Abstract: Prof. Angela Shiflet in computer science and mathematics and Prof. George Shiflet in biology are Fulbright Specialists. In January, 2015, they participated in a three-week collaborative project at University “Magna Græcia” of Catanzaro in Italy, in the Department of Medical and Surgical Sciences, hosted by Prof. Mario Cannataro. While there, the three along with Prof. Pietro Hiram Guzzi started a project to develop educational modules on high-performance-computing bioinformatics algorithms. Drs. Cannataro and Guzzi have written a book, Data Management of Protein Interaction Networks (Wiley, 2011), and regularly teach bioinformatics and HPC. Upon returning to the United States, the Drs. Shiflet applied to have undergraduates Daniel Couch and Dmitriy Kaplun be Blue Waters Interns in subsequent years, working on the project. The NSF-funded Blue Waters Project, which provides a stipend for the intern, supports “experiences involving the application of high-performance computing to problems in the sciences, engineering, or mathematics” (http://computationalscience.org/bwsip/). Each student participated in a two-week workshop at the National Center for Supercomputing Applications (NCSA) facilities on the University of Illinois Urbana-Champaign campus. In the 2016-2017 year of the project, Kaplun wrote sequential and HPC programs and performed timings to accompany a pair of educational modules, “What Are the Chances?--Hidden Markov Models” and “Viterbi Hidden Markov Models,” available at http://www.wofford.edu/ecs/. Hidden Markov Models (HMM) are used in numerous applications that involve recognition, such as image tracking in sports, speech or facial recognition, handwriting analysis, language translation, cryptanalysis, predicting protein structure, aligning multiple nucleotide sequences, and discovering locations of genes. After an introductory vignette, the first module explains the mathematics behind HMM, particularly probability, and develops a sequential HMM forward algorithm to determine the likelihood of a hidden sequence of states. After motivating the need for HPC, the module also discusses a parallel forward algorithm, its implementation, and timings with speedups, as developed by the intern. To aid students, the module contains sixteen Quick Review Questions, many with multiple parts; three exercises; and five projects. Using similar pedagogical features, the second module discusses the Viterbi algorithm to solve another type of HMM problem, decoding. Completed sequential and parallel C with OpenMP programs are available upon request by instructors. Students and faculty members in a bioinformatics course at University “Magna Græcia” of Catanzaro used the materials, which Ph.D. student Chiara Zucco assisted in incorporating and evaluating.

Abstract: The undergraduate course in Computational Engineering at Federal University of Juiz de Fora, Brazil, was created in 2008 as a joint initiative of two distinct departments in the University, Computer Science, located in the Exact Science Institute, and Applied and Computational Mechanics, located in the School of Engineering. First freshmen began in 2009 and graduated in 2014. This work presents the curriculum structure of this pioneering full bachelor's degree in Computational Engineering in Brazil.

Abstract: Computational thinking is a much-used concept in the computer science education. Here we examine the concept from the viewpoint of the extended cognition hypothesis. The analysis reveals that the extent of the concept is limited by its strong historical roots in computer science and software engineering. According to the extended cognition hypothesis, there is no meaningful distinction between human cognitive functions and the technology. This standpoint promotes a broader interpretation of the human-technology interaction. Human cognitive processes spontaneously adapt available technology enhanced skills when technology is used in cognitively relevant levels and modalities. A new concept technology synchronized thinking is presented to denote this conclusion. More diverse and practical approach is suggested for the computer science education.

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.