The Moderating Role of Training Program Design in the Relationship Between Technological Changes and Academic Performance of Imaging Equipment Technology Students at KMTC Rift Valley Region, Kenya
Main Article Content
Keywords
Technological Changes, training program design, academic performance, imaging equipment technology, KMTC Rift Valley Region
Abstract
Technological advancements in imaging equipment have transformed medical training worldwide, yet many students in Kenya struggle to adapt to these changes, resulting in poor academic performance in Imaging Equipment Technology (IET) courses at Kenya Medical Training College (KMTC) in Kenya. Despite significant investment in new technologies, gaps in training program design and limited access to modern equipment have contributed to suboptimal student outcomes. This study aimed to examine the moderating role of training program design on the relationship between technological changes and the academic performance of IET students. The study was anchored on the Technology Acceptance Model (TAM) and Goal Theory. A descriptive cross-sectional research design was adopted. The target population comprised 105 final-year IET students, 27 faculty members, and 4 departmental administrators across four KMTC campuses. A sample of 80 students was selected using proportionate stratified random sampling, while all faculty and administrators were included through a census due to their small numbers. Data were collected using structured questionnaires, pretested through a pilot study for content validity and reliability (Cronbach’s alpha = 0.82). Descriptive analysis showed that students perceived limited access to modern imaging technologies and inadequate exposure to AI-driven equipment as major contributors to poor performance (overall mean = 3.61). Pearson correlation indicated a strong positive relationship between technological changes and student performance (r = 0.487, p < 0.01), while training program design showed a weaker but significant positive relationship (r = 0.156, p < 0.05). Multiple regression analysis before moderation revealed that technological changes significantly predicted academic performance (β = 0.479, p < 0.05; R² = 0.24). Training program design was found to positively moderate this relationship, showing a significant effect (β = 0.406, p = 0.018) demonstrating that training program design strengthened the relationship between technological changes and academic performance. The study concluded that technological changes affect IET student performance, and well-structured training programs enhance students’ capacity to adapt to these changes. It is recommended that KMTC improve access to modern imaging technologies, adopt blended and problem-based learning approaches, and strengthen faculty capacity to optimize students’ academic performance.
References
Alos, N., Alkhorayef, M., Al-Senan, R., Sulieman, A., & Bradley, D. (2015). Imaging equipment technology: Trends in pediatric and adult diagnostic modalities. Radiology Technology Journal, 44(2), 112–120.
Antwi, W. K., Akudjedu, T. N., & Botwe, B. O. (2021). Artificial intelligence in medical imaging practice in Africa: A qualitative survey of radiographers’ perspectives. Radiography, 27(3), 826-832. https://doi.org/10.1016/j.radi.2021.01.002
Atalay, M. K., Baird, G. L., Stib, M. T., George, P., Oueidat, K., & Cronan, J. J. (2022). The impact of emerging technologies on residency selection by medical students in 2017 and 2021, with a focus on diagnostic radiology. Academic Radiology, 29(10), 1576-1585.
Autor, D. H., Dorn, D., & Hanson, G. H. (2013). The geography of trade and technology shocks in the United States. American Economic Review, 103(3), 220-225.
Basser, P. (2022). Detection of stroke by portable, low-field MRI: a milestone in medical imaging. Science Advances, 8(16), eabp9307. https://doi.org/10.1126/sciadv.abp9307
Bercovich, E., & Javitt, M. C. (2018). Medical imaging: from roentgen to the digital revolution, and beyond. Rambam Maimonides Medical Journal, 9(4), e0034.
Byl, J. L., Sholler, R., Gosnell, J. M., Samuel, B. P., & Vettukattil, J. J. (2020). Moving beyond two-dimensional screens to interactive three-dimensional visualization in congenital heart disease. The international journal of cardiovascular imaging, 36(8), 1567-1573.
Cascio, W. F., & Montealegre, R. (2016). How technology is changing work and organizations. Annual Review of Organizational Psychology and Organizational Behavior, 3, 349-375.
Chen, X., Wang, X., Zhang, K., Fung, K. M., Thai, T. C., Moore, K., ... & Qiu, Y. (2022). Recent advances and clinical applications of deep learning in medical image analysis. Medical Image Analysis, 79, 102444.
Chuenjitwongsa, S., Oliver, R. G., & Bullock, A. D. (2018). Competence, competency‐based education, and undergraduate dental education: a discussion paper. European Journal of Dental Education, 22(1), 1-8.
Coakley, S., Young, R., Moore, N., England, A., O'Mahony, A., O'Connor, O. J., ... & McEntee, M. F. (2022). Radiographers’ knowledge, attitudes and expectations of artificial intelligence in medical imaging. Radiography, 28(4), 943-948.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Durán-Guerrero, J. A., Ulloa-Guerrero, L. H., & Salazar-Díaz, L. C. (2019). Blended learning: An effective methodology for teaching radiology to medical students. Revista de la Facultad de Medicina, 67(2), 273-277. https://doi.org/10.15446/revfacmed.v67n2.68045
Feinberg, D. A., & Setsompop, K. (2018). Ultra-fast MRI of the human brain with simultaneous multi-slice imaging. Journal of Magnetic Resonance, 291, 24-33. https://doi.org/10.1016/j.jmr.2018.04.002
General Medical Council. (2017). Excellence by design: Standards for postgraduate curricula. https://www.gmc-uk.org/education/standards-guidance-and-curricula/standards-and-outcomes/excellence-by-design
Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572-2593. https://doi.org/10.1111/bjet.12815
Grealish, L. (2016). Realizing the potential of clinical education to support nursing students’ learning. Nurse Education in Practice, 16(1), 213–214.
Herrman, J. W. (2019). Creative teaching strategies for the nurse educator (3rd ed.). F.A. Davis.
Horbach, S. P., & Halffman, W. (2018). The changing forms and expectations of peer review. Research Integrity and Peer Review, 3(1), 1-15.
Kurowecki, D., Lee, S. Y., Monteiro, S., & Finlay, K. (2021). Resident physicians' perceptions of diagnostic radiology and the declining interest in the specialty. Academic Radiology, 28(2), 261-270.
Linaker, K. L. (2015). Pedagogical approaches to diagnostic imaging education: A narrative review of the literature. Journal of Chiropractic Humanities, 22(1), 9-16. https://doi.org/10.1016/j.echu.2015.09.002
Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation: A 35-year odyssey. American Psychologist, 57(9), 705.
Locke, E. A., & Latham, G. P. (2013). Goal setting theory: The current state. In New developments in goal setting and task performance (pp. 623-630). Routledge.
Low, E. Z., O’Sullivan, N. J., Sharma, V., Drumm, B., & Murphy, M. C. (2022). Assessing medical students’ perception and educational experience during COVID-19 pandemic. Irish Journal of Medical Science, 191(2), 577-585. https://doi.org/10.1007/s11845-021-02640-5
Malliori, A., & Pallikarakis, N. (2022). Breast cancer detection using machine learning in digital mammography and breast tomosynthesis: A systematic review. Health and Technology, 12(5), 893-910.
Mary, M. (2008). Factors influencing physicians' acceptance of telemedicine technology. Journal of Medical Systems, 32(1), 12-25.
Mellacher, P., & Scheuer, T. (2021). Wage inequality, labor market polarization and skill-biased technological change: an evolutionary (agent-based) approach. Computational Economics, 58(2), 233-278.
Mojiri, M., & Moghimbeigi, A. (2011). Awareness and attitude of radiographers towards radiation protection. Journal of Paramedical Sciences, 2(4), 2–6.
Mpalanyi, M., Nalweyiso, I. D., & Mubuuke, A. G. (2020). Perceptions of radiography students toward problem-based learning almost two decades after its introduction at Makerere University, Uganda. Journal of Medical Imaging and Radiation Sciences, 51(4), 639-644. https://doi.org/10.1016/j.jmir.2020.08.014
Mwaniki, J. (2020). Socio-technical factors influencing clinical learning among radiography students in Kenya. International Journal of Health Sciences and Research, 10(8), 45-52.
Oberländer, M., Beinicke, A., & Bipp, T. (2020). Digital competencies: A review of the literature and
applications in the workplace. Computers & Education, 146, 103752.
O'Brien, J., Montgomery, A., O’Neill, A. C., Maher, N., & Kavanagh, E. C. (2018). Competency-based medical education in radiology: A guide for trainees and trainers. Clinical Radiology, 73(9), 765-772.
Ondari, G., Mwaniki, J., Ondari, D., & Mburu, S. (2019). Trial-and-error teaching: Challenges of clinical supervision in resource-limited training centers. African Journal of Health Professions Education, 11(3), 88-93.
Paulu, R. (2018). Radiology training programs in Sub-Saharan Africa: A review of the current landscape. West African Journal of Radiology, 25(1), 1-8.
Ramsden, W. H., & Roberts, T. E. (2015). Workplace based assessment in clinical radiology – Diverse practice and its implications. MedEdPublish, 4(2), 15. https://doi.org/10.15694/mep.2015.006.0015
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13-35. https://doi.org/10.1016/j.compedu.2018.09.009
Sousa, M. J., & Rocha, Á. (2019). Digital learning: Developing skills for digital transformation of organizations. Future Generation Computer Systems, 91, 327-334.
Tang, A., Tam, R., Cadrin-Chênevert, A., Guest, W., Chong, J., Barfett, J., ... & Canadian Association of Radiologists (CAR) Artificial Intelligence Working Group. (2018). Canadian Association of Radiologists white paper on artificial intelligence in radiology. Canadian Association of Radiologists Journal, 69(2), 120-135.
The Royal College of Radiologists. (2016). Specialty training curriculum for clinical radiology. https://www.rcr.ac.uk/sites/default/files/cr_curriculum-2016_final_15_november_2016_0.pdf
Van de Venter, R., Skelton, E., Matthew, J., Woznitza, N., Tarroni, G., Hirani, S. P., ... & Malamateniou, C. (2023). Artificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research: a pilot study. Insights into Imaging, 14(1), 1-13.
Vavasseur, A., Muscari, F., Meyrignac, O., Nodot, M., Dedouit, F., Revel-Mouroz, P., ... & Mokrane, F. Z. (2020). Blended learning of radiology improves medical students’ performance, satisfaction, and engagement. Insights into Imaging, 11(1), 1-12. https://doi.org/10.1186/s13244-020-00866-w
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
Wanjiku, J., Bell, G., & Wachira, B. (2018). The impact of refresher training on the performance of medical imaging technologists in Kenya. Journal of Radiology Nursing, 37(2), 125-131.
Webb, A. (2022). Introduction to biomedical imaging. John Wiley & Sons.
Yang, J. J., Li, J., Mulder, J., Wang, Y., Chen, S., Wu, H., ... & Pan, H. (2015). Emerging information technologies for enhanced healthcare. Computers in industry, 69, 3-11.
Zain, N. M., Fadil, N. F. M., & Hadi, A. A. (2018). Learning Management System: An experience and perception study from medical imaging lecturers and scholars in a private university. International Journal of Interactive Mobile Technologies, 12(7), 174-180. https://doi.org/10.3991/ijim.v12i7.9644
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Damaris Jepkorir Kiplagat , Department of Administration Medical Education, Moi University, P.O. Box 4606 – 30100, Eldoret, Kenya
Department of Administration Medical Education, Moi University, P.O. Box 4606 – 30100, Eldoret, Kenya
Franklin Boibanda, Department of Administration Medical Education, Moi University, P.O. Box 4606 – 30100, Eldoret, Kenya
Department of Administration Medical Education, Moi University, P.O. Box 4606 – 30100, Eldoret, Kenya
Simon Kang’ethe , Department of Medical Education, School of Medicine, Moi University, P.O. Box 4606 – 30100, Eldoret, Kenya
Department of Medical Education, School of Medicine, Moi University, P.O. Box 4606 – 30100, Eldoret, Kenya