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1. International Perspectives on Intersecting Engineering's Grand Challenges and the UN's Sustainable Development Goals

   White, C. K. ; Tull, R. G. ; Zastavker, Y. V. ; Sigamoney, R. ( June 2018 , ASEE Annual Conference proceedings)

   Growing complexity and magnitude of the challenges facing humanity require new ways of understanding and operationalizing solutions for more healthy, sustainable, secure, and joyful living. Developed almost contemporaneously but separately, the National Academy of Engineering's 14 Grand Challenges (GCs) and United Nation’s 17 Sustainable Development Goals (GCs) describe and call for solutions to these challenges. During the 2017 meetings for the UNESCO Kick-off for Engineering Report II in Beijing, the Global Grand Challenges Summit in Washington, DC, and the World Engineering Education Forum (WEEF) in Malaysia, we expanded our work to include international perspectives on ways that the GCs and SDGs could be more strongly connected. Within this context we ask, "How can educators integrate best practices to nurture and support development of globally competent students who will reach the goals as the Engineers of 2020?" and "How can connectivity and alignment of curricula to the GCs and SDGs foster students’ development?" Conclusions from the UNESCO’s meeting were that educators and stakeholders still have much to do with respect to sharing the 17 SDGs with engineering audiences around the world. This conclusion was reiterated at WEEF when an informal poll among participants from around the world revealed that knowledge ofmore »both the GCs and the SDGs was not as wide-spread as we had initially assumed. There were several engineering educators who were learning about both of these constructs for the very first time. This led to concerns posed by students participating in the Malaysia conference as part of the Student Platform for Engineering Education Development (World SPEED). The student teams from India, Colombia, Brazil, and Korea acknowledged potential disadvantages associated with learning in the environments created by educators unequipped with knowledge of topics covered by the GCs, and the SDGs. The students were further concerned that their faculty and mentors would not be able to create educational environments that allow for development of intentional learning and conscientious projects associated the GCs and SDGs. The report here will discuss ways that the GCs and SDGs are driving international conversations about engineering curricula, diversity and inclusion, and partnerships for the goals.« less
2. Education innovation to meet Sustainable Development Goals (SDGs)

   Trotz, M. ( July 2019 , 12 th Natural Resource Management & Research Symposium “BELIZE, ‘THROUGH THE BOTTLENECK’”)

   Meeting the UN Sustainable Development Goals (SDGs) requires innovations in education to build key competencies in all learners. Learning objectives for SDGs identified by UNESCO like the “Integrated problem-solving competency,” if integrated properly with high school curriculum, can contribute sustainable development solutions for Belize. Additionally, the 3rd international conference of SIDS http://www.sids2014.org) under the theme, “The sustainable development of small island developing states through genuine and durable partnerships,” stressed investment in education and training, including through partnerships with migrants and diaspora communities, with “concrete, focused, forward-looking and action oriented programmes.” The Sagicor Visionaries Challenge, a sustainability challenge launched by the Caribbean Examinations Council (CXC), the Caribbean Science Foundation, and the Ministries of Education across 12 Caribbean countries in 2012, represented an example of such a partnership that fostered many key competencies now needed for meeting the SDGs. It asked secondary school students in the Caribbean to identify a challenge facing their school and or community, propose a sustainable and innovative solution, and show how that solution uses Science Technology Engineering and Mathematics (STEM) as well as got the support of the school community. For its inaugural year, teacher and student sensitization workshops were organized in each country. Teachers supervised themore »student projects with support from mentors who were either local or virtual, including many members of the Caribbean diaspora. 175 projects entered the competition, representing 900 students ranging in age from 11 to 19. Experience from the inaugural year, which saw Belize’s Bishop Martin Secondary emerge the regional challenge winner, demonstrated interest by young people of the Caribbean in many of the themes listed in the SIDS outcomes like climate change, sustainable energy, disaster risk reduction, sustainable oceans and seas, food security and nutrition, water and sanitation, sustainable transportation, sustainable consumption and production, and health and non-communicable diseases. Reflection on student projects from Belize from the 2013 challenge, as well as current examples of teacher led inquiry-based projects for CXC’s School Based Assessments (SBAs), offer multiple opportunities for ensuring reef to ridge sustainable development in Belize and the rest of the Caribbean.« less
3. Defining and Assessing Systems Thinking in Diverse Engineering Populations

   Mosyjowski, E.A. ; Daly, S.R. ; Lattuca, L. ( January 2019 , Proceedings of the American Society for Engineering Education Conference)

   Engineers are called to play an important role in addressing the complex problems of our global society, such as climate change and global health care. In order to adequately address these complex problems, engineers must be able to identify and incorporate into their decision making relevant aspects of systems in which their work is contextualized, a skill often referred to as systems thinking. However, within engineering, research on systems thinking tends to emphasize the ability to recognize potentially relevant constituent elements and parts of an engineering problem, rather than how these constituent elements and parts are embedded in broader economic, sociocultural, and temporal contexts and how all of these must inform decision making about problems and solutions. Additionally, some elements of systems thinking, such as an awareness of a particular sociocultural context or the coordination of work among members of a cross-disciplinary team, are not always recognized as core engineering skills, which alienates those whose strengths and passions are related to, for example, engineering systems that consider and impact social change. Studies show that women and minorities, groups underrepresented within engineering, are drawn to engineering in part for its potential to address important social issues. Emphasizing the importance of systemsmore »thinking and developing a more comprehensive definition of systems thinking that includes both constituent parts and contextual elements of a system will help students recognize the relevance and value of these other elements of engineering work and support full participation in engineering by a diverse group of students. We provide an overview of our study, in which we are examining systems thinking across a range of expertise to develop a scenario-based assessment tool that educators and researchers can use to evaluate engineering students’ systems thinking competence. Consistent with the aforementioned need to define and study systems thinking in a comprehensive, inclusive manner, we begin with a definition of systems thinking as a holistic approach to problem solving in which linkages and interactions of the immediate work with constituent parts, the larger sociocultural context, and potential impacts over time are identified and incorporated into decision making. In our study, we seek to address two key questions: 1) How do engineers of different levels of education and experience approach problems that require systems thinking? and 2) How do different types of life, educational, and work experiences relate to individuals’ demonstrated level of expertise in solving systems thinking problems? Our study is comprised of three phases. The first two phases include a semi-structured interview with engineering students and professionals about their experiences solving a problem requiring systems thinking and a think-aloud interview in which participants are asked to talk through how they would approach a given engineering scenario and later reflect on the experiences that inform their thinking. Data from these two phases will be used to develop a written assessment tool, which we will test by administering the written instrument to undergraduate and graduate engineering students in our third study phase. Our paper describes our study design and framing and includes preliminary findings from the first phase of our study.« less
4. The Deep Learning Epilepsy Detection Challenge: Design, Implementation, and Test of a New Crowd-Sourced AI Challenge Ecosystem

   Kiral, Isabell ; Roy, Subhrajit ; Mummert, Todd ; Braz, Alan ; Tsay, Jason ; Tang, Jianbin ; Asif, Umar ; Schaffter, Thomas ; Mehmet, Eren ; Picone, Joseph ; et al ( April 2019 , Challenges in Machine Learning Competitions for All (CiML))

   The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowd-sourced AIchallengeecosystem Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal Rosen-Zvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer† * These authors contributed equally to this work † Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com ◊ Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section J. Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmet is with the University of Illinois at Urbana-Champaign, USA. All other authors are with IBM Research in USA, Israel and Australia. Introduction This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert datamore »scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. Participation is even further restricted in the context of any challenge run on confidential use cases or with sensitive data. Recently, we designed and ran a deep learning challenge to crowd-source the development of an automated labelling system for brain recordings, aiming to advance epilepsy research. A focus of this challenge, run internally in IBM, was the development of a platform that lowers the barrier of entry and therefore mitigates the risk of excluding interested parties from participating. The challenge: enabling wide participation With the goal to run a challenge that mobilises the largest possible pool of participants from IBM (global), we designed a use case around previous work in epileptic seizure prediction [3]. In this “Deep Learning Epilepsy Detection Challenge”, participants were asked to develop an automatic labelling system to reduce the time a clinician would need to diagnose patients with epilepsy. Labelled training and blind validation data for the challenge were generously provided by Temple University Hospital (TUH) [4]. TUH also devised a novel scoring metric for the detection of seizures that was used as basis for algorithm evaluation [5]. In order to provide an experience with a low barrier of entry, we designed a generalisable challenge platform under the following principles: 1. No participant should need to have in-depth knowledge of the specific domain. (i.e. no participant should need to be a neuroscientist or epileptologist.) 2. No participant should need to be an expert data scientist. 3. No participant should need more than basic programming knowledge. (i.e. no participant should need to learn how to process fringe data formats and stream data efficiently.) 4. No participant should need to provide their own computing resources. In addition to the above, our platform should further • guide participants through the entire process from sign-up to model submission, • facilitate collaboration, and • provide instant feedback to the participants through data visualisation and intermediate online leaderboards. The platform The architecture of the platform that was designed and developed is shown in Figure 1. The entire system consists of a number of interacting components. (1) A web portal serves as the entry point to challenge participation, providing challenge information, such as timelines and challenge rules, and scientific background. The portal also facilitated the formation of teams and provided participants with an intermediate leaderboard of submitted results and a final leaderboard at the end of the challenge. (2) IBM Watson Studio [6] is the umbrella term for a number of services offered by IBM. Upon creation of a user account through the web portal, an IBM Watson Studio account was automatically created for each participant that allowed users access to IBM's Data Science Experience (DSX), the analytics engine Watson Machine Learning (WML), and IBM's Cloud Object Storage (COS) [7], all of which will be described in more detail in further sections. (3) The user interface and starter kit were hosted on IBM's Data Science Experience platform (DSX) and formed the main component for designing and testing models during the challenge. DSX allows for real-time collaboration on shared notebooks between team members. A starter kit in the form of a Python notebook, supporting the popular deep learning libraries TensorFLow [8] and PyTorch [9], was provided to all teams to guide them through the challenge process. Upon instantiation, the starter kit loaded necessary python libraries and custom functions for the invisible integration with COS and WML. In dedicated spots in the notebook, participants could write custom pre-processing code, machine learning models, and post-processing algorithms. The starter kit provided instant feedback about participants' custom routines through data visualisations. Using the notebook only, teams were able to run the code on WML, making use of a compute cluster of IBM's resources. The starter kit also enabled submission of the final code to a data storage to which only the challenge team had access. (4) Watson Machine Learning provided access to shared compute resources (GPUs). Code was bundled up automatically in the starter kit and deployed to and run on WML. WML in turn had access to shared storage from which it requested recorded data and to which it stored the participant's code and trained models. (5) IBM's Cloud Object Storage held the data for this challenge. Using the starter kit, participants could investigate their results as well as data samples in order to better design custom algorithms. (6) Utility Functions were loaded into the starter kit at instantiation. This set of functions included code to pre-process data into a more common format, to optimise streaming through the use of the NutsFlow and NutsML libraries [10], and to provide seamless access to the all IBM services used. Not captured in the diagram is the final code evaluation, which was conducted in an automated way as soon as code was submitted though the starter kit, minimising the burden on the challenge organising team. Figure 1: High-level architecture of the challenge platform Measuring success The competitive phase of the "Deep Learning Epilepsy Detection Challenge" ran for 6 months. Twenty-five teams, with a total number of 87 scientists and software engineers from 14 global locations participated. All participants made use of the starter kit we provided and ran algorithms on IBM's infrastructure WML. Seven teams persisted until the end of the challenge and submitted final solutions. The best performing solutions reached seizure detection performances which allow to reduce hundred-fold the time eliptologists need to annotate continuous EEG recordings. Thus, we expect the developed algorithms to aid in the diagnosis of epilepsy by significantly shortening manual labelling time. Detailed results are currently in preparation for publication. Equally important to solving the scientific challenge, however, was to understand whether we managed to encourage participation from non-expert data scientists. Figure 2: Primary occupation as reported by challenge participants Out of the 40 participants for whom we have occupational information, 23 reported Data Science or AI as their main job description, 11 reported being a Software Engineer, and 2 people had expertise in Neuroscience. Figure 2 shows that participants had a variety of specialisations, including some that are in no way related to data science, software engineering, or neuroscience. No participant had deep knowledge and experience in data science, software engineering and neuroscience. Conclusion Given the growing complexity of data science problems and increasing dataset sizes, in order to solve these problems, it is imperative to enable collaboration between people with differences in expertise with a focus on inclusiveness and having a low barrier of entry. We designed, implemented, and tested a challenge platform to address exactly this. Using our platform, we ran a deep-learning challenge for epileptic seizure detection. 87 IBM employees from several business units including but not limited to IBM Research with a variety of skills, including sales and design, participated in this highly technical challenge.« less
5. Civil Engineering Students’ Beliefs about the Technical and Social Implications of Global Warming and When Global Warming Will Impact Them Personally and Others

   https://doi.org/10.18260/1-2--34285  

   Katz, Andrew ; Shealy, Tripp ; Godwin, Allison ( January 2020 , American Society for Engineering Education Annual Conference & Exposition)

   The United Nations recognizes reducing the effects of global warming as a Sustainable Development Goal (SDG) (#13). This goal is interconnected and critical to improving health and education, reducing inequality, and spurring economic growth globally. Civil engineers will play a vital role in meeting this goal. To understand how civil engineering students perceive global warming, we surveyed a national sample of civil engineering students in their final semester of college (n = 524). We asked them (a) if they recognize both the technical and social issues associated with global warming and (b) when they believe global warming will start to have a severe effect on themselves, others, and the planet. Civil engineering students are significantly more likely to recognize the technical issues associated with global warming than social issues. In particular, the majority of students understand global warming is an immediate issue for the environment, engineering, health, and science, but less than half recognize global warming presents social justice, poverty, and national security issues. Moreover, civil engineering students hold an inverse relationship between spatial distance and the timing of the effects of global warming. The majority of students believe global warming is currently having a severe impact on plant andmore »animal species, the environment, people in developing countries, and the world's poor but do not recognize themselves in this group. Instead, civil engineering students predominantly believe the effects of global warming will start to have a serious impact on themselves, their family, and people in their community in 25 to 50 years. These results are troubling because if those beliefs translate into students waiting to address climate change for another two to five decades locks in more emissions and increases the chance of future and more severe global humanitarian crises. Educational interventions are needed to change these perspectives about time and impact.« less