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1. Exploring composite narratives as a methodology to understand and share research findings in engineering education

   Sajadi, Susan ; Kellam, Nadia N. ; Brunhaver, Samantha R. ( June 2023 , American Society for Engineering Education)

   In this methods paper, the development and utility of composite narratives will be explored. Composite narratives, which involve combining aspects of multiple interviews into a single narrative, are a relatively modern methodology used in the qualitative research literature for several purposes: to do justice to complex accounts while maintaining participant anonymity, summarize data in a more engaging personal form and retain the human face of the data, illustrate specific aspects of the research findings, enhance the transferability of research findings by invoking empathy, illuminate collective experiences, and enhance research impact by providing findings in a manner more accessible to those outside of academia. Composite narratives leverage the power of storytelling, which has shown to be effective in studies of neurology and psychology; i.e., since humans often think and process information in narrative structures, the information conveyed in story form can be imprinted more easily on readers’ minds or existing schema. Engineering education researchers have increasingly begun using narrative research methods. Recently, researchers have begun exploring composite narratives as an approach to enable more complex and nuanced understandings of engineering education while mitigating potential issues around the confidentiality of participants. Because this is a relatively new methodology in higher education more broadly and in engineering education specifically, more examples of how to construct and utilize composite narratives in both research and practice are needed. This paper will share how we created a composite narrative from interviews we collected for our work so that others can adapt this methodology for their research projects. The paper will also discuss ways we modified and enhanced these narratives to connect research to practice and impact engineering students. This approach involved developing probing questions to stimulate thinking, learning, and discussion in academic and industrial educational settings. We developed the composite narratives featured in this paper from fifteen semi-structured critical incident interviews with engineering managers about their perceptions of adaptability. The critical incidents shared were combined to develop seven composite narratives reflecting real-life situations to which engineers must adapt in the workplace. These scenarios, grounded in the data, were taken directly to the engineering classroom for discussion with students on how they would respond and adapt to the presented story. In this paper, we detail our process of creating one composite narrative from the broader study and its associated probing questions for research dissemination in educational settings. We present this detailed account of how one composite narrative was constructed to demonstrate the quality and trustworthiness of the composite narrative methodology and assist in its replication by other scholars. Further, we discuss the benefits and limitations of this methodology, highlighting the parts of the data brought into focus using this method and how that contrasts with an inductive-deductive approach to qualitative coding also taken in this research project. 

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2. Bottom-up psychosocial interventions for interdependent privacy: Effectiveness based on individual and content differences

   https://doi.org/10.1145/3544548.3581117  

   Washburn, Renita ; Tanni, Tangila Islam ; Solihin, Yan ; Kapadia, Apu ; Amon, Mary Jean ( April 2023 , Bottom-up psychosocial interventions for interdependent privacy: Effectiveness based on individual and content differences)

   Although a great deal of research has examined interventions to help users protect their own information online, less work has examined methods for reducing interdependent privacy (IDP) violations on social media (i.e., sharing of other people's information). This study tested the effectiveness of concept-based (i.e., general information), fact-based (i.e., statistics), and narrative-based (i.e., stories) educational videos in altering IDP-relevant attitudes and multimedia sharing behaviors. Our study revealed concept and fact videos reduced sharing of social media content that portrayed people negatively. The narrative intervention backfired and increased sharing among participants who did not believe IDP violations to be especially serious; however, the narrative intervention decreased sharing for participants who rated IDP violations as more serious. Notably, our study found participants preferred narrative-based interventions with real world examples, despite other strategies more effectively reducing sharing. Implications for narrative transportation theory and advancing bottom-up (i.e., user-centered) psychosocial interventions are discussed. 

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3. What I Wish My Instructor Knew: Navigating COVID-19 as an Underrepresented Student - Evidence Based Research

   Sealey, Zaniyah V. Lewis ( July 2021 , ASEE Virtual Annual Conference Content Access)

   In March 2020, the global COVID-19 pandemic forced universities across the United States to immediately stop face-to-face activities and transition to virtual instruction. While this transition was not easy for anyone, the shift to online learning was especially difficult for STEM courses, particularly engineering, which has a strong practical/laboratory component. Additionally, underrepresented students (URMs) in engineering experienced a range of difficulties during this transition. The purpose of this paper is to highlight underrepresented engineering students’ experiences as a result of COVID-19. In particular, we aim to highlight stories shared by participants who indicated a desire to share their experience with their instructor. In order to better understand these experiences, research participants were asked to share a story, using the novel data collection platform SenseMaker, based on the following prompt: Imagine you are chatting with a friend or family member about the evolving COVID-19 crisis. Tell them about something you have experienced recently as an engineering student. Conducting a SenseMaker study involves four iterative steps: 1) Initiation is the process of designing signifiers, testing, and deploying the instrument; 2) Story Collection is the process of collecting data through narratives; 3) Sense-making is the process of exploring and analyzing patterns of the collection of narratives; and 4) Response is the process of amplifying positive stories and dampening negative stories to nudge the system to an adjacent possible (Van der Merwe et al. 2019). Unlike traditional surveys or other qualitative data collection methods, SenseMaker encourages participants to think more critically about the stories they share by inviting them to make sense of their story using a series of triads and dyads. After completing their narrative, participants were asked a series of triadic, dyadic, and sentiment-based multiple-choice questions (MCQ) relevant to their story. For one MCQ, in particular, participants were required to answer was “If you could do so without fear of judgment or retaliation, who would you share this story with?” and were given the following options: 1) Family 2) Instructor 3) Peers 4) Prefer not to answer 5) Other. A third of the participants indicated that they would share their story with their instructor. Therefore, we further explored this particular question. Additionally, this paper aims to highlight this subset of students whose primary motivation for their actions were based on Necessity. High-level qualitative findings from the data show that students valued Grit and Perseverance, recent experiences influenced their Sense of Purpose, and their decisions were majorly made based on Intuition. Chi-squared tests showed that there were not any significant differences between race and the desire to share with their instructor, however, there were significant differences when factoring in gender suggesting that gender has a large impact on the complexity of navigating school during this time. Lastly, ~50% of participants reported feeling negative or extremely negative about their experiences, ~30% reported feeling neutral, and ~20% reported feeling positive or extremely positive about their experiences. In the study, a total of 500 micro-narratives from underrepresented engineering students were collected from June – July 2020. Undergraduate and graduate students were recruited for participation through the researchers’ personal networks, social media, and through organizations like NSBE. Participants had the option to indicate who is able to read their stories 1) Everyone 2) Researchers Only, or 3) No one. This work presents qualitative stories of those who granted permission for everyone to read. 

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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. Building a collaborative, university-based science-in-action video storytelling model that translates science for public engagement and increases scientists' relatability

   https://doi.org/10.3389/fcomm.2022.1049648  

   Seidel, Dena K. ; Morin, Xenia K. ; Staffen, Marissa ; Ludescher, Richard D. ; Simon, James E. ; Schofield, Oscar ( January 2023 , Frontiers in Communication)

   Collaborating scientists and storytellers successfully built a university-based science-in-action video storytelling model to test the research question: Can university scientists increase their relatability and public engagement through science-in-action video storytelling? Developed over 14 years, this science storytelling model produced more than a dozen high-visibility narratives that translated science to the public and featured scientists, primarily environmental and climate scientists, who are described in audience surveys as relatable people. This collaborative model, based on long-term trusting partnerships between scientists and video storytellers, documented scientists as they conducted their research and together created narratives intended to humanize scientists as authentic people on journeys of discovery. Unlike traditional documentary filmmaking or journalism, the participatory nature of this translational science model involved scientists in the shared making of narratives to ensure the accuracy of the story's science content. Twelve science and research video story products have reached broad audiences through a variety of venues including television and online streaming platforms such as Public Broadcasting Service (PBS), Netflix, PIVOT TV, iTunes, and Kanopy. With a reach of over 180 million potential public audience viewers, we have demonstrated the effectiveness of this model to produce science and environmental narratives that appeal to the public. Results from post-screening surveys with public, high school, and undergraduate audiences showed perceptions of scientists as relatable. Our data includes feedback from undergraduate and high school students who participated in the video storytelling processes and reported increased relatability to both scientists and science. In 2022, we surveyed undergraduate students using a method that differentiated scientists' potential relatable qualities with scientists' passion for their work, and the scientists' motivation to help others, consistently associated with relatability. The value of this model to scientists is offered throughout this paper as two of our authors are biological scientists who were featured in our original science-in-action videos. Additionally, this model provides a time-saving method for scientists to communicate their research. We propose that translational science stories created using this model may provide audiences with opportunities to vicariously experience scientists' day-to-day choices and challenges and thus may evoke audiences' ability to relate to, and trust in, science. 

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