Examining the mediating role of college based capacities on the relationship between technology integration and teacher preparation program Implementation Quality in Southern Nation, Nationalities and People Region, Ethiopia

doi:10.21203/rs.3.rs-4973025/v1

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Examining the mediating role of college based capacities on the relationship between technology integration and teacher preparation program Implementation Quality in Southern Nation, Nationalities and People Region, Ethiopia

https://doi.org/10.21203/rs.3.rs-4973025/v1

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The study investigated the mediating role of college-based capacities in the relationship between technology integration and implementation quality of teacher preparation programs in colleges within the South Nation, Nationalities, and Peoples region. A quantitative methods design employed, utilizing cross-sectional surveys with 294 participants from four teacher education colleges. In a study using structural equation modeling, it found that technological pedagogical content knowledge (TPACK) had a significant impact on the quality of implementation, the provision of materials, and outcomes such as instructor effectiveness. The study also revealed that college capacities, including leadership, resources, and the attitudes of diploma graduates, played a mediating role in the relationship between technology integration and implementation quality. Correlation analysis showed strong positive relationships between technology integration, and factors such as instructor efficacy, leadership support, resource availability, and attitudes toward technology integration. The findings highlight technology integration significance to the proposed model and its interconnectedness with organizational and human elements influencing effective technology use in teacher education. Theoretically, the results align with resource-based and transformational leadership theories, emphasizing strategic resource utilization and leadership practices enhancing teaching and learning through technology. Practically, a comprehensive approach investing in TPACK competencies, instructional leadership, and resource provision recommended for impactful educational reforms. The study contributes an original model clarifying the dynamics between technology integration, college capacities, and implementation quality in teacher preparation contexts.

Technology integration

In-college capacity

Implementation quality

Teacher preparation

The field of education is undergoing a digital transformation, and teacher preparation programs are not exempt. As Abraham et al. [1] As future educators expected to be proficient in using technology for teaching and learning, it is crucial to examine how teacher-training programs themselves are influencing technology to enhance their effectiveness. This study will investigate the specific technology integration impacts of a teacher preparation program, going beyond mere motivation to analyze tangible outcomes. In exploring the impact of technology as an out-of-college factor on the quality of implementation in teacher preparation programs, it is crucial to consider various institutional capacities that play a significant role in shaping the effectiveness of these programs. To delve deeper into the specific aspects concerning college instructors' implementation competences, diploma graduate teachers' attitudes, provision of materials, and instructional leadership efficacy.

The competencies of college instructors in effectively integrating technology into teacher training programs are vital for successful implementation. Changes in teaching methods, such as shifting towards incentive-based approaches, had noted in the context of Ethiopian education [3, 18]. However, disparities in motivation and enthusiasm between public and private school teachers highlight the importance of enhancing instructors' competences in utilizing technology tools for effective pedagogy. The attitudes of diploma-graduate teachers towards technology can greatly influence the success of teacher preparation programs. Documented evidence suggests that young Ethiopian teachers often feel pressured to perform well academically, with a focus on exam scores and class rank [44] This pressure may influence their receptiveness to technology integration in teaching practices. Understanding and addressing these attitudes is crucial in designing effective technology-driven training programs for future educators.

Access to appropriate technology resources and materials is a critical factor in the successful implementation of technology-driven programs. The availability of on-the-job training, career counseling, and mentoring opportunities can help address skill gaps among teachers and prepare them for the evolving job market. As [4] note, ensuring that teacher preparation programs have access to relevant technological tools and resources is essential for equipping future educators with the skills needed to excel in a digital learning environment. Effective instructional leadership within educational institutions is essential for driving technological advancements in teacher preparation programs. The societal status of teachers and the support they receive from instructional leadership can influence their willingness to adopt innovative teaching methods [20]. Strong instructional leadership can foster a culture of continuous improvement and provide the necessary resources and training to empower instructors in leveraging technology for enhanced teaching practices.

The study suggests that a thorough examination of institutional capacities in relation to technology integration in teacher preparation programs can help identify areas for improvement and develop strategies to improve implementation quality, thereby fostering a technologically proficient teaching workforce.

As [13] the integration of technology in education is a critical factor influencing the quality of teacher preparation programs (TPPs). Chai et al. 's systematic review highlights the importance of leadership, resources, and teacher abilities in integrating technology effectively. However, the successful integration of technology into TPPs remains a complex challenge. The quality of TPP implementation, particularly in relation to technology integration, is crucial for preparing pre-service teachers effectively utilize technology in their future classrooms.

This study addresses this gap by investigating the mediating roles of college instructors' curriculum implementation competencies, diploma graduate teachers' attitudes towards technology, the provision of educational materials, and instructional leadership capacity in promoting quality implementation. The research aims to provide valuable insights into optimizing technology integration effects to enhance the overall quality and effectiveness of TPPs.

Research indicates that college instructional leadership capacity is crucial in guiding faculty as they integrate technology into their teaching practices. Curriculum implementation competencies of college instructors directly influence how well technology employed in classrooms. The provision of educational materials can either facilitate or obstruct technology integration in the classroom [36]. A gap remains in understanding how these elements interact to mediate the relationship between technological factors and the implementation quality of TPPs.

Theoretical framework

For effective technology enhanced education, the Technological Pedagogical subject Knowledge (TPACK) framework highlights the connections between pedagogy, subject knowledge, and technology [36]. The purpose of this study is to investigate how pre-service teachers' growth of TPP implementation quality affected by technological integration. Through the perspective of the Technological Pedagogical Content Knowledge framework, the interaction between technology and elements including instructional leadership capability, instructor competences, diploma graduate instructors' attitudes, and the availability of educational materials investigated. According to the [36] developed TPACK framework, technical knowledge (TK), pedagogical knowledge (PK), and content knowledge (CK) interact to enable successful technology enhanced instruction. The framework highlights the importance of these three knowledge categories in providing efficient and meaningful learning experiences.

Instructional leadership capacity

The TPACK framework emphasizes the role of technological instructional leadership in promoting the efficient use of technology in education. The [51] study discusses how Effective school leadership influences educators' implementation of educational technologies, fostering environments that cultivate teachers' TPACK. Leaders with a strong understanding of TPACK can provide guidance, resources, and support, enhancing the attitude and receptiveness of diploma graduate teachers towards technology use.

Instructor Competencies

The TPACK framework emphasizes the importance of combining technological, pedagogical, and content knowledge for effective technology integration in teaching. As [47] highlight the role of professional development programs in enhancing teachers' capabilities and confidence in using technology effectively. Strong TPACK instructors can design and deliver technology-enhanced learning experiences, catering to diverse learning styles.

Diploma Graduate Teachers' Attitudes

The TPACK framework suggests that integrating technology in the learning process can significantly influence diploma graduate teachers' attitudes and engagement. A meta-analysis by [34] highlights those positive attitudes towards learning increase when technology effectively integrated into the curriculum. Factors such as relevance ease of use, and perceived effectiveness of technology-mediated learning activities can influence teachers' attitudes and willingness.

Provision of Educational Materials

The TPACK framework emphasizes the significance of aligning educational materials with technological, pedagogical, and content knowledge. Technology-enabled materials, like digital resources and interactive simulations, can enhance the learning experience for diploma graduate teachers. According to [41] adequate material availability is crucial for successful adoption of TPACK among teachers. Instructors with strong TPACK can create effective materials that support student learning and commitment.

In summary, the TPACK framework provides a comprehensive theoretical foundation to understand the interplay between technology and the in college capacities such as instructional leadership capacity, instructor competencies, diploma graduate teachers' attitudes, and the provision of educational materials. By addressing the TPACK requirements, educational institutions and instructors can foster a technology-rich learning environment that supports the quality of TPP implementation.

RQ- To what extent do college instructional leadership capacity, college instructors' curriculum implementation competencies, diploma graduate teachers' attitudes, and the provision of educational materials mediate the relationship between technological factors and the implementation quality of teacher preparation programs?

Hypothesis

H1: Technology integration in teacher preparation programs will have a positive indirect effect on TPP implementation quality, mediated by college instructors' curriculum implementation competencies.

This hypothesis suggests that technology alone does not automatically enhance program quality. Instead, its positive effect channeled through instructors' ability effectively integrate technology into their teaching practices.

H2: Technology integration in teacher preparation programs will have a positive indirect effect on TPP implementation quality, mediated by diploma graduate teachers' attitudes toward technology.

This hypothesis posits that positive student attitudes toward technology act as a bridge between technology use and overall program quality. If teachers are resistant or disengaged with technology, its potential benefits may diminished.

H3: Technology integration in teacher preparation programs will have a positive indirect effect on TPP implementation quality, mediated by the provision of appropriate educational materials.

This hypothesis emphasizes that technology's impact maximized when paired with high quality resources. Simply providing technology without considering its pedagogical application and content relevance may not yield the desired outcomes.

H4: Technology integration in teacher preparation programs will have a positive indirect effect on TPP implementation quality, mediated by instructional leadership capacity in promoting and supporting overall program effectiveness.

The framework suggests that technological factors, including college instructional leadership capacity, curriculum implementation competencies, diploma graduate teachers' attitudes, and educational materials, play a crucial role in influencing the quality of teacher preparation programs, providing a comprehensive view of their interaction.

In summary, conceptual framework provides a structured approach to understanding how technological factors influence the quality of teacher preparation programs. By identifying and elaborating on the mediating roles of instructional leadership capacity, curriculum implementation competencies, diploma graduate teacher attitudes, and educational materials, this model supports a comprehensive investigation into enhancing the implementation quality of teacher preparation as shaped by the evolving technological landscape. Future research based on this framework can guide practical interventions aimed at improving teacher preparation programs to meet contemporary educational needs.

Quantitative survey research follows a positivist paradigm, focusing on the systematic collection and analysis of empirical data to identify universal laws and causal relationships [31, 37 and 50]. The research approach of this study is typically deductive, starting with a theoretical framework and designing a survey instrument to test hypotheses. As mentioned by [31] the researchers of this study used is the cross-sectional design, which collects data from a single point in time. As [37] the chosen design depends on the research questions, objectives, and researcher control over variables.

5.1. Population

The study examines four teacher education colleges located in the Southern Nations, Nationalities, and Peoples' Region (SNNPR) of Ethiopia: Arbaminch, Bonga, Dilla, and Hossana Colleges of Education. Researchers often study all colleges within the SNNPR for various reasons, including research goals, college characteristics, and practical considerations.

Evaluating the entire population within a target region is crucial for understanding the full range of a program's effects due to centralized funding. This approach ensures inclusivity and equity in a centralized system, as different student groups may experience the program differently. Comprehensive evaluation provides policymakers and administrators with robust data to inform decisions, refining and improving the program to meet the needs of all students and achieve its objectives.

The target populations for this study include instructors, instructional leaders, and diploma-graduate teachers from the colleges' catchment areas. A multistage sampling technique is used to select diploma-graduate teachers from the colleges' catchment areas, including all primary schools in the selected woredas and city administrations, with regular diploma-graduate teachers graduating from 2018–2022. The total population comprises 520 diploma-graduate teachers, 777 college instructors, and 115 college instructional leaders. Dilla College has the largest total population, followed by Hossana and Bonga, while Arbaminch College has the smallest population of 1415.

5.2. Sample size

The target sample size is 294 participants, calculated using the Cochran formula for a population under 50,000. Sampling technique proportional stratified sampling used to draw samples from the three participant groups. Diploma-graduate teachers, college instructors, and college instructional leaders. A 50% of the sample selected from each participant group. For diploma-graduate teachers, college instructional leaders and college instructors, a systematic sampling approach used, selecting every k^th participant from the list (where k = N/n) at each college.

5.3. Measure

Instructors' curriculum implementation competences can have a significant impact on the quality and effectiveness of the teacher preparation program. Several standardized questionnaires have developed to assess college instructors' curriculum implementation competences, including, curriculum Implementation Competence Scale (CICS) - Developed by [44], measures instructors' competence across 5 domains content, pedagogy, assessment, technology, and professionalism. Sample of items I am knowledgeable about the subject matter. In addition, I effectively model appropriate teaching strategies for my students. By [10] employed the CICS to investigate the relationship between instructors' curriculum implementation competences and teacher candidates' learning outcomes. Based on the survey questionaries’ the researchers adapting contextually to evaluate the instructor’s curriculum implementation competences. A 12-items curriculum implementation competency a questionnaire used, with a five-point Likert scale. The scale's internal consistency value was 0.910.

Teacher Preparation Program Evaluation Survey (TPPES) - Developed by the National Council on Teacher Quality, assesses teachers' perceptions of the quality and effectiveness of their preparation program. I how well did your preparation program equip you to meet the needs of diverse learners? [26] Employed the TPPES to evaluate the effectiveness of different teacher preparation pathways. Based on the survey questionaries’ the researchers adapting contextually to evaluate the diploma graduate teachers attitude towards the program as well the profession. An 8-items diploma graduate teacher’s attitude a questionnaire used, with a five-point Likert scale. The scale's internal consistency value was 0.830.

Program Resource Adequacy Scale (PRAS) - Developed by the Council for the Accreditation of Educator Preparation (CAEP), assesses the sufficiency of program resources to support candidate learning. Sample Items. My program provided a wide variety of high-quality instructional materials. I had access to up-to-date technological tools and resources. The program has sufficient physical facilities (e.g. classrooms, technology labs) to support candidate learning. In addition, Candidates have access to high-quality instructional resources aligned to program curriculum. By [52] employed the PRAS to evaluate the adequacy of program resources across different teacher preparation pathways. By [16] investigated how the quality of educational materials provided in programs, measured by the TPPRI, impacts teacher candidates' learning and practice. Based on the survey questionaries’ the researchers adapting contextually to evaluate the provision of educational materials. A 7-items provision of educational materials a questionnaire used, with a five-point Likert scale. The scale's internal consistency value was 0.878.

Factors like instructional leadership skills, content expertise, pedagogical knowledge, and ability to model effective teaching practices can influence how well teacher candidates are able to learn and apply the content and skills from their preparation program. A few standardized questionnaires have developed to assess the competence of instructional leaders in teacher preparation programs. Instructional leadership Competence Scale (ILCS) Developed by the University Council for Educational Administration, measures leaders' competence across domains like visioning, instructional management, and professional development. Based on the survey questionaries’ the researchers adapting contextually to evaluate the instructional leadership competences. Sample items Program leaders have a clear vision for high-quality teacher preparation. Instructional leaders provide regular, meaningful feedback to instructor. A 13-items instructional leadership competency a questionnaire used, with a five-point Likert scale. The scale's internal consistency value was 0.923.

5.4. Analytical strategy

SPSS version 26 used to assess the model fit of the study variables. The hypotheses tested using smart PLS-4 structural equation modeling (SEM) was established. The estimated standardized path coefficients and fit statistics reported. To test the hypotheses, the following steps followed via collecting data on the independent variable Technology integration TPACK and the dependent variables (implementation quality of TPP).

The present analysis employs a path analysis framework or structural equation modeling (SEM) to clarify the intricate relationships among various technology integration TPACK and educational variables in the context of TPP implementing quality. This model quantitatively captures the influence of technology integration TPACK on indicators of educational efficacy, including instructor effectiveness, diploma graduate attitudes, educational materials, instructional leadership capacities, and their collective affect a quality implementation.

6.1. Quality criteria analysis

R square result analysis

Table 1

R square result analysis
	R-square	R-square adjusted
Diploma	0.053	0.050
Material	0.237	0.234
Dependent	0.430	0.420
Instructor	0.113	0.110
Leader	0.230	0.227

The r-squared and adjusted r-squared values for the different predictors (diploma, material, dependent/ quality of implementation/, instructor, and leader) interpretation of r-squared and adjusted r-squared

The r-squared value indicates that approximately 5.3% of the variation in the dependent / quality of implementation/ explained by the diploma graduate teachers attitude. The adjusted r-squared value, which adjusts for the number of predictors in the model, is slightly lower at 5.0%. This suggests that diploma has a relatively small explanatory power for the dependent variable. Diploma may not be a strong predictor of the outcome. Other variables or factors could be contributing more significantly to the variation in the dependent variable.

The r-squared value indicates that approximately 23.7% of the variation in the quality of implementation explained by the material provision. The adjusted r-squared value is very close, at 23.4%, suggesting that material provision is a good predictor and its explanatory power not significantly diminished when accounting for the number of predictors. Material provision appears to be a meaningful predictor of the quality of TPP, contributing significantly to the explanation of its variation.

The r-squared value indicates that 11.3% of the variation in the dependent variable explained by the instructor competences. The adjusted r-squared value is slightly lower at 11.0%. This suggests that instructor competences have a modest explanatory power. Instructor competences is a moderate predictor of the TPP implementation quality. Its contribution is more significant than diploma graduate teachers attitude but less than material provision.

The r-squared value indicates that 23.0% of the variation in the dependent variable explained by the leader competencies. The adjusted r-squared value is very close, at 22.7%, which indicates that instructional leadership competences is a good predictor. Instructional leaders is also a meaningful predictor of the dependent variable, similar in explanatory power to material provision. Using these insights, one can prioritize the predictors that have higher r-squared and adjusted r-squared values for more focus in research, intervention, or decision-making processes.

Inner model list analysis

Table 2

Inner model list analysis
	VIF
diploma graduate teacher dependent ( implementation quality)	1.158
provision of material dependent ( implementation quality )	1.726
Technology integration TPACK factors diploma graduate teacher	1.000
Technology integration TPACK factors material provision	1.000
Technology integration TPACK factors dependent (implementation quality)	1.434
Technology integration TPACK factors instructor competencies	1.000
Technology integration TPACK factors leaders competencies	1.000
instructor competencies dependent ( implementation quality )	1.645
leaders competencies dependent ( implementation quality)	1.909

The provided data includes variance inflation factors (VIF) for various models, with different predictor variables. VIF is a measure of multi-collinearity, which indicates how much the variance of a regression coefficient inflated due to collinearity with other predictors. VIF interpretation VIF = 1 refers no correlation between the predictor and other variables. 1 < VIF < 5 refers moderate correlation but not severe enough to authorization corrective measures. VIF > 5 indicates high correlation indicating significant multi-collinearity.

Low multi-collinearity the VIF values are all below 2, indicating that multi-collinearity is not a significant issue in the models. This suggests that the predictors can used together without causing instability in the regression coefficients. Model reliability the low VIF values (close to 1) for technology integration TPACK factors across multiple predictors imply that technology integration TPACK-related factors are independent and reliable predictors. This suggests that technology integration TPACK can confidently use in regression models without multi-collinearity concerns. Focus on leader’s competencies the highest VIF value (1.909) for leader’s competencies indicates a relatively higher degree of multi-collinearity with dependent variables. While this is still within an acceptable range, it suggests that leader’s competencies may have overlapping information with other predictors. Care should take when interpreting the coefficients of leader’s competencies in regression models. Provision of material and instructor competencies the moderate VIF values for provision of material (1.726) and instructor competencies (1.645) indicate that these variables have some multi-collinearity but are still acceptable predictors. Their inclusion in regression models should not pose significant issues. The VIF analysis reveals that multi-collinearity is not a major concern in the provided models. All VIF values are well below the threshold of 5, indicating that the predictors can be used together without causing significant instability in the regression estimates. Ensure careful interpretation when this variable is included in models. Comprehensive approach continue using a diverse set of predictors (diploma graduate teacher, provision of material, and instructor competencies) as they do not exhibit problematic multi-collinearity. By understanding and addressing multi-collinearity, researchers and educators can make more decisions that are informed and build reliable statistical models.

Model fit analysis

Table 3

Model fit analysis
Model fit	Structured model	Estimated model
SRMR	0.072	0.138

The provided data includes several fit indices for two different models a structured model and an estimated model. The fit indices used are SRMR (Standardized Root Mean Square Residual), SRMR (Standardized Root Mean Square Residual) Structured Model (0.072). This value is below the commonly accepted threshold of 0.08, indicating a good fit.

Validity and reliability analysis

The provided data includes various metrics for assessing the construct validity and reliability of different variables: Cronbach’s alpha, Composite Reliability (rho a and rho c), and Average Variance Extracted (AVE). Threshold commonly accepted threshold for acceptable reliability is 0.70. All variables have Cronbach’s alpha values above 0.70, indicating good internal consistency and reliability. Interpretation all constructs are reliable based on Cronbach’s alpha. Composite reliability (rho a and rho c) threshold values above 0.70 are considered acceptable. All variables have rho a and rho c values well above 0.70, indicating good composite reliability. All constructs demonstrate good composite reliability. Average variance extracted (AVE) threshold is values above 0.50 considered acceptable, indicating that more than 50% of the variance of the indicators captured by the construct.

Table 4

Validity and reliability analysis
Construct validity of variables	Cronbach’s alpha	Composite reliability (rho a)	Composite reliability (rho c)	Average variance extract
Diploma graduate teacher (intermediate)	0.830	0.905	0.885	0.542
Material provision(intermediate)	0.878	0.881	0.906	0.579
Technology integration TPACK	0.837	0.840	0.891	0.672
Dependent	0.703	0.703	0.871	0.771
Instructor competencies(intermediate)	0.910	0.917	0.924	0.503
Instructional leadership performance	0.923	0.925	0.933	0.520

Diploma graduate teacher (0.542), material provision (0.579), technology integration TPACK (0.672), dependent (0.771), instructor competencies (0.503), and instructional leadership performance (0.520) all have AVE values above 0.50. Therefore, all constructs have acceptable convergent validity, as they all have AVE values above 0.50.

Construct validity all constructs have good convergent validity as indicated by the AVE values being above 0.50. This means that the constructs well represented by their indicators. Reliability the high values of Cronbach’s alpha and composite reliability (rho a and rho c) indicate that the constructs are reliable and consistently measured. The constructs instructor competencies and instructional leadership performance exhibit particularly high reliability with Cronbach’s alpha values of 0.910 and 0.923, respectively.

Model trustworthiness given the good reliability and validity metrics, the constructs can be trusted for further analysis and interpretation in research models. The dependent variable, despite having the lowest Cronbach’s alpha (0.703), still meets the acceptable threshold and shows strong convergent validity with an AVE of 0.771. Focus on improvement while all constructs meet the thresholds, instructor competencies has an AVE close to the threshold (0.503). Efforts could made further improve its indicators to increase this value.

The analysis indicates that all the constructs exhibit good reliability and validity, making them suitable for use in further research. The high reliability and acceptable AVE values suggests that the constructs are well measured and accurately represent the underlying concepts.

Table 5

Discriminate validity analysis
Heterotrait-monotrait ratio (HTMT)	Diploma graduate teacher	Material provision	Technology integration TPACK	Dependent	Instructor competencies
Diploma graduate teacher attitudes (intermediate)
Educational Material provision(intermediate)	0.341
Technology integration TPACK independent	0.271	0.566
Dependent /quality of TPP/	0.427	0.529	0.720
Instructor competencies(intermediate)	0.350	0.533	0.365	0.500
Instructional leadership performance (intermediate)	0.274	0.626	0.538	0.637	0.627

The heterotrait-monotrait (HTMT) ratio is a measure used in structural equation modeling to assess the discriminant validity of constructs. Discriminant validity ensures that different constructs are indeed distinct from each other. An HTMT value greater than 0.90 often considered problematic, suggesting that two constructs may not be distinct. However, some literature suggests a more conservative threshold of 0.85.

Diploma graduate teacher and material provision (0.341) this value is well below the threshold of 0.85, indicating good discriminant validity between these two constructs. Diploma graduate teacher and technology integration TPACK (0.271) this value is also well below the threshold, indicating good discriminant validity. Diploma graduate teacher and dependent (0.427) again, this value is below the threshold, supporting discriminant validity. Diploma graduate teacher and instructor competencies (0.350) this value suggests good discriminant validity. Diploma graduate teacher and instructional leadership performance (0.274) this value is below the threshold, indicating good discriminant validity.

Material provision and technology integration (0.566) this value is below the threshold, indicating good discriminant validity. Material provision and dependent (0.529) this value is below the threshold, indicating good discriminant validity. Material provision and instructor competencies (0.533) this value suggests good discriminant validity. Material provision and instructional leadership performance (0.626) this value is below the threshold, indicating good discriminant validity. Technology integration and dependent (0.720) this value is below the threshold, indicating good discriminant validity. Technology integration and instructor competencies (0.365) this value suggests good discriminant validity.

Technology integration and instructional leadership performance (0.538) this value is below the threshold, indicating good discriminant validity. Dependent and instructor competencies (0.500) this value suggests good discriminant validity. Dependent and instructional leadership performance (0.637) this value is below the threshold, indicating good discriminant validity. Instructor competencies and instructional leadership performance (0.627) this value is below the threshold, indicating good discriminant validity. In conclusion, your HTMT analysis indicates good discriminant validity between the constructs, suggesting that the measures used are appropriately distinct. This supports the robustness and reliability of your research findings.

Normality analysis

The provided data includes statistical measures for five variables such as diploma graduate attitude, material provision, technology integration, instructor implementation competencies, and instructional leader’s capacity.

Table 6

Normality analysis
	Standard deviation	Excess kurtosis	skewness	Number of observation
Diploma	1.000	-0.351	-0.505	294.000
Material	1.000	-0.688	0.152	294.000
Technology integration TPACK	1.000	-0.702	-0.072	294.000
Instructor	1.000	-0.308	-0.064	294.000
Leader	1.000	-0.445	-0.233	294.000

Observations about the mean of 0.000 and a standard deviation of 1.000 are smart. This does indeed point towards standardization, a common technique employed to bring different variables onto a comparable scale [28]. This is particularly useful when dealing with multivariate analysis, as it prevents variables with larger scales from dominating the analysis [19]. A skewness value near zero suggests a symmetrical distribution resembling the normal distribution. This favorable characteristic for many parametric statistical tests that often rely on the assumption of normality [23].

6.2. Major result analysis

Pathways to Effective Technology Integration analysis

The diagram appears to be a path analysis or a structural equation model representing the relationships between different variables related to technology integration TPACK and TPP implementation.

Technology integration has a significant impact on all the variables measured. The strongest influence is on material and leader, with path coefficients of 0.487 and 0.480 respectively. This suggests that improvements or changes in technology integration might strongly influence educational materials and instructional leadership factors. The analysis reveals that technology integration exerts a significant positive influence across all variables under investigation. Notably, the path coefficients for educational materials (0.487) and instructional leadership (0.480) are particularly high, indicating that enhancements in technology integration could substantially affect these critical components of the educational process. This suggests that institutional investments in technology integration resources may lead to transformative changes in how educational materials are developed and delivered, as well as how instructional leadership exercised within educational settings.

Instructor influences dependent (quality implementation) path coefficient = 0.084 The instructor has a less significant but positive influence on the dependent variable, suggesting that while the instructor's role is important, it might not be the most critical factor within this model. While the instructor’s influence on the dependent variable is present (path coefficient = 0.084), it is comparatively lower than that of the technology integration TPACK-mediated variables. This finding indicates that, although instructors play an essential role in the educational landscape, their impact in this model less pronounced than that of technology integration TPACK and instructional leadership. This lower path coefficient may suggest a need for additional support and resources for instructors to maximize their effectiveness within the educational ecosystem.

Diploma graduate teachers attitude influences dependent path coefficient = 0.180. The diploma graduate teachers attitude has a moderate positive influence on the dependent variable, indicating that the positive realization of a diploma graduate teachers attitude has a notable impact on the TPP implementation quality considered in this model. Provision of material influences implementation quality path coefficient = 0.006. The path from material to the dependent variable is very weak, suggesting that material alone might not significantly affect the outcome directly. The impact of the diploma graduate teacher on the TPP implementation quality is moderate (path coefficient = 0.180), highlighting its importance in shaping educational outcomes. Conversely, the path from educational materials to the TPP implementation quality is insignificant (path coefficient = 0.006), suggesting that the mere availability of educational materials may not do for meaningful learning outcomes such as implementation quality of teacher preparation program efficacy. This underscores the necessity of focusing on the application and integration of such materials within diverse pedagogical strategies rather than solely their provision.

Leader influences implementation quality path coefficient = 0.246. Instructional leadership has a relatively strong positive influence on the dependent variable, indicating the importance of instructional leadership in affecting the outcome. Instructional leadership emerges as a critical factor influencing the dependent variable, with a path coefficient of 0.246. This finding underscores the pivotal role that educational leaders play in fostering a conducive learning environment. Investments in instructional leadership development programs could be instrumental in enhancing educational effectiveness and outcomes, as strong instructional leadership appears to correlate positively with improved quality of TPP implementation.

Technology integration is a crucial factor in improving educational quality, according to a model. It positively influences instructors, materials, instructional leadership, and diploma graduates' attitudes. A balanced approach, including improvements in teaching quality, instructional leadership, and diploma program support, can lead to better outcomes. Strategic investments in technology integration infrastructure recommended.

Path coefficient analysis

The provided data appears to be a simplified representation of the path coefficients from a structural equation model or path analysis.

Table 7

Path coefficient analysis
	Diploma	Material	Dependent	instructors	Leader
Diploma			0.180
Material			0.006
Technology integration TPACK	0.231	0.487	0.364	0.337	0.480
Dependent
Instructors			0.084
Leader			0.246

Path coefficients diploma to dependent is 0.180, material to dependent is 0.006, technology integration TPACK to diploma is 0.231, technology integration TPACK to material is 0.487, technology integration TPACK to dependent is 0.364, technology integration TPACK to instructors is 0.337, technology integration TPACK to leader is 0.480, instructors to dependent is 0.084, and leader to dependent is 0.246.

Technology integration TPACK influences on diploma is moderate positive impact (0.231). Material is strong positive impact (0.487). Dependent is moderate positive impact (0.364). Instructors is moderate positive impact (0.337). Leader is strong positive impact (0.480). This implies that technology integration TPACK serves as a foundational factor influencing various aspects of the educational environment. Its strongest impacts are on material provision and leader’s efficacy, indicating that technology integration advancements significantly enhance educational materials availability and instructional leadership competences.

Diploma influences on dependent is moderate positive impact (0.180). This implies that achieving a diploma graduate teacher’s attitude positively has a notable influence on the TPP implementation quality. Material influences on TPP implementation quality is very weak positive impact (0.006). This implies that, the direct influence of materials on the TPP implementation quality is minimal; indicating that while materials are necessary; their direct impact on outcomes is limited without other supporting factors. Instructor’s influences on TPP implementation quality is weak positive influences (0.084). It implies that instructors have a positive but relatively weak direct impact on the dependent variable, suggesting that their influence might more effectively mediated through other variables like technology integration or instructional leadership. Leader influences on TPP implementation quality is moderate positive impact (0.246). Which mean that Instructional leadership has a significant positive influence on the dependent variable, highlighting the importance of strong instructional leadership in achieving better educational outcomes.

Enhancing educational results at teacher education institutes may achieved by investing in technology integration. In their [5] research, Baker & Inventado explore how technology might improve learning outcomes by enabling data-driven decision-making. Setting modern technology as a top priority together with bettering instructional leadership and diploma attitudes results in a more comprehensive strategy that produces superior outcomes. This paper by [38] demonstrates how integrating technology into teacher education may enhance teaching strategies and student learning. The effectiveness of teacher preparation programs (TPPs) depends on instructional leadership, which calls for robust leader development and ongoing professional training.

The importance of professional development and strong instructional leadership in teacher training programs emphasized in the [16] study. It is not enough only provide instructional resources; instead, effective use of technology and teaching methodologies is crucial [9].To improve educational performance overall, a holistic approach that strengthens teacher support, instructional leadership, and technological integration is essential. As [33] discusses the value of effective instructional leadership in creating a setting that encourages innovation in education.

Specific indirect effect matrix analysis

The provided data indicates specific indirect effects of technology integration TPACK on the dependent variable through different mediating factors.

Table 8

Specific indirect effect matrix analysis
	Specific indirect effects
Technology integration factors instructor competencies dependent	0.028
Technology integration factors material provision dependent	0.003
Technology integration factors diploma graduate teacher dependent	0.042
Technology integration factors leaders competencies dependent	0.118

The analysis of indirect effects reveals a nuanced interplay between technology integration factors and the quality of program implementation, mediated through various actors and resources. While all posited relationships demonstrate a positive association, the magnitude of these indirect effects varies significantly, suggesting strategic considerations for maximizing the impact of technology integration on quality educational outcomes.

Firstly, the study reveals a relatively weak indirect effect of technology integration factors on program implementation quality through instructor competencies (0.028). While statistically significant, this finding suggests that simply providing instructors with technology integration may not be sufficient substantially enhance program implementation. This aligns with existing literature emphasizing the importance of pedagogical training alongside Technology integration TPACK adoption [36]. Institutions should prioritize professional development programs that focus not only on technical skills but also on integrating technology integration effectively into pedagogical practices.

Similarly, the indirect effect through material provision is negligible (0.003), indicating that the mere availability of technology integration enhanced materials does not guarantee improved implementation quality. This finding underscores the importance of considering how materials integrated into the curriculum and utilized by both instructors and learners. As highlighted by [7], effective technology integration TPACK requires a holistic approach that aligns technology integration TPACK tools with pedagogical goals and learner needs.

The study identifies a moderate indirect effect through diploma graduate teachers (0.042), suggesting that technology integration can positively influence program implementation by supporting the qualifications and effectiveness of educators. This finding supports the growing body of research advocating for the integration of technology integration TPACK into teacher education programs [40]. Institutions should prioritize the development, implementation of technology integration enhanced teacher education programs that equip educators with the skills, and knowledge effectively influence technology integration in their classrooms.

The analysis's last finding, which highlights the crucial role that instructional leadership plays in influencing technology integration for educational progress, is the indirect effect through leaders' abilities (0.118). This result is consistent with studies showing how transformative instructional leadership is critical to the success of technology integration projects [21]. Institutions ought to fund programs for instructional leadership development that improve school administrators' ability to create, carry out, and maintain learning environments rich in technological integration.

This study's result emphasizes the intricate relationship between program implementation quality and technology integration elements. Technology integration can have a favorable impact on several program execution issues, but the extent of these benefits depends on deliberate alignment with educational objectives and strategic investments in human resources.

Correlation matrix analysis

The information provided looks to be a correlation matrix, illustrating the relationships between variables like attitudes of diploma graduates, the availability of materials, the factor related to technology integration, the quality of implementation (dependent), the competencies of instructors, and the competencies of instructional leadership. The values of correlation coefficients vary from − 1 to 1. A score of 1 denotes an ideal, happy relationship. A complete negative connection is represented by a -1. A value of 0 denotes no association.

Table 9

Correlation matrix analysis
	Diploma	Material	Technology integration TPACK	Dependent	instructors	leader
Diploma	1.000
Material	0.304	1.000
Technology integration TPACK	0.231	0.487	1.000
Dependent	0.353	0.418	0.555	1.000
Instructors	0.319	0.484	0.337	0.409	1.000
Leader	0.243	0.565	0.480	0.516	0.578	1.000

The correlation analysis have conducted reveals a complex interplay between various factors influencing educational outcomes. The findings highlight the interconnected nature of diploma graduate teacher attitudes, material provision, Technology integration quality of implementation represented as the “Dependent”, instructor effectiveness, and instructional leadership quality. The analysis correctly identifies the pivotal role of material provision in shaping the educational environment. The moderate to strong positive correlations between material provision and other variables, notably the strong positive correlation with instructional leadership quality (0.565), suggest that investing in high quality materials can have a high effect, positively influencing Technology integration instructor effectiveness, and instructional leadership practices. This make parallel with resource-based theories, which posit that access to and effective utilization of resources, including material resources, are crucial for organizational effectiveness [12].

Furthermore, the findings underscore the crucial role of Technology integration in contemporary educational settings. The strong positive correlation between Technology integration and the dependent variable (0.555) reinforces the growing body of evidence demonstrating the positive impact of Technology integration on quality implementation [42]. However, it is important to acknowledge that Technology integration is not a solution. Effective Technology integration pivots on various factors, including pedagogical approaches, instructor competency, and institutional support [11].

The analysis interestingly highlights the centrality of instructional leadership in shaping teacher educational outcomes. The strong positive correlations between instructional leadership and other variables, particularly the strong association with instructor effectiveness (0.578) and learner outcomes (0.516), underscore the crucial role of effective instructional leadership in fostering a positive and productive learning environment. This aligns with research highlighting the impact of transformational instructional leadership on school improvement efforts [32].

Invest in high-quality materials and technologies, with comprehensive implementation plans. Implement instructional leadership development programs to influence technology integration, support instructors, and foster a positive college climate. Provide ongoing professional development opportunities for instructors; empower them to shape school improvement efforts.

The availability of hardware, software, and digital resources shapes not only what taught but also how it taught [29]. However, access alone is not enough. As [8] argues, robust infrastructure, including reliable internet and technical support, is paramount to successful technology integration. Without it, even the most motivated educators face a difficult encounter.

This is where college instructional leadership capacity becomes crucial. Effective leadership goes beyond simply equipping faculty with technology; it is about fostering a culture of innovation and providing ongoing support [17] This includes professional development opportunities personalized to instructors' needs and a clear vision for technology's role in achieving institutional goals [45]. Of course, college instructors’ curriculum implementation Competencies are fundamental. As [48] highlight, pedagogical knowledge must evolve alongside technological advancements. Instructors need training not just on, how to use specific tools but also on how effectively integrate those into their teaching to achieve desired learning outcomes.

Finally, the diploma graduate teachers’ attitudes towards technology are a significant predictor of their future practices [39]. Pre-service training plays a vital role in shaping these attitudes. Providing opportunities for meaningful engagement with technology during their training can significantly affect their confidence and willingness to incorporate it into their future classrooms [46]. Inclusion of provision of educational materials is insightful. Access to current, high-quality resources, both digital and traditional, is crucial for effective technology integration [27]. This includes not only textbooks and digital content but also tools and platforms that support diverse learning styles and needs.

Theoretical implication

The study emphasizes the importance of Technology Pedagogical Content Knowledge in the educational model, highlighting its interconnectedness with other elements like instructor efficacy, instructional leadership, and diploma graduates' attitudes. It suggests that simply providing technology integration may not be enough significantly improve program implementation quality. This aligns with existing literature that emphasizes the importance of integrating pedagogical training alongside TPACK adoption [36]. The study further highlights the need for a holistic approach to technology integration, where tools aligned with pedagogical goals and learner needs [7]. This reinforces the theoretical understanding that effective technology integration requires a comprehensive strategy that includes both technical and pedagogical components.

The correlational analysis yielded significant theoretical implications, particularly in light of resource-based theories. The robust relationships identified between the availability of resources and variables such as the caliber of instructional leadership and the integration of technology underscore the importance of efficient resource utilization in organizational success. This finding aligns with [12] assertion that access to high-quality materials is crucial for academic achievement. Moreover, the strong relationship between the quality of implementation and technological integration further supports the notion that technology should embraced as an integral component of instructional practice in contemporary educational contexts. The findings corroborate the [42] conclusions, which highlight the positive impact of technology on academic performance, indicating that educational theoretical frameworks must revised to account for the evolving functions of these tools. The data also emphasize the importance of instructional leadership in influencing student outcomes. Theories underpinning transformational leadership should explored further in educational settings, given the substantial connections observed between instructional leadership and other factors, such as teacher performance. As [32] emphasize the importance of effective instructional leadership for school development initiatives. These results underscore the need for educational leaders to implement transformational strategies that promote student learning and teacher performance.

Practical implication

According to [49], the TPACK (Technological Pedagogical Content Knowledge) framework is an effective tool for promoting technology integration in education. This framework requires strategic investments in infrastructure and resources to enhance educational quality by encouraging innovative teaching approaches and technology integration into curricula. Effective instructional leadership and diploma graduate programs are essential for improved educational outcomes. According to [32] found that leaders with a clear vision for technology integration can enhance colleges' capacity to implement innovative strategies, and a balanced approach is most effective. A comprehensive model should not only provide educational materials but also focus on their effective utilization through sound teaching strategies and technological integration. By [15] suggest that professional development programs should target both technical skills and pedagogical strategies for effective technology integration in education.

Additionally, recent findings by [2] indicate that high-quality instructional materials directly correlate with higher student achievement, especially when educators well trained in their application. Institutions should prioritize professional development programs that integrate TPACK into pedagogical practices, as [22] research-aligning technology with pedagogical goals is crucial for improved educational effectiveness. Successful integration depends on teachers' ability to connect educational resources with teaching methodologies. Effective instructional leadership is crucial for enhancing educational outcomes as indicated by [25] findings. Leaders should invest in leadership development programs to equip themselves with the necessary skills to create a supportive school climate and guide educators through technology integration, ensuring that teachers supported and held accountable. Investment in high-quality educational materials is crucial for institutional success, and institutions should prioritize resource allocation towards innovative technologies that promote effective teaching practices [30]. Effective technology integration requires a comprehensive grasp of pedagogical strategies, instructor competencies, and institutional support frameworks, and actionable steps should take to implement strong professional development opportunities for educators [7].

Limitation

First, it is unclear if the observed associations are the result of the variables' interactions or other unmeasured factors because the study's correlational design hinders the ability to draw conclusions about connectedness. Furthermore, because the study's participants selected from a single diploma program in a particular region, the results may not fully reflect the larger educational context, which might limit their generalizability. A further constraint is to the self-report measures employed for the assessment of some variables, which may influenced by response biases and social desirability. Additionally, the study did not take into account all significant variables that could have an impact on technology integration and educational results, such as socioeconomic backgrounds and technical ability. The generalizability and strength of the results could improve by using more varied and representative samples and a wider range of factors in future study. Future research might also benefit from using experimental and longitudinal methods to assess the impact of technology integration on educational results over time and demonstrate causation.

Originality

Despite these drawbacks, by emphasizing the connections between college instructors, instructional leadership, resource availability, diploma graduate teachers, this study significantly adds to the body of knowledge on technology integration in education. It also emphasizes how crucial it is to approach technology integration holistically, matching TPACK tools with quality implementation in the teacher preparation. Investing in high-quality educational resources, encouraging instructional leadership to improve technology integration in educational contexts, and giving priority to professional development programs for instructors that incorporate TPACK are some of the practical implications for educators and policymakers.

Integrate technology in education; it is essential to invest in infrastructure and tools, as well as to establish training programs that cultivate strong educational leaders. Educational materials should not only provided but also integrated thoughtfully into the teaching process, aligning with pedagogical goals and learner needs. A holistic strategy that enhances all areas technology integration, instructional leadership, instructor competence, and diploma graduate attitudes is crucial for improving educational outcomes because of high positive influences of technology integration. Comprehensive professional development programs must cover both technical and pedagogical aspects of TPACK; ensuring educators can effortlessly integrate technology into their teaching. Additionally, robust leadership development initiatives needed to empower school leaders to support instructors effectively. Overall, a cooperative and interconnected approach to educational improvement should adopted, focusing on material resources, technology integration, leadership capacity, and instructor effectiveness to create dynamic learning environments that meet the diverse needs of learners.

In summary, this analysis underscores the importance of a comprehensive strategy that embraces the interconnectedness of technology integration factor, educational materials, instructor effectiveness, instructional leadership, and graduate attitudes. By focusing on enhancing these areas simultaneously, educational institutions can achieve sustainable improvements that go beyond lonely changes. The insights gained from this analysis highlight the necessity for a strategic approach that prioritizes instructional leadership, instructor capabilities, and effective material integration and diploma graduates positive outlook. Such a commitment not only fosters enhanced learning experiences but also covers the way for improved learner success in an ever-evolving educational landscape.

Funding

The authors did not receive support from any organization for the submitted work

Conflicts of Interest

Any known competing financial interests or personal relationships do not influence the work reported in this paper

Ethics approval

Ethical permission with a number of DU/IEBS/121/16 obtained from Dilla University IEBS institute ethics committee for this research on September 05/05/2016 E.C

Informed consent to participate

Informed consent was obtained from all individual participants included in the study

Author (s) Contribution Rate

Befkadu Legesse: Since this study is part of my dissertation, conducted the research, Investigation, Methodology, Formal analysis, writing the original draft, reviewing the reviewers’ comments, and editing the final draft. Dr.Dawit Legesse and Dr. Mesfin Molla: Supervision, Reviewing and editing.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request

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Examining the mediating role of college based capacities on the relationship between technology integration and teacher preparation program Implementation Quality in Southern Nation, Nationalities and People Region, Ethiopia

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1. Introduction

2. Statement of the Problem

3. Basic Research Question

4. Conceptual framework

5. Methods

5.1. Population

5.2. Sample size

5.3. Measure

5.4. Analytical strategy

6. Result of the study

6.1. Quality criteria analysis

6.2. Major result analysis

7. Discussion

8. Recommendations

9. Conclusion

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