VLC-UM: A Novel Virtual Laboratory using Machine Learning and Artificial Intelligence

Industry 4.0 technological development has led to the overall implementation of these technologies in all fields, both at school and at universities. One of the development is the virtual laboratory. Several studies have developed virtual laboratories in the form of learning media, materials, and models. However, no one has developed a smart virtual laboratory (VL) that can follow the capabilities of its personalized users. This study intends to develop a smart VL that is implemented in student practices of electric machines. Students can use this VL according to the achievement of their theoretical abilities of electric machines' practicum. This smart VL is called VLC-UM. VLC-UM uses an artificial intelligence (AI) algorithm (backward chaining) to determine the ability of students who will take practicum material. Whereas to determine the practicum modules that will be accessed and used by students, the algorithm used is forward chaining. Machine learning is used to classify the learning materials according to the students' ability. The findings from the testing results on the Virtual Laboratory state that the accuracy of determining the appropriate module for users is 80% and the determination of the appropriate category based on the user's ability is 90%.