• International Journal of Technology (IJTech)
  • Vol 15, No 3 (2024)

Factors Influencing Confidence in STEM Practices among Preservice Teachers

Factors Influencing Confidence in STEM Practices among Preservice Teachers

Title: Factors Influencing Confidence in STEM Practices among Preservice Teachers
Sian Hoon Teoh, Geethanjali Narayanan, Sharipah Ruzaina Syed Aris, Norezan Ibrahim, Badrul Isa

Corresponding email:

Cite this article as:
Teoh, S.H., Narayanan, G., Aris, S.R.S., Ibrahim, N., Isa, B., 2024. Factors Influencing Confidence in STEM Practices among Preservice Teachers. International Journal of Technology. Volume 15(3), pp. 687-696

Sian Hoon Teoh Department of Mathematics Education, Faculty of Education, Universiti Teknologi MARA, 42300 Bandar Puncak Alam, Selangor, Malaysia
Geethanjali Narayanan Department of Teaching English as a Second Language, Faculty of Education, Universiti Teknologi MARA, 42300 Bandar Puncak Alam, Selangor, Malaysia
Sharipah Ruzaina Syed Aris Department of Science Education, Faculty of Education, Universiti Teknologi MARA, 42300 Bandar Puncak Alam, Selangor, Malaysia
Norezan Ibrahim Department of Science Education, Faculty of Education, Universiti Teknologi MARA, 42300 Bandar Puncak Alam, Selangor, Malaysia
Badrul Isa Department of Art & Design Education, Faculty of Education, Universiti Teknologi MARA, 42300 Bandar Puncak Alam, Selangor, Malaysia
Email to Corresponding Author

Factors Influencing Confidence in STEM Practices among Preservice Teachers

Preservice teachers are more confident in teaching mathematics, specifically when they have a better understanding of the subject. In teaching and microteaching practices, content and understanding of mathematics pedagogy are monitored and evaluated. However, additional investigation on science, technology, engineering, and mathematics (STEM) skills and practices is needed to meet the current need to train teachers with 21st-century skills in order to educate future generations. Therefore, this study aimed to determine the way in which factors influencing effort, performances in algebra, and statistics were related to the self-confidence of preservice teachers in teaching STEM practices. To achieve this purpose, a correlational approach was used to collect information from 113 prospective science teachers at a public institution in Malaysia. In response to the questionnaire, participants provided impressions of the experience and level of confidence in using STEM. The result showed that the efforts of preservice teachers had a significant impact on the confidence in using STEM subjects in the mathematics classroom. Furthermore, the future of STEM education was in the collective and practical efforts to connect STEM with the real world.

Algebra; Confidence; Effort; Statistics; STEM (Science, Technology, Engineering and Mathematics)


Preservice teachers are evaluated based on subject knowledge, engagement in teaching, and application of teaching theory (Asare and Amo, 2023; Stinken-Rösner et al., 2023; Cai, Zhu, and Tian,  2022; Diamah et al., 2022; Baier et al., 2021; Engin and Tasgin, 2021). Teachers are expected to guide future generations toward academic and professional excellence (VanDerHeide and Marciano, 2022; Koh and Tan, 2021; Van-Driel and Berry, 2010), thereby indicating the significance of STEM (science, technology, engineering, and mathematics) skills in the current rapidly evolving digital world (Ryu, Mentzer and Knobloch, 2019). With increasing attention being paid to STEM education, the integration of STEM subjects into the real world by preservice teachers has become critical for success (Syafril et al., 2021)

Learning and teaching STEM subjects presents unique challenges for preservice teachers, as they are in a transition period from practicing and supervising while preparing to teach students (Pimthong and Williams, 2018). The effectiveness of previous university studies in increasing the awareness of integrating STEM elements into the curriculum remains a significant study gap.

To effectively guide and inspire students, preservice teachers need to proactively develop 21st-century skills, particularly in STEM education. By connecting previous learning experiences and current teaching practices, novice teachers can obtain valuable insights to effectively guide the efforts. Additionally, teachers’ participation in STEM education assists in bridging the gap between theory and practice and promotes the authenticity of STEM practices (Margot and Kettler, 2019; Holmlund, Lesseig, and Slavit, 2018). This implies that preservice teachers' willingness to put effort and actively engage in STEM practices is critical to building self-confidence, strengthening behavioral intentions, and creating more opportunities to engage students in these activities (Teoh et al., 2022; Asvial, Mayangsari, and Yudistriansyah, 2021; Margot and Kettler, 2019).

A strong knowledge of algebra and statistics is essential for preservice mathematics teachers to excel in STEM classes (Ng, 2019). This is because a higher level of algebra knowledge helps in increasing confidence in teaching mathematics concepts, and also allows teachers to effectively introduce a broader range of STEM concepts. Therefore, preservice teachers who have strong knowledge of algebra can confidently guide students in the problem-solving process, such as when faced with a problem comprising the concept of inverse proportionality in algebra. These problem-solving activities are key to larger STEM activities, such as painting a house. By using algebra knowledge, teachers can easily integrate additional STEM components into the classroom by engaging students in discussions, such as a way of selecting paints and brushes to paint efficiently and considering various factors that influence painting speed.

Aside from the above, a strong foundation in algebra and statistics also enables mathematics preservice teachers to excel in teaching concepts and integrate a variety of STEM elements into the lessons, thereby providing students with a more comprehensive and engaging educational experience. However, these disciplines can be challenging and require more effort to develop future students' talents (Khalil and Kier, 2021; Mergler and Spooner-Lane, 2012).

Assessing preservice teachers' confidence and effort in teaching STEM practices, particularly algebra and statistics, provides an opportunity to increase competency. The ability of teachers to effectively integrate the basic mathematical concepts into real-world applications of STEM is critical to preparing students for the demands of a rapidly evolving world of work that relies heavily on STEM skills (Starr et al., 2022; Black et al., 2021; Ng, 2019). By focusing on the role of algebra and statistics in STEM education, educational institutions and policymakers can develop targeted interventions to strengthen preservice teachers' pedagogical skills and content knowledge in these areas, thereby cultivating more competent and confident STEM educators.

This study aims to observe and understand the confidence level of preservice mathematics teachers regarding their efforts and competence in teaching STEM practices, with a particular focus on algebra and statistics. Subsequently, this study provides answers for the following questions:

·   First Research Question: What are the levels of confidence, effort, and achievement in algebra and statistics among preservice teachers?

·   Second Research Question: Is there a significant model that explains the relationship between confidence in STEM practices and the independent variables of effort, algebra, and statistics performance?

The results provide valuable insight into the factors influencing the confidence of prospective teachers in STEM teaching practices and help educational institutions and policymakers develop more effective strategies to support and enable teachers to become competent. In general, the goal is to improve the total quality of STEM education and create a more dynamic and innovative learning environment for students.

Experimental Methods

2.1. Study Design

     This study used a correlational design to collect quantitative data. The quantitative approach focused on examining the relationship between variables, namely confidence, effort, and mathematics achievement (algebra and statistics).

2.2. Sampling

To collect quantitative data, this study used cluster sampling, where samples were selected randomly from existing categories. In this study, the cluster included the entire sample of 941 individuals who were divided into 37 clusters. The similarity between the individuals in the population was that they enrolled in a preservice program in the science and mathematics discipline at a Malaysian public university. Additionally, the individuals had comparable characteristics that met the selection criteria. These characteristics satisfied investigations, which required the application of basic scientific knowledge, particularly algebra and statistics. During the study period, this group of preservice teachers was exposed to relevant information. The similarity of these characteristics was important for cluster sampling because it allowed meaningful comparisons and conclusions to be made regarding the particular group being studied (Raifman et al., 2022). Therefore, the cluster sampling was used to gain a better understanding of preservice teachers' experiences with STEM learning. Since all individuals in a given cluster shared similar characteristics, data collected from each cluster would possibly provide valuable insight into STEM learning experiences, attitudes, and perceptions.

The sample consisted of 6 clusters totaling 37 clusters, which were selected randomly. Random sampling was a fundamental principle because it helped ensure that the sample was representative of the entire population and reduced the possibility of bias during the selection process. Consequently, the number of participants from the 6 clusters was 113 people, depending on the willingness to participate. The sample size was considered sufficient from a statistical perspective. According to the central limit theorem, sample sizes generally had to be large enough to provide statistical power and accurate estimates (Stroock, 2010). Additionally, the clusters were homogeneous to each other due to the science and mathematics-focused nature of the studies at the university, but the internally heterogeneous grouping was apparent as they were all selected to enrol in the program through an entrance selection process. To eliminate possible bias due to the relatively small number of clusters, bootstrap sampling was used in data analysis (Cameron, Gelbach, and Miller, 2008). Bootstrap sampling is a resampling method that comprised repeatedly taking random samples and replacing the samples from the original data set. This method allowed the estimation of sample variability and helped increase the robustness of statistical inference, particularly with limited cluster sizes.

2.3. Data Collection

       The largest selection of preservice teachers was in formation to hold a virtual meeting. Efforts were made to participate in the investigation to ensure proper accountability. At the meeting, the overview of the investigation was presented to preservice teachers and they responded to the instrument.

2.4. Instrumentation

    Participants responded to two instruments, namely a questionnaire and a test. The primary instrument for questions concerned the perceptions in the context of beliefs and efforts in STEM practice. These building blocks were important aspects that needed to be changed in the transformation of STEM education (Ministry of Education Malaysia, 2013). Furthermore, the items were validated by two experts to establish valid conditions. Also, the construct validity of the questionnaire was established through factorial analysis. Based on the reliability test, Cronbach's alpha values for effort and confidence were 0.896 and 0.899, respectively.

In the context of construct validity, KMO tests showed adequate and high variability, with KMO = 0.889 > 0.50. This was because the value was closer to 1, indicating high variability in the data. Additionally, Bartlett test showed that there were sufficient correlations among the variables with chi-square = 981.455, df = 66, and significance < 0.05. The result signified that the associated probability was less than 0.05. Statistically, there were two extracted factors which referred to components account for the variance among the 12 variables. The percentage of variance accounted for by the component was 67.85% of cumulative variance.

In this study, the two components were rotated orthogonally, and the variable loadings (for some items) on the factors were shown in Table 1. Subsequently, only factor loadings greater than 0.5 were described. First, items 14, 15, 8, 6, and 7 were highly correlated to the first component (confidence) with factor loadings of 0.873, 0.800, 0.786, 0.730, and 0.685, respectively. In this analysis, item 9 was rejected because the variation in factor loadings between the two components was very small, namely 0.535 - 0.533 = 0.012, less than 0.2, which was called the limit value for determining the remaining significant factor loadings. Second, the analysis showed that items 1, 2, 3, 4, 12, and 13 for the second component (effort) were highly correlated with factor loadings (for several items) of 0.873, 0.800, 0.786, 0.730, and 0.685, respectively.

Table 1 Rotated Component with a sample of the items


Component 1

Component 2

14. My confidence level in how to conduct STEM education is high.



15. I am confident enough to introduce STEM education to my friends or community.



13. I need to prepare myself to apply STEM in my practicum.



12. I will put a lot of effort into supporting the development of STEM education.



        The second instrument was an algebra and statistics test. In this process, the test questions were adapted from standardized examinations (California Department of Education, 2008a; 2008b). Several examples of the items were shown in Table 2. In the reliability test, Cronbach's alpha values for statistics and algebra were 0.795 and 0.620, respectively.

Table 2 Sample items


Sample item


1.       Rico’s first three test scores in biology were 65, 90, and 73. What was his mean score?



1.       Lisa typed a 1000-word essay at an average rate of 20 words per minute. If she started typing at 6:20 p.m. and did not have any breaks, at what time did Lisa finish typing the essay?

2.       Stephanie is reading a 456-page book. During the past 7 days, she has read 168 pages. If she continues reading at the same rate, how many more days will it take her to complete the book?

Results and Discussion

3.1. Finding 1: Level of confidence in STEM practices

        Finding 1 analyzed the data to provide descriptive statistics regarding levels of trust and other factors. Subsequently, the following questions were answered:

Question One: What are the levels of confidence, effort, and achievement in algebra and statistics among preservice teachers?

        Confidence and effort were measured on a Likert Scale ranging from ‘1’ for ‘strongly disagree’ to ‘5’ for ‘strongly agree’. Table 3 shows the level of confidence (mean = 4.263, standard deviation = 0.579) and effort (mean = 3.993, standard deviation = 0.704). The result implied that the level of confidence was rated slightly higher. Additionally, it was observed that preservice teachers needed to put more effort into studying algebra because their scores (in Table 3) were low, with mean = 5.080 (total score = 10 marks). The statistical performance was a bit higher (mean =6.204) than algebra score.

Table 3 Descriptive Statistics



Bootstrapa (1000 bootstrap samples)


Std. Error

95% Confidence Interval




Mean, M






Std. Deviation



















Std. Deviation












Algebra Achievement (total =10 marks)







Std. Deviation












Statistics Achievement







Std. Deviation












 3.2. Finding 2: Model of confidence in STEM practices

        Finding 2 analyzed the data to present the inferential statistics of confidence level and other factors. Subsequently, the following study question was answered:

Question Two: Is there a significant model that explains the relationship between confidence in STEM practices and the independent variables of effort, algebra, and statistics performance?

        Least square Regression with the stepwise method was conducted on the set of data, which included dependent (confidence) and independent variables (effort, algebra, and statistics performance). Table 4 shows the regression model with R2 = 0.546, indicating that 54.6% of the variation in the dependent variable (confidence) was explained in the independent variable of effort but not by other variables (algebra and statistics).

Table 4 Model Summary



R Square

Adjusted R Square

Std. Error of the Estimate






a. Predictors: (Constant), Effort

        The model was significantly presented since ANOVA analysis in Table 5 shows that F = 133.346 with p < 0.05.

Table 5 ANOVAa


Sum of Squares


Mean Square






















a. Dependent Variable: Confidence; b. Predictors: (Constant), Effort

        According to Table 6, the model was significantly constructed with only one independent variable (effort) for the dependent (Confidence).

Table 6 The Regression Model


Unstandardized Coefficients

Standardized Coefficients




Std. Error















a. Dependent Variable: Confidence

        According to Table 7, algebra and statistics performance did not contribute to the model.

Table 7 Excluded Variables


Beta In



Partial Correlation

Collinearity Statistics



Statistics Achievement






Algebra Achievement






a.        Dependent Variable: Confidence; b. Predictors in the Model: (Constant), Effort

        Although algebra and statistics performance did not contribute to the level of confidence, these two variables showed a fairly significant relationship. Table 8 shows that the correlation coefficient for the two variables was 0.646, where p < 0.05. The results also showed a strong positive relationship between confidence and effort, with the correlation coefficient being 0.739 (p < 0.05). Meanwhile, the relationship between effort and algebra performance was weak and negative, with a correlation coefficient of -0.19 (p < 0.05).

Table 8 Correlations

The two Variables

Pearson Correlation

Sig. (2-tailed)

95% Confidence Intervals (2-tailed)a



Confidence and Effort





Confidence and Algebra Achievement





Confidence and Statistics Achievement





Effort and Algebra Achievement





Effort and Statistics Achievement





Algebra Achievement and Statistics Achievement






4.    Discussion

    The results suggested that preservice teachers could be more confident in STEM practices. Influencing factors showed that teachers obtained confidence by completing academic assignments and practicing independently. However, results in algebra and statistics did not contribute to the degree of certainty. Even though algebra and statistics were both important and related to each other, as shown by the correlation coefficient, they did not contribute to the level of confidence in STEM practices. The results showed a slightly negative correlation between effort and algebra. This could be interpreted as the need to try harder when teachers were weak in algebra. Therefore, preservice teachers relied heavily on the efforts for any improvement. Despite the results, teachers’ commitment to STEM practices significantly influenced the development. It was suggested that universities needed to create more assignments and projects to support STEM practices for preservice teachers.

        Further efforts were needed to develop pedagogy in STEM teaching for preservice teachers, as it was suggested that teachers should do more to develop the skills of the future students (Lee, Hsu, and Cheng, 2022; Khalil and Kier, 2021; Leung, 2020; Pellas et al., 2020; Mergler and Spooner-Lane, 2012). However, equipping preservice teachers with comprehensive and high-quality STEM knowledge, particularly pedagogical knowledge, was a challenge. The challenges of teaching was in informal educational experiences, such as integrating technological tools into the learning environment (Gu et al., 2023; Guan et al., 2023; Love and Hughes, 2022; Neo et al., 2022). This scenario also showed the importance of trying to acquire more experience in integrating STEM subjects into the classroom. The experience could come from informal activities, such as collaborative activities, which had been suggested to improve preservice teachers' STEM skills (Berisha and Vula, 2021; Kim and Keyhani, 2019).
      Preservice teachers also had lower algebra scores compared to the statistics. The results suggested that preservice teachers needed training to improve algebra skills as highlighted in previous studies (Johar et al., 2023). Although there was a relationship between the two variables, namely effort and algebra performance, the relationship was negative. This negative sign indicated that more effort was needed for improvement.


    In conclusion, the implications of this study were categorized into three. First, it was suggested that preservice teachers could acquire confidence in STEM practices through their personal efforts, commitment, and practice. Therefore, universities should develop more assignments and projects that explicitly targeted STEM practices. This would enable preservice teachers to develop greater confidence and competence in teaching STEM subjects. Second, this study described the importance of developing pedagogical skills in STEM education for prospective teachers. To teach STEM subjects effectively, preservice teachers needed to have content and pedagogical knowledge that would enable teachers to teach complex concepts effectively and engage students in meaningful learning experiences. Several activitiess, such as integrating technology tools into the learning environment and engaging in collaborative activities could also contribute to preservice teachers' STEM knowledge. Third, this study described the lower algebra scores of preservice teachers compared to the statistics, and the negative correlation between effort and algebra performance. The results suggested the need for targeted training and support to improve preservice teachers' algebra knowledge. In summary, teachers could remain confident in implementing STEM subjects by acquiring additional STEM-related knowledge in order to have a strong foundation in both science and mathematics. Additionally, the future of STEM education relied on collaborative and practical efforts to connect STEM with real-world applications.


This study is part of a larger study supported by the Special Research Grant (Geran Penyelidikan Khas) from Universiti Teknologi MARA (UiTM). Grant: 600-RMC/GPK5/3 (040/2020).

Supplementary Material
R1-IE-6366-20230801172451.pdf This is a list of corrections.

Asare, P.Y., Amo, S.K., 2023. Developing Preservice Teachers’ Teaching Engagement Efficacy: A Classroom Managerial Implication. Cogent Education, Volume 10(1), pp. 117

Asvial, M., Mayangsari, J., Yudistriansyah, A., 2021. Behavioral Intention of e-Learning: A Case Study of Distance Learning at a Junior High School in Indonesia due to the COVID-19 Pandemic. International Journal of Technology, Volume 12(1), pp. 54–64

Baier, F., Maurer, C., Dignath, C., Kunter, M., 2021. Fostering Pre-service Teachers’ Theoretical Knowledge Application: Studying with and without Text-based Cases. Instructional Science, Volume 49(6), pp. 855876

Berisha, F., Vula, E., 2021. Developing Pre-service Teachers Conceptualization of STEM and STEM Pedagogical Practices. Frontiers in Education, Volume 6, p. 585075

Black, S.E., Muller, C., Spitz-Oener, A., He, Z., Hung, K., Warren, J.R., 2021. The Importance of STEM: High School Knowledge, Skills and Occupations in an Era of Growing Inequality. Research Policy, Volume 50(7), p. 104249

Cai, Z., Zhu, J., Tian, S., 2022. Preservice Teachers’ Teaching Internship Affects Professional Identity: Self-efficacy and Learning Engagement as Mediators. Frontiers in Psychology, Volume 13, p. 1070763

California Department of Education, 2008a. Algebra and Functions. Available online at: https://www.hemetadultschool.org/pdf/MATH_F.pdf, Accessed on October 8, 2022

California Department of Education, 2008b. Data Analysis, and Probability. Available online at: http://ccasillas.weebly.com/uploads/3/0/8/0/30800917/stats,_probability.pdf, Accessed on October 8, 2022

Cameron, A.C., Gelbach, J., Miller, D.L., 2008. Bootstrap-Based Improvements for Inference with Clustered Errors. The Review of Economics and Statistics, Volume 90, pp. 414–427

Diamah, A., Rahmawati, Y., Paristiowati, M., Fitriani, E., Irwanto, I., Dobson, S., Sevilla, D., 2022. Evaluating the Effectiveness of Technological Pedagogical Content Knowledge-based Training Program in Enhancing Pre-service Teachers’ Perceptions of Technological Pedagogical Content Knowledge. Frontiers in Education, Volume 7, p, 897447

Engin, M.Ç., Tasgin, A., 2021. Pre-service Teachers’ Professional Commitment and their Ability to Choose Teaching Techniques. Journal of Education and Future, Volume 21, pp. 29–40

Gu, X., Tong, D., Shi, P., Zou, Y., Yuan, H., Chen, C., Zhao, G., 2023. Incorporating STEAM Activities into Creativity Training in Higher Education. Thinking Skills and Creativity, Volume 50, p. 101395

Guan, J.-Q., Wang, L.-H., Chen, Q., Jin, K., Hwang, G.-J., 2023. Effects of a Virtual Reality-based Pottery Making Approach on Junior High School Students’ Creativity and Learning Engagement. Interactive Learning Environments, Volume 31(4), pp. 2016–2032

Holmlund, T.D., Lesseig, K., Slavit, D., 2018. Making Sense of “STEM Education” in K-12 Contexts. International Journal of STEM Education, Volume 5(1), pp. 1–18

Johar, R., Desy, D., Ramli, M., Sasalia, P., Walker, H.-C. O., 2023. Preservice Teachers’ Noticing Skills in Relation to Student Misconceptions in Algebra. European Journal of Educational Research, Volume 12(2), pp. 865–879

Khalil, D., Kier, M., 2021. Equity-Centered Design Thinking in STEM Instructional Leadership. Journal of Cases in Educational Leadership, Volume 24(1), pp. 69–85

Kim, M. S., Keyhani, N., 2019. Understanding STEM Teacher Learning in an Informal Setting: a Case Study of a Novice STEM Teacher. Research and Practice in Technology Enhanced Learning, Volume 14(1), p. 9

Koh, D., Tan, A.L., 2021. Singaporean Pre-service Teachers’ Perceptions of STEM Epistemic Practices and Education. Journal of STEM Teacher Education, Volume 56(2), pp. 1–30

Lee, S.W.-Y., Hsu, Y.-T., Cheng, K.-H., 2022. Do Curious Students Learn More Science in an Immersive Virtual Reality Environment? Exploring the Impact of Advance Organizers and Epistemic Curiosity. Computers & Education, Volume 182, p. 104456

Leung, A., 2020. Boundary Crossing Pedagogy in STEM Education. International Journal of STEM Education, Volume 7(1), p. 15

Love, T.S., Hughes, A.J., 2022. Engineering Pedagogical Content Knowledge: Examining Correlations with Formal and Informal Preparation Experiences. International Journal of STEM Education, Volume 9(1), p. 29

Margot, K.C., Kettler, T., 2019. Teachers’ Perception of STEM Integration and Education: A Systematic Literature Review. International Journal of STEM Education, Volume 6(1). pp. 1–16

Mergler, A.G., Spooner-Lane, R., 2012. What Pre-Service Teachers Need to Know to Be Effective at Values-Based Education. Australian Journal of Teacher Education, Volume 37(8), pp. 66–81

Ministry of Education Malaysia, 2013. Malaysia Education Blueprint 2013-2025 (Preschool to PostSecondary Education). Putrajaya. Available online at: https://www.moe.gov.my/menumedia/media-cetak/penerbitan/dasar/1207 malaysia-education-blueprint-2013-2025/file, Accessed on October 8, 2022

Neo, M., Lee, C.P., Tan, H.Y., Neo, T.K., Tan, Y.X., Mahendru, N., Ismat, Z., 2022. Enhancing Students’ Online Learning Experiences with Artificial Intelligence (AI): The MERLIN Project. International Journal of Technology, Volume 13(5), pp. 1023–1034

Ng, S.B., 2019. Exploring STEM Competences for the 21st Century. UNESCO International Bureau of Education. Available online at: https://unesdoc.unesco.org/ark:/48223/pf0000368485, Accessed on October  8, 2022

Pellas, N., Dengel, A., Christopoulos, A., 2020. A Scoping Review of Immersive Virtual Reality in STEM Education. IEEE Transactions on Learning Technologies, Volume 13(4), pp. 748–761

Pimthong, P., Williams, J., 2018. Preservice Teachers’ Understanding of STEM Education. Kasetsart Journal of Social Sciences, Volume 2018, pp. 1–7

Raifman, S., DeVost, M.A., Digitale, J.C., Chen, Y.H., Morris, M.D. 2022. Respondent-Driven Sampling: A Sampling Method for Hard-to-Reach Populations and Beyond. Current Epidemiology Reports, Volume. 9 (1), pp. 38–47

Ryu, M., Mentzer, N., Knobloch, N., 2019. Preservice Teachers’ Experiences of STEM Integration: Challenges and Implications for Integrated STEM Teacher Preparation. International Journal of Technology and Design Education, Volume 29(3), pp. 493–512

Starr, C.R., Ramos Carranza, P., Simpkins, S.D., 2022. Stability and Changes in High School Students’ STEM Career Expectations: Variability Based on STEM Support and Parent Education. Journal of Adolescence, Volume 94(6), pp. 906–919

Stinken-Rösner, L., Hofer, E., Rodenhauser, A., Abels, S., 2023. Technology Implementation in Pre-Service Science Teacher Education Based on the Transformative View of TPACK: Effects on Pre-Service Teachers’ TPACK, Behavioral Orientations and Actions in Practice. Education Sciences, Volume 13(7), p. 732

Stroock, D.W., 2010. Probability Theory: An Analytic View. 2nd Edition. Cambridge University Press

Syafril, S., Rahayu, T., Al-Munawwarah, S.F., Satar, I., Halim, L.B., Yaumas, N.E., Pahrudin, A., 2021. Mini Review: Improving Teachers’ Quality In STEM-Based Science Teaching-Learning In Secondary School. Journal of Physics: Conference Series, Volume 1796(1), p. 012072

Teoh, S.H., Mohamed, S.R., Mohd, A.H., Rasid, N.S.M., Yusof, M.M.M., 2022. Creating Engagement Opportunity for Math Learning. International Journal of Technology, Volume 13(5), pp. 1013–1022

VanDerHeide, J., Marciano, J.E., 2022. Preservice Teachers Taking Action: Enacting Teaching Practices and Pedagogical Theories in an After-School Literacy Club Field Experience. Journal of Teacher Education, Volume 73(2), pp. 175–187

Van-Driel, J.H., Berry, A., 2010. Pedagogical Content Knowledge. In: International Encyclopedia of Education. Third Edition, Elsevier, pp. 656–661