Model Based Approach To Understand And Develop Technology Enhanced System For Engineering Education¶
Abstract¶
Since year 1993, through the biyearly joint report called Science and Engineering Indicators, National Science Foundation (NSF) is indicating a constant reduction in the overall engineering enrollment and the number of successful graduates for various engineering majors. According to Becker (2010), Grau-Valldosera & Minguillón (2011) and Wallace & Mutooni (1997), there is a direct correlation between the traditional learning mechanism and abstract nature of the engineering concepts to the dropouts.
Therefore in order to encourage the student engagement in the overall learning process, we have developed a mobile-based in-class supplemental learning assistant. In the testing phase, students from multiple strategically selected groups were taught with and without the technology assistant in controlled environment. The results are suggesting that the mobile-based learning assistant is useful for the initial acquisition of the learning concepts in mathematics and physics for the students. Structural equation model analysis of the qualitative data collection also suggests that the designed model to predict the behavioral intention of the test participants is accurate and it predicts the behavioral intention of the test participants with fair amount of accuracy. This indicates that the response data collected from the test participants is reliable as well as valid.
Bio
Kushal Abhyankar is a PhD candidate in the Interactions Design and Modeling Lab at Wright State University. Previous to the enrollment in PhD program, he has MS degree in Biomedical Engineering and MS degree in Human Factors Engineering, both from Wright State University.