The Development of an Interactive Video Aided Learning Module for Milling Machine Subject - Mohd Redzuan, Muhammad Hisyamuddin; Rohanai, Reyanhealme; Mohamed Salleh, Muhammad; Ghazali, Mohamad Hafiz; Abdul Rahman, Shahrul Nizam (Research and Innovation in Technical and Vocational Education and Training, 2023)
The development of a comprehensive learning module has been one of the important learning materials to enhance student understanding during teaching and learning sessions. However, there was some evidence showing that technical and vocational students specifically faced difficulties catching up with the technical topics to master in the class, and the learning module seems to have improved. Thus, this study focuses on developing an interactive video-aided learning module for one of the technical subjects which are milling machine subjects to facilitate learning for milling machine subjects among technical and vocational students. The researcher used the ADDIE model as a guidance model in developing this learning module. There are five phases involved in the development of this learning module, namely, the analysis phase, design phase, development phase, implementation phase, and evaluation phase. In this study, the researcher used an expert validation form to obtain feedback on the suitability of interactive video-aided learning modules in terms of content, format, visual, and interactive video elements. A total of three experts consisting of vocational college lecturers in the field of industrial machining and university lecturers in the field of multimedia have been selected as the experts in evaluating the module validation. The data obtained from the three expert validations’ will be analyzed using SPSS software. The result shows that the mean result for the entire module aspect analysis obtained a high mean value (M = 4.80) indicating strong positive agreement among the experts. In conclusion, this Interactive Video Aided Learning Module for the Milling Machine Subject can be used as a reference and guide in assisting better experience of the milling machine learning environment.