Evaluating the competencies of university teachers in content, pedagogical, and technological knowledge

Abstract

This paper reports the findings of the university teachers’ competencies on content, pedagogical, and technological knowledge based on TPACK model. Infomed by the cross-sectional research design, this research applied structural equation modeling and machine learning as the major statistical tools to analyse the data derived from self-administered survey and qualitative narratives of the 405 teachers involved in teacher education programs at 23 community and constituent campuses of Tribhuvan University, Nepal. Among the six types of knowledge viz. content, pedagogical, technological, content-pedagogical, technological-content, and technological-pedagogical knowledge observed, technology-related knowledge is found comparatively inadequate in the teachers. Since technological knowledge is the main predictor among these types of knowledge, it should be integrated in instructional planning, learning management system, and use of the digital devices.

Keywords:

• Competencies
• TPACK
• SEM
• machine learning
• university teachers

REVIEWING EDITOR:

• Gustavo Cunha de Araújo, Universidade Federal do Tocantins, Brazil

SUBJECTS:

• Teachers and Teacher Education
• Classroom Practice
• Higher Education

Background

Faculty of Education of Tribhuvan University (FoE-TU hereafter) has been running teacher education programmes since its establishment in 1959. Extending the Basic Teacher Training Programme established in 1947, FoE-TU started university degree programmes especially in Bachelor and Master levels targeting to produce trained teachers required for the schools in the nation. It is the largest institution of the nation in terms of both the number of students and affiliated campuses that run higher education programmes including teacher education programmes. Currently, FoE-TU has run Bachelor Degree, Master Degree, Degree of Master in Philosophy, and Doctoral Degree in education specializing English, Mathematics, Science, and other Social Sciences in education. FoE-TU produces trained human resources, such as teachers, teacher educators, educational planners and managers, educational researchers, curriculum designers, and all sorts of human resources needed for the educational sector. Although, FoE-TU has served widely in education sector since its inception, the questions regarding its performance are always pertinent (Gautam, 2016). Scholars have raised issues concerning its curricula, courses delivered, training and instructional programmes and practices designed to the prospective teachers, ICT integration, and the performance that the graduates perform in schools and classrooms (Awasthi, 2010; Dhakal & Pant, 2016; Shrestha, 2008). Therefore, underpinning on these issues, this study has investigated the competencies of teacher educators particularly relating content knowledge, pedagogical knowledge, and technological knowledge.

The integration of technology in education has become more prominent in the current technological era (Ribeiro-Navarrete et al., 2021). Twenty-first century is widely perceived as a transitional period for integrating digital technology and education in the process of transforming learning (Van Laar et al., 2017). The role of teachers includes not only imparting knowledge but also acting as a mediator and active facilitator to support students (Alcántara-Porcuna et al., 2022; Osborne et al., 2019). Instructors/facilitators in the twenty-first century require knowledge to use technologies into their classroom instruction (Benton-Borghi, 2013; Taimalu & Luik, 2019). Advancements in pedagogy, technology, and content areas are prerequisites for teachers who desire to improve their professionalism and bring transformation in student learning (Ding et al., 2019; Chaudhary & Chaudhary, 2022). Through the effective implementation of ICT knowledge and skills, teachers can stay up-to-date in their professionalism, keep track of scientific discoveries, and incorporate innovative strategies into their lessons (Oliveira et al., 2019; Thoonen et al., 2011).

Teachers are expected to lead the use of technology in the classroom and serve as role models in the present information age (Karaca et al., 2013b; Li et al., 2019; Singh, 2021). However, only a few higher education institutions have taken some successful initiatives to incorporate technologies into their pedagogical practice in Nepal (Laudari, 2019; Laudari & Prior, 2020). Majority of teacher education programs are still conducted face-to-face in teaching and learning. Researches (Joshi, Khanal, et al., 2023; Rana et al., 2020) have shown that many in-service teachers are not well-prepared to teach using technologies since they are not aware of use technologies in their classroom instruction. Dhakal and Pant (2016) pointed out that there was no specific course on technology use at TU and other universities in Nepal. Nevertheless it is a challenging process to integrate technology in education, it needs to be done as it has a wider impact on diverse conditions and causes (Karaca et al., 2013b). Researchers (Baylor & Ritchie, 2002; Hew & Brush, 2007; Karaca et al., 2013a) found that teacher’s competencies in using technology(-ies) are most important factors which influence technology integration in instructional activities. Prior research defined technology competencies as the technical know-how and abilities needed to apply technologies, operating knowledge and skills in educational settings (Ocak & Karanfil, 2021). However, the recent research focused that it is much more than technical skills that teachers feel a need for integrating technologies into their lessons (Alenezi, 2017; Valverde-Berrocoso et al., 2021). Rather, it demands teachers to integrate the pedagogical, content, and technological competencies in their instructional activities (Koehler & Mishra, 2005). For this, the technological pedagogical and content knowledge (TPACK) model that Mishra and Koehler (2006) developed provide more inputs and insights for the researchers, teachers and teacher educators. The main focus of the model was technology, content, and pedagogy (Mishra & Koehler, 2006; Rodríguez Moreno et al., 2019) and proposes to investigate teachers’ knowledge (Estellés & Fischman, 2021). In the case of the higher education in Nepal, to maintain the quality of education, it is necessary to improve the classroom situation (Acharya et al., 2022) through pedagogy and technology integration for preparing equitable educators in higher education (McDonough et al., 2023).

Recent literature emphasize on TPACK of teacher educators (Laudari & Prior, 2020) and importance of developing teachers’ TPACK for technology integration in teacher training programs (Chen & Jang, 2019; Joo et al., 2018). However, there is a lack of literature which focused on exploring in-services teachers’ TPACK for technology integration in educational activities at universities (Ammade et al., 2020; Andrews et al., 2019; Cochran-Smith et al., 2020; Lavidas et al., 2021). Thus, this research seeks to unpack the status of university teachers’ competencies based on TPACK model, and also the effect of the integration of content, pedagoigical, and technological knowledge for shaping content pedagogical knowledge (CPK), technological content knowledge (TCK), and technology pedagogical knowledge (TPK) in teacher educators. Thus, underpinning the aforementioned intents, this study is guided by the following research questions.

1. What is the status of university teachers’ competencies based on TPACK model?

2. What is the relationship between the TPACK related competencies of higher education teachers?

3. What is the effect of content, pedagogical, and technological knowledge of higher education teachers on CPK, TCK, and TPK?

The Government of Nepal (GoN) institutionalized teacher development programme in 1971 when it made a 10-month training mandatory. It started providing training allowance and additional benefits to those teachers who receive training packages and teach at schools accordingly. Since then, national policy documents (Education Sector Plan [ESP]-2021; National Education Policy [NEP]-2019; School Sector Development Plan [SSDP]-2016; School Sector Plan [SSP]-2021; Sustainable Development Goal-Nepal National Framework [SDG-NNF]-2019; The Fifteenth Plan, 2019) in education all have stressed the role of teacher training and development to improve the quality of education. Higher education teacher competencies include the knowledge of content, pedagogy, and technology; knowledge about learners, learning environment, and classroom management; communication and collaboration skills; enthusiasm for continuous learning and professional development; and legal bases and professional conduct. In line with these competencies, The Fifteenth Plan-2019 further made provision of teacher professional development packages in alignment with the national teacher competency framework and national framework for teacher preparation. ESP-2021 has also emphasized teachers’ professional development and capacity enhancement in school education in Nepal. Recently, SSP-2021 has also made provisions to support teachers’ professional development. So, the study seeks to answer the above-mentioned research questions.

Literature review

The TPACK model is one of the most effective approaches to combining education and technology. Scholars leading to appropriate this model in education contended that Pedagogical Content Knowledge (PCK) ought to incorporate a greater understanding of technology, considering its ubiquitous presence in the modern world and the various advantages and convenience it offers (Akturk & Ozturk, 2019; Hill & Uribe-Florez, 2019; Kaleli, 2021). TPACK has recently been applied in either technology-based teacher training activities or evaluation of teachers’ knowledge and skills in technology integration (Agyei & Voogt, 2010; Chai et al., 2012; Valtonen et al., 2019). Teacher professional development is mainly targeted to improve teacher’s performance, particularly focusing on their ability to integrate content knowledge, pedagogical abilities, technological knowledge, and technology while delivering content instruction in their classrooms (Pazim, 2021; Lopes & Oliveira, 2020). A long-standing fundamental qualification for teaching a subject, content knowledge is a crucial need for teacher certification (Hill, 2007). Jacob et al. (2020) explained that teacher with better content knowledge (CK) knows how to teach the subject to a specific audience and also s/he is expected to have better results with students. A teacher who is less qualified in subject matter hardly ensure the such results with students (Whalen, 2020). Özden (2008) emphasized that content knowledge always has positive influence on pedagogical content knowledge and effective instructional practices in classroom. Kara (2021) argue that TPACK competencies of pre-service visual arts teachers were low in terms of technology knowledge, but high in content knowledge. And also, TPACK competencies vary based on gender, year of study, and academic achievement. Tatto (2021) mentioned that instructors should regularly keep their profession’s knowledge base up to date. In the similar vein, Karlberg and Bezzina (2022) and Tanang and Abu (2014) emphasized teachers should dedicate themselves to high-quality professional development aimed at advancing their knowledge.

Levy (2018) argued that the teachers who have digitally informed knowledge and perceive technology as an opportunity can improve themselves step by step without training. However, they need to be aware of the digital tools that may help them unleash their full teaching potential. Similarly, Yu and Franz (2017) suggested that teachers should be able to identify the ways in which information technology might help or hinder the achievement of a goal. It helps the instructors in the classroom, along with other thoughts, discourse about the degrees of confidence and capability of educators to utilize technology in the classroom. Luzon and Cubillas (2022) found that teachers’ level of technological knowledge (TK) is significantly correlated with the pedagogical and content knowledge which clarifies that there is an association among the three types of knowledge.

The current advancement in technologies has added a challenge for teachers to incorporate it into their class plans (Santos & Castro, 2020). Effective use of ICT in the classroom requires successful integration of technology and pedagogical processes during lesson preparation (Janssen et al., 2019; Mlambo et al., 2020). Transformative competence involves the process of converting educational content into technology-driven formats through effective pedagogy (Angeli & Valanides, 2013; Virkus et al., 2020). Hence, teacher should conduct each step of transformative competence (Ioannou & Angeli, 2015). Mailizar et al. (2021) identified that the strongest predictor of TPACK was TCK; and TK was the main predictor of TCK which implies teachers must have a good understanding of technology to improve their technological content knowledge, which in turn influences their TPACK (Castéra et al., 2020; Hu & Venketsamy, 2022; Pamuk et al., 2015). Mailizar et al. (2021) identified that PK was the strongest predictor of TPK and PCK (Celik et al., 2014; Habibi et al., 2020; Pamuk et al., 2015). Their result resonates with the result of Mailizar et al. (2021) who widely believed that teacher pedagogical knowledge significantly affected teachers’ pedagogical content knowledge. Additionally, this result supports Earle (2002) who also argue that technology integration in education is not entirely dependent on technology utilization.

Theoretical framework

TPACK (Mishra & Koehler, 2006) is a theoretical framework based on Shulman’s (1986) PCK theory for evaluating and identifying teachers’ knowledge on the pedagogically meaningful use of ICT in education (Koehler et al., 2013). Teachers’ pedagogical and content knowledge is regarded as a cohesive and interdependent unit by PCK and cannot be separated from one another (Chai et al., 2012). TPACK that emphasized PCK has recently been extended to include technology as an additional knowledge for effective teaching with the interaction among subject content, pedagogy, and technology. TPACK consists of seven knowledge bases in which TK, PK, and CK are core knowledge bases and technological content knowledge (TCK), pedagogical content knowledge (PCK), technological pedagogical knowledge (TPK), and TPACK are established from the interactions and comprising among the core bases (Habibi et al., 2020). Habibi et al. (2020) define TPACK area as: TK is the knowledge of how to use different technologies, PK is the knowledge of different teaching and learning approaches and theories of learning, CK is the knowledge of subject matter. In a similar manner, PCK is the knowledge of how to combine the CK and PK to make the content understandable, TCK is the knowledge of how to utilize appropriate technology to support teaching and learning approaches without considering the subject matter, TPK is the Knowledge of how ICT is used by content experts and TPACK is the knowledge of how to combine these different areas and how to use appropriate pedagogical approaches for certain content with appropriate ICT knowledge and skills. Generated from the above-mentioned theoretical understanding of TPACK, the following conceptual framework (Figure 1) shows how these different types of knowledge are mapped out for integrating technology in teacher education. The major research hypotheses of the research were as follows:

H1: There is significant effect of content knowledge on pedagogical content knowledge.

H2: There is significant effect of content knowledge on technological content knowledge.

H3: There is significant effect of content knowledge on technological pedagogical knowledge.

H4: There is significant effect of pedagogical knowledge on pedagogical content knowledge.

H5: There is significant effect of pedagogical knowledge on technological content knowledge.

H6: There is significant effect of pedagogical knowledge on technological pedagogical knowledge.

H7: There is significant effect of technological knowledge on pedagogical content knowledge.

H8: There is significant effect of technological knowledge on technological content knowledge.

H9: There is significant effect of technological knowledge on technological pedagogical knowledge.

Figure 1. Conceptual framework.

Methodology

Study setting

This study is a part of National Priority Research entitled ‘Teacher Edu cation Program in Nepal: Reorienting Policies and Practices’ conducted by Dean Office, Faculty of Education in collaboration with Research Centre for Educational Innovation and Development (CERID), and Open and Distance Education Centre (ODEC), supported by Research Directorate, Tribhuvan University Nepal. Under Tribhuvan University, Faculty of Education (FOE here onwards) is one of the faculties that develop teachers required for both school and tertiary education in Nepal. It is the largest faculty of TU in terms of student enrolment and the number of affiliated campuses. Among the 62 constituent and 1082 affiliated campuses of Tribhuvan University, 26 constituent and 590 affiliated campuses across the country run the teacher education programs directly designed, developed, and monitored by FOE. It has offered diverse educational programs in both Bachelor’s and Master’s degrees in a wider range of subject areas including English education, Nepali education, Mathematics education, Social Studies, Science and Information and Communication Technology education. The cross-sectional survey design was employed in the study. It applied self-administered teacher survey and self-rating scale as the tools for data collection for investigating the university teacher competencies necessarily stressed in the TPACK model.

Sample and sampling technique

All teachers working under FOE at Tribhuvan University (TU) Nepal were considered as the population of the research. For the selection of teachers, 23 campuses were selected using stratified random sampling procedure. The sample campuses represented campus types (constituent, community, and private) based on TU affiliation, ecological belts (mountain, hill, Terai, and Kathmandu Valley) across the country. The sample comprises six Qualiti Assurance and Accredition (QAA) accredited and 17 non-QAA accredited campuses runing both Bachelor and Master’s level program. The details of selected sample campuses are displayed in Table 1. The study targeted to select 30 teachers from each campus randomly, however, some campuses had less than the required number of teacher educators. Hence, only 473 teachers were participated in the survey. Still, because of some non-responses and outliers cases in the data, responses of 405 teacher educators were included in this research.

Table 1. Selection of sample campuses under TU-FOE.

Types of campus	Mountain	Hills	Kathmandu valley	Tarai	Total
Constituent	1	5	2	4	12
Community	1	2	–	4	7
Private	–	–	1	3	4
Total	2	7	3	11	23

Research instrument

Self-administering instrument of the questionnaire was employed in the research which is theoretically based on TPACK model (Chai et al., 2012; Habibi et al., 2020; Koehler et al., 2013; Mishra & Koehler, 2006). In total 26 Likert five-point rating as excellent, good, fair, low competency, and poor competency scale types of items were included in the research instrument. The pilot testing was conducted on two campuses (one constituent and one community) that are not included in the sample to validate the questionnaire. The reliability of the instrument was ensured by Cronbach’s Alpha method and found to be 0.94 which exceed the threshold criteria of 0.70 (Cohen et al., 2018; Creswell, 2012). Additionally, the reliability of the instrument was ensured by composite reliability (CR) (see Table 2) which also edceed the threshold value of 0.70 (Civelek, 2018). For the validity the content and face validity were ensured by getting feedback of teachers, sharing and presentation of instrument among education related experts of Nepal. Based on the feedback of the teachers and experts, the items were improved and some more items were added and modified as per the requirement. Moreover, the validity of the instrument also ensured by convergent and discriminant validity method. The value of Average variance extracted (AVE) found to be >0.50 in each case except as PK indicating that the convergent validity was ensured (Nusair & Hua, 2010). The value of AVE is 0.47 in case of PK, which is also acceptable for validity (Lam, 2012). Additionally, the discriminant validity was ensured by Heterotrait–Monotrait (HTMT) analysis (Voorhees et al., 2016) which as presented in Table 2. In total 26 items were included in the research under six-dimensions as content knowledge (CK), pedagogical knowledge (PK), technological knowledge (TK), pedagogical content knowledge (PCK), technological content knowledge (TCK), and technological pedagogical knowledge (TPK). The dimensions were also conformed by confirmatory factor analysis (CFA) which is presented in Table 3.

Table 2. Detail of reliability and validity.

Factors	CR	AVE	HTMT analysis
			CK	PK	TK	PCK	TCK	TPK
CK	0.88	0.66
PK	0.78	0.47	0.68
TK	0.88	0.56	0.24	0.41
PCK	0.87	0.62	0.50	0.55	0.52
TCK	0.89	0.68	0.33	0.44	0.86	0.66
TPK	0.93	0.76	0.33	0.44	0.81	0.64	0.90

Table 3. Status of competency of teachers regarding CK, TK, PK, PCK, TCK, and TPK (n = 405).

Statements	Mean	SD	t-Value	FL
Content knowledge (CK)	4.10	0.67	32.92*
CCK1	4.18	0.77	30.98*	0.77
CCK2	4.21	0.79	30.85*	0.81
CCK3	3.99	0.78	25.32*	0.86
CCK4	4.00	0.77	26.13*	0.80
Pedagogical knowledge (PK)	3.98	0.55	35.87*
CPK1	4.10	0.69	32.25*	0.70
CPK2	3.96	0.70	27.66*	0.64
CPK3	3.83	0.70	23.77*	0.70
CPK4	4.01	0.73	27.93*	0.70
Technological knowledge (TK)	3.60	0.71	16.87*
CTK1	3.72	0.90	16.23*	0.63
CPK2	3.47	0.88	10.73*	0.69
CPK3	3.50	0.90	11.26*	0.69
CPK4	3.78	0.84	18.62*	0.83
CPK5	3.28	0.97	5.73*	0.86
CPK6	3.83	0.93	17.93*	0.77
Pedagogical content knowledge (PCK)	4.13	0.61	37.63*
CPCK1	4.18	0.71	33.40*	0.77
CPCK2	4.21	0.69	35.40*	0.84
CPCK3	4.04	0.72	28.93*	0.78
CPCK4	4.10	0.75	29.58*	0.75
Technological content knowledge (TCK)	3.70	0.73	19.39*
CTCK1	3.73	0.82	17.85*	0.78
CTCK2	3.75	0.84	18.03*	0.84
CTCK3	3.77	0.84	18.44*	0.84
CTCK4	3.55	0.85	13.14*	0.83
Technological pedagogical knowledge (TPK)	3.69	0.76	18.25*
CTPK1	3.74	0.82	17.96*	0.89
CTPK2	3.65	0.86	15.29*	0.86
CTPK3	3.76	0.83	18.47*	0.86
CTPK4	3.59	0.82	14.49*	0.88

FL: factor loading.

*p < 0.05.

Data collection procedure

The ethical approval was taken from the Ethical Review Board of Tribhuvan University (ERB-TU) before starting the data collection. Twenty-three researchers were selected and appointed for collecting data from the sample campuses across the country. Before the researchers were deployed to the respective campuses, a two-day orientation was organized in the Dean’s Office, TU Kathmandu, and the Principal Investigator and Co-Investigators including the members of the expert team oriented the researchers to make them familiar with using research tools in the field. The data was collected in face-to-face mode in February 2023. Separate participant consent was taken from each participant in written form (with personal details and signature) assuring with the confidentiality of the information they provide before their participation in self-administered survey form.

Data analysis technique

Mean, standard deviation (SD), one-sample t-test, Confrimatory Factor Analysis (CFA), structural equation modeling (SEM), and machine learning were major statistical techniques used in the research. Before the analysis, data were cleaned by excluding non-response cases and outliers in the data. Mean, SD, and one-sample t-test were used to calculate the status of competency of teachers on CK, TK, PK, PCK, TCK, and TPK. CFA was employed to ensured the factors in related domains as CK, TK, PK, PCK, TCK, and TPK. The SEM was employed to find the effect of each of CK, TK, and PK on PCK, TCK, and TPK. Additionally, the machine learning was employed to find the effect of all items (different from corresponding dimension) on mean score of each dimensions as CK, TK, PK, PCK, TCK, and TPK. The SPSS 23, AMOS-23, and JASP were major statistical software used in the research for data analysis.

Findings

Table 3 shows that the level of competency of the teachers found to be significantly high in each item and dimension. However, based on six-dimensions of competencies, the result was comparatively high in CK (Mean = 4.10, SD = 0.67), PK (Mean = 3.98, SD = 0.55), and PCK (Mean = 4.13, SD = 0.61) and low in TK (Mean = 3.60, SD = 0.71), TCK (Mean = 3.70, SD = 0.73), and TPK (Mean = 3.69, SD = 0.76). With respect to items, teachers are less competent in designing assignment for assessment (Mean = 3.99, SD = 0.78), using novel assignment techniques for evaluation (Mean = 3.83, SD = 0.70), skills in developing digital materials (Mean = 3.28, SD = 0.97), applying assessment design in classroom teaching (Mean = 4.04, SD = 0.72), developing digital instructional materials using technology (Mean = 3.55, SD = 0.85), and the use of digital resources for assessment and feedback (Mean = 3.59, SD = 0.82). Whereas, they were found more competent on the content knowledge in the subject that they teach (Mean = 4.18, SD = 0.77), using different teaching methods to teach the course (Mean = 4.10, SD = 0.69), skills in using writing and presentation tools (Mean = 3.83, SD = 0.93), delivering developed content as per interest of the students (Mean = 4.21, SD = 0.69), developing assignments and test papers by using technology (Mean = 3.77, SD = 0.84), and using digital resources in sharing the content (Mean = 3.76, SD = 0.83) as compared to remaining items in the corresponding dimensions as CK, TK, PK, PCK, TCK, and TPK, respectively.

Figure 2 shows that the relationship between the differentiation dimensions of competencies as CK, TK, PK, PCK, TCK, and TPK. The high correlation was measured between TK with TCK (r = 0.76) and TPK (r = 0.73) and TCK with TPK (r = 0.86) whereas moderate correlation was measured between PCK with CK (r = 0.44), PK (r = 0.46), and TK (r = 0.46) and PK with CK (r = 0.56). Furthermore, the relation found to be low (Burns & Dobson, 1983) in remaining cases however the relationship found to be positively significant in all cases.

Figure 2. Correlation between the dimensions of compentencies.

Model fit indices of structural equation modeling

Total variables observed were 26 and the sample size is 405 hence the sample size was sufficient for SEM analysis (Joshi, Adhikari, Khanal, Belbase, et al., 2023; MacCallum et al., 1996). Because of having good fit in the indicators as root mean square error of approximation (RMSEA), goodness-of-fit statistic (GFI), adjusted goodness-of-fit statistic (AGFI), and standardized root mean square residual (SRMR), normed-fit index (NFI), comparative fit index (CFI), Tucker-Lewis index (TLI), and incremental fit index (IFI) in the model the significant value of the Chi-square was considered in the models (Bentler & Bonett, 1980). The RMSEA is 0.06 (<0.08), GFI is 0.89, AGFI is 0.87 (near to threshold value 0.90), and SRMR is 0.06 (<0.08). Further, NFI is 0.91 (>0.09), CFI is 0.95 (>0.90), TLI is 0.94 (>0.90), and IFI is 0.95, all indices showed a good fit (Bentler & Bonett, 1980; Byrne, 1989; Hooper et al., 2008; Hu & Bentler, 1999; Joshi, Adhikari, Khanal, et al., 2023; Kline, 2016; MacCallum et al., 1996; Sarker & Chakraborty, 2021).

Results based on structural equation modeling (SEM)

Figure 3 shows that CK, TK, PK explaining 46, 75, and 67% variances on PCK, TCK, and TPK, respectively. The figure shows that the technological knowledge is found to be main predictor to the pedagogical content knowledge (Beta = 0.38), technological content knowledge (Beta = 0.80), and technological pedagogical knowledge (Beta = 0.74). However, the content knowledge also significant predictor to the pedagogical content knowledge (Beta = 0.27), technological content knowledge (Beta = 0.13), and technological pedagogical knowledge (Beta = 0.11). Additionally, the pedagogical knowledge has only a significant effect on pedagogical content knowledge (Beta = 0.23) however the results found to be insignificant regarding technological content knowledge (Beta = 0.05), and technological pedagogical knowledge (Beta = 0.09). It is also important to higlight that all the results either significant or insignificant are positive.

Figure 3. Effect of CK, TK, and PK on PCK, TCK, and TPK.

The itemwise effect to each of the dimensions are presented in Table 4. The contribution of technological content knowledge is found to be higher as compared to remaining items except as technological knowledge in TK, however, CTK2 and CTK2 are main predictors to the TK. In pedagogical knowledge, the contribution of content knowledge related items is found to be high as compared to remaining items except as PK related items however the CPK1 and CPK3 are main predictors to the latent variables as pedagogical knowledge. Similarly, the pedagogical knowledge have more contribution to content knowledge except as CK related items. CCK2 is the main predictor to the content knowledge. With respect to TPK, PTK, and TCK related items have more contribution as compared to remaining items whereas the same results were measured in TPK however other than PCK related variables have very poor roles to determine PCK.

Table 4. Item wise effect on CK, TK, PK, PCK, TCK, and TPK (n = 405).

Items	TK	PK	CK	TPK	TCK	PCK
CTPK4	0.02	0.01	0.00	0.18	0.08	0.00
CTPK3	0.02	0.01	0.00	0.16	0.07	0.00
CTPK2	0.02	0.01	0.00	0.16	0.07	0.00
CTPK1	0.02	0.01	0.00	0.21	0.09	0.00
CTCK4	0.04	0.00	0.01	0.06	0.13	0.00
CTCK3	0.04	0.00	0.01	0.07	0.15	0.00
CTCK2	0.04	0.00	0.01	0.06	0.14	0.00
CTCK1	0.03	0.00	0.00	0.05	0.10	0.00
CPCK4	0.01	0.01	0.01	0.00	0.00	0.16
CPCK3	0.01	0.01	0.01	0.00	0.00	0.19
CPCK2	0.02	0.02	0.02	0.00	0.00	0.26
CPCK1	0.01	0.01	0.01	0.00	0.00	0.19
CTK1	0.13	0.00	−0.01	0.01	0.02	0.01
CTK2	0.22	0.01	−0.01	0.02	0.04	0.01
CTK3	0.18	0.00	−0.01	0.01	0.03	0.01
CTK4	0.10	0.00	0.00	0.01	0.02	0.01
CTK5	0.09	0.00	0.00	0.01	0.02	0.01
CTK6	0.07	0.00	0.00	0.01	0.01	0.00
CPK4	0.00	0.16	0.03	0.01	0.00	0.01
CPK3	0.00	0.17	0.03	0.01	0.00	0.01
CPK2	0.00	0.14	0.02	0.00	0.00	0.01
CPK1	0.00	0.18	0.03	0.01	0.00	0.01
CCK1	−0.01	0.03	0.20	0.00	0.01	0.01
CCK2	−0.01	0.04	0.27	0.00	0.01	0.02
CCK3	−0.01	0.03	0.20	0.00	0.01	0.01
CCK4	−0.01	0.03	0.16	0.00	0.00	0.01

Results of machine learning

Regularized linear regression was performed under machine learning to find the item wise effects on mean scores of six dimensions as CK, TK, PK, PCK, TCK, and TPK. Table 5 shows that the mean square error (MSE) found to be high in content knowledge and low in technological content knowledge indicating that the data points are dispersed extensively around its mean in CK and the data points are disprsed norrowly in TCK as compared to remaining dimensions. Similar results were measeared in terms of root mean square error (RMSE), and mean absolute error (MAE). However, the mean absolute percentage error (MAPE) is found to be high in TK (122.33%) and low in CK (89.73) as compared to the remaining dimensions indicating that the average magnitude of error produced by model is high in TK and low in CK. Table 5 further shows that the model explaninng 42% (CK) to 77% (TCK) variance with respect to different dimensions. The model was used with n (Train) = 259, n (Validation) = 65, and n (Test) = 81.

Table 5. Evaluation metrices of the models (n = 405).

Indicators	CK	PK	TK	PCK	TCK	TPK
MSE	0.96	0.59	0.39	0.43	0.21	0.35
RMSE	0.98	0.77	0.62	0.66	0.45	0.59
MAE	0.69	0.56	0.48	0.52	0.37	0.42
MAPE	89.73%	214.22%	122.33%	100.13%	108.77%	105.76%
R^2	0.42	0.44	0.58	0.41	0.77	0.65
λ	0.04	0.04	0.02	0.02	0.01	0.02
Validation MSE	0.35	0.58	0.35	0.59	0.19	0.26

Note. The model is optimized with respect to the validation set mean squared error.

Table 6 shows that the CPK1 ($\beta$ = 0.20), CPK4 ($\beta$ = 0.17), and CPCK1 ($\beta$ = 0.11) are main predictors to the TK (Figure 4(a,a1)) whereas CTK4 ($\beta$ = 0.13) and CPCK2 ($\beta$ = 0.05) are main predictors to PK (Figures 4(b,b1)). Similarly, CTCK2 ($\beta$ = 0.22), CCK1 ($\beta$ = −0.17) and CTPK4 ($\beta$ = 0.16) are main predictor to the TK (Figures 4(c,c1)) with highest beta value however the CCK1 has negative effect on the TK.

Figure 4. (a) Variable trace plot of CK. (a1) Lambda evaluation plot of CK. (b) Variable trace plot of PK. (b1) Lambda evaluation plot of PK. (c) Variable trace plot of TK. (c1) Lambda evaluation plot of TK. (d) Variable trace plot of PCK. (d1) Lambda evaluation plot of PCK. (e) Variable trace plot of TCK. (e1) Lambda evaluation plot of TCK. (f) Variable trace plot of TPK. (f1) Lambda evaluation plot of TPK.

Table 6. Item wise effect on CK, TK, PK, PCK, TCK, and TPK (n = 405).

Items	Beta values
	CK	PK	TK	PCK	TCK	TPK
CCK1		0.12	−0.17	0.13	0.05	0.00
CCK2		0.06	0.00	0.01	0.03	−0.01
CCK3		0.16	0.00	0.05	−0.01	0.07
CCK4		0.20	0.04	0.05	−0.05	0.00
CPK1	0.20		0.00	0.05	0.02	0.00
CPK2	0.03		0.07	0.00	0.00	0.00
CPK3	0.02		0.00	0.01	−0.04	0.04
CPK4	0.17		0.09	0.11	−0.02	0.00
CTK1	−0.01	0.01		−0.14	0.03	0.06
CTK2	0.00	0.00		0.00	0.15	0.00
CTK3	0.00	0.00		−0.06	0.00	0.08
CTK4	0.00	0.13		0.12	0.03	0.01
CTK5	−0.05	0.00		0.09	0.05	0.01
CTK6	0.00	0.00		0.08	0.11	0.01
CPCK1	0.11	0.00	0.08		0.00	0.00
CPCK2	0.02	0.05	−0.06		0.09	0.05
CPCK3	0.03	0.00	0.00		0.04	0.00
CPCK4	0.06	0.04	0.00		0.00	0.02
CTCK1	0.06	0.00	0.15	0.15		0.15
CTCK2	0.00	0.03	0.22	0.03		0.23
CTCK3	0.00	0.00	0.08	0.01		0.23
CTCK4	0.00	0.00	0.08	0.00		0.21
CTPK1	0.00	0.00	0.08	0.00	0.33
CTPK2	0.00	0.00	0.04	0.02	0.13
CTPK3	0.00	0.00	0.00	0.22	0.00
CTPK4	0.00	0.00	0.16	0.07	0.16

Table 6 further shows that the CCK1 ($\beta$ = 0.13), CTK1 ($\beta$ = −0.14), CTCK1 ($\beta$ = 0.15), and CTPK3 ($\beta$ = 0.22) are main predictors of the PCK with highest absolute beta value. However, CTK1 has negative effect having negative beta value (Figures 4(d,d1)). The CTK2 ($\beta$ = 0.15), CTPK1 ($\beta$ = 0.33), and CTPK4 ($\beta$ = 0.16) have high and positive effect on TCK as compared to remaining items (Figures 4(e,e1)) because of having highest beta value. Similarly, CTCK1 ($\beta$ = 0.15), CTCK2 ($\beta$ = 0.23), CTCK3 ($\beta$ = 0.23), and CTCK4 ($\beta$ = 0.21) have the highest effect on TPK as compared to remaining items with positive highest beta value (Figures 4(f,f1)).

Discussion

The main aim of the research was to study the status of university teachers’ competencies based on the TPACK model. The findings show that the teachers are more competent in content and pedagogical knowledge. All teachers were chosen from the education stream, hence they have some sort of competency in their pedagogical knowledge which they study in their university courses. The cause also may be that the university has a provision for pedagogical engagement of teachers in education stream like instructing to prepare instructional planning (lesson, unit, and annual planning), preparation and use of materials, orientation, and supervision of teaching practices to prospective teachers and others. The competency of teachers in the domain of technological knowledge which are more important and related to 21st century skills (Joshi, Neupane, et al., 2021) was found to be lower as compared to other domains of knowledge and competence The similar results were found in Nepal in the context of ICT competency (Joshi, Chitrakar, et al., 2021) and digital pedagogical skills of teachers (Joshi, Khanal, et al., 2023) however both studies were limited to mathematics teachers of school and university level. Which also may cause that the teachers fromareas like remote hills and mountains have limited access and resources of technological infrastructure, such as access of internet and digital resources (Devkota, 2021). Equally, the colleges have limited access to well-managed computer labs and other digital materials. Hence, concerned bodies of the government and TU should have further plans to enhance the technological competencies of teachers. Furthermore, cloud computing adoption model (Okai et al., 2014) can be more appropirate to enhance the such technological knowledge of the university teachers of diverse areas.

Regarding the application of technological and content knowledge of teachers in student assessment, teachers are found to have limited competency in designing assignments for assessment, using novel assignment techniques for evaluation, applying assessment design in classroom teaching, developing test papers, and use of digital resources for assessment and feedback. Hence, teacher training programs, workshops, and seminars from the university as well as University Grants Commission should have more focus in enhancing necessary digital skills on assessment practices. The teachers’ competencies to develop and use of digitalized instructional materials are found to be comparatively low. Therefore, such trainings and workshops should focus more on the skills of audio-visual and text material development. Skills of using and writing presentation tools and sharing content-related digital resources are fundamental to the teacher educators. Hence, necessary digital equipment and digital pedagogical skills-related training should be provided to teaching faculties for effective integration of technology into classroom instruction (Laudari, 2019).

The relationship between all types of competencies as CK, TK, PK, PCK, TCK, and TPK found to be positively significant and interconnected which shows the enhancement in any type of competency supports to improve others (Habibi et al., 2020). PCK, TCK, and TPK are highly affected by technological knowledge. Hence TK related skills particularly involving instructional planning, operating LMS, preparation and presentation of instructional activities through the digital means, assessing students using digital tools, and developing digital materials are prerequisite for the teachers. Therefore, such knowledge and skills need to be incorporated in the contents of teacher education programs. Also, workshops, trainings, and seminars designed and organized for teacher educators need to address the issues and challenges of teachers related to their digital competency and digital strategies. Furthermore, CK and PK are significant predictors of the PCK, which is aligned with the finding of the study by Mailizar et al. (2021) that PK was the strongest predictor of PCK (Celik et al., 2014; Habibi et al., 2020; Pamuk et al., 2015), hence, further improvements are also needed in exploring content knowledge, designing assessment and digital materials, use of different instructional methods, and sharing materials. In this regard, Santos and Castro (2020) point out the need of more structured alternative approaches that prepare teachers to integrate technology while accomplishing the above-mentioned predictors in connection with 21st century learning. Thus, it was stated that technological pedagogical content knowledge serve as the knowledge foundation for teaching in the twenty-first century (Liu, 2011). It implies that TK as well as PCK should always be in emphasis in teacher education policies and programmes. The TPACK theory states that only the proper blending of technology, pedagogy, and content knowledge guarantee the proper achievement of students (Apau, 2017; Mishra & Koehler, 2006).

The contribution of having skills to develop instructional planning the using digital devices, using learning management system tools, and using evaluation tools are the determinant factors to the technological knowledge. Falloon (2020) often relates this competency as teacher digital competency which requires teachers to produce and apply digitally-sourced information and blend pedagogical, content, and technological knowledge. Hence, every teacher educator is expected to promote such competency for the enhancement and application of technological knowledge. The competence of using different teaching methods and novel assignment techniques for the student evaluation are the main predictors of pedagogical knowledge. However, their level was found to be significantly high. Hence, universities that are delivering teacher education programmes should have an additional focus on the use of diverse teaching methods and strategies of using digital technologies while implementing instructional activities, approaches, and practices (Lei et al., 2013). More importantly, the teachers should be able to combine pedagogical, content, and technological competencies into their lesson plans. The integration of technology attributes successful and pleasant environment in the classroom and the instructional activities implemented there (Koehler & Mishra, 2005).

Moreover, content knowledge has a better role to determine pedagogical knowledge and pedagogical content knowledge which suggests that teachers having a better knowledge of contents has also better at employing good pedagogical practices in their instruction (Kleickmann et al., 2015). Additionally, the teachers having competent in designing assignment for assessment has the highest prediction power to determine content knowledge (Jacob et al., 2020). This implies that the teachers can only design good assignment if they have better understanding and clarity in their contents that they need to teach. However, the prediction power of pedagogical knowledge-related items was found to be more as compared to others types of knowledge. Hence, the advancement of pedagogical knowledge only ensures the advancement of content knowledge.

The use of digital resources in teaching as well as assessment and feedback are better performed by those who have good technological pedagogical knowledge. Hence, every teacher should have additional focus on the use of digital resources and digital assessment practices for enhancing their technological pedagogical knowledge (Topping, 2023). However, the role of technological content knowledge has better prediction power to the TPK and vice versa. Therefore, teachers should be sensitive and serious about enhancing these competencies for enhancing technological and pedagogical content knowledge. Moreover, the role of other items except as PCK-related items was found to be negligible hence the enhancement of competencies on pedagogical content knowledge is only possible while enhancing the knowledge of easily deliver subjective knowledge to the interest of the students, developed content as per the interest of the students, and applying assessment design in classroom teaching should be promoted.

The result of machine learning shows that teacher competencies in using different teaching methods, searching materials online, and delivering content knowledge as per the interest of the students have a more positive prediction role to determine content knowledge (Bisen et al., 2021). Hence such competencies should be promoted in the future more. Moreover, having skills to develop instructional planning and developing digital materials which may be an issue to justify more by further research. The role of pedagogical knowledge-related items is the main predictor of pedagogical knowledge which is similar to the findings of SEM. Moreover, the uses of digital resources develop and deliver contents by using technology are the effective contributors to the technological knowledge. Hence, the teachers’ habits in sharing and using digital technology should additionally be promoted and ensured tech-infused pedagogy (Weisberg & Dawson, 2023). However, the competency of teachers in the content knowledge in the subject they teach has a negative role to determine technological knowledge.

For pedagogical content knowledge, the content knowledge at teaching subject, having skills for using collaboration and communication tools, use of digital resources while delivering contents, providing feedback, and assessing have positive roles (Berry et al., 2016). However, having skills to develop instructional planning and using evaluation tools by using digital devices has a negative role to determine pedagogical content knowledge. This happens when teachers are more occupied with digital devices and do not pay less attention to promote their content knowledge which ultimately affects the students’ learning achievement (Robertson, 2003). The skills in using LMS, writing, and presentation tools, enabling to use of digital resources in teaching, planning instruction, assessment, and feedback have high predictive power to the technological content knowledge hence these skills and using habits should be highly promoted in the future. However, the role of competence in designing assignments for assessment, selecting and grading materials, using novel assignment techniques, and searching materials from online sources have slightly negative role to determine technological content knowledge which seems to be an unexpected result of the research and need to justify by further research. Furthermore, skills of using digital resources to develop content, deliver it, design assignments, test papers, and digital instructional materials by using technology have a positive role to determine technological skills. For developing such skills, TU has to organize professional development training and programs. To enhance a teacher’s performance, including their content knowledge, pedagogical skills, technological expertise, and technology integration abilities in the classroom, professional development is required (Lopes & Oliveira, 2020; Pazim, 2021). Therefore, these skills should be promoted by organizing training, workshops, seminars, and other digital awareness programs for enhancing technological pedagogical knowledge related competencies of teachers.

Conclusions

The findings revealed that the level of competencies of teachers in content, pedagogy, and technology-related competencies of teachers is found to be significantly high. However, the technology-related competencies are found low as compared to others. The technological knowledge-related competency of teachers has the main role to determine pedagogical content knowledge, technological content knowledge, and technological pedagogical knowledge-related competencies. Development and use of digital materials and assessment techniques as well as digital content sharing through digital resources still need to be improved. The findings of the research are highly applicable to the University Grants Commission, TU and other universities, the Government of Nepal, trainers, and other stakeholders to know the existing competencies of university teachers so that they can consider these findings to make further plans for teachers’ skills enhancement and professional development. The results of the research are also important for all campuses and teachers at that level for making further teacher’s professional development training. However, this study is limited to the teachers of FOE-TU hence further similar study is needed by taking teachers of other universities as well as school level. The study included teachers and teacher educators from the Faculty of Education in general. Hence, further study is needed particularly targeting the teachers and teacher educators of different departments and subjects. Finally, the findings of this study are based on survey design. Any study in the future needs to consider qualitative approaches of data collection and analysis to explore teacher competencies and professional development.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was funded in part through a National Priority Area Research Grant Award (Award Number: TU-NPAR-078/79-ERG-02), Research Directorate, Research Coordination, and Development Council (RCDC), Tribhuvan University, Nepal, 2022.

Notes on contributors

Bishnu Khanal

Dr. Bishnu Khanal is an Associate Professor in the Department of Mathematics Education in the Mahendra Ratna Campus, Tribhuvan University, Kathmandu, Nepal. Currently, Dr. Khanal is an Assistant Dean at the Faculty of Education, Tribhuvan University. Dr. Khanal earned his Ph.D. in Education (Mathematics Education) from the Tribhuvan University. He also earned M.Phil. in Education and M.Ed. in Mathematics Education from Tribhuvan University. His research interest includes learning strategies and styles, teaching styles and strategies, teaching approaches, assessment of student achievement and integration of ICT in teaching and learning mathematics, and other cross-cutting issues regarding education.

Kamal Raj Devkota

Dr. Kamal Raj Devkota is an Assistant Professor of English Education currently working at the Central Department of Education, Tribhuvan University, Nepal. He teaches applied linguistics, language philosophy, and research methodology to both the graduate and postgraduate students. Equally, he supervises graduate students involved in different research studies in language education for the fulfillment of their graduate degrees. He has led several national and international research in education and technology, language education, literacy, and intergenerational learning. He has authored several research papers published in national and international journals. More recently, he is involved in policy and programme evaluation targeting school education for the Ministry of Education, Science, and Technology for the Government of Nepal.

Kamal Prasad Acharya

Dr. Kamal Prasad Acharya is an Assistant Professor of science education, teaching at the Central Department of Education, Tribhuvan University, Nepal. Dr. Acharya earned his Ph.D. in Education (Science Education) from the Tribhuvan University. He is the author of science education national and international peer-reviewed research journals in the field of the science curriculum, science teacher professional development, and participatory action research. He has published more than fifty research articles in national and international peer-reviewed journals. His areas of expertise are school gardening, social entrepreneurship, inquiry-based learning, and participatory action research.

Krishna Prasad Sharma Chapai

Mr. Krishna Prasad Sharma Chapai is an Assistant Professor at Babai Multiple Campus, Bardiya Mid-West University Nepal. He received his MPhil degree in Mathematics Education from the Nepal Open University and earned his M.Ed. in Mathematics Education from Tribhuvan University. His research interest includes Educational Technology, ethno mathematics, applied mathematics, quantitative research, and other issues related to Mathematical pedagogy.

Dirgha Raj Joshi

Dr. Dirgha Raj Joshi is a Faculty of Mahendra Ratna Campus, Tahachal, Tribhuvan University Nepal. He completed his Ph.D. in Education from Banaras Hindu University India and earned M.Ed. in Mathematics Education from Tribhuvan University. His research interest includes educational technology, digital competency, applied mathematics, quantitative research, structural equation modeling, machine learning, and other issues related to digital pedagogy. He has been working as a facilitator in digital pedagogy, mathematics teaching-related software and application, quantitative data analysis tools (SPSS, JASP), Structural Equation Modeling (AMOS), referencing tools (Mendeley, Zotero), qualitative data analysis tools (Atlas.ti), academic/scientific writing and other teaching-learning-related software and applications.