Prerpints.org logo
Preprint
Article

This version is not peer-reviewed.

Understanding Teacher Readiness for Online Teaching and Learning: A Study of Cambodian Higher Education

A peer-reviewed article of this preprint also exists.

Submitted:

09 October 2024

Posted:

11 October 2024

You are already at the latest version

Abstract
This study examines Cambodian higher education teachers' readiness for online teaching and learning (OTL) using a structural equation modeling (SEM) approach. The framework centers around three key dimensions: teachers' self-efficacy in technological, pedagogical, and content knowledge (TPACK), their perceived online teaching presence, and the institutional support they receive. A quantitative survey was administered to 140 teachers at the university level. Teachers' online teaching experience positively influenced their TPACK self-efficacy. Online teaching experience also had a significant positive impact on teachers perceived online teaching presences. Furthermore, teachers' perceptions of institutional support were positively associated with their online teaching experience. These results highlight the critical role of teachers' online teaching experience in shaping their readiness for OTL. The findings suggest that targeted professional development programs and institutional support mechanisms can effectively enhance teachers' self-efficacy, online teaching presence, and perceptions of institutional support in Cambodian higher education.
Keywords: 
Online Teaching and Learning
;  
TPACK
;  
Institutional Support
;  
Higher Education
;  
SEM
Subject: 
Social Sciences  -   Education

Introduction

The TPACK framework has strongly influenced research and practice in teacher education and professional development and inspired extensive research and scholarship. Since 2009, there have been over 1200 journal articles and book chapters, over 315 dissertations and 28 books with TPACK as the central construct (Zhang & Tang, 2021). Early in 2001, Pierson began to use the concept of TPCK. Pierson’s TPCK referred to “Technology assisting PCK”. Niess changed TPCK from a static concept to a dynamic one (Zhang & Tang, 2021). To effectively promote OTL readiness and competencies, professional development and teacher training programs need to be tailored to the teachers ‘various needs and backgrounds. Consequently, OTL research has been aimed at identifying the factors that may explain why or why not teachers consider themselves ready for OTL experience is one of these factors (Scherer et al., 2023).
The COVID-19 pandemic prompted the adoption of new technologies, but traditional teaching methods still dominate. Quality and relevance, equitable access, institutional governance and management, strategic investment, and the alignment of higher education with national development are anticipated challenges (Sok & Bunry, 2023). Some concerns with online learning regarding motivation of students, time management, and delay of feedback have been identified as potential limitations. However, these limitations could be addressed by having an instructor that is present and available through responses to students and timely feedback on assignments which also fosters a sense of belongingness (Martin et al., 2019; Zhang et al., 2022). Instructors require plenty of opportunities for students to participate and to be engaged with each other, the material, the service-learning agency, and the instructors themselves (Branscum, 2024). The digitalization situation in Cambodia is contradictory, with limited skills for using the internet, smartphones, and social networks in rural areas. Access to the internet is still a challenge, but the government is working to solve this problem. Blended learning has become the dominant response to the COVID-19 pandemic in education (Vialichka, 2021). Additionally, the research highlights the need for continuous support for both technical and pedagogical aspects of online instruction (Fabriz et al., 2021). Providing training and development for faculty members is key, as those who are open to change tend to have higher satisfaction with online and distance education (Chan et al., 2021).
More research is needed on the specific factors that influence teacher readiness, beyond just attitudes, technological competency, pedagogy, training, and time constraints. The existing frameworks are broad and require more nuanced studies (Baran et al., 2011; Scherer et al., 2021; Uerz et al., 2018). There is a lack of research comparing teacher readiness across different geographical locations and types of institutions. Most studies focus on a single context. Examining gaps in readiness between urban and rural areas could provide important insights (Adedoyin & Soykan, 2023; Almaiah et al., 2020; Chea et al., 2022; Hasyim et al., 2024).

Literature Review

Technological Pedagogical Content Knowledge (TPACK)

Technological Pedagogical Content Knowledge (TPCK) was introduced to the educational research field as a theoretical framework for understanding teacher knowledge required for effective technology integration (Mishra & Koehler, 2006a). TPACK is a theoretical lens for understanding teacher readiness for online teaching and learning. The framework is about designing and evaluating teacher knowledge for effective student learning in various content. Recent developments in digital technology and the COVID-19 pandemic have moved education online. This has made educators to upgrade their digital literacy and professional identity and TPACK is a useful tool to improve teaching practices (Su, 2023). Zgheib et al. (2023) found that faculty readiness to teach online, female faculty are more prepared than male faculty in course design and attitude towards online learning. More years of teaching experience enhances pedagogy and course design. However, challenges such as unreliable internet and lack of advanced skills for interactive activities were noted. Technical skills had a big impact on faculty readiness, it’s time to shift from traditional to innovative teaching methods in online education.
Çam & Koç (2024) conducted a study on a professional development program to enhance teacher’s TPACK. The program increased participants’ self-confidence and ability to integrate technology into teaching. The study confirmed earlier research that TPACK-oriented training affects teachers to transfer their knowledge into practice. Moreover, Paetsch et al. (2023) found a positive relationship between pandemic-related experience, technology integration self-efficacy and support for ICT integration in post-pandemic teaching. Moreover, Paetsch et al. (2023) found a positive relationship between pandemic-related experience, technology integration self-efficacy and support for ICT integration in post-pandemic teaching.

Teacher’s Self Efficacy

Teacher self-efficacy (TSE) is the teacher’s belief in their ability to teach students and impact student learning outcomes (Gordon et al., 2023; Ramakrishnan & Salleh, 2019). It is a crucial factor that affects various aspects of teaching including instructional strategies, classroom management and student engagement (Amin Mydin et al., 2022; Gordon et al., 2023). Researchers have developed several instruments to measure TSE such as the Teacher Self-Efficacy Scale (TSES) and the Teacher’s Sense of Efficacy Scale (TSES) (Corry & Stella, 2018; Gordon et al., 2023). These scales assess teachers’ belief in their ability to perform specific teaching tasks and overall confidence in their teaching.
Many studies have found that online teaching self-efficacy is related to years of teaching experience, grade level taught and level of technological proficiency (Dolighan & Owen, 2021; Yang & Du, 2024). Teachers with higher self-efficacy are more confident in managing online learning environments and more likely to try out new teaching methods. Research indicates that educators frequently have difficulties in properly incorporating technology into their instructional methods, thus undermining their self-efficacy in online teaching (Corry & Stella, 2018; Dolighan & Owen, 2021). This challenge is especially evident for educators with less experience or training in online instruction.

Support and Professional Development

Access to sufficient support and professional development opportunities is essential for improving teachers' preparedness for online instruction and learning. Continuous training, mentorship, and collaborative networks can enable educators to acquire the knowledge, skills, and confidence necessary to manage the intricacies of online education (Archambault & Crippen, 2009). Robust support and professional development are essential for facilitating teachers' smooth transition to online and blended education formats. Studies indicate that specialized training programs may markedly enhance educators' digital skills, self-assurance, and understanding of optimal online teaching techniques (Horvitz & Beach, 2011; Rafique, 2024). Studies have repeatedly shown that these programs may significantly enhance teacher self-efficacy, especially when customized to address the individual needs and concerns of the participating instructors (Baroudi & Shaya, 2022; Corry & Stella, 2018; Dolighan & Owen, 2021).

Research Questions

The current study intends to evaluate the association between teacher experience and preparation for online context. The study gives useful insights into the elements impacting teacher preparedness by addressing the following research questions: (1): To what degree does teaching experience impact the instructors' preparedness for Online Teaching and Learning (OTL) across many dimensions, such as Technological Pedagogical Content Knowledge (TPACK), self-efficacy, perceived online teaching presence, and perceived institutional support? (2): How do the correlations between teaching experience and preparedness for OTL change across the dimensions?

Participants

The quantitative sample contained 140 instructors from several higher education institutions in Cambodia. These individuals were picked using a random selection approach with a representative range of teaching disciplines, years of experience, and expertise with online teaching platforms. The selection criteria were meant to capture a wide range of views and experiences, thereby improving the reliability and generalizability of the findings. The academics that participated in the survey were from different academic areas, including sciences, humanities, social sciences, and professional studies. Additionally, the sample consisted of teachers with varying degrees of skill, from rookie instructors to experienced educators, to offer a thorough assessment of how experience effects readiness for online teaching.

Methodology

Research Design

This study adopts a quantitative research approach, employing an online survey to acquire data from instructors in higher education institutions in Cambodia. The survey approach facilitates the collection of numerical data, which may be statistically examined to understand the correlations between variables. Structural Equation Modeling (SEM) is used to analyze the intricate interactions between the variables.

Data Collection and Procedure

The online survey form was utilized to collect the data, which stressed critical areas such as technical capabilities, pedagogical techniques for online learning, institutional support mechanisms, and personal attitudes toward online education. The survey was sent through the email and Telegram. The data-collecting period was extended from March to June 2024, while maintaining their identity and anonymity.

Measurement

This survey was particularly created to assess instructors' preparation for online teaching and learning in higher education institutions in Cambodia. The poll consisted of 29 questions and was separated into three primary sections: self-efficacy, perceived online teaching presence, and perceived institutional support. The readiness for OTL measure contained three dimensions. Teachers’ self-efficacy in their TPACK, perceived online teaching presence, and the perceived institutional support. These dimensions covered teachers’ readiness perceptions of core aspects of knowledge, teaching, and support. Teachers responded to the items using a 5-point agreement scale (1= strongly disagree, 5 = strongly agree).

Reliability

The reliability indices provided for the constructs in the measurement model offer insight into the consistency and reliability of the measures used.
Table 1. Reliability indices of the Readiness Constructs.
Table 1. Reliability indices of the Readiness Constructs.
Variable α Ordinal α ω₁ ω₂ ω₃ AVE
TCK 0.904 0.916 0.861 0.861 0.861 0.852
TPK 0.952 0.964 0.938 0.938 0.940 0.874
TCPK 0.946 0.958 0.932 0.932 0.936 0.857
CoI 0.940 0.969 0.943 0.943 0.944 0.890
CA 0.967 0.979 0.964 0.964 0.974 0.879
Feedback 0.884 0.929 0.888 0.888 0.888 0.870
IS 0.945 0.963 0.948 0.948 0.957 0.822
Note: α = Cronbach alpha; ω₁ = Omega; AVE = Average variance extracted.

Data Analysis

The data were examined using SEM, using the Jamovi statistical program, which provides tools for model definition, estimation, and assessment. SEM was chosen for its capacity to handle complicated interactions between observable and latent variables, enabling a complete study of both measurement and structural models. The model fit was computed using multiple indices, including the chi-square test and Standardized Root Mean Square Residual (SRMR).

Findings

SEM for Teacher’s Readiness in OTL

SEM was used to evaluate a number of constructs, including Clarity of Instruction (CoI), Cognitive Activation (CA), Feedback, Technological Content Knowledge (TCK), Technological Pedagogy Knowledge (TPK), and Technological Content Pedagogy Knowledge (TCPK), in order to determine how prepared teachers were for Online Teaching and Learning (OTL). The overall fit of the SEM was tested using multiple fit indices. The chi-square (X²) value for the user model was 309, with p = 0.996 > 0.05, suggesting a very strong fit (Peugh & Feldon, 2020). Additionally, the Baseline Model generated an X² = 146,613, and p < .001. Despite the huge chi-square value in the Baseline Model, the user model's fit indices gave more useful information.
Table 2. Fit Indices and Model Comparisons.
Table 2. Fit Indices and Model Comparisons.
Fit Index Value p-value Result
Model Tests
User Model χ² = 309, df = 378 0.996 Very good fit
Baseline Model χ² = 146,613, df = 435 < .001 Poor fit
Fit Indices
SRMR 0.038 Excellent fit
RMSEA 0.000 1 Excellent fit
CFI 1.000 Perfect fit
TLI 1.001 Perfect fit
NNFI (Bentler-Bonett) 1.001 Good fit
RNI 1.000 Good fit
NFI (Bentler-Bonett) 0.998 Perfect fit
RFI (Bollen's) 0.998 Perfect fit
IFI (Bollen's) 1.000 Perfect fit
PNFI 0.867 Good fit
Note: SRMR = Standardized Root Mean Square Residual; RMSEA = Root Mean Square Error of Approximation; CFI = Comparative Fit Index; TLI = Tucker-Lewis Index; NNFI = Bentler-Bonett Non-normed Fit Index; RNI = Relative Noncentrality Index; NFI = Bentler-Bonett Normed Fit Index; RFI = Bollen's Relative Fit Index; IFI = Bollen's Incremental Fit Index; PNFI = Parsimony Normed Fit Index.
The Comparative Fit Index (CFI) and Tucker-Lewis Index (TLI) both showed a perfect match (Goretzko et al., 2024; Stone, 2021a). Other fit indices validated the model's adequacy with values of 1.001 and 1.000, respectively.

Parameters Estimates of Oline Teaching Experience

The parameter estimates were used to look into the influence of online teaching experience (Online Exp) on several elements of instructors' attitudes and readiness for online teaching. The study focuses on a number of dependent variables, including Technological Content Knowledge (TCK), Technological Pedagogical Knowledge (TPK), and various dimensions of perceived online teaching presence (POTP), such as Clarity of Instruction, Cognitive Activation, and Feedback. The impact on Institutional Support (SI) is also considered.
Table 3. Parameters estimates of online teaching experiences on Dependent variables.
Table 3. Parameters estimates of online teaching experiences on Dependent variables.
95% Confidence Intervals
Dep Pred Estimate SE Lower Upper β z p
TCK OTL 0.164 0.0478 0.0699 0.257 0.223 3.42 < .001
TPK OTL 0.206 0.0574 0.0935 0.318 0.254 3.59 < .001
TCPK OTL 0.225 0.0555 0.1159 0.334 0.274 4.05 < .001
CoI OTL 0.19 0.0584 0.0757 0.305 0.234 3.26 0.001
CA OTL 0.197 0.048 0.1027 0.291 0.26 4.1 < .001
Feedback OTL 0.218 0.0681 0.085 0.352 0.258 3.21 0.001
IS OTL 0.175 0.0589 0.0595 0.29 0.221 2.97 0.003
Note: Dep= Dependent Variable; Pred = Predictor; OTL = Online Teaching and Learning; SE = Standard Error; TCK = Technological content knowledge; TPK = Technological Pedagogy knowledge; TCPK = Technological Content Pedagogy knowledge; POTP = Perceived Online Teaching Presence; CoI = Clarity of Instruction; CA = Cognitive Activation; IS = Institutional Support.
There was a substantial positive correlation between online teaching experience and TCK (β = 0.223, p <.001), TPK (β = 0.254, p <.001), and TCPK (β = 0.274, p <.001). This shows that increasing online teaching experience correlates with better levels of technology competence among teachers. Online teaching experience had a significant positive effect on the clarity of instruction (β = 0.234, p = 0.001), cognitive activation (β = 0.26, p < .001), and feedback (β = 0.258, p = 0.001) dimensions of POTP. This indicates that more online teaching experience enhances teachers' perceptions of their instructional practices. Online teaching experience had a significant positive relationship with PIS (β = 0.221, p = 0.003), suggesting that teachers with more online teaching experience perceive greater institutional support.

Measures of Indicators and Factors

The measurement model results presented evaluate the relationships between observed variables (indicators) and their corresponding latent constructs (factors) within the context of teachers' perceptions and readiness for online teaching. We analyze the estimates, standard errors (SE), 95% confidence intervals, standardized loadings (β), z-values, and p-values for each observed variable across various latent constructs.
Table 4. Measurement Model.
Table 4. Measurement Model.
Latent Observed Estimate SE 95% Confidence Intervals β z p
Lower Upper
Online Online teaching 1 0 1 1 1
TCK tpack1 1 0 1 1 0.845
tpack2 1.161 0.041 1.08 1.242 0.981 28.1 < .001
TPK tpack3 1 0 1 1 0.935
tpack4 0.949 0.022 0.905 0.993 0.887 42.7 < .001
tpack5 1.02 0.018 0.983 1.056 0.953 54.8 < .001
tpack6 1.008 0.020 0.968 1.047 0.942 50 < .001
TCPK tpack7 1 0 1 1 0.945
tpack8 0.951 0.022 0.908 0.994 0.899 43.2 < .001
tpack9 0.948 0.022 0.904 0.991 0.896 43 < .001
tpack10 0.981 0.018 0.945 1.016 0.927 54.1 < .001
CoI presence1 1 0 1 1 0.937
presence2 0.957 0.024 0.909 1.005 0.897 39 < .001
presence3 1.038 0.018 1.001 1.075 0.972 55.2 < .001
presence4 0.989 0.019 0.951 1.027 0.927 50.8 < .001
CA presence5 1 0 1 1 0.874
presence6 1.11 0.026 1.057 1.162 0.97 41.5 < .001
presence7 1.074 0.024 1.027 1.122 0.939 44.2 < .001
presence8 1.077 0.024 1.028 1.125 0.941 43.6 < .001
presence9 1.063 0.023 1.016 1.109 0.929 45 < .001
presence10 1.055 0.022 1.01 1.1 0.922 46.4 < .001
presence11 1.072 0.02 1.033 1.111 0.937 53.6 < .001
Feedback presence12 1 0 1 1 0.975
presence13 0.899 0.024 0.85 0.947 0.876 36.3 < .001
IS IS1 1 0 1 1 0.912
IS2 1 0.028 0.945 1.055 0.912 35.7 < .001
IS3 1.052 0.029 0.994 1.11 0.96 35.4 < .001
IS4 0.936 0.031 0.875 0.996 0.853 30.2 < .001
IS5 0.924 0.036 0.853 0.995 0.842 25.5 < .001
IS6 0.992 0.030 0.931 1.052 0.904 32.3 < .001
Note: SE = Standard Error.
The measurement model demonstrates that the observed variables accumulated high and statistically significant loadings on their corresponding latent constructs.

Discussion

The excellent model fit indices, including a nonsignificant chi-square value, underscore the robustness of the SEM model used in this study (Goretzko et al., 2021; Stone, 2021b). These findings are consistent with those of Goretzko et al. (2021) and Shi et al. (2018), who found that high CFI and TLI values indicated strong model fit in educational research. The favorable link between online teaching experience and TCK is consistent with other research that emphasizes the relevance of experience in establishing technical competence among teachers. his validates the estimate's accuracy and reinforces Mishra & Koehler’s (2006b) conclusions on the importance of technology content knowledge in the digital age.
The considerable influence of online teaching experience on TPK is similar to the findings of Chai et al. (2013) which stress the cruciality of teaching experience in improving teachers' pedagogical understanding of technology. According to Koehler et al. (2013), the association between online experience and TCPK emphasizes the necessity of thorough knowledge integration in effective online education. Their findings also show that the creation of TPACK is critical for instructors to successfully integrate technology into their teaching methods. The significant positive effects on clarity of instruction, cognitive activation, and feedback dimensions of POTP corroborate the findings of Diamah et al. (2022) on efficiency of a training program based on technology pedagogical content knowledge and Bolkan (2016) on clear instruction, which can employ a variety of effective teaching behaviors, such as those that reflect the instruction, to increase the odds that students experience success in their courses. It was argued that the reason clarity works to influence student success is because of its ability to reduce learners’ cognitive load experienced as receiver apprehension. The strong association between online teaching experience and PIS elucidates that institutional support is crucial in allowing effective online teaching (Al-Samarraie et al., 2018). The strong association between online teaching experience and PIS elucidates that institutional support is crucial in allowing effective online teaching.

Implications and Conclusion

Several major recommendations may be made to teachers, universities (HEIs), and policymakers (MoEYS) to strengthen the readiness of teachers for online teaching and learning. Teachers should engage in continual professional development opportunities to strengthen practical skills. A study by Chea et al. (2022) proposed the TPACK framework for effective technology integration in teaching. Research has shown that teacher collaboration and the sharing of best practices can significantly improve online teaching skills and student outcomes (Chea et al., 2022; Dede et al., 2008; Prestridge, 2019). Several studies have demonstrated that instructors with a growth mindset are more effective in moving to online and blended learning environments (Dweck, 2024; Ertmer & Ottenbreit-Leftwich, 2010). Studies have underlined the significance of explicit learning objectives, regular feedback, and cognitive activation tactics to maintain student engagement and learning in online contexts (Means et al., 2010; Snook et al., 2009)
Higher education institutions (HEIs) should invest in reliable digital infrastructure, including stable internet connectivity and user-friendly learning management systems, to support online teaching and learning (Means et al., 2010), and equipping teachers with the necessary skills and knowledge for effective online instruction (Baran & Correia, 2014; Schmidtet al., 2009b).
Providing technical assistance and tools for instructors is vital to address the difficulties and strengthen their online teaching abilities (Ertmer & Ottenbreit-Leftwich, 2010; Hermien & Wiyatini, 2019). It is encouraged to develop a culture of creativity and cooperation among teachers for effective online teaching approaches (Dede et al., 2008; Prestridge, 2019). Several studies emphasized the significance of clear guidelines and policy for the quality and consistency (Hermien & Wiyatini, 2019), collaboration with various HEIs to address gaps in teacher readiness and competencies (Baran et al., 2011), and promotion of the online and blended learning integration to enhance access and flexibility for students (Means et al., 2010). Further research should undertake longitudinal studies to gauge how teachers' perceptions of OTL have changed. It is advisable to explore the viewpoints of students and other relevant stakeholders in order to have a broader understanding of online teaching and learning in HEIs in Cambodia.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Acknowledgement

The author would like to thank esteemed participants for their participation in the study.

Disclosure statement

The author declares no potential conflicts of interest with respect to the research, authorship, or publication of this article.

Ethical approval

This study was conducted in accordance with ethical standards. All participants provided informed consent prior to involvement in the study.

References

  1. Adedoyin, O. B., & Soykan, E. (2023). Covid-19 pandemic and online learning: the challenges and opportunities. Interactive Learning Environments, 31(2), 863–875. [CrossRef]
  2. Almaiah, M. A., Al-Khasawneh, A., & Althunibat, A. (2020). Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic. Education and Information Technologies, 25(6), 5261–5280. [CrossRef]
  3. Al-Samarraie, H., Teng, B. K., Alzahrani, A. I., & Alalwan, N. (2018). E-learning continuance satisfaction in higher education: a unified perspective from instructors and students. Studies in Higher Education, 43(11), 2003–2019. [CrossRef]
  4. Amin Mydin, A., Alaklabi, S., & Alomar, A. (2022). A Review of Teachers’ Self-Efficacy and to What Extent it is Influenced by Instructional Leadership in Educational Institutions. International Transaction Journal of Engineering, 13(9), 1–12. [CrossRef]
  5. Archambault, L., & Crippen, K. (2009). Examining TPACK among K-12 online distance educators in the United States. Contemporary Issues in Technology and Teacher Education, 9(1), 71–88.
  6. Baran, E., & Correia, A. P. (2014). A professional development framework for online teaching. TechTrends, 58(5), 95–101. [CrossRef]
  7. Baran, E., Correia, A. P., & Thompson, A. (2011). Transforming online teaching practice: critical analysis of the literature on the roles and competencies of online teachers. Distance Education, 32(3), 421–439. [CrossRef]
  8. Baroudi, S., & Shaya, N. (2022). Exploring predictors of teachers’ self-efficacy for online teaching in the Arab world amid COVID-19. Education and Information Technologies, 27(6), 8093–8110. [CrossRef]
  9. Bolkan, S. (2016). The Importance of Instructor Clarity and Its Effect on Student Learning: Facilitating Elaboration by Reducing Cognitive Load. Communication Reports, 29(3), 152–162. [CrossRef]
  10. Branscum, A. Y. (2024). Best Practices of Online Teaching in Service-Learning. In JSLHE (Vol. 18).
  11. Çam, Ş. S., & Koç, G. (2024). Professional Development Program to Develop Teacher Educators’ Technological Pedagogical Content Knowledge. SAGE Open, 14(2). [CrossRef]
  12. Chai, C. S., Koh, J. H. L., & Tsai, C.-C. (2013). A Review of Technological Pedagogical Content Knowledge. Educational Technology & Society, 16(2), 31–51.
  13. Chan, R. Y., Bista, K., & Allen, R. M. (2021). Online Teaching and Learning in Higher Education during COVID-19: International Perspectives and Experiences. Online Teaching and Learning in Higher Education during COVID-19: International Perspectives and Experiences, 1–266. [CrossRef]
  14. Chea, P., Bo, C., & Minami, R. (2022). Working Paper Series No. 134 Cambodian Secondary School Teachers’ Readiness for Online Teaching During the Covid-19 Pandemic (134).
  15. Corry, M., & Stella, J. (2018). Teacher self-efficacy in online education: A review of the literature. In Research in Learning Technology (Vol. 26). Association for Learning Technology. [CrossRef]
  16. Dede, C., Ketelhut, D. J., Whitehouse, P., Breit, L., & McCloskey, E. M. (2008). A Research Agenda for Online Teacher Professional Development. Http://Dx.Doi.Org/10.1177/0022487108327554, 60(1), 8–19. [CrossRef]
  17. 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, 7. [CrossRef]
  18. Dolighan, T., & Owen, M. (2021). Teacher Efficacy for Online Teaching During the COVID-19 Pandemic. Brock Education Journal, 30(1), 95–116.
  19. Dweck, C. S. (2024). Personal Perspectives on Mindsets, Motivation, and Psychology. Motivation Science, 10(1), 1–8. [CrossRef]
  20. Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher Technology Change: How Knowledge, Confidence, Beliefs, and Culture Intersect. 42(3), 255–284. www.iste.org/jrte.
  21. Fabriz, S., Mendzheritskaya, J., & Stehle, S. (2021). Impact of Synchronous and Asynchronous Settings of Online Teaching and Learning in Higher Education on Students’ Learning Experience During COVID-19. Frontiers in Psychology, 12, 733554. [CrossRef]
  22. Gordon, D., Blundell, C., Mills, R., & Bourke, T. (2023). Teacher self-efficacy and reform: a systematic literature review. The Australian Educational Researcher, 50(3), 801–821. [CrossRef]
  23. Goretzko, D., Pham, T. T. H., & Bühner, M. (2021). Exploratory factor analysis: Current use, methodological developments and recommendations for good practice. Current Psychology, 40(7), 3510–3521. [CrossRef]
  24. Goretzko, D., Siemund, K., & Sterner, P. (2024). Evaluating Model Fit of Measurement Models in Confirmatory Factor Analysis. Educational and Psychological Measurement, 84(1), 123–144. [CrossRef]
  25. Hasyim, N., Arismunandar, Butarbutar, R., Ramli, A. M., & Malik Nur, I. D. (2024). Mind mapping of teachers’ readiness for online teaching and learning: A reflective study of urban and suburban areas. Cogent Education, 11(1). [CrossRef]
  26. Hermien, N., & Wiyatini, T. (2019). Teaching in a Digital Age - Second Edition. Kesehatan Masyarakat, 252. https://repository.poltekkes-smg.ac.id/index.php?p=show_detail&id=23725&keywords.
  27. Horvitz, B., & Beach, A. (2011). Professional Development to Support Online Teaching. Journal of Faculty Development, 9(2), 24–32.
  28. Koehler, M. J., Mishra, P., & Cain, W. (2013). What is Technological Pedagogical Content Knowledge (TPACK)? Journal of Education, 193(3), 13–19. [CrossRef]
  29. Martin, F., Ritzhaupt, A., Kumar, S., & Budhrani, K. (2019). Award-winning faculty online teaching practices: Course design, assessment and evaluation, and facilitation. The Internet and Higher Education, 42, 34–43. [CrossRef]
  30. Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2010). Evaluation of Evidence-Based Practices in Online Learning: A Meta-analysis and Review of Online Learning Studies. [CrossRef]
  31. Mishra, P., & Koehler, M. J. (2006a). Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge. Teachers College Record: The Voice of Scholarship in Education, 108(6), 1017–1054. [CrossRef]
  32. Mishra, P., & Koehler, M. J. (2006b). Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge. Teachers College Record: The Voice of Scholarship in Education, 108(6), 1017–1054. [CrossRef]
  33. Mishra, P., & Koehler, M. J. (2006c). Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge. Teachers College Record: The Voice of Scholarship in Education, 108(6), 1017–1054. [CrossRef]
  34. Paetsch, J., Franz, S., & Wolter, I. (2023). Changes in early career teachers’ technology use for teaching: The roles of teacher self-efficacy, ICT literacy, and experience during COVID-19 school closure. Teaching and Teacher Education, 135. [CrossRef]
  35. Peugh, J., & Feldon, D. F. (2020). “How Well Does Your Structural Equation Model Fit Your Data?”: Is Marcoulides and Yuan’s Equivalence Test the Answer? CBE—Life Sciences Education, 19(3), es5. [CrossRef]
  36. Prestridge, S. (2019). Categorising teachers’ use of social media for their professional learning: A self-generating professional learning paradigm. Computers & Education, 129, 143–158. [CrossRef]
  37. Rafique, R. (2024). Supporting teachers through online professional development: a small-scale qualitative study with teachers in Bangladesh. Open Learning: The Journal of Open, Distance and e-Learning, 39(2), 112–131. [CrossRef]
  38. Ramakrishnan, R., & Salleh, N. M. (2019). Teacher’s Self-Efficacy: A Systematic Review. International Journal of Academic Research in Business and Social Sciences, 8(12). [CrossRef]
  39. Scherer, R., Howard, S. K., Tondeur, J., & Siddiq, F. (2021). Profiling teachers’ readiness for online teaching and learning in higher education: Who’s ready? Computers in Human Behavior, 118, 106675. [CrossRef]
  40. Scherer, R., Siddiq, F., Howard, S. K., & Tondeur, J. (2023). The more experienced, the better prepared? New evidence on the relation between teachers’ experience and their readiness for online teaching and learning. Computers in Human Behavior, 139. [CrossRef]
  41. Schmidt, D. A., Baran, E., Thompson, A. D., Mishra, P., Koehler, M. J., & Shin, T. S. (2009). Technological Pedagogical Content Knowledge (TPACK): The Development and Validation of an Assessment Instrument for Preservice Teachers. In Journal of Research on Technology in Education (Vol. 123). www.iste.org.
  42. Shi, D., Lee, T., & Terry, R. A. (2018). Revisiting the Model Size Effect in Structural Equation Modeling. Structural Equation Modeling, 25(1), 21–40. [CrossRef]
  43. Snook, I., O’Neill, J., Clark, J., O’Neill, A.-M., & Openshaw, R. (2009). Invisible Learnings? A Commentary on John Hattie ’ s book : Visible Learning : A synthesis of over 800 meta-analyses relating to achievement. In New Zealand Journal of Educational Studies (Vol. 44, Issue 1). https://www.routledge.com/Visible-Learning-A-Synthesis-of-Over-800-Meta-Analyses-Relating-to-Achievement/Hattie/p/book/9780415476188.
  44. Sok, S., & Bunry, R. (2023). Higher Education in Cambodia. In L. P. Symaco & M. Hayden (Eds.), International Handbook on Education in South East Asia (pp. 1–24). Springer. [CrossRef]
  45. Stone, B. M. (2021a). The Ethical Use of Fit Indices in Structural Equation Modeling: Recommendations for Psychologists. Frontiers in Psychology, 12. [CrossRef]
  46. Stone, B. M. (2021b). The Ethical Use of Fit Indices in Structural Equation Modeling: Recommendations for Psychologists. Frontiers in Psychology, 12, 783226. [CrossRef]
  47. Su, Y. (2023). Delving into EFL teachers’ digital literacy and professional identity in the pandemic era: Technological Pedagogical Content Knowledge (TPACK) framework. Heliyon, 9(6), e16361. [CrossRef]
  48. Uerz, D., Volman, M., & Kral, M. (2018). Teacher educators’ competences in fostering student teachers’ proficiency in teaching and learning with technology: An overview of relevant research literature. Teaching and Teacher Education, 70, 12–23. [CrossRef]
  49. Vialichka, U. (2021). Country report Cambodia. https://www.dvv-international.de/fileadmin/files/Inhalte_Bilder_und_Dokumente/News/2022/01_Digitalisation_in_ALE_in_Asia_Country_report_Cambodia.pdf.
  50. Yang, X., & Du, J. (2024). The effect of teacher self-efficacy, online pedagogical and content knowledge, and emotion regulation on teacher digital burnout: a mediation model. BMC Psychology, 12(1), 51. [CrossRef]
  51. Zgheib, G., Al Daia, R., & Serhan, M. (2023). A contextual approach for exploring faculty readiness to teach online. Heliyon, 9(10). [CrossRef]
  52. Zhang, W., & Tang, J. (2021). Teachers’ TPACK Development: A Review of Literature. Open Journal of Social Sciences, 09(07), 367–380. [CrossRef]
  53. Zhang, Y., Tian, Y., Yao, L., Duan, C., Sun, X., & Niu, G. (2022). Individual differences matter in the effect of teaching presence on perceived learning: From the social cognitive perspective of self-regulated learning. Computers & Education, 179, 104427. [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
Prerpints.org logo

Preprints.org is a free preprint server supported by MDPI in Basel, Switzerland.

facebook logotwitter logolinkedin logo
bsky
MDPI Initiatives
Important Links
Subscribe

Disclaimer

Terms of Use

Privacy Policy

Privacy Settings

© 2026 MDPI (Basel, Switzerland) unless otherwise stated