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1. Developing a flexible learning activity on biodiversity and spatial scale concepts using open‐access vegetation datasets from the National Ecological Observatory Network

   https://doi.org/10.1002/ece3.7385  

   Styers, Diane M. ; Schafer, Jennifer L. ; Kolozsvary, Mary Beth ; Brubaker, Kristen M. ; Scanga, Sara E. ; Anderson, Laurel J. ; Mitchell, Jessica J. ; Barnett, David ( March 2021 , Ecology and Evolution)

   Biodiversity is a complex, yet essential, concept for undergraduate students in ecology and other natural sciences to grasp. As beginner scientists, students must learn to recognize, describe, and interpret patterns of biodiversity across various spatial scales and understand their relationships with ecological processes and human influences. It is also increasingly important for undergraduate programs in ecology and related disciplines to provide students with experiences working with large ecological datasets to develop students’ data science skills and their ability to consider how ecological processes that operate at broader spatial scales (macroscale) affect local ecosystems. To support the goals of improving student understanding of macroscale ecology and biodiversity at multiple spatial scales, we formed an interdisciplinary team that included grant personnel, scientists, and faculty from ecology and spatial sciences to design a flexible learning activity to teach macroscale biodiversity concepts using large datasets from the National Ecological Observatory Network (NEON). We piloted this learning activity in six courses enrolling a total of 109 students, ranging from midlevel ecology and GIS/remote sensing courses, to upper‐level conservation biology. Using our classroom experiences and a pre/postassessment framework, we evaluated whether our learning activity resulted in increased student understanding of macroscale ecology and biodiversity concepts and increased familiarity with analysis techniques, software programs, and large spatio‐ecological datasets. Overall, results suggest that our learning activity improved student understanding of biological diversity, biodiversity metrics, and patterns of biodiversity across several spatial scales. Participating faculty reflected on what went well and what would benefit from changes, and we offer suggestions for implementation of the learning activity based on this feedback. This learning activity introduced students to macroscale ecology and built student skills in working with big data (i.e., large datasets) and performing basic quantitative analyses, skills that are essential for the next generation of ecologists.

    

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2. Practical Skills for Students in Mechatronics and Robotics Education

   Berry, C. ; Gennert, M. ; Reck, R. ( June 2020 , ASEE annual conference exposition proceedings)

   In September 2019, the fourth and final workshop on the Future of Mechatronics and Robotics Education (FoMRE) was held at a Lawrence Technological University in Southfield, MI. This workshop was organized by faculty at several universities with financial support from industry partners and the National Science Foundation. The purpose of the workshops was to create a cohesive effort among mechatronics and robotics courses, minors and degree programs. Mechatronics and Robotics Engineering (MRE) is an integration of mechanics, controls, electronics, and software, which provides a unique opportunity for engineering students to function on multidisciplinary teams. Due to its multidisciplinary nature, it attracts diverse and innovative students, and graduates better-prepared professional engineers. In this fast growing field, there is a great need to standardize educational material and make MRE education more widely available and easier to adopt. This can only be accomplished if the community comes together to speak with one clear voice about not only the benefits, but also the best ways to teach it. These efforts would also aid in establishing more of these degree programs and integrating minors or majors into existing computer science, mechanical engineering, or electrical engineering departments. The final workshop was attended by approximately 50 practitioners from industry and academia. Participants identified many practical skills required for students to succeed in an MRE curriculum and as practicing engineers after graduation. These skills were then organized into the following categories: professional, independent learning, controller design, numerical simulation and analysis, electronics, software development, and system design. For example, professional skills include technical reports, presentations, and documentation. Independent learning includes reading data sheets, performing internet searches, doing a literature review, and having a maker mindset. Numerical simulation skills include understanding data, presenting data graphically, solving and simulating in software such as MATLAB, Simulink and Excel. Controller design involves selecting a controller, tuning a controller, designing to meet specifications, and understanding when the results are good enough. Electronics skills include selecting sensors, interfacing sensors, interfacing actuators, creating printed circuit boards, wiring on a breadboard, soldering, installing drivers, using integrated circuits, and using microcontrollers. Software development of embedded systems includes agile program design, state machines, analyzing and evaluating code results, commenting code, troubleshooting, debugging, AI and machine learning. Finally, system design includes prototyping, creating CAD models, design for manufacturing, breaking a system down into subsystems, integrating and interfacing subcomponents, having a multidisciplinary perspective, robustness, evaluating tradeoffs, testing, validation, and verification, failure, effect, and mode analysis. A survey was prepared and sent out to the participants from all four workshops as well as other robotics faculty, researchers and industry personnel in order to elicit a broader community response. Because one of the biggest challenges in mechatronics and robotics education is the absence of standardized curricula, textbooks, platforms, syllabi, assignments, and learning outcomes, this was a vital part of the process to achieve some level of consensus. This paper presents an introduction to MRE education, related work on existing programs, methods, results of the practical skills survey, and then draws conclusions based upon these results. It aims to create the foundation for standardizing the development of student skills in mechatronics and robotics curricula across institutions, disciplines, majors and minors. The survey was completed by 94 participants and it was clear that there is a consensus that the primary skills students should have upon completion of MRE courses or a program is a broader multidisciplinary systems-level perspective, an ability to problem solve, and an ability to design a system to meet specifications. 

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3. Lived Experiences of African American Engineering Students at a PWI Through the Lens of Navigational Capital

   Damas, S. ; Benson, L. ( February 2022 , 2022 CoNECD (Collaborative Network for Engineering & Computing Diversity))

   There are significant disparities between the conferring of science, technology, engineering, and mathematics (STEM) bachelor’s degrees to minoritized groups and the number of STEM faculty that represent minoritized groups at four-year predominantly White institutions (PWIs). Studies show that as of 2019, African American faculty at PWIs have increased by only 2.3% in the last 20 years. This study explores the ways in which this imbalance affects minoritized students in engineering majors. Our research objective is to describe the ways in which African American students navigate their way to success in an engineering program at a PWI where the minoritized faculty representation is less than 10%. In this study, we define success as completion of an undergraduate degree and matriculation into a Ph.D. program. Research shows that African American students struggle with feeling like the “outsider within” in graduate programs and that the engineering culture can permeate from undergraduate to graduate programs. We address our research objective by conducting interviews using navigational capital as our theoretical framework, which can be defined as resilience, academic invulnerability, and skills. These three concepts come together to denote the journey of an individual as they achieve success in an environment not created with them in mind. Navigational capital has been applied in education contexts to study minoritized groups, and specifically in engineering education to study the persistence of students of color. Research on navigational capital often focuses on how participants acquire resources from others. There is a limited focus on the experience of the student as the individual agent exercising their own navigational capital. Drawing from and adapting the framework of navigational capital, this study provides rich descriptions of the lived experiences of African American students in an engineering program at a PWI as they navigated their way to academic success in a system that was not designed with them in mind. This pilot study took place at a research-intensive, land grant PWI in the southeastern United States. We recruited two students who identify as African American and are in the first year of their Ph.D. program in an engineering major. Our interview protocol was adapted from a related study about student motivation, identity, and sense of belonging in engineering. After transcribing interviews with these participants, we began our qualitative analysis with a priori coding, drawing from the framework of navigational capital, to identify the experiences, connections, involvement, and resources the participants tapped into as they maneuvered their way to success in an undergraduate engineering program at a PWI. To identify other aspects of the participants’ experiences that were not reflected in that framework, we also used open coding. The results showed that the participants tapped into their navigational capital when they used experiences, connections, involvement, and resources to be resilient, academically invulnerable, and skillful. They learned from experiences (theirs or others’), capitalized on their connections, positioned themselves through involvement, and used their resources to achieve success in their engineering program. The participants identified their experiences, connections, and involvement. For example, one participant who came from a blended family (African American and White) drew from the experiences she had with her blended family. Her experiences helped her to understand the cultures of Black and White people. She was able to turn that into a skill to connect with others at her PWI. The point at which she took her familial experiences to use as a skill to maneuver her way to success at a PWI was an example of her navigational capital. Another participant capitalized on his connections to develop academic invulnerability. He was able to build his connections by making meaningful relationships with his classmates. He knew the importance of having reliable people to be there for him when he encountered a topic he did not understand. He cultivated an environment through relationships with classmates that set him up to achieve academic invulnerability in his classes. The participants spoke least about how they used their resources. The few mentions of resources were not distinct enough to make any substantial connection to the factors that denote navigational capital. The participants spoke explicitly about the PWI culture in their engineering department. From open coding, we identified the theme that participants did not expect to have role models in their major that looked like them and went into their undergraduate experience with the understanding that they will be the distinct minority in their classes. They did not make notable mention of how a lack of minority faculty affected their success. Upon acceptance, they took on the challenge of being a racial minority in exchange for a well-recognized degree they felt would have more value compared to engineering programs at other universities. They identified ways they maneuvered around their expectation that they would not have representative role models through their use of navigational capital. Integrating knowledge from the framework of navigational capital and its existing applications in engineering and education allows us the opportunity to learn from African American students that have succeeded in engineering programs with low minority faculty representation. The future directions of this work are to outline strategies that could enhance the path of minoritized engineering students towards success and to lay a foundation for understanding the use of navigational capital by minoritized students in engineering at PWIs. Students at PWIs can benefit from understanding their own navigational capital to help them identify ways to successfully navigate educational institutions. Students’ awareness of their capacity to maintain high levels of achievement, their connections to networks that facilitate navigation, and their ability to draw from experiences to enhance resilience provide them with the agency to unleash the invisible factors of their potential to be innovators in their collegiate and work environments. 

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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))

   null (Ed.)

   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 data 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. 

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5. The concerns and perceived challenges students faced when traditional in-person engineering courses suddenly transitioned to remote learning

   Yu, Fan ; Milord, Johanna ; Orton, Sarah L. ; Flores, Lisa. Y. ; & Marra, Rose. M. ( June 2022 , 2022 ASEE Annual Conference)

   This research evaluates the impact of switching college engineering courses from in-person instruction to emergency remote learning among engineering students at a university in the Midwest. The study aimed to answer the question: What were the concerns and perceived challenges students faced when traditional in-person engineering courses suddenly transitioned to remote learning? The goal of this study is to uncover the challenges students were facing in engineering online courses and to understand students’ concerns. Our findings can help improve teaching instruction to provide students with previously unavailable educational assistance for online engineering courses. We collected online survey responses during weeks 8 and 9 of the academic semester, shortly after the COVID-19 shutdown and emergency transition to remote learning in Spring 2020. The survey included two open-ended questions which inquired about students’ feedback about moving the class online, and one two-item scale which assessed students’ confidence in online engineering learning. Data analysis for the open-ended questions was guided by the theoretical framework - Social Cognitive Career Theory [1] that explores how context, person factors and social cognitions contribute to career goals, interests and actions. A phenomenological approach [2] was conducted to understand the experience of these students. Open coding and axial coding [2] methods were used to create initial categories then themes related to students' concerns and challenges. Data from the two-item scale was evaluated using descriptive statistics: means, standard deviations, and ranges. Four main themes with separate sub-categories emerged from the student responses: 1) Instructor’s ability to teach course online (Instructional limitations, Seeking help, Increased Workload), 2) Student’s ability to learn online (Time Management, Lower engagement and motivation, Harder to absorb material, Hard to focus, Worry about performance), 3) Difficulties outside of class (Technology issues), and 4) No concerns. Students seemed more concerned about their ability to learn the material (48% of responses) than the instructor’s ability to teach the material (36% of responses). The instructional limitations or lack of instructional support (22% of responses) and time management (12% of responses) were among the major concerns in the sub-categories. The results from two-item scale indicated participants' s confidence in their ability to master their classroom knowledge was at an intermediate level via online instruction (6/10), and participants' confidence in the instructor's ability to teach knowledge in online classes is moderate to high (7/10). The results align with the open-ended question response in which students were somewhat more concerned about their ability to learn than the instructor’s ability to teach. The themes and analysis will be a valuable tool to help institutions and instructors improve student learning experiences. 

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