Abstract:
A vast majority of Science, Technology, Engineering, Mathematics (STEM) courses and pedagogical frameworks concentrate on teaching the fundamental concepts and theoretica...Show MoreMetadata
Abstract:
A vast majority of Science, Technology, Engineering, Mathematics (STEM) courses and pedagogical frameworks concentrate on teaching the fundamental concepts and theoretical underpinnings of the tools related to the subject. While this aspect is important, we recognize that the teaching methods in a majority of the STEM courses today are broken; there is a major discrepancy between the skills and mindsets in technical classes and the ones that are useful to solve actual problems in “the real world”. Therefore, we suggest a new teaching framework called Data-X where entrepreneurial teaching methods developed in the Berkeley Method of Entrepreneurship are applied to advanced technical topics. Through inductive learning and by practicing story creation, stakeholder generation, adaptation, ideation, innovation processes, and by having a diverse mix of students being coached by a network of expert advisors, this highly applied teaching method empowers students to pursue and find solutions to open-ended projects and problems. The Data-X framework has been implemented and tested for three semesters in a UC Berkeley course called Applied Data Science for Venture Applications. In the class the students pick up, become comfortable, and utilize state-of-the-art tools in Data Science, Machine Learning, and Artificial Intelligence. The results, feedback, and testimonials we have received upon offering the class have been overwhelmingly positive, and we propose that the ideas and concepts behind Data-X can help fix many problems in modern STEM education.
Published in: 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC)
Date of Conference: 17-20 June 2018
Date Added to IEEE Xplore: 16 August 2018
ISBN Information: