Basis for Deep Learning Model of Discrete Event System for Information Technology Course Design

Abstract

The development of technologies is changing the traditional concept of teaching and learning in classrooms. In particular, information technology (IT) courses are rapidly adopting such changes as IT education's purpose is to cultivate students' capability to develop technologies by experiencing them in classrooms. In IT courses, deep learning has been used to improve teaching and learning ability through the use of sensing technology and related hardware/ software. In teaching and learning, asynchronous, concurrent, and stochastic events occur because teachers and students have their protocols and behaviors in the related activities. Therefore, the design and teaching of courses should be based on a discrete event system that deals with discrete events. On the basis of this concept, we propose a deep learning model with the Petri net method to establish a logical modular system for IT course design and teaching. Because Petri nets are useful in simulation and analysis for the system modeling of asynchronous, concurrent, and stochastic events, we investigate how to use a Petri net to establish a deep learning model to develop and evaluate the curriculum of IT courses. The results of this study will contribute to building an efficient IT learning system that comprises sensing technology, information transmission, information processing, and feedback, which will require collaboration with pedagogical experts in the future.