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1. Youth-Community Invention: Community Epistemologies and Expansive Iterative Design

   Calabrese Barton, A. ( January 2020 , American Education Research Association)

   Objectives We examine the community epistemologies in youth’s iterative refinements of STEM-rich inventions across settings and time. Iteration in STEM-rich engineering/invention work refers to re-thinking ideas/designs within prototyping processes (Cunningham & Kelly, 2017). The objective of this paper is to examine the political dimensions of iteration through a) how iteration involves pre- and post-design “lives” of inventions especially towards new social futures, and b) the intentional incorporation of cultural epistemologies towards advancing new forms of legitimate inventor knowledge/practice (Yosso, 2005). Framing We draw from critical justice and consequential learning studies. Critical justice focuses on recognizing diversity and addressing structural inequalities perpetuated through systemic racism and classism. It seeks re-shifted relations of power and position within multiple scales-of-activity in learning, intersected with historicized injustices in learning environments. Consequential learning examines what matters to people, and how associated values and practices, when coordinated through social activity, allows for imagining new social futures (Gutierrez, 2012). Viewing the iterative process of inventing through a justice-oriented consequential lens calls into question traditional modes of knowing, and challenges/expands who and what areas of expertise are recognized and valued. Methods Our study takes place in two community makerspaces in mid-sized cities. Both center community engagement and support youth in designing/inventing to address problems they and their communities care about. Both also support minoritized youth in inventing through engagement with a wide range of community/STEM stakeholders. In researcher-educator roles, we collaborated with both makerspaces to establish programs supporting youth in sustained engagement in STEM and making/inventing in culturally-sustaining ways. In our two-year, longitudinal critical ethnography, data were generated in weekly community making sessions between 2016-2018. Data include artifacts, youth conversation groups, and videos capturing youth interaction with STEM and community experts at various stages in their design process. Analysis involved multiple stages and levels of coding based on open-coding and constant comparison procedures. Findings We ground our paper in four in-depth longitudinal cases of youth’s iterative design work: Nila’s light-up #stopracism sign; Su’zanne’s massaging slipper, Sharon’s geodesic play dome, and Jazmyn’s portable fan. Across cases, we illustrate three findings. First, youth located broader injustices within local making/inventing discourses with support from community and STEM allies, suggesting youth drew from multiple epistemologies, some grounded in community cultural wealth, others in STEM. For example, Su’Zanne drew from a familial culture of care and resistance in recognizing injustices nested in homelessness while iterating a way to make her slipper “more massaging.” The geodesic dome youth-makers drew from collective solidarity/resistance in making a structure for younger peers due to unjust lack of play infrastructure. Second, iterative engagement involving community wealth afforded further design and inventing experiences and expanded ownership over inventions across many stakeholders. For example, youth turned Nila’s #stopracism sign on during group discussions when they felt that racism needed to be foregrounded. Third, the afterlife of youth invention processes impacted the emergent inventor-maker culture through influencing the iterative process. Significance Iterations expand hybridization of cultural knowledge/practice and STEM-rich inventing, re-shaping whose cultural knowledge matters, and fostering justice-oriented collective outcomes. 

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2. Impacts of the Marine Technology Boom: Ocean Literacy Now Requires Data Literacy

   https://doi.org/10.5334/cjme.78  

   Miller, Danielle ; Nolan, Hannah ( June 2023 , Current: The Journal of Marine Education)

   New technologies are continually being placed in the ocean, constantly collecting ocean data in real-time. As a result, Data Literacy is now a necessary learning goal for supporting students' Ocean Literacy. The newest ships in the U.S. Academic Research Fleet, the Regional Class Research Vessels (RCRVs), are being built with the aim of supporting data literacy through outreach and education, with aid from a forthcoming real-time data portal. To understand how the RCRV’s outreach and education initiatives can best support data and ocean literacy, while also facilitating intentional engagement with minoritized populations, a three-phase research strategy was conducted over three years. The objective was to determine promising practices in data literacy education and shipboard outreach that are also culturally responsive. The first phase of the research interviewed experts in the fields of teaching, data literacy, shipboard education, and community engagement in order to generate recommendations. The second phase was an assessment of a three-day data literacy high-school curriculum utilizing research vessel data. The third phase examined the success of potential culturally responsive data literacy curricular frameworks and teaching practices in an afterschool pilot program for Latinx youth. The research determined that in a world where students have never ending access to data, data literacy education must be scaffolded throughout a student's life. Data used in education must be contextual and relatable and the best tools for data literacy learning are designed for teachers and students. As new knowledge is being generated about the ocean through new technologies continually collecting data, ocean literacy can no longer exist without data literacy. 

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3. (Designing for) learning computational STEM and arts integration in culturally sustaining learning ecologies

   https://doi.org/10.1108/ILS-01-2020-0018  

   Champion, Dionne N. ; Tucker-Raymond, Eli ; Millner, Amon ; Gravel, Brian ; Wright, Christopher G. ; Likely, Rasheda ; Allen-Handy, Ayana ; Dandridge, Tikyna M. ( November 2020 , Information and Learning Sciences)

   null (Ed.)

   Purpose The purpose of this paper is to explore the designed cultural ecology of a hip-hop and computational science, technology, engineering, and mathematics (STEM) camp and the ways in which that ecology contributed to culturally sustaining learning experiences for middle school youth. In using the principles of hip-hop as a CSP for design, the authors question how and what practices were supported or emerged and how they became resources for youth engagement in the space. Design/methodology/approach The overall methodology was design research. Through interpretive analysis, it uses an example of four Black girls participating in the camp as they build a computer-controlled DJ battle station. Findings Through a close examination of youth interactions in the designed environment – looking at their communication, spatial arrangements, choices and uses of materials and tools during collaborative project work – the authors show how a learning ecology, designed based on hip-hop and computational practices and shaped by the history and practices of the dance center where the program was held, provided access to ideational, relational, spatial and material resources that became relevant to learning through computational making. The authors also show how youth engagement in the hip-hop computational making learning ecology allowed practices to emerge that led to expansive learning experiences that redefine what it means to engage in computing. Research limitations/implications Implications include how such ecologies might arrange relations of ideas, tools, materials, space and people to support learning and positive identity development. Originality/value Supporting culturally sustaining computational STEM pedagogies, the article argues two original points in informal youth learning 1) an expanded definition of computing based on making grammars and the cultural practices of hip-hop, and 2) attention to cultural ecologies in designing and understanding computational STEM learning 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. Culturally Sustaining STEM Learning in the Gullah Community

   Ciphrah, Regina ; Boyd, Fenice ; Ellis, Jamelle ( January 2022 , National Science Foundation (NSF) STEM for All Video Showcase)

   Culture plays a significant role in students’ learning and understanding. Teaching and learning are most effective when teachers make curricula relevant to students’ cultural, linguistic and experiential backgrounds, and when they take time to know and understand their students and the community they serve. The Culturally Sustaining STEM (CS-STEM) Institute is a feasibility study designed to support professional learning of in-service and preservice teachers in community-based settings. Funded by NSF AISL and ITEST programs, the study is comprised of three goals: supporting preservice and in-service teachers’ practices of culturally-sustaining STEM pedagogy, enhancing pre-adolescent learners’ knowledge of historical and current STEM practices in Gullah Geechee culture, inviting dialogue that critique and challenge perceptions of who participates in STEM agency and how through a Community-based Participatory Research (CBPR) model. A primary component of the study was a pilot summer 2021 STEM camp for fifth and sixth grade students in Georgetown, SC, located within the geographical footprint of the Gullah Geechee Cultural Heritage Corridor. The multi-PI team utilizes constant comparative analytic methods to analyze transcripts from observations and interviews, as well as the educators’ products to measure how the educators understand and apply culturally sustaining pedagogies (e.g., lesson plans). They also measure how the youth convey their understandings of culturally-embedded scientific content and practices by using constant comparative and multimodal analysis of transcripts from interviews and observations, as well as youth-generated artifacts. This research addresses why CS-STEM is important, development and enactment of CS-STEM curriculum, and how CS-STEM increases STEM literacy. 

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