Engineering design learning for high school and college first-year students in a STEM battlebot design project

Abstract

Background There is a worldwide trend to include engineering design in high school curricula as a bridge course to higher-level STEM education and to increase high school students’ interest in STEM fields. This study used a battlebot design curriculum to compare engineering design learning between high school and college first-year students and then proposed suggestions for curriculum planning that promoted the continuity of learning between different levels of engineering design education. Results This study used the creative product analysis matrix (CPAM) and lag sequential analysis (LSA) to explore the possible similarities and differences between the two groups’ understanding of engineering design. The results show that college first-year students were significantly better than high school students in CPAM, but the two groups were similar in their reflections on engineering design behaviors, indicating that the noncumulative learning results must be taken seriously. Conclusions Higher-order engineering design thinking skills take a longer time to develop than technical skills. For both high school and college first-year students, it is important to enhance their higher-order engineering design thinking skills to promote higher engineering design performance. Moreover, high school students could be provided with convenient processing tools and easy-to-use, hands-on techniques to increase their technical skills. Educators from institutions of higher education and K-12 schools should work together to develop pedagogical models that provide rigorous, well-rounded education and outstanding engineering design instructions to most effectively cultivate STEM talent.

Towards a Framework for Performance Management and Machine Learning in a Higher Education Institution

“…There are input, hidden and output layers. Equipped with the ability to grasp complex nonlinear relationships in big data sets, MLPs work well for problems of classification, regression, and go beyond the scope of their application to solve pattern recognition etc [10].…”