DOI: 10.11648/j.ijvetr.20261202.14 ISSN: 2469-8199

Quality Assurance in Drone Maintenance Training for Workforce Development in Elect-Elect Engineering Technology in Polytechnics in South-South Nigeria

Osakwe Adonis, Igwebuike Dimkpa, Caroline Ogbowu, Gerald Ezeka, Oputa Richard
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This study examined quality assurance in drone maintenance training as a foundation for sustainable workforce development in Electrical/Electronic Engineering Technology education in polytechnics in South-South Nigeria. The study adopted a descriptive research design. A purposive sampling technique was used to select 253 respondents, comprising 181 Electrical/Electronic Engineering Technology lecturers from eleven polytechnics and 72 industrial drone maintenance officers from drone-related industries in South-South Nigeria. Data were collected using a structured questionnaire titled Drone Maintenance Module Questionnaire (DMMQ). The instrument was subjected to face and content validation by three experts, while its reliability was established using Cronbach's alpha, which yielded an average reliability coefficient of 0.89. Data were analyzed using mean, standard deviation, and independent-samples t-test. A criterion mean of 3.50 was used to determine acceptance of the questionnaire items, while the null hypotheses were tested at the 0.05 level of significance. The findings showed that the quality assurance components were required for effective drone maintenance training, with grand mean scores of 4.00 for Electrical/Electronic Engineering Technology lecturers and 4.04 for industrial drone maintenance officers. The identified drone maintenance competencies also received grand mean scores of 4.07 and 4.05, respectively. The independent-samples t-test showed no significant difference between the two respondent groups for quality assurance components (t = .83, p = .408) and drone maintenance competencies (t = .40, p = .691). The study concludes that effective drone maintenance training requires a comprehensive quality assurance system encompassing relevant curriculum content, competent instructors, functional workshops, adequate tools and equipment, practical and competency-based training, appropriate assessment, industry collaboration, safety practices, regulatory compliance, and continuous programme improvement. It recommends phased and collaborative investment in training drones and diagnostic equipment, alongside stronger quality assurance mechanisms and industry linkages, to support employability, entrepreneurship, adaptability, and sustainable workforce development.

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