A study on measuring self-efficacy in engineering modeling and design courses

Abstract

Preparing future engineers to model and design engineering systems is one of the primary objectives of engineering education. Rapid advances in technologies such as high performance computing, rapid prototyping through additive manufacturing, robotics, automation, nanotechnology and instrumentation have increased the complexity of engineering systems. The engineering design process involves knowledge of multiple domains of engineering and collaborative work among multi-disciplinary teams. The design process is also complicated by the safety, practicality and cost constraints. In light of these challenges, the engineering education needs to maintain its focus on principles of engineering design that can effectively prepare engineering graduates to meet the challenges posed by rapid technological growth in engineering and manufacturing technologies. The effectiveness of engineering education in modeling and design courses, traditionally, is measured through quizzes, exams and course projects that are aimed at measuring level of developed skills. For engineering students to be successful, it is not only essential that they possess required skills and competencies but should also have the belief that they will be able to perform with those skills. This self-belief in one's ability to perform assigned tasks for attainment of a specified objective has been described as "self-efficacy" construct in terms of Bandura's Social Cognitive Theory. An important research question is how to measure the developed self-confidence of engineering students in modeling and design courses. To address this question, present study proposes development of a self-efficacy measure. The proposed measure has been used to collect pre and post course data on self-efficacy through student surveys in engineering modeling and design courses at Arkansas Tech University. The collected data is analyzed with Statistical Package for the Social Sciences (SPSS). The data analysis involves computation of correlations and reliability coefficients, t-tests and analysis of variance (ANOVA).