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This study investigated the underlying factors that influence potential TVET teachers' decision to pursue a teaching career in order to recruit more teachers. The motivation of pre-service TVET graduates obtaining a postgraduate diploma in technical education was therefore investigated using the QUAN-QUAL approach to collect quantitative data via a researcher-created, self-administered questionnaire and qualitative data via written essays and narratives using the triangulation mixed method design. Participants were selected from two cohorts (N=78) of students enrolled in the various departments for the Postgraduate Diploma in Technical Education (PGDTE) program of the University of Nigeria. According to the quantitative analysis, excellent role models from previous teachers, the demanding nature of the job role, a willingness to impart relevant knowledge and skills, a willingness to assist financially disadvantaged students in gaining marketable job skills, and the country's presumed demand for TVET teachers were the primary motivators for pre-service teachers. The qualitative analysis found seven (7) themes, three of which were motivational: desire to assist students and community/sharing expertise; personal dream/calling/passion to teach; and the desire to financially aid students who are incapacitated. However, the gender aspect revealed that male and female pre-service TVET teachers showed significant differences in their altruistic and intrinsic impulses. Following that, the two sets of data were compared and investigated. The ramifications of the findings were then examined, as well as their significance in enhancing hiring measures through setting of standards for tech-voc education programs to improve on the status of TVET teachers to attract quality graduates of technical education programs who can teach as TVET teachers before and after completing their programs.

This study investigated the underlying factors that influences pre-service Nigerian (technical vocational education and training, TVET) teachers’ decision to pursue a teaching career which aids in recruiting more teachers. Preservice TVET teachers are the teachers who are being prepared to teach in a vocation requiring technical skills. The motivation of these pre-service TVET teachers obtaining a postgraduate diploma in technical education was investigated using the quantitative research design approach to collect data via a researcher-created, self-administered questionnaire. Participants were selected from two cohorts (N = 78) of students enrolled in the various departments for the Postgraduate Diploma in Technical Education (PGDTE) program of the University of Nigeria. According to the quantitative analysis, excellent role models from previous teachers, the demanding nature of the job role, a willingness to impart relevant knowledge and skills, a willingness to assist financially disadvantaged students in gaining marketable job skills, and the country’s presumed demand for TVET teachers were the primary motivators for pre-service teachers. However, the gender aspect revealed that male and female pre-service TVET teachers showed significant differences in their altruistic and intrinsic impulses when the non-parametric Mann-Whitney U tests were utilized to analyze extracted data on gender. The ramifications of the findings were then examined, as well as their significance in enhancing hiring measures through setting of standards for technical and vocational education programs in the universities to improve on the status of pre-service TVET teachers to attract quality graduates of technical education programs who can teach as TVET teachers before and after completing their programs.

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Many effective quality systems to maintain the robots’ autonomous task expansion process in construction industries for various applications over the years have yet to be well established. This study, therefore, presents a simple deep/neural network algorithm to diverse robotics tasks on building construction—bricklaying, grasping, cutting materials, and aerial robot obstacle avoidance and highlight the strengths of these algorithms in real-world robotics applications in building sites. Our findings revealed that the amount of tasks robots encountered in real-world environments is extremely challenging for existing robotic control algorithms to handle. Also, our algorithm when evaluated against other conventional learning algorithms can be a more powerful tool with the capacity to learn features directly from data, making it an excellent choice for such robotics applications in building construction. In other words, our algorithm can teach robots the ability to “work,” “think,” “know,” and “understand” their surroundings. It can also improve customer satisfaction, speed up the building process, and improve the productivity of building development teams. This chapter, however, contributes to classifications of autonomous robotics application development in construction literature. Although the problem addressed in this chapter is based on building construction, the algorithms presented are designed to be generalizable to related tasks.