Aceh.

Analysis of the moderating effect variable on the relationship between independent and dependent
variables was evaluated by using the Structural Equational Modeling-Partial Least Square approach (known as
SEM-PLS or called PLS). The PLS approach is suitable for prediction purposes. Also, PLS is not based on
many assumptions, such as data does not have to be ordinarily multivariate (indicators with categories, ordinal
scales, intervals, or ratios can be used on the same model) and do not require a large number of samples.
Furthermore, PLS is not only suitable for prediction purposes but also can be used to explain whether there is a
relationship between latent variables (Ghozali, 2014; Latan & Ramli, 2013; Wong, 2013).

The data analysis started by summing the correct answers on the test item and the total score on the
questionnaire for each indicator of research variables. Furthermore, the analysis of the relationship and the
moderating effect was performed by using SEM-PLS with SmartPLS 3.2.8 software (Ringle, Wende, & Becker,
2015).

Research variables

This research consists of three types of variables, namely exogenous variable (technological
competencies), moderating variable (teachers’ attitudes in technology integration), as well as an endogenous
variable (science teachers’ TPACK). The interaction of the three variables formed the following research model
(Figure 1).

Figure 1. Research model with moderating effect

Table 1. The summary of evaluation of outer and inner models with reflective indicators

Criteria Rule of thumb

FL 0.50 – 0.60
AVE > 0.50
Cross Loading >0.70
The square root of AVE value and the
correlation between latent constructs

The square root of AVE > correlation between latent constructs

CR > 0.60
R2 0.67 (strong), 0.33 (moderate), and 0.19 (weak)
f2 0.35 (strong), 0.15 (moderate), and 0.02 (weak)
Q2 Q2 > 0 represent the model has predictive relevance.

Q2 < 0 represents the model has no predictive relevance. Furthermore, in order
to obtain the value of Q2, we used the following formula.
Q2 = 1 – (1-R2) (1)

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Evaluation of PLS model
The evaluation of the PLS model was performed by assessing the outer model and inner model. Evaluation

of the outer model with reflective indicators consists of convergent validity, discriminant validity, and reliability.
Convergent validity was obtained from the factor loading value and Average Value Extracted (AVE).
Discriminant validity was obtained from the cross-loading value and compared the value from the square root of
AVE with the correlation value between constructs. The reliability test that used and suitable for reflective
indicators is Composite Reliability (CR) because the Cronbach’s alpha value tends to be a lower bound
estimate. Furthermore, the evaluation of the inner model was obtained from the value of R2 for endogenous
variable, effect size (f2), and Q2 to determine the relevance of predictions from the models that have been
designed (Ghozali, 2014; Ghozali & Latan, 2015; Latan & Ramli, 2013; Wong, 2013). The evaluation criteria for
the PLS model shown in Table 1.

RESULTS AND DISCUSSION

The results of the PLS analysis are reported by using the two-step approach. The first step focused on
reporting the results from the outer model. The second step focused on reporting the results from the inner
model. Furthermore, because all indicators are reflective, the interaction approach that used was the product
indicator (Ghozali & Latan, 2015; Latan & Ramli, 2013).

The outer model evaluation results

Evaluation of the outer model was performed twice. Based on the results of the first evaluation of the outer
model (appendix), researchers excluded all indicators with loading values <0.6. It aimed to get a composite
reliability value >0.6. After all indicators with values below 0.6 are excluded, re-estimation was performed. The
re-estimation results shown in Figure 2.

Figure 2. Graphical result of the outer model evaluation

The convergent validity value was determined from the factor loading and AVE values. The value of the

factor loading (the correlation between the indicator and the latent construct) for each indicator used in the
model must be higher than 0.60. The results of evaluations (Table 2) showed that there are no indicators with
a value below 0.5. Furthermore, the AVE values for each construct must be higher than 0.5. Table 2 showed
that all constructs have AVE values greater than 0.5.

The value of reliability in this research was obtained from the Composite Reliability (CR) value. This is
caused by the value of Cronbach’s alpha on SEM-PLS is lower (under-estimate) than the composite reliability
value (Ghozali & Latan, 2015; Wong, 2013). Based on Table 2, each of the research variables had a CR value
greater than 0.7.

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The determination of the discriminant validity was obtained from the value of cross-loading. However,
because of the value of cross-loading is similar to the factor loading value, the researcher only showed the
factor loading values. In the SmartPLS v3.2.8, comparison between the square root of AVE values and
correlation values between constructs can be seen from the value of the Fornell-Larcker Criterium. The
square root of AVE values is displayed diagonally, and the value below is the correlation value between
constructs (Table 3). So, it showed that the values of the square root of AVE are higher than the correlation
values; it can be concluded that the model is valid because it has met the discriminant validity. After all of the
variables that used are valid and reliable, then proceed with a test of the influence between variables and
moderating effects otherwise known as inner model evaluation.

Table 2. The results of convergent validity and reliability test

VAR* Indicators FL** AVE CR

SciTe-TPACK TPACK4 0.680 0.554 0.787
TPACK10 0.740

TPACK16 0.807

TCom TCOM1 0.704 0.575 0.844
TCOM3 0.742

TCOM4 0.822

TCOM5 0.760

TeAtt TEATT1 0.742 0.542 0.778
TEATT2 0.829

TEATT3 0.623

Description: *Variables; **Factor Loading

Table 3. Fornell-Larcker criterium for discriminant validity

Variables SciTe-TPACK TCom TeAtt

SciTe-TPACK 0.744

TCom 0.461 0.758

TeAtt 0.482 0.416 0.736

The inner model evaluation results

Evaluation of the inner model aims to see the significance of all path estimates. The predictive power of
the outer model can be seen from the R2 value of the endogenous variable. Furthermore, in order to obtain
the relevance of predictions, we determined by the value of Q2 (1). In SmartPLS, R2 value was obtained
through PLS Algorithm.

Table 4. The R2 value and rating from PLS algorithm result

Variable R2 R2 adjusted Rating

SciTe-TPACK 0.408 0.327 Moderate

Based on R2 value (Table 4), it showed that 40.8% of science teachers’ TPACK is influenced by

technology competencies and teachers’ attitudes in technology integration and the remaining 59.2%
influenced by other variables outside of this research model. This is due to technology that used as indicators
in technology competencies and the attitudes towards technology integration was information and
communication technology. Meanwhile, the technology used as indicators in science teachers’ TPACK
consists of digital and analog technology. According to Koehler et al. (Koehler et al., 2013), technology in the
TPACK can apply to all technology tools and resources. Therefore, not all technological capabilities within
technology competencies and attitudes towards technology indicators affect science teachers' TPACK. The
value of 0.408 also showed that the research model has a moderate rating.

Based on the calculation result (1), we obtained a Q2 value of 0.408 > 0, mean that the model built has
predictive relevance. Specifically, when inner model evaluation results showed predictive relevance, it
accurately predicts the data points of indicators in reflective inner models of endogenous constructs (Hair et
al., 2014). Therefore, it exhibits that technology competencies and attitudes towards technology integration
influencing science teachers TPACK in Banda Aceh. Furthermore, the values of effect size (Table 5) and
path coefficient (Table 6) were obtained from the bootstrapping method through SmartPLS.

The results of bootstrapping about effect size (Table 5) showed that all of the variable relationships affect
the science teachers’ TPACK variable. However, only the relationship between technology competencies and
teachers’ attitudes has a moderate effect on science teacher TPACK, while the other two relationships have a

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weak effect. According to the description of R2 value, this result caused by the difference in technology in
variable indicators. Interestingly, the combination of technological competencies and attitudes towards
technology integration can improve relations with TPACK to become moderate. It showed that teachers could
improve their TPACK if they have good technology competence and a positive attitude in integrating
technology. According to Chow and Pepe (2015), the teachers will integrate technology into their learning
activities if they feel that they have sufficient knowledge in the use of technology. Therefore, the government
or schools should be investing more in a teacher’s professional development rather than just purchasing
pieces of technology.

Table 5. The effect size values from bootstrapping

Relationships f2 Description

TCom → SciTe-TPACK 0.117 Weak
TCom*TeAtt → SciTe-TPACK 0.158 Moderate
TeAtt → SciTe-TPACK 0.088 Weak

Table 6. The path coefficient values from bootstrapping

Relationships Mean SD t-statistics

TCom → SciTe-TPACK 0.29 0.09 3.22
TCom*TeAtt → SciTe-TPACK 0.36 0.06 5.83
TeAtt → SciTe-TPACK 0.26 0.10 2.59

The results of bootstrapping about path coefficients (Table 6) showed that all of the relationships have a

value of t-statistics higher than t table. It means all of the three relationships have a significant effect on the
science teachers’ TPACK. These results indicate that technology competency, attitudes towards technology
integration, and the relationship of competence and attitudes in technology integration affect science teachers'
TPACK. It showed that the better the competence and attitude of the teacher, the better the ability of their
TPACK. These results support the previous research conducted by Yerdelen-Damar et al. (2017) and Karaca
et al. (Karaca et al., 2013), showed that technology competence and attitudes towards technology integration
have a significant effect on technology integration or in this study is the ability of their TPACK. Therefore, to
improve the ability of science teachers' TPACK, it must notice their technological competencies and attitudes
towards technology integration.

The results of hypothesis testing

The analysis of hypothesis testing was obtained based on the value of t-statistics that obtained from the
bootstrapping method (Table 6). In the H1 hypothesis test, it showed that the value of t-statistics from the
relationship between technology competencies and science teachers’ TPACK of 3.22>1.96, then H0 was
rejected. It means technology competencies have a positive relationship with the science teachers’ TPACK.
The value of t-statistics that obtained also showed that technology competencies have a significant effect on
the science teachers’ TPACK.

Furthermore, in the H2 hypothesis test showed that the t-statistic value on the interaction between
technology competencies and teachers’ attitudes towards science teachers’ TPACK obtained a value of
5.83>1.96, then H0 was rejected. It means the teachers’ attitude is a moderator variable or able to moderate
the relationship between technology competency and science teachers’ TPACK. Table 6 also showed that the
teachers’ attitudes also have a significant effect on science teachers’ TPACK. Therefore, based on the model
that built and the results of hypothesis testing, showed that technology competencies, attitudes towards
technology integration, and the relationship of competencies and attitudes towards technology integration are
predictors of science teachers’ TPACK.

The results obtained by this study are consistent with previous research on the relationship between
technological competencies, attitudes in technology integration, and TPACK capabilities. Research conducted
by Yerdelen-Damar et al. (2017) showed that the competencies and attitudes of teachers have a direct effect
on teachers’ TPACK. Besides, the research also showed that there was a positive influence between
technological competencies and the attitude of teachers in technology integration. Other research results from
Karaca et al. (2013) showed that technology competencies and teachers’ attitudes have a direct effect on
technology integration. The research also showed exciting results where technology competencies have a
significant influence on teachers’ attitudes and beliefs. It means the teachers will have positive attitudes and
beliefs if they have good technology competencies. The results of these studies indicate that technology
competencies and attitudes towards technology integration are predictors in technology integration.

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Specifically, technology competencies and attitudes towards technology integration affect the teacher's
Technological Knowledge (TK). TK is the main component forming TPACK. The type of technology used as
an indicator in this component is ICT. It was similar to the type of technology used as an indicator in
technology competence and attitudes towards technology integration. According to the research results from
Yurdakul and Coklar (2014), it showed that ICT usage had a significant effect on the teachers’ TPACK.
Specifically, ICT usage affects the TK of teachers. Therefore, it showed that the relationship formed from
technology competencies and attitudes in technology integration towards TPACK was caused by indicators of
TK components specifically consisting of ICT.

Interpretation of interaction effects

Interpretation of the two-way interaction effects using an Excel file template obtained from the Dawson
(2019). The template facilitates researchers to interpret the moderating effects of teacher attitudes in the
relationship between technology competency and TPACK. The results of the interpretation in the form of a
chart shown in Figure 3.

Figure 3. The result of two-way interaction effects

Figure 3 showed that the higher the teachers’ attitude, then the higher the degree of technology

competencies and TPACK. It means the teachers’ attitude strengthens the positive relationship between
technology competencies and science teachers’ TPACK. These results indicated that the teachers' attitudes
have an essential role in technology integration; in this case, it is strengthening the relationship between
technology competencies and science teachers' TPACK.

Based on the research results, it appears that attitudes play an essential role in integrating technology. It
caused by attitudes towards technology integration affected the acceptance and use of technology in learning
activities (Scherer, Tondeur, Siddiq, & Baran, 2018). However, this factor is still often ignored (Niederhauser &
Lindstrom, 2018). The government is still focused on providing technology tools and facilities. They consider
technology integration to take place automatically when technology is available (Baturay et al., 2017; Ertmer,
1999; Niederhauser & Lindstrom, 2018). Specifically, it can be seen from the “Smart City” program from the
Aceh Province government. This program only focuses on providing internet services in all Aceh Province
Vocational Schools (Saifullah, 2017).

According to Ertmer (1999), attitudes often ignored due to attitudes are the intrinsic factors of the teacher.
Intrinsic factors are abstract and personal. Therefore, this problem is more challenging to solve than solving
problems related to extrinsic factors (Baturay et al., 2017; Ertmer, 1999; Vongkulluksn, Xie, & Bowman, 2018).
This problem can be solved by choosing and using technology that often used by the teacher. So, the
teachers are easy to integrate the technology into their learning activities. The statement is in line with the
interview results with the Physics teacher stating that the teacher uses his smartphone as a learning media,
by giving it to groups of students in alternately. This method was used to overcome the problem of technology
availability and was obtained by the teacher when they take part in MGMP activities. This information
indicated that the use of mobile learning must be encouraged in schools because of its easy to use (Yulisman,
2017). It showed that the teachers’ positive attitude would emerge if the available technology is a technology

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that is acquainted with them, and the technology is accessible for them to use. Furthermore, the teachers'
positive attitude will help them find ways to integrate technology into their learning activities.

According to Birkollu (2017) and Buabeng-Andoh (2012), the positive attitude of teachers in the application
of technology will facilitate them in adopting and integrating technology into their learning activities. It means,
if the teacher is interested in using technology, then they will find ways to integrate the technology into their
learning activities. Therefore, instead of focusing on increasing the competence and ability to integrate
teacher technology, it is imperative to pay attention to the teachers’ attitudes on the use of technology. The
results of interviews with Physics teachers also showed that training activities using technology for teachers
should not be focused on how to use technology only, but how to use technology in learning activities.

The results of interviews from Biology teachers showed that teachers want to integrate technology, but they
are still constrained by an organizational factor. Although he has good technology competencies and a positive
attitude in technology integration, the process of integrating technology into learning activities cannot be
performed because of constraints in school facilities, such as electricity, the lack of tools and complicated
licensing in the use of technological tools. It means organizational factor can hamper individual factor in
technology integration. According to Niederhauser (Niederhauser & Lindstrom, 2018), five factors influence the
integration of technology into learning activities, namely environmental, technological, individual, organizational,
and pedagogical considerations.

CONCLUSION

Technology competencies have a positive relationship and a significant effect on the science teachers’
TPACK. Furthermore, the variable of teachers’ attitudes in technology integration is a moderator variable or
able to moderate the relationship of technology competencies and science teachers’ TPACK. The results from
the interpretation of the interaction effects also indicated that the teachers’ attitudes strengthen the positive
relationship between technology competencies and science teachers’ TPACK.

The results of this research indicated that the teachers’ attitudes have an essential role in integrating
technology into learning activities. Therefore, researchers, teachers, or education providers should not ignore
teachers' attitudes. Furthermore, further information is needed on the influence of other factors that influence
the integration of TPACK, especially in Indonesia.

ACKNOWLEDGMENT

We would like to thank Indonesia Endowment Fund for Education, abbreviated as LPDP (Lembaga
Pengelola Dana Pendidikan), the Faculty of Teacher Training and Education-Universitas Syiah Kuala,
Education and Culture Office of Kota Banda Aceh, and junior high school science teachers in Kota Banda Aceh
for supporting us in order to collect research data.

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