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1. Engineers' Situated Use of Digital Resources to Augment their Workplace Learning Ecology

   https://doi.org/10.1109/FIE49875.2021.9637421  

   Le, Hieu-Trung ; Johri, Aditya ( October 2021 , 2021 IEEE Frontiers in Education Conference (FIE))

   This Research FULL PAPER extends recent scholarship on the role of technology in workplace learning in professional engineering and computing settings. Digitization of work practices has made a noticeable impact on how engineers gain expertise and solve problems they encounter at work. In this paper we use a workplace learning ecology lens to examine engineers' situated information seeking to identify practices associated with the use of online resources. Building on a previous qualitative interview study, we developed and administered a survey consisting of 16 items to assess use of online resources across learning experiences. We found high use of online resources but with variations among the use of specific resources by field, problem, and learning goal. LinkedIn, Twitter, YouTube, Wikipedia, Reddit, and technology vendor websites were the primary online platforms used by respondents for both learning and problem-solving. Respondents placed different levels of trust in online resources. Social media, especially Twitter, was trusted least across all sources. The highest trust was placed on websites of technology vendors. Findings from this work can help create better educational content as well as pedagogical interventions that use online resources for training the future workforce. 

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2. Engineering Students’ Perceptions of Mathematical Modeling in a Learning Module Centered on a Hydrologic Design Case Study

   https://doi.org/10.1007/s40753-020-00131-8  

   Merck, Madeline F. ; Gallagher, Melissa A. ; Habib, Emad ; Tarboton, David ( July 2021 , International Journal of Research in Undergraduate Mathematics Education)

   null (Ed.)

   Abstract Engineering students need to spend time engaging in mathematical modeling tasks to reinforce their learning of mathematics through its application to authentic problems and real world design situations. Technological tools and resources can support this kind of learning engagement. We produced an online module that develops students’ mathematical modeling skills while developing knowledge of the fundamentals of rainfall-runoff processes and engineering design. This study examined how 251 students at two United States universities perceived mathematical modeling as implemented through the online module over a 5-year period. We found, subject to the limitation that these are perceptions from not all students, that: (a) the module allowed students to be a part of the modeling process; (b) using technology, such as modeling software and online databases, in the module helped students to understand what they were doing in mathematical modeling; (c) using the technology in the module helped students to develop their skill set; and (d) difficulties with the technology and/or the modeling decisions they had to make in the module activities were in some cases barriers that interfered with students’ ability to learn. We advocate for instructors to create modules that: (a) are situated within a real-world context, requiring students to model mathematically to solve an authentic problem; (b) take advantage of digital tools used by engineers to support students’ development of the mathematical and engineering skills needed in the workforce; and (c) use student feedback to guide module revisions. 

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3. 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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4. Work-in-Progress: Novel Ethnographic Investigations of Engineering Work Practices

   Jesiek, B. ; Johri, A. ; Brozina, C. ; Korte, R. ( June 2020 , Proceedings of the American Society for Engineering Education Virtual Conference, 2020.)

   There remains a lack of research on professional engineering work practices [1]. This deficiency is troubling because engineering education is organized and reorganized based on claims and assumptions about what professional engineering work is or will be. Without well-researched and trustworthy representations of practice, it is questionable whether engineering educators can adequately prepare future engineers for workplace realities. Although it is important that the preparation of future engineers not be tied solely to the workforce, there is a significant “disconnect between engineers in practice and engineers in academe” [2, p. 18]. If educators want to prepare students for professional success – including by assuming roles as future leaders and change agents – concrete images of engineering work are critical resources for rethinking engineering education [1]. The need for such resources is even more urgent given ongoing changes to engineering work under the forces of globalization, new organizational configurations, and new technologies of communication, design, and production. More research is needed to document images that are often discounted by students and even faculty, i.e., portrayals of engineering practice that emphasize its non-technical and non-calculative sides, as well as its non-individual aspects [3-4]. The aim of this work-in-progress paper is to introduce an exploratory project that will test innovative approaches to data collection and analysis for rapidly generating new knowledge about engineering practice. Traditionally, engineering practices have primarily been studied using in-depth ethnographic field research, requiring researchers to embed themselves as participant observers in the workplace. Yet technical work increasingly involves open workspaces and geographically distributed teams, frequent changes in job roles and team composition, and many layers of digital abstraction and collaboration. It thus may not be feasible or optimal to perform on-site research for extended periods of time. The main aim of this paper is to introduce method innovations for conducting field research which can potentially generate higher quality data more efficiently. Before doing so, we briefly overview prior research on engineering practice. 

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5. Work-in-Progress: Novel Ethnographic Investigations of Engineering Work Practices.

   Jesiek, B. ; Johri, A. ; Brozina, C. ; Korte, R. ( July 2020 , Proceedings of ASEE 2020)

   null (Ed.)

   There remains a lack of research on professional engineering work practices [1]. This deficiency is troubling because engineering education is organized and reorganized based on claims and assumptions about what professional engineering work is or will be. Without well-researched and trustworthy representations of practice, it is questionable whether engineering educators can adequately prepare future engineers for workplace realities. Although it is important that the preparation of future engineers not be tied solely to the workforce, there is a significant “disconnect between engineers in practice and engineers in academe” [2, p. 18]. If educators want to prepare students for professional success – including by assuming roles as future leaders and change agents – concrete images of engineering work are critical resources for rethinking engineering education [1]. The need for such resources is even more urgent given ongoing changes to engineering work under the forces of globalization, new organizational configurations, and new technologies of communication, design, and production. More research is needed to document images that are often discounted by students and even faculty, i.e., portrayals of engineering practice that emphasize its non-technical and non-calculative sides, as well as its non-individual aspects [3-4]. The aim of this work-in-progress paper is to introduce an exploratory project that will test innovative approaches to data collection and analysis for rapidly generating new knowledge about engineering practice. Traditionally, engineering practices have primarily been studied using in-depth ethnographic field research, requiring researchers to embed themselves as participant observers in the workplace. Yet technical work increasingly involves open workspaces and geographically distributed teams, frequent changes in job roles and team composition, and many layers of digital abstraction and collaboration. It thus may not be feasible or optimal to perform on-site research for extended periods of time. The main aim of this paper is to introduce method innovations for conducting field research which can potentially generate higher quality data more efficiently. Before doing so, we briefly overview prior research on engineering practice. 

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