An online engineering education framework based on the predictors of adaptability and fuzzy inference system

Online engineering education utilizes the internet and information communication technologies as media of learning. Unfortunately, there is a dearth of frameworks developed that address the issues of online learning based on predictors of adaptability of engineering students and faculty members. This paper aims to identify the issues of online learning, establish the causal-effect relationship between socio-demographic factors and adaptability, and propose an Artificial Intelligence-driven framework using mixed methods. The author engaged 886 engineering students and 61 faculty members from the Technological University of the Philippines in Manila and Taguig Campuses in the Philippines. Based on test results, fewer disruptions, more ICT devices, lower monthly internet bills, faster internet speed, and age significantly predict the students’ adaptability to online learning. Meanwhile, faculty members with faster internet speed and higher monthly income have significantly higher adaptability to online learning. The author proposed a framework using a Fuzzy Inference System, which can be used for an accurate and timely decision-making process. Further test results confirmed that the framework is consistently accurate and significantly faster than the conventional method Hence, the proposed framework is a viable decision-making tool for large datasets and complex use cases in online engineering education.