Trainers’ Perspectives on CBET Implementation in Enhancing ICT Employability Skills for Students in Bungoma County TVET Institutions

Trainers’ Perspectives on CBET Implementation in Enhancing ICT Employability Skills for Students in Bungoma County TVET Institutions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Trainers’ Perspectives on CBET Implementation in Enhancing ICT Employability Skills for Students in Bungoma County TVET Institutions Collins Wabwile Walucho, Hoseah Kiplagat, Simon Wanami This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9554893/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Although Technical and Vocational Education and Training (TVET) institutions in Kenya have adopted Competency-Based Education and Training (CBET) to align education with labor market demands, there is lack of empirical data on how trainers perceive CBET implementation within ICT courses in fostering technical and soft skills relevant for student employability. This study investigated trainers’ perspectives on CBET Implementation in enhancing ICT employability skills for students in Bungoma County TVET institutions. This study employed mixed methods research design grounded on interpretivist research paradigm and competency theoretical framework. The study adhered to ethical research principles of anonymity and informed consent. The study filled the knowledge gap by offering empirical findings on the perceptions of ICT trainers towards CBET implementation in ICT courses and student employability skills. Data was collected from 41 respondents using structured questionnaires and semi structured interviews from 32 purposely sampled ICT trainers and 9 ICT Heads of Departments correspondingly in 9 TVET institutions in Bungoma County. Descriptive and inferential statistics and qualitative thematic analysis were used to analyze quantitative and qualitative data respectively. The results of the study reveal that implementation strategies (β = 0.438, p = 0.014) and the characteristics of the trainer (β = 0.331, p = 0.048) have a significant impact on the integration of CBET in ICT courses. The results pointed to the significant knowledge gap in emerging areas like Artificial Intelligence, Data science, Machine learning and Full-stack development. The most notable challenges were lack of adequate resources, evolving ICT field and the insufficient time dedicated to practical training. The study suggested that TVET institutions should incorporate Problem-based learning practices and enhance human resources through integrating emerging areas in ICT courses and addressing identified challenges to ensure CBET effectively enhances career adaptability and employability of ICT students in the contemporary dynamic labor market. CBET Employability ICT Kenya Trainers Perspectives TVET Figures Figure 1 Figure 2 Figure 3 1.0 INTRODUCTION The global economy has been transformed fundamentally because of digital transformation, resulting in an increased need for experts in ICT. Kenya faces an urgent workforce need because it aims to use technology for economic expansion together with societal advancement (CBET Policy, 2018 ). The designed CBET system prepares graduates to master real-world technical abilities, which lead to better employment possibilities along with progress toward national development targets. Students need to acquire ICT employability skills to enable them to perform successfully in the job market. Ayonmike, Okwelle, and Dibua ( 2014 ) explored the comprehensive role of CBET in developing the employability skills as a vital element towards national security and sustainable development. A blend of practical education programs and external business ties with technology-based education resources should be adopted in schools to optimize their ICT learning achievements (Mutohhari et al., 2021 ). Prepared CBET curricula directly contribute to preparing candidates to work by increasing levels of employability skills, in particular in technical fields such as ICT (Baker, 2021 ). Despite the observed policy changes and the key investments into TVET, some gaps in ICT employability skills are still present, and consequently, a very high unemployment rate among the youths, including those that have attended institutions of TVET exists (Wasike, 2020). This implies the existence of a possible gap between the training and the skills that are needed in the labor market. Although most recent studies have explored graduate employability and employer satisfaction, a significant gap has been identified in interpreting the views of such trainers who are front-runners in implementing the CBET curriculum. Their perceptions of the effectiveness of this training, the difficulties they encounter, and their perspective regarding how effective CBET is in preparing trainees to obtain jobs in ICT-related fields remain largely unexplored, especially within specific regions, such as Bungoma County. Therefore, this study addresses this gap by reporting empirical data using the trainers' perspectives, which are vital in determining policy and enhancing quality TVET in Kenya. 1.1 Statement of the Problem Although Technical and Vocational Education and Training (TVET) institutions in Kenya have adopted Competency-Based Education and Training (CBET) to align education with labor market demands, there is lack of empirical data on how trainers perceive CBET within ICT courses in fostering technical and soft skills relevant for student employability. It is unclear how strongly TVET trainers who are the primary implementers of the CBET curriculum are confident that the CBET framework is effectively preparing ICT graduates with the required ICT skills for the labor market, and thus far, it is not easy to align training with the requirements of the labor market. This study therefore, addressed lack of empirical facts on how trainers perceive the CBET implementation in ICT courses and student employability skills by providing empirical findings on the perspectives of trainers to improve the CBET curriculum and present data-driven recommendations for the TVET administrators, policymakers, and trainers to align ICT Courses training and the industry demands in Bungoma County. 1.2 Research Objectives The study was guided by the following specific objectives: To assess the opinions of ICT trainers on the influence of CBET implementation on ICT employability skills in TVET institutions in Bungoma County, Kenya. To identify the challenges faced by ICT trainers in implementing CBET to enhance ICT employability skills in TVET institutions in Bungoma County, Kenya. To explore strategies perceived by ICT trainers on CBET implementation towards enhancing ICT employability skills in TVET institutions in Bungoma County, Kenya. 1.3 Research Hypothesis The study tested the following hypotheses: H 01 Competency-Based Education and Training (CBET) implementation positively influences ICT employability skills among students in TVET institutions in Bungoma County, Kenya. H 02 Challenges such as limited resources, inadequate infrastructure, and insufficient training negatively affect the effective implementation of CBET in enhancing ICT employability skills of students in TVET institutions in Bungoma County, Kenya. H 03 Implementation of strategic interventions perceived by ICT trainers on CBET implementation, including industry collaboration, curriculum adaptation, and capacity building for trainers, significantly enhances ICT employability skills of students in Bungoma County TVET institutions. 1.4 Justification and Significance of the Study The study is justified by lack of adequate literature on the opinions of trainers as the primary implementers of CBET curriculum and the persistent constraints in the labor market, especially the ICT sector, which has led to high levels of youth unemployment in Kenya (Barasa et al., 2023 ). The study is relevant to various stakeholders in TVET and ICT education. The findings offer critical empirical evidence that policymakers, trainers, and government agencies can use to support the development and transformation of the TVET policies of the nation, as well as counties, to align them with the CBET curriculum to be delivered to satisfy the labor market. 1.5 Scope and Limitations of the Study The study focused on trainers’ perspectives on CBET Implementation in enhancing ICT employability skills for students in Bungoma County TVET institutions. The study maintained a specific operational boundary to conduct a manageable investigation while acknowledging the inherent constraints. This study had limitations that could impede the generalizability of its findings. The study focused on the opinions of trainers as the primary implementers of CBET and ICT Courses training which meant that it presented one-sided opinions when referring to the complicated question of CBET and ICT employability skills. It failed to uphold other important stakeholders, including the students, employers in the industry, and policymakers. The lack of these viewpoints did not sufficiently triangulate the information to acquire a more holistic idea of the challenges and achievements of the CBET curriculum. Thus, the research should be understood within the defined scope and resource constraints, as it was a momentary outlook of trainers' perspectives in a particular geographic and professional setting. In addition, the study is constrained by the ever evolving nature of the generative spark in ICT, whereby the fast-changing developments in AI and automation can possibly surpass the static modular form of contemporary ICT Courses and CBET. This creates a new technical lag that can impact the trainers’ perception of the existing employability skills. 1.6 Theoretical Framework This study was based on competency theory framework. Human practices offer different ways to understand how multiple elements affect the combination of CBET implementation and its consequences for developing skills. Competency theory was useful in this study by showing how teachers see the link between ICT related skills building and employability training that is part of the curriculum. Trainer evaluations on CBET's success as a program stem from their views on how effectively it cultivates necessary competencies demanded by ICT professionals. When trainers identify CBET as providing complete necessary competencies to students, they tend to view it as effective (Manase, 2024 ). Trainers who detect inadequacies between CBET-developed competencies and employer-specified skills are likely to report negative views about the program (Zhong, 2024 ). 1.7 Conceptual Framework Variables refer to measurable attributes that can assume diverse values from the study respondents (Mugenda & Mugenda, 2009 ). This study involved opinions, challenges and strategies perceived by ICT trainers to enhance CBET in TVET institutions in Bungoma County as independent variables, TVET Policy and CBET policy framework which mandates a transformation from knowledge-based to skill-focused and industry-driven training in TVET as the intervening variables and ICT employability skills as the dependent variable. Figure 1 shows the conceptual framework of the study: 2.0 LITERATURE REVIEW 2.1 Competence-Based Education and Training (CBET) Competence-Based Education and Training (CBET) focuses on developing essential capabilities for different professional disciplines. Educational institutions recognize CBET as a fundamental approach to developing vocational and technical education because it helps learners acquire essential workplace skills. The study findings by Ayonmike et al. ( 2014 ) show that CBET enables students to obtain skills that follow industrial market requirements, enhancing their job readiness and supporting state progress and defense. The CBET framework uses outcome-based defined curriculums to teach learners employable workplace skills (Ayonmike-et.al, 2014). Competence-Based Education and Training works as a fundamental method in vocational education because it combines educational results with marketplace requirements. The development of CBET followed traditional educational models, which transformed into systems focused on skill acquisition and employability applications, according to Ayonmike et al. ( 2014 ). Training centers follow the CBET principles, which stress student-led learning. This type of learning defines skills by performance standards and tests these skills for proof that can be seen (CBET Policy, 2018 ). Lack of resources, untrained teachers, and unclear information about the CBET curriculum are among the problems affecting CBET implementation, according to Tambwe (2019). Various regions face similar obstacles while implementing the CBET framework, which proves the necessity for appropriate institutional reform and staff training at institutions that adopt CBET. 2.2 ICT in TVET: Kenyan Perspective Information and Communication Technology (ICT) has a key role in Technical and Vocational Education and Training (TVET), so it helps the teaching and learning processes through enabling greater access to information and creating interactive educational experiences. The technology sector changes so fast that TVET needs information and communication technology as an essential component to educate a skilled workforce. The adoption of Information and Communication Technology (ICT) within TVET education has created a new vocational training environment through which students gain modern workforce essential employability skills. ICT provides educators with various teaching approaches that allow them to conduct dynamic, interactive educational encounters (Zhong, 2024 ). The study relates directly to how Mekonnen et al. (2024) outline institutional Competency development through holistic programs. Strategically implementing ICT in TVET is necessary for skill development that keeps up with changes in technology and meets the needs of the market. Their study proves that when educational programs match industry requirements, the development of employability skills becomes more effective according to Garcia and Martinez (2022). The study proved that when ICT serves as a facilitator, curriculum alignment strongly affects the growth of employability skills. Information and communication technology is more than just an extra resource, according to a study. It changes the whole educational system to help students do their best (Garcia, 2022). There are ongoing problems with integrating ICT, mainly because of digital inequality, shortage of trainers and a lack of infrastructure (Tambwe, 2019). 2.3 Trainers' Perceptions and CBET Implementation: Global Perspective The successful implementation of CBET curriculum relies on the trainers' perspectives and attitudes. Trainer perception of CBET affects their capability and readiness to put its principles into practice and thus affects student learning results. The attitudes of trainers guide both policy-making and the delivery of employability programs in Kenya (Barasa et al., 2023 ). The successful implementation of CBET in the Ethiopian vocational environment depends on instructor approval according to Tekle Areaya and Habtamu (2024). The perception of CBET as a practical approach by trainers leads to active inclusion of its principles and involvement in its delivery processes (Barasa et al., 2023 ). The planning and implementation of CBET programs requires trainer involvement. Such participation creates a feeling of ownership which enables trainers to share their problems and aid in creating efficient implementation strategies. Proper training is essential for instructors to acquire the required competencies needed to deliver effective CBET practices (Tambwe, 2019). The training curriculum should focus on competency-based assessment methods together with curriculum development techniques and educational technology application in teaching practices. Effective delivery of CBET depends heavily on how organizations distribute their resources. Educational materials alongside proper equipment combined with relevant technology must be available to trainers when they prepare CBET training courses at a high professional level (Tekle et al., 2024 ). Inadequate resources create challenges for trainers attempting to deliver CBET properly because it leads to dissatisfaction among learners. Educational institutions can create better learning conditions for CBET through trainer involvement during planning stages, resource provision, and addressing the challenges to enhance student academic achievements (Garcia, 2022). 3.0 MATERIALS AND METHODS 3.1 Study Design The study used a mixed methods approach to give in-depth findings of how trainers perceive CBET implementation in enhancing ICT employability skills for students in Bungoma County TVET institutions. The study design used both quantitative and qualitative methods so that all aspects of the study could be fully understood (Jilcha, 2020 ). Since numerical data and personal experiences were gathered in this study, there was need to use both quantitative and qualitative study methods to get holistic findings (Arslan, 2023 ). The study involved providing questionnaires to the sampled ICT trainers to assess their precise opinions on the CBET curriculum. The qualitative part of the study involved the use of semi-structured interviews with the sampled ICT heads of department to give their opinions about their real-life experiences, challenges, and recommendations about CBET (Mazhar et al., 2021 ). 3.2 Research Paradigm The study was grounded on interpretivist research paradigm since it was appropriate to study the realities and subjective experiences of TVET trainers. This paradigm proposes that reality is a product of society and could be determined by how people interpret it (Pervin & Mokhtar, 2022). The interpretive approach made possible to explore how and why the skill mismatch existed in a deep and nuanced manner. Such a philosophical position informed the qualitative methodology by focusing on the detailed data-gathering procedures to provide detailed insights on the issues, strengths, and challenges of the CBET and ICT courses training through the ICT trainers’ lenses in TVET institutions in Bungoma County. 3.3 Study Area The study was conducted in TVET institutions located in 9 administrative sub-counties of Bungoma County, situated in Kenya. Bungoma County is situated between 0° 28’ and 1° 30’ North latitude and 34° 20’ and 35° 15’ East longitude per the Fluxes maps. Bungoma County has 9 sub-counties with TVET institutions distributed as shown in Table 1 : Table 1 Distribution of TVET institutions in 9 Sub-Counties in Bungoma County. S/No Sub-County Name of Ward Name of Institution 1. Kanduyi East Sang'alo The Bungoma National Polytechnic 2. Kabuchai Kabuchai Kisiwa National Polytechnic 3. Bumula Siboti Musakasa Technical Training Institute 4. Kimilili Kimilili Matili Technical Training Institute 5. Sirisia Lwandanyi Sirisia Technical and Vocational College 6. Tongaren Milima Bungoma North Technical and Vocational College 7. Webuye West Machakha Webuye West Technical and Vocational College 8. Mt Elgon Chepyuk ward Mt. Elgon TVC 9. Bumula South Bukusu Cardinal Maurice Otunga TVC 3.4 Target Population The study target group included ICT trainers who train in the selected 9 TVET institutions situated within Bungoma County. The selected TVET institutions for this study comprises of the 2 national polytechnics, 3 technical institutes and 4 technical and vocational colleges. This population was selected because they participate directly in implementing CBET while providing knowledge about its success in building student ICT employability skills. How trainers see the influence of CBET and how they come up with ways to make things better depend a lot on their opinions (Tekle et al., 2024 ). A total of 41 ICT trainers in the selected TVET institutions were sampled to participate in the study to find out how strong the quantitative analysis is and how well the qualitative understanding is through descriptive and inferential statistical power determination (Ayonmike-et.al, 2014). 3.5 Sample Size and Sampling Technique Purposive sampling was suitable in this study since ICT trainers who applied Competence-Based Education and Training (CBET) provided the best information regarding its effectiveness. The study by Nyimbili ( 2024 ) explains that purposive sampling assists the researcher to locate effective information based on some features of the targeted study that might remain concealed with random sampling (Nyimbili & Nyimbili, 2024 ). The sample size of this study was determined using Yamane formula, which is used to compute the sample size in a finite population. The Yamane formula is expressed as follows; n = N÷ (1 + N (e) 2 ) where; N is the population size; n is the sample size and e is margin of error (5%) with 95% confidence interval. A total of the sampled 9 Technical and Vocational Education and Training (TVET) institutions spread across nine sub-counties in Bungoma were selected for the study. Three ICT trainers and one head of department were sampled from each Technical and Vocational College (TVC), four ICT trainers and one head of department were sampled from each national polytechnic and Technical training institutions because of institutions’ large size and number of students, to attain 41 trainers for the study sample. Table 2 shows the numbers of sampled trainers and Heads of Departments in every TVET institution in Bungoma County: Table 2 TVET Institutions in Bungoma County with respective sampled trainers Sub-County TVET Institutions in Bungoma County National Polytechnics Technical Training Institutes Technical and Vocational Colleges Total Number of Sampled TVET Institutions Total Number of sampled ICT Trainers Total Number of sampled ICT Heads of Departments (HODs) Total Number of sampled Trainers and HODs Mt. Elgon - 1 - 1 4 1 5 Kimilili - 1 - 1 4 1 5 Bumula - 1 1 2 7 2 9 Kanduyi 1 - - 1 4 1 5 Webuye West - - 1 1 3 1 4 Webuye East - - - - - - - Kabuchai 1 - - 1 4 1 5 Sirisia - - 1 1 3 1 4 Tongaren - - 1 1 3 1 4 Total 2 3 4 9 32 9 41 3.6 Data Collection Instruments The study combined mixed methods for data collection through both quantitative and qualitative data collection instruments. Structured questionnaires were the primary quantitative data collection tool used in the study to collect data from ICT trainers. Questionnaires were administered to a total of 32 ICT trainers teaching in TVET institutions within the Bungoma County to give a preliminary quantitative data with qualitative data gathered from 9 ICT Heads of Departments. The questionnaires gathered information on the views of the ICT trainers within the TVET institutions in Bungoma County on CBET implementation and student employability skills. The questionnaires included Likert rating scale to evaluate the teaching efficacy of CBET in respect to the particular skills, and open-ended questions that focused on trainers concerning curriculum suitability and training materials. To obtain qualitative data, semi-structured interviews were used to supplement questionnaires to obtain data from 9 ICT Heads of Departments in the study. According to the study by Mazhar et al. ( 2021 ), using semi-structured interviews, helps to effectively investigate the perspectives of the respondents. 3.7 Validity and Reliability of Study Instruments A study instrument achieves validity when it appropriately assesses the intended items, yet reliability occurs when the instrument maintains consistent output measurements. The subject experts, consisting of thesis supervisors, performed additional validation checks on these instruments (Garcia, 2022). To evaluate instrument reliability, a distinct group of ICT trainers, excluded from the main study participants, undertook the pilot test. The pilot test served to improve the instruments by detecting unclear wordings and maintaining consistent terminology. Cronbach's alpha analysis was conducted to verify internal consistency and reliability of the questionnaire. The Cronbach's alpha score of 0.749 as shown in Table 3 below indicates acceptable internal consistency and reliability of the research instruments used in the study (Taber, 2018 ). Table 3 Reliability Score of Study Instruments Frequentist Scale Reliability Statistics 95% CI Coefficient Estimate Std. Error Lower Upper Coefficient α 0.749 0.041 0.668 0.830 3.8 Data Collection Procedures The study methodology involved questionnaire distribution followed by semi-structured interview procedures. The study carried out a pilot study involving ICT trainers to confirm and ensure the correctness of the data collection tools. The most suitable study techniques are those which coincide statistical significance and a large population sample as they are efficient and accurate in data collection (Nyimbili & Nyimbili, 2024 ). The questionnaires collected the opinion of the trainers on Competence-Based Education and Training (CBET) in ICT employability skills. To gather qualitative data, the study used semi-structured interviews with heads of department in the 9 TVET institutions in Bungoma County. Data collection process began with seeking NACOSTI license and Bungoma County TVET director research permit, consent acquisition, and proceeded with questionnaire and interview anonymity and administration. These procedures helped to provide a holistic framework to conduct the study (Nolan, 2021 ). 3.9 Data Analysis The study utilized a combination of quantitative and qualitative data analysis methods, utilizing appropriate analytical frameworks. The statistical analysis of questionnaire-based quantitative data helped to determine the study findings. The study conducted descriptive statistics to present trainer assessments of CBET effectiveness through central value descriptions and measurement of data spread and used inferential statistics to test hypotheses and find variable relationships, which helped to fully understand what the data means (Adams, 2020 ). The study used a Multiple Linear Regression Model to investigate how several factors predict student employability. The model was significant with Ϝ= 3.213 and ρ = 0.028. The Explanatory Power (R-Square value) was 0.322, indicating that the factors which include Implementation Strategies (β = 0.438), Trainer Characteristics (β = 0.331), Facilitating Factors (β = -0.029) and Challenges (β = 0.030) indicate 32.20% of the variation in ICT employability skills. Thematic analysis was used to analyze interview data through a method that successfully detects patterns and themes within textual information. The study adopted thematic analysis by putting data into groups, then summarizing themes, and finally interpreting the results based on the literature reviews from credible sources. The qualitative data from the ICT heads of departments gave more insightful information about the opportunities and challenges facing CBET curriculum. 4.0 RESULTS AND DISCUSSION The study analysis was guided by the objectives that were focused on investigating opinions of trainers on CBET and ICT Courses training for student employability skills, challenges in CBET implementation, strategies perceived by trainers in enhancing CBET implementation and ICT employability skills of ICT students. The study applied a triangulation convergent design where quantitative and qualitative data were collected simultaneously and analyzed separately to guarantee independent integrity. Quantitative data through structured questionnaires was analyzed through descriptive statistics to determine the broad patterns whereas qualitative data through open-ended questions was subjected to thematic analysis to capture the nuanced perspectives of ICT trainers. The quantitative and qualitative analysis was then merged during the interpretation through comparison and contrasting of the statistical outcomes with the developed themes, enabling the qualitative information to serve as a deeper contribution to the quantitative trends, in the way CBET implementation in ICT courses enhance the employability skills of students. The study achieved a 100% response rate from the targeted respondents. All the 32 ICT trainers who were sampled participated in the study returned duly completed questionnaires, and similarly, all the 9 Heads of Departments (HoDs) got the chance to be interviewed. The findings were presented using descriptive statistics such as frequencies, percentages, means, inferential statistics and multiple linear regression analysis to identify the relationships between the variables, then followed by detailed interpretations of the findings. 4.1 Background information of the Respondents The background information assisted in interpreting the results and creating an insight into experiences and qualifications of the respondents that might affect their views on the implementation of CBET within TVET institutions. The study included 19 trainers (59.4%) female, and 13 trainers (40.6%) male. The age was considered to be a significant demographic characteristic since it could affect professional experience of trainers, their flexibility to Competency-Based Education and Training (CBET), and their understanding of the development of ICT employability skills. The age distribution of the 32 trainers who took part in the study is such that most of them were relatively young to middle-life career professionals. The most numerous group was trainers aged between 30 and 39 years and they made up 46.9% of the respondents. This shows that a large percentage of the trainers in TVET institutions in Bungoma County are within their prime working age and this could have a positive impact on the implementation of CBET and ICT-related practices since they are likely to have been exposed to the modern methods of training. Another 25.0% of the respondents were between 21–29 years old and they were early-career trainers capable of introducing new insights and increased flexibility to ICT integration in teaching and learning. The 40–49 years old group formed 18.8% of the sample with 50–59 years group forming 9.4% being the smallest group as shown in Fig. 2 : 4.1.1 Training on CBET methodologies CBET training is very important because it empowers trainers with the skill and knowledge needed to successfully give competency-based training and improve ICT employability skills on trainees. Figure 3 shows the findings on CBET methodology training among the trainers: The results indicate that a big percentage of the trainers had been trained on the CBET methodologies and implementation. Two-third of the 32 respondents (28 trainers) said that they had attended CBET training, and only 4 trainers (12.5%) had not attended CBET training. Such a high exposure implies that the majority of trainers in the TVET institutions located in Bungoma County are highly equipped with the basic knowledge that may enable them to apply the Competency-Based Education and Training. The fact that a relatively low percentage of trainers did not receive CBET training indicates a vacuum that may have to be filled by institutions in enhancing a comprehensive and complete implementation of CBET practices in all departments. 4.2 Regression analysis The level at which the independent variables were able to predict the dependent variable (ICT employability skills) was determined by means of regression analysis. The analysis will contain the model summary, coefficients, and significance to demonstrate the strength, direction, and significance of the relationships between the variables as shown in Table 4 below: Table 4 Model Summary Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .568 a .322 .222 .38182 a. Predictors: (Constant), Characteristics of the Trainer, Challenges, Strategies, Facilitating Factors The multiple linear regression model depicts that the independent variables (Trainer Characteristics, Challenges, Strategies, and Facilitating Factors) and the dependent variable (Employability Factors) have a light positive relationship as demonstrated by the value of R = 0.568. The R 2 of 0.322 suggests that the four predictors of the model contributes to 32.2% of the variation in ICT employability factors when they are together. This indicates a reasonable explanatory power of the model, bearing in mind that the notion of employability is determined by numerous contextual and institutional factors, which were not considered in the study. The Adjusted R Square (0.222) implies that even with the number of predictors and sample size, 22.2% of the variability of the employability factors remains to be explained by the model. The decrease in R 2 to Adjusted R 2 is accounted by the social science research and indicates that the predictors are not meaningless due to the relatively low sample size. Standard Error of the Estimate (0.38182) shows that the predictions made by the model are not much different and thus acceptable as far as the survey data using perception is concerned. Table 5 shows the ANOVA analysis: Table 5 ANOVA The results of the ANOVA also show that the regression model is significant (F = 3.213, p = 0.028). This means that a combination of trainer characteristics, challenges, strategies, and facilitating factors, is a very strong predictor of ICT employability factors. Therefore, the null hypothesis that the independent variables does not have a significant effect on the employability factors is rejected and this supports the overall appropriateness of the regression model in explaining the differences in employability results as shown in Table 6 : ANOVA Model Sum of Squares df Mean Square F Sig. 1 Regression 1.874 4 .468 3.213 .028 b Residual 3.936 27 .146 Total 5.810 31 a. Dependent Variable: Employability Factors b. Predictors: (Constant),Trainer Characteristics, Challenges, Strategies, Facilitating Factors Table 6 Coefficients Coefficients Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 2.565 .442 5.800 .000 Challenges .011 .061 .030 .176 .862 Facilitating Factors − .010 .063 − .029 − .164 .871 Strategies .226 .086 .438 2.628 .014 Trainer Characteristics .148 .072 .331 2.073 .048 a. Dependent Variable: Employability Factors The regression coefficients analysis reveals that implementation strategies positively and significantly impact the factors of employability (B = 0.226, β = 0.438, p = -0.014). This results shows that successful CBET programs such as curriculum alignment to industry demands, industry relationship, adoption of learner-based instructional strategies and high competency based assessment are extremely critical in enhancing ICT employability skills. The fact that the beta coefficient is a standardized value, implies that strategies are the most potent predictor in the model, which is why they are so critical in translating the CBET principles into meaningful outcomes in order to convert them into employability. The findings indicated a statistically significant and positive relationship between trainer characteristics and the employability skills (B = 0.148, β = 0.331 p = 0.048). This hints that the professional qualification, trainers’ confidence on the implementation of the CBET, pedagogical competence, and industry exposure plays a significant role in the development of ICT employability skills. The finding pinpoints the importance of investing in the continuous professional development and training that focuses on the CBET among ICT trainers in TVET institutions. The issues associated with implementation of CBET however, did not significantly affect employability factors (B = 0.011, β = 8030, p = 0.862). Although there may be lack of resources, infrastructure and institutional barriers, the results show that there is no direct significant impact on the results of the employability in case the strategies are good and the trainers are qualified. This shows that the obstacles can either have an indirect effect or can be alleviated by placing the right instruction and institutional responses. On the same note, it was determined that the factors of facilitating did not have a statistically significant impact on factors of employability (B = -0.010, β = -0.029, p = 0.871). The relative and negligible negative value indicates that even the simple presence of enabling influences like policies, equipment, or institutional support systems, is unlikely to yield to productive effects on the improvement of employability unless these enabling influences are used well by good implementation plans and training skills. 4.3 Hypothesis Testing H01: The implementation of Competency-Based Education and Training has a positive impact on ICT employability skills of students in TVET institutions in Bungoma County, Kenya. The results of the regression analysis show that the implementation plans of CBET positively and statistically impact ICT employability skills (B = 0.226, β = 0.438, p = 0.014). This means that the efficacy of the CBET framework such as connecting ICT curriculums to industry needs, the encouragement of industry relations, the embracing of student-centered pedagogy and the emphasis of competency based assessments are key drivers that enhance the employability skills of students. The standardized beta coefficient shows that the implementation strategies of CBET hold the strongest predictor in the model; an element that enhances the significance of the implementation strategies in actualizing the principles of CBET into meaningful employability outcomes. These findings support H01 and confirm that the use of CBET has a positive effect on the ICT employability skills. H02: Challenges such as inadequate resources, poor infrastructure and inadequate training have negative impact on the implementation of CBET in enhancing ICT employability skills in TVET institutions. The challenges related to the implementation of CBET did not significantly influence the employability factors (B = 0.011, = 0.30, p = 0.862). Even though other issues such as scarce resources, poor infrastructure and institutional factors are also present, it does not appear that they directly affect ICT employability outcomes except when there are no proper implementation plans and qualified trainers. This is a pointer that the difficulties can only indirectly influence or can be reversed with proper instructional and institutional interventions. H02 is therefore not supported by the regression results. H03: Strategic interventions as perceived by ICT trainers regarding implementation of CBET such as industry collaboration, curriculum modification and trainers’ capacity building to enhance the ICT employability skills in TVET institutions in Bungoma County Kenya. The characteristics of the trainers were observed to have a significant and positive impact on the employability factors (B = 0.148, β = 0.331 = 0.048). It means that the qualification, pedagogical ability, and confidence of trainers in the use of CBET, and industry exposure plays an important role in developing ICT employable skills. Likewise, facilitating factors were found not to have a significant impact on employability (B = -0.010, β = 0.029, p = 0.871), and thus, resources or support mechanisms should be actively engaged based on effective strategies and trainers. The results of the regression analysis indicate that implementation strategies and trainer characteristics are significant predictors (32.2) of the implementation of CBET in ICT courses and explain the difference in the skills of the student in employability. This statistical finding can be directly associated with the study by Baker and Taylor ( 2021 ), which underscores that the translation of an elaborated curriculum to a classroom performance highly depends on the overlap of the knowledge of trainers and the efficient implementation strategy. Moreover, the high impact of the characteristics of trainers confirms the claims of Barasa et al. ( 2023 ) about the importance of pedagogical competence in Bungoma County TVET institutions. Although the model validates the idea of strategy interventions and effective human resources as the main success drivers, the insignificance of challenges indicates that their contribution can be negated by effective instructional leadership, which is reinforced by Mazhar et al. ( 2021 ) on the significance of established systems in ensuring integrity in education. The study therefore highlights the need for policy makers and TVET administrators to prioritize strengthening CBET implementation strategies and enhancing trainer capacity as key pathways to improving ICT employability outcomes. 4.4 Qualitative Findings 4.4.1 HoDs’ Opinions on the Current CBET Curriculum The qualitative analysis of the responses indicate 5 key emerging themes concerning trainers’ perceptions of CBET implementation in ICT courses. Theme 1: Effectiveness and Industry Relevance of the CBET Curriculum Several respondents perceived the CBET curriculum as effective and aligned with industry needs, particularly due to industry involvement in curriculum development and the emphasis on practical skills. These views suggest that CBET is achieving its core objective of preparing trainees for the job market. This theme indicates that HoDs recognize CBET as an improvement over traditional training approaches, especially in enhancing employability-oriented competencies. The findings are connected to the research of Ayonmike et al. ( 2014 ), who stressed that vocational education can never be productive without the industrial market where it is applied. The request of trainers to create more organized partnerships confirms the suggestions of Mazhar et al. ( 2021 ), which claim that more concrete systems of industry-institutional collaborations are needed to make sure that the results of training are also applicable to the requirements of the job market. Theme 2: CBET Assessment is Practice-based but Demanding Operationally. Most respondents also admitted that active concomitant assessments of CBET are useful in assessing applied competence and preparedness of trainees. The HODs mention that CBET assessments are time consuming, they need more trainers and are difficult to administer especially when the resources are limited and the number of trainees are many. This theme brings about a dilemma between the pedagogical strengths of CBET assessment and realities of practice in TVET institutions. This contradiction between the pedagogical excellence of CBET test and the high cost of administrative and resources is similar to what Fredrick ( 2025 ) found that CBET is hampered by an imbalance between evaluation demands and staff in Kenyan polytechnics. The nature of the respondents to state that lack of space and equipment is an obstacle to practical assessment is directly related to resources acquisition challenges as found by the study conducted by Ringeera et al. ( 2025 ). These operational constraints suggests that socio-material reality of the institutions has not been fully predetermined so far by intensive assessment demands as pre-supposed by the CBET Policy ( 2018 ). Theme 3: Low Confidence in CBET Implementation A majority of respondents expressed low to moderate confidence regarding CBET implementation although there was a variation in confidence levels, due to rushed rollout, inadequate training, weak institutional support and limited resources. This theme indicates that confidence in CBET improves over time but remains constrained by systemic challenges. The findings are in tandem with the argument by Muthomi et al. ( 2023 ), who states that the pedagogical transformation necessary in CBET has to be accompanied by the uninterrupted technical upskilling. The results indicate that trainers have a lot of experience, but their performance cannot be enhanced with the absence of confidence and exposure to Industry technologies, which supports the idea that the key factors to successful delivery of the curriculum are trainer features and training. Theme 4: Key challenges and Strategies to improve CBET and Strategies to Bridge the Gap. The interviews with HoDs indicated that there is a good conceptual and high potential aspect of CBET implementation in ICT, yet its performance is limited due to the systemic, institutional, and resources-linked problem. Although the majority of HoDs had no significant discrepancy between core ICT competences, there are gaps in advanced, emerging ICT dimensions, soft skills, and entrepreneurship, mainly because of the lack of resources, problems with the capacity of trainers, and the lack of a strong connection with the industry. The findings indicate that institutional capacity has been lagging behind the policy ambitions leading to the partial and ineffective implementation of CBET. The findings were consistent with the research conducted by Mgaya ( 2022 ) and Barasa et al. ( 2023 ), which reported resource acquisition as one of the major issues of TVET institutions in Bungoma County. The findings indicate that the "socio-material" environment, which is examined in vocational literature in terms of the need of physical tools in the process of knowledge building, is one of the critical bottlenecks that will not allow the full manifestation of CBET opportunities as stated in national strategic plans (CBET Policy, 2018 ). Theme 5: Industry Relevance and Skills Mismatch in Emerging ICT Areas Most HoDs agreed that core ICT skills align well with industry needs, indicating no major mismatch at foundational levels. However, slight but important mismatches were identified in advanced and fast-evolving ICT areas, such as data science, Artificial Intelligence and advanced programming. These variations were attributed to limited trainer exposure and obsolete equipment rather than curriculum design. These findings show a dynamic skills gap where the implementation of CBET lags behind the industry innovation and demands despite the curricula being relevant. Without regular curriculum updating and industry-supported training, ICT graduates risk falling behind in competitive job markets. This is backed by the report that the exposure of equipment and trainers cannot match the high demanding computing courses although the relevant curriculum is in place, which Baker and Taylor ( 2021 ) argue is crucial in the process of successful implementation of a designed curriculum into real mastery, and thus the availability of modern training tools. Such dynamic skills gap in the developed regions corresponds with the studies of Ayonmike et al. ( 2014 ) which emphasized that the effectiveness of vocational education is very often reduced when the institutional ability falls behind the fast industrial innovations. It also supports the conclusion made by Nolan and Wilson ( 2021 ) to have an established institutional-industry collaboration to close the gap between classroom teaching and workplace requirements. 5.0 CONCLUSION AND RECOMMENDATIONS The study relates the findings with existing literatures, draws conclusions based on the study objectives, and proposes recommendations informed by the identified gaps. 5.1 Conclusion In conclusion, CBET can be successfully used as the means of improving the underlying ICT employability skills, especially technical and soft. Nevertheless, there are still gaps in both advanced and emerging ICT fields like Artificial Intelligence, full-stack development and machine learning that suggest the extent to which CBET fulfills its primary goals but cannot fully equip the trainees with the latest and advanced ICT fields implying that ICT curriculum development requires the incorporation of emerging fields of ICT development making it essential to amend the ICT curriculum to integrate these new fields of study. The CBET framework has to transform from a robust framework to a training approach that is adaptable and embedded into the industry. This requires moving beyond mere equipping to the establishment of deep institutional and industry relationships that would go on to ensure that training remains an embodiment of applied socio-material workplace realities. Competency-Based Education and Training (CBET) as adopted in the TVET institutions is not only a paradigm shift of the traditional heavy instruction based teaching but a modern competence based model of teaching that will withstand the demands of the modern ICT labor market. Although CBET has a robust conceptual framework and is highly effective in developing the basic technical and soft skills levels, its implementation success heavily depends on the subjective experiences and professional proficiency of the trainers who are at the center of curriculum implementation. The human factor including trainer confidence, pedagogical skill and industry exposure and experience creates a link between curriculum design and student employability. The findings indicate that perceived effectiveness of CBET is not simply a product of institutional policy but rather it is determined by the capacity of the trainers to operate within an environment with limited resources and yet be able to cope with the higher technological changes. The absence of significant correlation between the employability outcomes and implementation challenges is an indication that effective mitigation of the infrastructural shortcomings can be achieved by competency training, aimed at long-term outcomes. Finally, the study affirms that although CBET enhances student employability, its effectiveness and orientation are solely determined by continuous capacity building of trainers and adaptability of the curriculum. 5.2 Recommendations 5.2.1 Policy Level Recommendations Infrastructure and Resource Funding: The Ministry of Education and TVETA ought to enhance capitation to specifically upgrade the ICT laboratories to match modern standards and offer computing equipment and software that are necessary in training ICT Courses. Structured collaboration between TVET institutions and the Industry: Establish a policy-supported model and industry relationships to provide joint evaluation, organized industrial placements to the trainees and compulsory industrial attachments to the trainer to match the innovations within the market. Standardized Quality Assurance: The assessment bodies such as Curriculum Development, Assessment and Certification Council (CDACC), Kenya National Qualifications Framework (KNQF) and other accredited Qualification Awarding Bodies and institutions should enhance external and internal moderation of assessments and establish strong digital assessment infrastructure to ensure integrity of assessments and certifications. 5.2.2 Institutional Level Recommendations Emerging areas and Curriculum Adaptation: The TVET institutions need to be proactive to include emerging areas into the ICT courses, including Artificial intelligence (AI), Data science, and Full-stack Development to address the existing lack of high-level emerging ICT skills in the ICT curriculum. Trainer Capacity Building and Retooling: Institutions should emphasize on continuous professional development (CPD) on the new ICT trend and CBET-associated assessment procedures to enhance the confidence and competence of trainers. The TVET administrators should reevaluate the trainee to trainer ratios and work with the government to employ more trainers to enhance practical and personalized student training. Enhancement of Soft Skills and Problem-Based Learning: The TVET Institutions should enhance problem-based learning (PBL) and industry-based projects to explicitly incorporate soft skills in ICT technical units. 5.3 Suggestions for Further Studies Future studies should examine the long-term employability outcomes of CBET graduates in the ICT sector using tracer studies. Comparative studies between CBET and conventional ICT courses training models across different TVET institutions would provide deeper insights into effectiveness variations. Further research is suggested on the integration of micro-credentials, industry partnerships and emerging learning areas in CBET frameworks, to enhance credibility of assessment and employability of ICT students. Declarations Author Contribution Statement All authors contributed to the conception and design of this study. Concept note, material preparation, data collection, analysis and report writing were conducted by CW. The manuscript was reviewed by Dr. HK and Dr. SW from concept note to approval of the final manuscript. Ethics Declaration The study maintained principles of informed consent, anonymity and confidentiality as protective measures for the rights and welfare of participants. The authors obtained official ethical clearance through National Commission for Science and Technology (NACOSTI) and the Ministry of Education to initiate its data collection activities. Participation in the study was voluntary and participants were assured that all data will be confidential through both secure storage and pseudonym identities during interviews, results publication and dissemination. Competing interests The authors declare that no competing interests exist. Funding There was no funding received for this study. Author Contribution All authors contributed to the conception and design of this study. Concept note, material preparation, data collection, analysis and report writing were conducted by CW. The manuscript was reviewed by Dr. HK and Dr. SW from concept note to approval of the final manuscript. Acknowledgement All glory to God for his endless mercy and blessings of life, good health, and leading me through this entire process. I am sincerely grateful for the outstanding insights and support granted to me by the outstanding lecturers and supervisors; Dr. Hoseah Kiplagat and Dr. Simon Wanami for the professional review, guidance and mentorship that has made me to attain this outstanding academic heights. Special gratitude to my parents, family and technology education colleagues for holding my hand and encouraging me to accomplish this research work; they are my most significant source of inspiration. Data Availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. References Adams, R., & Osborne, M. (2020). The Impact of Curriculum Design on Employability Skills: A Case Study from Australia. Journal of Education and Work, 33 (3), 207–226. Arslan, G., & Demir, F. (2023). The role of Competency-based education in improving vocational education. Vocational Education Journal , 22 (1), 45–60. Ayonmike, C., Okwelle, P., & Dibua, E. (2014). Competency-Based Education and Training in Technical Vocational Education: Implications for Sustainable National Security and Development. Journal of Educational Policy and Entrepreneurial Study (JEPER) , 1 (2), 290–300. Baker, R., & Brown, M. (2021). The impact of a Competency-based curriculum on job readiness: Evidence from Australian vocational education. Journal of Education and Work , 34 (1), 63–81. https://doi.org/10.1080/13639080.2020.1841445 Baker, S., & Taylor, P. (2021). Curriculum Implementation and Its Effects on Vocational Training Outcomes. International Journal of Training and Development , 25 (1), 45–63. Barasa, O., Manasi, E., & Wepukhulu, R. (2023). Trainers’ Pedagogical Competence in Technical and Vocational Education and Training Institutions in Bungoma County, Kenya. https://doi.org/10.30574/ijsra.2023.8.2.0281 CBET policy (2018). Competency-based education and training CBET policy framework. https://www.education.go.ke/sites/default/files/2022-05/COMPETENCY-BASED-EDUCATION-AND-TRAINING-CBET-POLICY-FRAMEWORK1.pdf Fredrick, K. (2025). Assessing the Implementation of Competency-Based Education and Training in Kenya: A Case Study of Nyeri National Polytechnic, Kenya. IOSR Journal of Research & Method in Education (IOSR-JRME) . Garcia, L., Rego, M., Sáez-Gambín, D., & González-Geraldo, J. (2022). Transversal Competences and Employability of University Students: Converging towards Service-Learning. 10.3390/educsci12040265 Jilcha, K. (2020). Study Design and Methodology. IntechOpen. DOI: 10.5772/intechopen. 85731. Maiyo, J. (2020). Utility of government initiatives in technical, vocational, and Education Training Institutions. https://erepository.kibu.ac.ke/items/016720c0-f64b-454e-83ec-6830d4111b74 Manase, G., & Nyamu, E. (2024). Influence of a Dynamic CBET Curriculum on TVET Graguates’. Employability Skills , 12 (8), 65–76. Mazhar, S. A., Anjum, R., Anwar, A. I., & Khan, A. A. (2021). Methods of data collection: A fundamental tool of study. Journal of Integrated Community Health (ISSN 419–9113) , 10(1), 6–10. Mgaya, S. (2022). Challenges facing learners’ acquisition of employability competencies under competency-based education and training approach in vocational education and training centres in Tanzania. African Journal of Accounting and Social Science Studies , 4 (2). Mugenda, Q. M., & Mugenda, A. G. (2009). Research Methods: Quantitative and Qualitative Approaches . ACTS. Muthomi, K., Boit, J. M., & Sanga, P. L. (2023). Influence of Trainers’ Competencies on Competency-Based Education and Training Implementation in Public Technical Institutions in Meru County, Kenya. The Educator: A Journal of the School of Education Moi University , 3 (1), 110–128. Mutohhari, F., Sutiman, S., Nurtanto, M., Kholifah, N., & Samsudin, A. (2021). Difficulties in Implementing 21st Century Skills Competence in Vocational Education Learning. International Journal of Evaluation and Research in Education , 10 (4), 1229–1236. Nolan, T., & Wilson, J. (2021). The effectiveness of industry-led workshops in improving Employability skills. Journal of Education and Work, 34(4), 413–397. https://doi.org/10.1080/13639080.2021.416659 Nyimbili, F., & Nyimbili, L. (2024). Types of Purposive Sampling Techniques with Their Examples and Application in Qualitative Study Studies, British Journal of Multidisciplinary and Advanced Studies: English Lang., Teaching, Literature. Linguistics and Communication , 5 (1), 90–99. Pervin, N., & Mokhtar, M. (2022). The Interpretivist Research Paradigm: A Subjective Notion of a Social Context. International Journal of Academic Research in Progressive Education and Development , 11 (2). 10.6007/IJARPED/v11-i2/12941 Ringeera, C. K., Ngeera, F. G., & Muriithi, S. (2025). Financial Preparedness for the Implementation of CBET Curriculum in Public Technical Institutions in Mt. Kenya East Region, Kenya. International Journal of Professional Practice , 13 (3), 26–35. Taber, K. S. (2018). The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education. Res Sci Educ , 48 , 1273–1296. https://doi.org/10.1007/s11165-016-9602-2 Tekle, A., Areaya, S., & Habtamu, G. (2024). Stakeholders' perceptions of occupational Competency assessment and certification systems in Ethiopia’s TVET programs, Higher Education, Skills and Work-Based Learning, Vol. ahead-of-print No. ahead - of - print . https://doi.org/10.1108/HESWBL-02-2024-0030 Wasike, J., Ingendi, J., & Maiyo, J. (2020). The Relationship between ICT Adoption and Student Enrolment in TVET Institutions in Bungoma County, Kenya. 10.36348/jaep . 2020. V04i10.003. Zhong, Z., & Juwaheer, S. (2024). Digital competence development in TVET with a Competency-based whole-institution approach. Vocat Tech Edu DOI 10.54844/vte 2024.0591 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9554893","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":631425406,"identity":"8025282a-b581-4bff-8d03-a707de3f11fe","order_by":0,"name":"Collins Wabwile Walucho","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFklEQVRIiWNgGAWjYFAC5gYGxgYgzc7ccICBQUJOnh3EM7DAo4URqoWZEaTFwtiw5wBIiwRxWoBkRWLDjQSQMG4tBscPtkn83GEjz8/M2Hi44I8EY+PM51c3/CiQYOBv707AquVMYptk75k0w5nNjA2HZ7ZJMLNL55Td7AE6TOLM2Q3YtJgdSGyTZmw7nGBwGKiFt0GCjXF2TtoNHqAWA4lc7FrOP0TSwvNHgofh5pm0m3/wabmBbAsPm4QEww32Y7fx2WJ/42GzZW8b1C+8bRIGhj05bLdlDCR4cPlFsj/54I2fbcAQY28+/JnnT139fPbjz26++WMjx9/ei1ULNsBjACaJVQ4C7A9IUT0KRsEoGAXDHwAASUlmW+t4KDwAAAAASUVORK5CYII=","orcid":"","institution":"University of Eldoret","correspondingAuthor":true,"prefix":"","firstName":"Collins","middleName":"Wabwile","lastName":"Walucho","suffix":""},{"id":631425407,"identity":"a8bc71ab-184c-436b-9bcc-1ca01acf901a","order_by":1,"name":"Hoseah Kiplagat","email":"","orcid":"","institution":"University of Eldoret","correspondingAuthor":false,"prefix":"","firstName":"Hoseah","middleName":"","lastName":"Kiplagat","suffix":""},{"id":631425408,"identity":"76bee8a5-4ee2-428e-9bed-21daa6e84134","order_by":2,"name":"Simon Wanami","email":"","orcid":"","institution":"University of Eldoret","correspondingAuthor":false,"prefix":"","firstName":"Simon","middleName":"","lastName":"Wanami","suffix":""}],"badges":[],"createdAt":"2026-04-28 13:39:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9554893/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9554893/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108165984,"identity":"10810c7b-dbd7-4b20-9c8b-82f1a4f19f9d","added_by":"auto","created_at":"2026-04-30 05:52:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":100939,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Framework\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource: (Author, 2025)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9554893/v1/1b7e0a3d23b366f3531df350.png"},{"id":108182758,"identity":"9798b617-73ca-4629-872c-9a7c207b4c35","added_by":"auto","created_at":"2026-04-30 08:59:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29506,"visible":true,"origin":"","legend":"\u003cp\u003eAge of ICT trainers\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9554893/v1/2c0914229840231d663d7f5f.png"},{"id":108182719,"identity":"638281f6-c1f8-44f2-9749-8121fd2836eb","added_by":"auto","created_at":"2026-04-30 08:59:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":25257,"visible":true,"origin":"","legend":"\u003cp\u003eTraining on CBET Methodologies\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9554893/v1/9a98ea54cf4a6d79b5daeebc.png"},{"id":108804265,"identity":"e4c930f3-f9c6-4cb7-8cc0-a1b2fb912bbb","added_by":"auto","created_at":"2026-05-08 15:18:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":609284,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9554893/v1/b31e408b-cc8d-4ece-b993-4d14508e4c30.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eTrainers’ Perspectives on CBET Implementation in Enhancing ICT Employability Skills for Students in Bungoma County TVET Institutions\u003c/p\u003e","fulltext":[{"header":"1.0 INTRODUCTION","content":"\u003cp\u003eThe global economy has been transformed fundamentally because of digital transformation, resulting in an increased need for experts in ICT. Kenya faces an urgent workforce need because it aims to use technology for economic expansion together with societal advancement (CBET Policy, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The designed CBET system prepares graduates to master real-world technical abilities, which lead to better employment possibilities along with progress toward national development targets. Students need to acquire ICT employability skills to enable them to perform successfully in the job market. Ayonmike, Okwelle, and Dibua (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) explored the comprehensive role of CBET in developing the employability skills as a vital element towards national security and sustainable development. A blend of practical education programs and external business ties with technology-based education resources should be adopted in schools to optimize their ICT learning achievements (Mutohhari et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Prepared CBET curricula directly contribute to preparing candidates to work by increasing levels of employability skills, in particular in technical fields such as ICT (Baker, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the observed policy changes and the key investments into TVET, some gaps in ICT employability skills are still present, and consequently, a very high unemployment rate among the youths, including those that have attended institutions of TVET exists (Wasike, 2020). This implies the existence of a possible gap between the training and the skills that are needed in the labor market. Although most recent studies have explored graduate employability and employer satisfaction, a significant gap has been identified in interpreting the views of such trainers who are front-runners in implementing the CBET curriculum. Their perceptions of the effectiveness of this training, the difficulties they encounter, and their perspective regarding how effective CBET is in preparing trainees to obtain jobs in ICT-related fields remain largely unexplored, especially within specific regions, such as Bungoma County. Therefore, this study addresses this gap by reporting empirical data using the trainers' perspectives, which are vital in determining policy and enhancing quality TVET in Kenya.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Statement of the Problem\u003c/h2\u003e \u003cp\u003eAlthough Technical and Vocational Education and Training (TVET) institutions in Kenya have adopted Competency-Based Education and Training (CBET) to align education with labor market demands, there is lack of empirical data on how trainers perceive CBET within ICT courses in fostering technical and soft skills relevant for student employability. It is unclear how strongly TVET trainers who are the primary implementers of the CBET curriculum are confident that the CBET framework is effectively preparing ICT graduates with the required ICT skills for the labor market, and thus far, it is not easy to align training with the requirements of the labor market. This study therefore, addressed lack of empirical facts on how trainers perceive the CBET implementation in ICT courses and student employability skills by providing empirical findings on the perspectives of trainers to improve the CBET curriculum and present data-driven recommendations for the TVET administrators, policymakers, and trainers to align ICT Courses training and the industry demands in Bungoma County.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Research Objectives\u003c/h2\u003e \u003cp\u003eThe study was guided by the following specific objectives:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo assess the opinions of ICT trainers on the influence of CBET implementation on ICT employability skills in TVET institutions in Bungoma County, Kenya.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo identify the challenges faced by ICT trainers in implementing CBET to enhance ICT employability skills in TVET institutions in Bungoma County, Kenya.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo explore strategies perceived by ICT trainers on CBET implementation towards enhancing ICT employability skills in TVET institutions in Bungoma County, Kenya.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Research Hypothesis\u003c/h2\u003e \u003cp\u003eThe study tested the following hypotheses:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH\u003c/b\u003e \u003csub\u003e \u003cb\u003e01\u003c/b\u003e \u003c/sub\u003e Competency-Based Education and Training (CBET) implementation positively influences ICT employability skills among students in TVET institutions in Bungoma County, Kenya.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH\u003c/b\u003e \u003csub\u003e \u003cb\u003e02\u003c/b\u003e \u003c/sub\u003e Challenges such as limited resources, inadequate infrastructure, and insufficient training negatively affect the effective implementation of CBET in enhancing ICT employability skills of students in TVET institutions in Bungoma County, Kenya.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH\u003c/b\u003e \u003csub\u003e \u003cb\u003e03\u003c/b\u003e \u003c/sub\u003e Implementation of strategic interventions perceived by ICT trainers on CBET implementation, including industry collaboration, curriculum adaptation, and capacity building for trainers, significantly enhances ICT employability skills of students in Bungoma County TVET institutions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.4 Justification and Significance of the Study\u003c/h2\u003e \u003cp\u003eThe study is justified by lack of adequate literature on the opinions of trainers as the primary implementers of CBET curriculum and the persistent constraints in the labor market, especially the ICT sector, which has led to high levels of youth unemployment in Kenya (Barasa et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The study is relevant to various stakeholders in TVET and ICT education. The findings offer critical empirical evidence that policymakers, trainers, and government agencies can use to support the development and transformation of the TVET policies of the nation, as well as counties, to align them with the CBET curriculum to be delivered to satisfy the labor market.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e1.5 Scope and Limitations of the Study\u003c/h2\u003e \u003cp\u003eThe study focused on trainers\u0026rsquo; perspectives on CBET Implementation in enhancing ICT employability skills for students in Bungoma County TVET institutions. The study maintained a specific operational boundary to conduct a manageable investigation while acknowledging the inherent constraints. This study had limitations that could impede the generalizability of its findings. The study focused on the opinions of trainers as the primary implementers of CBET and ICT Courses training which meant that it presented one-sided opinions when referring to the complicated question of CBET and ICT employability skills. It failed to uphold other important stakeholders, including the students, employers in the industry, and policymakers. The lack of these viewpoints did not sufficiently triangulate the information to acquire a more holistic idea of the challenges and achievements of the CBET curriculum. Thus, the research should be understood within the defined scope and resource constraints, as it was a momentary outlook of trainers' perspectives in a particular geographic and professional setting. In addition, the study is constrained by the ever evolving nature of the generative spark in ICT, whereby the fast-changing developments in AI and automation can possibly surpass the static modular form of contemporary ICT Courses and CBET. This creates a new technical lag that can impact the trainers\u0026rsquo; perception of the existing employability skills.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e1.6 Theoretical Framework\u003c/h2\u003e \u003cp\u003eThis study was based on competency theory framework. Human practices offer different ways to understand how multiple elements affect the combination of CBET implementation and its consequences for developing skills. Competency theory was useful in this study by showing how teachers see the link between ICT related skills building and employability training that is part of the curriculum. Trainer evaluations on CBET's success as a program stem from their views on how effectively it cultivates necessary competencies demanded by ICT professionals. When trainers identify CBET as providing complete necessary competencies to students, they tend to view it as effective (Manase, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Trainers who detect inadequacies between CBET-developed competencies and employer-specified skills are likely to report negative views about the program (Zhong, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e1.7 Conceptual Framework\u003c/h2\u003e \u003cp\u003eVariables refer to measurable attributes that can assume diverse values from the study respondents (Mugenda \u0026amp; Mugenda, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This study involved opinions, challenges and strategies perceived by ICT trainers to enhance CBET in TVET institutions in Bungoma County as independent variables, TVET Policy and CBET policy framework which mandates a transformation from knowledge-based to skill-focused and industry-driven training in TVET as the intervening variables and ICT employability skills as the dependent variable. Figure\u0026nbsp;1 shows the conceptual framework of the study:\u003c/p\u003e \u003c/div\u003e"},{"header":"2.0 LITERATURE REVIEW","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Competence-Based Education and Training (CBET)\u003c/h2\u003e \u003cp\u003eCompetence-Based Education and Training (CBET) focuses on developing essential capabilities for different professional disciplines. Educational institutions recognize CBET as a fundamental approach to developing vocational and technical education because it helps learners acquire essential workplace skills. The study findings by Ayonmike et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) show that CBET enables students to obtain skills that follow industrial market requirements, enhancing their job readiness and supporting state progress and defense. The CBET framework uses outcome-based defined curriculums to teach learners employable workplace skills (Ayonmike-et.al, 2014).\u003c/p\u003e \u003cp\u003eCompetence-Based Education and Training works as a fundamental method in vocational education because it combines educational results with marketplace requirements. The development of CBET followed traditional educational models, which transformed into systems focused on skill acquisition and employability applications, according to Ayonmike et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Training centers follow the CBET principles, which stress student-led learning. This type of learning defines skills by performance standards and tests these skills for proof that can be seen (CBET Policy, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Lack of resources, untrained teachers, and unclear information about the CBET curriculum are among the problems affecting CBET implementation, according to Tambwe (2019). Various regions face similar obstacles while implementing the CBET framework, which proves the necessity for appropriate institutional reform and staff training at institutions that adopt CBET.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.2 ICT in TVET: Kenyan Perspective\u003c/h2\u003e \u003cp\u003eInformation and Communication Technology (ICT) has a key role in Technical and Vocational Education and Training (TVET), so it helps the teaching and learning processes through enabling greater access to information and creating interactive educational experiences. The technology sector changes so fast that TVET needs information and communication technology as an essential component to educate a skilled workforce. The adoption of Information and Communication Technology (ICT) within TVET education has created a new vocational training environment through which students gain modern workforce essential employability skills. ICT provides educators with various teaching approaches that allow them to conduct dynamic, interactive educational encounters (Zhong, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The study relates directly to how Mekonnen et al. (2024) outline institutional Competency development through holistic programs.\u003c/p\u003e \u003cp\u003eStrategically implementing ICT in TVET is necessary for skill development that keeps up with changes in technology and meets the needs of the market. Their study proves that when educational programs match industry requirements, the development of employability skills becomes more effective according to Garcia and Martinez (2022). The study proved that when ICT serves as a facilitator, curriculum alignment strongly affects the growth of employability skills. Information and communication technology is more than just an extra resource, according to a study. It changes the whole educational system to help students do their best (Garcia, 2022). There are ongoing problems with integrating ICT, mainly because of digital inequality, shortage of trainers and a lack of infrastructure (Tambwe, 2019).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Trainers' Perceptions and CBET Implementation: Global Perspective\u003c/h2\u003e \u003cp\u003eThe successful implementation of CBET curriculum relies on the trainers' perspectives and attitudes. Trainer perception of CBET affects their capability and readiness to put its principles into practice and thus affects student learning results. The attitudes of trainers guide both policy-making and the delivery of employability programs in Kenya (Barasa et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The successful implementation of CBET in the Ethiopian vocational environment depends on instructor approval according to Tekle Areaya and Habtamu (2024). The perception of CBET as a practical approach by trainers leads to active inclusion of its principles and involvement in its delivery processes (Barasa et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe planning and implementation of CBET programs requires trainer involvement. Such participation creates a feeling of ownership which enables trainers to share their problems and aid in creating efficient implementation strategies. Proper training is essential for instructors to acquire the required competencies needed to deliver effective CBET practices (Tambwe, 2019). The training curriculum should focus on competency-based assessment methods together with curriculum development techniques and educational technology application in teaching practices. Effective delivery of CBET depends heavily on how organizations distribute their resources. Educational materials alongside proper equipment combined with relevant technology must be available to trainers when they prepare CBET training courses at a high professional level (Tekle et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Inadequate resources create challenges for trainers attempting to deliver CBET properly because it leads to dissatisfaction among learners. Educational institutions can create better learning conditions for CBET through trainer involvement during planning stages, resource provision, and addressing the challenges to enhance student academic achievements (Garcia, 2022).\u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Study Design\u003c/h2\u003e \u003cp\u003eThe study used a mixed methods approach to give in-depth findings of how trainers perceive CBET implementation in enhancing ICT employability skills for students in Bungoma County TVET institutions. The study design used both quantitative and qualitative methods so that all aspects of the study could be fully understood (Jilcha, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Since numerical data and personal experiences were gathered in this study, there was need to use both quantitative and qualitative study methods to get holistic findings (Arslan, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The study involved providing questionnaires to the sampled ICT trainers to assess their precise opinions on the CBET curriculum. The qualitative part of the study involved the use of semi-structured interviews with the sampled ICT heads of department to give their opinions about their real-life experiences, challenges, and recommendations about CBET (Mazhar et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Research Paradigm\u003c/h2\u003e \u003cp\u003eThe study was grounded on interpretivist research paradigm since it was appropriate to study the realities and subjective experiences of TVET trainers. This paradigm proposes that reality is a product of society and could be determined by how people interpret it (Pervin \u0026amp; Mokhtar, 2022). The interpretive approach made possible to explore how and why the skill mismatch existed in a deep and nuanced manner. Such a philosophical position informed the qualitative methodology by focusing on the detailed data-gathering procedures to provide detailed insights on the issues, strengths, and challenges of the CBET and ICT courses training through the ICT trainers\u0026rsquo; lenses in TVET institutions in Bungoma County.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Study Area\u003c/h2\u003e \u003cp\u003eThe study was conducted in TVET institutions located in 9 administrative sub-counties of Bungoma County, situated in Kenya. Bungoma County is situated between 0\u0026deg; 28\u0026rsquo; and 1\u0026deg; 30\u0026rsquo; North latitude and 34\u0026deg; 20\u0026rsquo; and 35\u0026deg; 15\u0026rsquo; East longitude per the Fluxes maps. Bungoma County has 9 sub-counties with TVET institutions distributed as shown in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of TVET institutions in 9 Sub-Counties in Bungoma County.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS/No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSub-County\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eName of Ward\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eName of Institution\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKanduyi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEast Sang'alo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe Bungoma National Polytechnic\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKabuchai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKabuchai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKisiwa National Polytechnic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBumula\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSiboti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMusakasa Technical Training Institute\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKimilili\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKimilili\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMatili Technical Training Institute\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSirisia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLwandanyi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSirisia Technical and Vocational College\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTongaren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMilima\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBungoma North Technical and Vocational College\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWebuye West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMachakha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWebuye West Technical and Vocational College\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMt Elgon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChepyuk ward\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMt. Elgon TVC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBumula\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSouth Bukusu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCardinal Maurice Otunga TVC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Target Population\u003c/h2\u003e \u003cp\u003eThe study target group included ICT trainers who train in the selected 9 TVET institutions situated within Bungoma County. The selected TVET institutions for this study comprises of the 2 national polytechnics, 3 technical institutes and 4 technical and vocational colleges. This population was selected because they participate directly in implementing CBET while providing knowledge about its success in building student ICT employability skills. How trainers see the influence of CBET and how they come up with ways to make things better depend a lot on their opinions (Tekle et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A total of 41 ICT trainers in the selected TVET institutions were sampled to participate in the study to find out how strong the quantitative analysis is and how well the qualitative understanding is through descriptive and inferential statistical power determination (Ayonmike-et.al, 2014).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Sample Size and Sampling Technique\u003c/h2\u003e \u003cp\u003ePurposive sampling was suitable in this study since ICT trainers who applied Competence-Based Education and Training (CBET) provided the best information regarding its effectiveness. The study by Nyimbili (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) explains that purposive sampling assists the researcher to locate effective information based on some features of the targeted study that might remain concealed with random sampling (Nyimbili \u0026amp; Nyimbili, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The sample size of this study was determined using Yamane formula, which is used to compute the sample size in a finite population. The Yamane formula is expressed as follows; n\u0026thinsp;=\u0026thinsp;N\u0026divide; (1\u0026thinsp;+\u0026thinsp;N (e)\u003csup\u003e2\u003c/sup\u003e) where; N is the population size; n is the sample size and e is margin of error (5%) with 95% confidence interval. A total of the sampled 9 Technical and Vocational Education and Training (TVET) institutions spread across nine sub-counties in Bungoma were selected for the study. Three ICT trainers and one head of department were sampled from each Technical and Vocational College (TVC), four ICT trainers and one head of department were sampled from each national polytechnic and Technical training institutions because of institutions\u0026rsquo; large size and number of students, to attain 41 trainers for the study sample. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the numbers of sampled trainers and Heads of Departments in every TVET institution in Bungoma County:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTVET Institutions in Bungoma County with respective sampled trainers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSub-County\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eTVET Institutions in Bungoma County\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNational Polytechnics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTechnical Training Institutes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTechnical and Vocational Colleges\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal Number of Sampled TVET Institutions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal Number of sampled ICT Trainers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal Number of sampled ICT Heads of Departments (HODs)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal Number of sampled Trainers and HODs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMt. Elgon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKimilili\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBumula\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKanduyi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWebuye West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWebuye East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKabuchai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSirisia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTongaren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Data Collection Instruments\u003c/h2\u003e \u003cp\u003eThe study combined mixed methods for data collection through both quantitative and qualitative data collection instruments. Structured questionnaires were the primary quantitative data collection tool used in the study to collect data from ICT trainers. Questionnaires were administered to a total of 32 ICT trainers teaching in TVET institutions within the Bungoma County to give a preliminary quantitative data with qualitative data gathered from 9 ICT Heads of Departments. The questionnaires gathered information on the views of the ICT trainers within the TVET institutions in Bungoma County on CBET implementation and student employability skills. The questionnaires included Likert rating scale to evaluate the teaching efficacy of CBET in respect to the particular skills, and open-ended questions that focused on trainers concerning curriculum suitability and training materials. To obtain qualitative data, semi-structured interviews were used to supplement questionnaires to obtain data from 9 ICT Heads of Departments in the study. According to the study by Mazhar et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), using semi-structured interviews, helps to effectively investigate the perspectives of the respondents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Validity and Reliability of Study Instruments\u003c/h2\u003e \u003cp\u003eA study instrument achieves validity when it appropriately assesses the intended items, yet reliability occurs when the instrument maintains consistent output measurements. The subject experts, consisting of thesis supervisors, performed additional validation checks on these instruments (Garcia, 2022). To evaluate instrument reliability, a distinct group of ICT trainers, excluded from the main study participants, undertook the pilot test. The pilot test served to improve the instruments by detecting unclear wordings and maintaining consistent terminology. Cronbach's alpha analysis was conducted to verify internal consistency and reliability of the questionnaire. The Cronbach's alpha score of 0.749 as shown in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e below indicates acceptable internal consistency and reliability of the research instruments used in the study (Taber, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReliability Score of Study Instruments\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eFrequentist Scale Reliability Statistics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficient α\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Data Collection Procedures\u003c/h2\u003e \u003cp\u003eThe study methodology involved questionnaire distribution followed by semi-structured interview procedures. The study carried out a pilot study involving ICT trainers to confirm and ensure the correctness of the data collection tools. The most suitable study techniques are those which coincide statistical significance and a large population sample as they are efficient and accurate in data collection (Nyimbili \u0026amp; Nyimbili, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The questionnaires collected the opinion of the trainers on Competence-Based Education and Training (CBET) in ICT employability skills. To gather qualitative data, the study used semi-structured interviews with heads of department in the 9 TVET institutions in Bungoma County. Data collection process began with seeking NACOSTI license and Bungoma County TVET director research permit, consent acquisition, and proceeded with questionnaire and interview anonymity and administration. These procedures helped to provide a holistic framework to conduct the study (Nolan, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Data Analysis\u003c/h2\u003e \u003cp\u003eThe study utilized a combination of quantitative and qualitative data analysis methods, utilizing appropriate analytical frameworks. The statistical analysis of questionnaire-based quantitative data helped to determine the study findings. The study conducted descriptive statistics to present trainer assessments of CBET effectiveness through central value descriptions and measurement of data spread and used inferential statistics to test hypotheses and find variable relationships, which helped to fully understand what the data means (Adams, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The study used a Multiple Linear Regression Model to investigate how several factors predict student employability. The model was significant with Ϝ= 3.213 and ρ\u0026thinsp;=\u0026thinsp;0.028. The Explanatory Power (R-Square value) was 0.322, indicating that the factors which include Implementation Strategies (β\u0026thinsp;=\u0026thinsp;0.438), Trainer Characteristics (β\u0026thinsp;=\u0026thinsp;0.331), Facilitating Factors (β = -0.029) and Challenges (β\u0026thinsp;=\u0026thinsp;0.030) indicate 32.20% of the variation in ICT employability skills.\u003c/p\u003e \u003cp\u003eThematic analysis was used to analyze interview data through a method that successfully detects patterns and themes within textual information. The study adopted thematic analysis by putting data into groups, then summarizing themes, and finally interpreting the results based on the literature reviews from credible sources. The qualitative data from the ICT heads of departments gave more insightful information about the opportunities and challenges facing CBET curriculum.\u003c/p\u003e \u003c/div\u003e"},{"header":"4.0 RESULTS AND DISCUSSION","content":"\u003cp\u003eThe study analysis was guided by the objectives that were focused on investigating opinions of trainers on CBET and ICT Courses training for student employability skills, challenges in CBET implementation, strategies perceived by trainers in enhancing CBET implementation and ICT employability skills of ICT students. The study applied a triangulation convergent design where quantitative and qualitative data were collected simultaneously and analyzed separately to guarantee independent integrity. Quantitative data through structured questionnaires was analyzed through descriptive statistics to determine the broad patterns whereas qualitative data through open-ended questions was subjected to thematic analysis to capture the nuanced perspectives of ICT trainers. The quantitative and qualitative analysis was then merged during the interpretation through comparison and contrasting of the statistical outcomes with the developed themes, enabling the qualitative information to serve as a deeper contribution to the quantitative trends, in the way CBET implementation in ICT courses enhance the employability skills of students. The study achieved a 100% response rate from the targeted respondents. All the 32 ICT trainers who were sampled participated in the study returned duly completed questionnaires, and similarly, all the 9 Heads of Departments (HoDs) got the chance to be interviewed. The findings were presented using descriptive statistics such as frequencies, percentages, means, inferential statistics and multiple linear regression analysis to identify the relationships between the variables, then followed by detailed interpretations of the findings.\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Background information of the Respondents\u003c/h2\u003e \u003cp\u003eThe background information assisted in interpreting the results and creating an insight into experiences and qualifications of the respondents that might affect their views on the implementation of CBET within TVET institutions. The study included 19 trainers (59.4%) female, and 13 trainers (40.6%) male. The age was considered to be a significant demographic characteristic since it could affect professional experience of trainers, their flexibility to Competency-Based Education and Training (CBET), and their understanding of the development of ICT employability skills. The age distribution of the 32 trainers who took part in the study is such that most of them were relatively young to middle-life career professionals. The most numerous group was trainers aged between 30 and 39 years and they made up 46.9% of the respondents. This shows that a large percentage of the trainers in TVET institutions in Bungoma County are within their prime working age and this could have a positive impact on the implementation of CBET and ICT-related practices since they are likely to have been exposed to the modern methods of training. Another 25.0% of the respondents were between 21\u0026ndash;29 years old and they were early-career trainers capable of introducing new insights and increased flexibility to ICT integration in teaching and learning. The 40\u0026ndash;49 years old group formed 18.8% of the sample with 50\u0026ndash;59 years group forming 9.4% being the smallest group as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e4.1.1 Training on CBET methodologies\u003c/h2\u003e \u003cp\u003eCBET training is very important because it empowers trainers with the skill and knowledge needed to successfully give competency-based training and improve ICT employability skills on trainees. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the findings on CBET methodology training among the trainers:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results indicate that a big percentage of the trainers had been trained on the CBET methodologies and implementation. Two-third of the 32 respondents (28 trainers) said that they had attended CBET training, and only 4 trainers (12.5%) had not attended CBET training. Such a high exposure implies that the majority of trainers in the TVET institutions located in Bungoma County are highly equipped with the basic knowledge that may enable them to apply the Competency-Based Education and Training. The fact that a relatively low percentage of trainers did not receive CBET training indicates a vacuum that may have to be filled by institutions in enhancing a comprehensive and complete implementation of CBET practices in all departments.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Regression analysis\u003c/h2\u003e \u003cp\u003eThe level at which the independent variables were able to predict the dependent variable (ICT employability skills) was determined by means of regression analysis. The analysis will contain the model summary, coefficients, and significance to demonstrate the strength, direction, and significance of the relationships between the variables as shown in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e below:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Summary\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eModel Summary\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted R Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStd. Error of the Estimate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.568\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.38182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ea. Predictors: (Constant), Characteristics of the Trainer, Challenges, Strategies, Facilitating Factors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe multiple linear regression model depicts that the independent variables (Trainer Characteristics, Challenges, Strategies, and Facilitating Factors) and the dependent variable (Employability Factors) have a light positive relationship as demonstrated by the value of R\u0026thinsp;=\u0026thinsp;0.568.\u003c/p\u003e \u003cp\u003eThe R\u003csup\u003e2\u003c/sup\u003e of 0.322 suggests that the four predictors of the model contributes to 32.2% of the variation in ICT employability factors when they are together. This indicates a reasonable explanatory power of the model, bearing in mind that the notion of employability is determined by numerous contextual and institutional factors, which were not considered in the study.\u003c/p\u003e \u003cp\u003eThe Adjusted R Square (0.222) implies that even with the number of predictors and sample size, 22.2% of the variability of the employability factors remains to be explained by the model. The decrease in R\u003csup\u003e2\u003c/sup\u003e to Adjusted R\u003csup\u003e2\u003c/sup\u003e is accounted by the social science research and indicates that the predictors are not meaningless due to the relatively low sample size. Standard Error of the Estimate (0.38182) shows that the predictions made by the model are not much different and thus acceptable as far as the survey data using perception is concerned. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the ANOVA analysis:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA The results of the ANOVA also show that the regression model is significant (F\u0026thinsp;=\u0026thinsp;3.213, p\u0026thinsp;=\u0026thinsp;0.028). This means that a combination of trainer characteristics, challenges, strategies, and facilitating factors, is a very strong predictor of ICT employability factors. Therefore, the null hypothesis that the independent variables does not have a significant effect on the employability factors is rejected and this supports the overall appropriateness of the regression model in explaining the differences in employability results as shown in Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e:\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eANOVA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSum of Squares\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.028\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003ea. Dependent Variable: Employability Factors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eb. Predictors: (Constant),Trainer Characteristics, Challenges, Strategies, Facilitating Factors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCoefficients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eCoefficients\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnstandardized Coefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandardized Coefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChallenges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFacilitating Factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrategies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrainer Characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003ea. Dependent Variable: Employability Factors\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe regression coefficients analysis reveals that implementation strategies positively and significantly impact the factors of employability (B\u0026thinsp;=\u0026thinsp;0.226, β\u0026thinsp;=\u0026thinsp;0.438, p = -0.014). This results shows that successful CBET programs such as curriculum alignment to industry demands, industry relationship, adoption of learner-based instructional strategies and high competency based assessment are extremely critical in enhancing ICT employability skills. The fact that the beta coefficient is a standardized value, implies that strategies are the most potent predictor in the model, which is why they are so critical in translating the CBET principles into meaningful outcomes in order to convert them into employability.\u003c/p\u003e \u003cp\u003eThe findings indicated a statistically significant and positive relationship between trainer characteristics and the employability skills (B\u0026thinsp;=\u0026thinsp;0.148, β\u0026thinsp;=\u0026thinsp;0.331 p\u0026thinsp;=\u0026thinsp;0.048). This hints that the professional qualification, trainers\u0026rsquo; confidence on the implementation of the CBET, pedagogical competence, and industry exposure plays a significant role in the development of ICT employability skills. The finding pinpoints the importance of investing in the continuous professional development and training that focuses on the CBET among ICT trainers in TVET institutions. The issues associated with implementation of CBET however, did not significantly affect employability factors (B\u0026thinsp;=\u0026thinsp;0.011, β\u0026thinsp;=\u0026thinsp;8030, p\u0026thinsp;=\u0026thinsp;0.862). Although there may be lack of resources, infrastructure and institutional barriers, the results show that there is no direct significant impact on the results of the employability in case the strategies are good and the trainers are qualified. This shows that the obstacles can either have an indirect effect or can be alleviated by placing the right instruction and institutional responses.\u003c/p\u003e \u003cp\u003eOn the same note, it was determined that the factors of facilitating did not have a statistically significant impact on factors of employability (B = -0.010, β = -0.029, p\u0026thinsp;=\u0026thinsp;0.871). The relative and negligible negative value indicates that even the simple presence of enabling influences like policies, equipment, or institutional support systems, is unlikely to yield to productive effects on the improvement of employability unless these enabling influences are used well by good implementation plans and training skills.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Hypothesis Testing\u003c/h2\u003e \u003cp\u003e \u003cb\u003eH01: The implementation of Competency-Based Education and Training has a positive impact on ICT employability skills of students in TVET institutions in Bungoma County, Kenya.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe results of the regression analysis show that the implementation plans of CBET positively and statistically impact ICT employability skills (B\u0026thinsp;=\u0026thinsp;0.226, β\u0026thinsp;=\u0026thinsp;0.438, p\u0026thinsp;=\u0026thinsp;0.014). This means that the efficacy of the CBET framework such as connecting ICT curriculums to industry needs, the encouragement of industry relations, the embracing of student-centered pedagogy and the emphasis of competency based assessments are key drivers that enhance the employability skills of students. The standardized beta coefficient shows that the implementation strategies of CBET hold the strongest predictor in the model; an element that enhances the significance of the implementation strategies in actualizing the principles of CBET into meaningful employability outcomes. These findings support H01 and confirm that the use of CBET has a positive effect on the ICT employability skills.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH02: Challenges such as inadequate resources, poor infrastructure and inadequate training have negative impact on the implementation of CBET in enhancing ICT employability skills in TVET institutions.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe challenges related to the implementation of CBET did not significantly influence the employability factors (B\u0026thinsp;=\u0026thinsp;0.011, =\u0026thinsp;0.30, p\u0026thinsp;=\u0026thinsp;0.862). Even though other issues such as scarce resources, poor infrastructure and institutional factors are also present, it does not appear that they directly affect ICT employability outcomes except when there are no proper implementation plans and qualified trainers. This is a pointer that the difficulties can only indirectly influence or can be reversed with proper instructional and institutional interventions. H02 is therefore not supported by the regression results.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH03: Strategic interventions as perceived by ICT trainers regarding implementation of CBET such as industry collaboration, curriculum modification and trainers\u0026rsquo; capacity building to enhance the ICT employability skills in TVET institutions in Bungoma County Kenya.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe characteristics of the trainers were observed to have a significant and positive impact on the employability factors (B\u0026thinsp;=\u0026thinsp;0.148, β\u0026thinsp;=\u0026thinsp;0.331\u0026thinsp;=\u0026thinsp;0.048). It means that the qualification, pedagogical ability, and confidence of trainers in the use of CBET, and industry exposure plays an important role in developing ICT employable skills. Likewise, facilitating factors were found not to have a significant impact on employability (B = -0.010, β\u0026thinsp;=\u0026thinsp;0.029, p\u0026thinsp;=\u0026thinsp;0.871), and thus, resources or support mechanisms should be actively engaged based on effective strategies and trainers.\u003c/p\u003e \u003cp\u003eThe results of the regression analysis indicate that implementation strategies and trainer characteristics are significant predictors (32.2) of the implementation of CBET in ICT courses and explain the difference in the skills of the student in employability. This statistical finding can be directly associated with the study by Baker and Taylor (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which underscores that the translation of an elaborated curriculum to a classroom performance highly depends on the overlap of the knowledge of trainers and the efficient implementation strategy. Moreover, the high impact of the characteristics of trainers confirms the claims of Barasa et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) about the importance of pedagogical competence in Bungoma County TVET institutions. Although the model validates the idea of strategy interventions and effective human resources as the main success drivers, the insignificance of challenges indicates that their contribution can be negated by effective instructional leadership, which is reinforced by Mazhar et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) on the significance of established systems in ensuring integrity in education. The study therefore highlights the need for policy makers and TVET administrators to prioritize strengthening CBET implementation strategies and enhancing trainer capacity as key pathways to improving ICT employability outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Qualitative Findings\u003c/h2\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1 HoDs\u0026rsquo; Opinions on the Current CBET Curriculum\u003c/h2\u003e \u003cp\u003eThe qualitative analysis of the responses indicate 5 key emerging themes concerning trainers\u0026rsquo; perceptions of CBET implementation in ICT courses.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTheme 1: Effectiveness and Industry Relevance of the CBET Curriculum\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSeveral respondents perceived the CBET curriculum as effective and aligned with industry needs, particularly due to industry involvement in curriculum development and the emphasis on practical skills. These views suggest that CBET is achieving its core objective of preparing trainees for the job market. This theme indicates that HoDs recognize CBET as an improvement over traditional training approaches, especially in enhancing employability-oriented competencies. The findings are connected to the research of Ayonmike et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), who stressed that vocational education can never be productive without the industrial market where it is applied. The request of trainers to create more organized partnerships confirms the suggestions of Mazhar et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which claim that more concrete systems of industry-institutional collaborations are needed to make sure that the results of training are also applicable to the requirements of the job market.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTheme 2: CBET Assessment is Practice-based but Demanding Operationally.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMost respondents also admitted that active concomitant assessments of CBET are useful in assessing applied competence and preparedness of trainees. The HODs mention that CBET assessments are time consuming, they need more trainers and are difficult to administer especially when the resources are limited and the number of trainees are many. This theme brings about a dilemma between the pedagogical strengths of CBET assessment and realities of practice in TVET institutions. This contradiction between the pedagogical excellence of CBET test and the high cost of administrative and resources is similar to what Fredrick (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) found that CBET is hampered by an imbalance between evaluation demands and staff in Kenyan polytechnics. The nature of the respondents to state that lack of space and equipment is an obstacle to practical assessment is directly related to resources acquisition challenges as found by the study conducted by Ringeera et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These operational constraints suggests that socio-material reality of the institutions has not been fully predetermined so far by intensive assessment demands as pre-supposed by the CBET Policy (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eTheme 3: Low Confidence in CBET Implementation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA majority of respondents expressed low to moderate confidence regarding CBET implementation although there was a variation in confidence levels, due to rushed rollout, inadequate training, weak institutional support and limited resources. This theme indicates that confidence in CBET improves over time but remains constrained by systemic challenges. The findings are in tandem with the argument by Muthomi et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who states that the pedagogical transformation necessary in CBET has to be accompanied by the uninterrupted technical upskilling. The results indicate that trainers have a lot of experience, but their performance cannot be enhanced with the absence of confidence and exposure to Industry technologies, which supports the idea that the key factors to successful delivery of the curriculum are trainer features and training.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTheme 4: Key challenges and Strategies to improve CBET and Strategies to Bridge the Gap.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe interviews with HoDs indicated that there is a good conceptual and high potential aspect of CBET implementation in ICT, yet its performance is limited due to the systemic, institutional, and resources-linked problem. Although the majority of HoDs had no significant discrepancy between core ICT competences, there are gaps in advanced, emerging ICT dimensions, soft skills, and entrepreneurship, mainly because of the lack of resources, problems with the capacity of trainers, and the lack of a strong connection with the industry.\u003c/p\u003e \u003cp\u003eThe findings indicate that institutional capacity has been lagging behind the policy ambitions leading to the partial and ineffective implementation of CBET. The findings were consistent with the research conducted by Mgaya (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Barasa et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which reported resource acquisition as one of the major issues of TVET institutions in Bungoma County. The findings indicate that the \"socio-material\" environment, which is examined in vocational literature in terms of the need of physical tools in the process of knowledge building, is one of the critical bottlenecks that will not allow the full manifestation of CBET opportunities as stated in national strategic plans (CBET Policy, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eTheme 5: Industry Relevance and Skills Mismatch in Emerging ICT Areas\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMost HoDs agreed that core ICT skills align well with industry needs, indicating no major mismatch at foundational levels. However, slight but important mismatches were identified in advanced and fast-evolving ICT areas, such as data science, Artificial Intelligence and advanced programming. These variations were attributed to limited trainer exposure and obsolete equipment rather than curriculum design.\u003c/p\u003e \u003cp\u003eThese findings show a dynamic skills gap where the implementation of CBET lags behind the industry innovation and demands despite the curricula being relevant. Without regular curriculum updating and industry-supported training, ICT graduates risk falling behind in competitive job markets. This is backed by the report that the exposure of equipment and trainers cannot match the high demanding computing courses although the relevant curriculum is in place, which Baker and Taylor (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) argue is crucial in the process of successful implementation of a designed curriculum into real mastery, and thus the availability of modern training tools. Such dynamic skills gap in the developed regions corresponds with the studies of Ayonmike et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) which emphasized that the effectiveness of vocational education is very often reduced when the institutional ability falls behind the fast industrial innovations. It also supports the conclusion made by Nolan and Wilson (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to have an established institutional-industry collaboration to close the gap between classroom teaching and workplace requirements.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5.0 CONCLUSION AND RECOMMENDATIONS","content":"\u003cp\u003eThe study relates the findings with existing literatures, draws conclusions based on the study objectives, and proposes recommendations informed by the identified gaps.\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Conclusion\u003c/h2\u003e \u003cp\u003eIn conclusion, CBET can be successfully used as the means of improving the underlying ICT employability skills, especially technical and soft. Nevertheless, there are still gaps in both advanced and emerging ICT fields like Artificial Intelligence, full-stack development and machine learning that suggest the extent to which CBET fulfills its primary goals but cannot fully equip the trainees with the latest and advanced ICT fields implying that ICT curriculum development requires the incorporation of emerging fields of ICT development making it essential to amend the ICT curriculum to integrate these new fields of study. The CBET framework has to transform from a robust framework to a training approach that is adaptable and embedded into the industry. This requires moving beyond mere equipping to the establishment of deep institutional and industry relationships that would go on to ensure that training remains an embodiment of applied socio-material workplace realities.\u003c/p\u003e \u003cp\u003eCompetency-Based Education and Training (CBET) as adopted in the TVET institutions is not only a paradigm shift of the traditional heavy instruction based teaching but a modern competence based model of teaching that will withstand the demands of the modern ICT labor market. Although CBET has a robust conceptual framework and is highly effective in developing the basic technical and soft skills levels, its implementation success heavily depends on the subjective experiences and professional proficiency of the trainers who are at the center of curriculum implementation.\u003c/p\u003e \u003cp\u003eThe human factor including trainer confidence, pedagogical skill and industry exposure and experience creates a link between curriculum design and student employability. The findings indicate that perceived effectiveness of CBET is not simply a product of institutional policy but rather it is determined by the capacity of the trainers to operate within an environment with limited resources and yet be able to cope with the higher technological changes. The absence of significant correlation between the employability outcomes and implementation challenges is an indication that effective mitigation of the infrastructural shortcomings can be achieved by competency training, aimed at long-term outcomes. Finally, the study affirms that although CBET enhances student employability, its effectiveness and orientation are solely determined by continuous capacity building of trainers and adaptability of the curriculum.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Recommendations\u003c/h2\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003e5.2.1 Policy Level Recommendations\u003c/h2\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eInfrastructure and Resource Funding: The Ministry of Education and TVETA ought to enhance capitation to specifically upgrade the ICT laboratories to match modern standards and offer computing equipment and software that are necessary in training ICT Courses.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eStructured collaboration between TVET institutions and the Industry: Establish a policy-supported model and industry relationships to provide joint evaluation, organized industrial placements to the trainees and compulsory industrial attachments to the trainer to match the innovations within the market.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eStandardized Quality Assurance: The assessment bodies such as Curriculum Development, Assessment and Certification Council (CDACC), Kenya National Qualifications Framework (KNQF) and other accredited Qualification Awarding Bodies and institutions should enhance external and internal moderation of assessments and establish strong digital assessment infrastructure to ensure integrity of assessments and certifications.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003e5.2.2 Institutional Level Recommendations\u003c/h2\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEmerging areas and Curriculum Adaptation: The TVET institutions need to be proactive to include emerging areas into the ICT courses, including Artificial intelligence (AI), Data science, and Full-stack Development to address the existing lack of high-level emerging ICT skills in the ICT curriculum.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTrainer Capacity Building and Retooling: Institutions should emphasize on continuous professional development (CPD) on the new ICT trend and CBET-associated assessment procedures to enhance the confidence and competence of trainers. The TVET administrators should reevaluate the trainee to trainer ratios and work with the government to employ more trainers to enhance practical and personalized student training.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEnhancement of Soft Skills and Problem-Based Learning: The TVET Institutions should enhance problem-based learning (PBL) and industry-based projects to explicitly incorporate soft skills in ICT technical units.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e5.3 Suggestions for Further Studies\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFuture studies should examine the long-term employability outcomes of CBET graduates in the ICT sector using tracer studies.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eComparative studies between CBET and conventional ICT courses training models across different TVET institutions would provide deeper insights into effectiveness variations.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFurther research is suggested on the integration of micro-credentials, industry partnerships and emerging learning areas in CBET frameworks, to enhance credibility of assessment and employability of ICT students.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eAuthor Contribution Statement\u003c/h2\u003e \u003cp\u003eAll authors contributed to the conception and design of this study. Concept note, material preparation, data collection, analysis and report writing were conducted by CW. The manuscript was reviewed by Dr. HK and Dr. SW from concept note to approval of the final manuscript.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics Declaration\u003c/h2\u003e \u003cp\u003eThe study maintained principles of informed consent, anonymity and confidentiality as protective measures for the rights and welfare of participants. The authors obtained official ethical clearance through National Commission for Science and Technology (NACOSTI) and the Ministry of Education to initiate its data collection activities. Participation in the study was voluntary and participants were assured that all data will be confidential through both secure storage and pseudonym identities during interviews, results publication and dissemination.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that no competing interests exist.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThere was no funding received for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the conception and design of this study. Concept note, material preparation, data collection, analysis and report writing were conducted by CW. The manuscript was reviewed by Dr. HK and Dr. SW from concept note to approval of the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eAll glory to God for his endless mercy and blessings of life, good health, and leading me through this entire process. I am sincerely grateful for the outstanding insights and support granted to me by the outstanding lecturers and supervisors; Dr. Hoseah Kiplagat and Dr. Simon Wanami for the professional review, guidance and mentorship that has made me to attain this outstanding academic heights. Special gratitude to my parents, family and technology education colleagues for holding my hand and encouraging me to accomplish this research work; they are my most significant source of inspiration.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdams, R., \u0026amp; Osborne, M. (2020). The Impact of Curriculum Design on Employability Skills: A Case Study from Australia. \u003cem\u003eJournal of Education and Work, 33\u003c/em\u003e(3), \u003cem\u003e207\u0026ndash;226.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArslan, G., \u0026amp; Demir, F. (2023). 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The Relationship between ICT Adoption and Student Enrolment in TVET Institutions in Bungoma County, Kenya. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.36348/jaep\u003c/span\u003e\u003cspan address=\"10.36348/jaep\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 2020. V04i10.003.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhong, Z., \u0026amp; Juwaheer, S. (2024). Digital competence development in TVET with a Competency-based whole-institution approach. \u003cem\u003eVocat Tech Edu DOI\u003c/em\u003e10.54844/vte 2024.0591\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CBET, Employability, ICT, Kenya, Trainers Perspectives, TVET","lastPublishedDoi":"10.21203/rs.3.rs-9554893/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9554893/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlthough Technical and Vocational Education and Training (TVET) institutions in Kenya have adopted Competency-Based Education and Training (CBET) to align education with labor market demands, there is lack of empirical data on how trainers perceive CBET implementation within ICT courses in fostering technical and soft skills relevant for student employability. This study investigated trainers\u0026rsquo; perspectives on CBET Implementation in enhancing ICT employability skills for students in Bungoma County TVET institutions. This study employed mixed methods research design grounded on interpretivist research paradigm and competency theoretical framework. The study adhered to ethical research principles of anonymity and informed consent. The study filled the knowledge gap by offering empirical findings on the perceptions of ICT trainers towards CBET implementation in ICT courses and student employability skills. Data was collected from 41 respondents using structured questionnaires and semi structured interviews from 32 purposely sampled ICT trainers and 9 ICT Heads of Departments correspondingly in 9 TVET institutions in Bungoma County. Descriptive and inferential statistics and qualitative thematic analysis were used to analyze quantitative and qualitative data respectively. The results of the study reveal that implementation strategies (β\u0026thinsp;=\u0026thinsp;0.438, p\u0026thinsp;=\u0026thinsp;0.014) and the characteristics of the trainer (β\u0026thinsp;=\u0026thinsp;0.331, p\u0026thinsp;=\u0026thinsp;0.048) have a significant impact on the integration of CBET in ICT courses. The results pointed to the significant knowledge gap in emerging areas like Artificial Intelligence, Data science, Machine learning and Full-stack development. The most notable challenges were lack of adequate resources, evolving ICT field and the insufficient time dedicated to practical training. The study suggested that TVET institutions should incorporate Problem-based learning practices and enhance human resources through integrating emerging areas in ICT courses and addressing identified challenges to ensure CBET effectively enhances career adaptability and employability of ICT students in the contemporary dynamic labor market.\u003c/p\u003e","manuscriptTitle":"Trainers’ Perspectives on CBET Implementation in Enhancing ICT Employability Skills for Students in Bungoma County TVET Institutions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-30 05:52:54","doi":"10.21203/rs.3.rs-9554893/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e2f8e035-2d3a-4131-b983-10334c60e5b6","owner":[],"postedDate":"April 30th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-04-30T10:58:17+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-30T11:10:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-30 05:52:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9554893","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9554893","identity":"rs-9554893","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}