Abstract: Introductory courses in computer science typically do not teach students much about computer hardware. Many courses in robotics have a tight integration of software and hardware leading to high performance at the cost of lower flexibility in the functionality of the programs. The emergence of low cost processors and rapid prototyping electronic platforms allows for a rectification of this situation. Students can be introduced to both hardware and software for parallel scientific computing by classroom co-design using simple low power microcontrollers such as the Atmel AVR used in Arduino. Experiences developing such a platform for calculating Pi using a Monte Carlo method, doing matrix multiplication and implementing a lattice Boltzmann solver are discussed.

Abstract: Enterprise Resource Planning Systems (ERPS) have continually extended their scope over the last decades. The evolution has currently reached a stage where ERPS support the entire value chain of an enterprise. This study deals with the rise of a new era, where ERPS is transformed into so-called interconnected Enterprise Systems (iES), which have a strong outside-orientation and provide a networked ecosystem open to human and technological actors (e.g. social media, Internet of Things). Higher education institutions need to prepare their students to understand the shift and to transfer the implications to today’s business world. Based on literature and applied learning scenarios the study shows existing approaches to the use of ERPS in teaching and elaborates whether and how they can still be used. In addition, implications are outlined and the necessary changes towards new teaching approaches for iES are proposed.

Abstract: To prepare future scientists, engineers, and technicians to harness big data and solve complex problems, undergraduates in STEM (Science, Technology, Engineering, and Mathematics) need to become competent in conducting basic data-enabled research, interpreting data, and applying findings across multiple disciplinary contexts. Integrating Computational and Data Science and Engineering (CDSE) coursework into the undergraduate curriculum that embeds authentic research experiences and follows a Course-based Under-graduate Research Experience (CURE) pedagogical model can address these needs. Collaborative project-based learning (CPBL) is identified as a practical approach to implement CURE and build student proficiency in these vital areas. This paper addresses the collaborative problem-solving environment and model-based learning assessment for two blended learning CDSE courses that we delivered to the students across multiple universities.