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1. Cogenerative Dialogues and Development of the Culturally Relevant Pedagogical Guidelines for Computational Thinking and Computer Science.

   Sirrakos, G. ( January 2023 , American Association for the Advancement of Curriculum Studies Annual Meeting)

   In this proposal, we will share some initial findings about how teacher and student engagement in cogenerative dialogues influenced the development of the Culturally Relevant Pedagogical Guidelines for Computational Thinking and Computer Science (CRPG-CSCT). The CRPG-CSCT’s purpose is to provide computer science teachers with tools to enhance their instruction by accurately reflecting students’ diverse cultural resources in the classroom. Additionally, the CRPG-CSCT will provide guidance to non-computer science teachers on how to facilitate the integration of computational thinking skills to a broad spectrum of classes in the arts, humanities, sciences, social sciences, and mathematics. Our initial findings shared here are part of a larger NSF-funded research project (Award No. 2122367) which aims to better understand the barriers to entry and challenges for success faced by underrepresented secondary school students in computer science, through direct engagement with the students themselves. Throughout the 2022-23 academic year, the researchers have been working with a small team of secondary school teachers, students, and instructional designers, as well as university faculty in computer science, secondary education, and sociology to develop the CRPG-CSCT. The CRPG-CSCT is rooted in the tenets of culturally relevant pedagogy (Ladson-Billings, 1995) and borrows from Muhammad’s (2020) work in Cultivating Genius: An Equity Framework for Culturally and Historically Responsive Literacy. The CRPG-CCT is being developed over six day-long workshops held throughout the academic year. At the time of this submission, five of the six workshops had been completed. Each workshop utilized cogenerative dialogues (cogens) as the primary tool for organizing and sustaining participants’ engagement. Through cogens, participants more deeply learn about students’ cultural capital and the value of utilizing that capital within the classroom (Roth, Lawless, & Tobin, 2000). The success of cogens relies on following specific protocols (Emdin, 2016), such as listening attentively, ensuring there are equal opportunities for all participants to share, and affirming the experiences of other participants. The goal of a cogen is to reach a collective decision, based on the dialogue, that will positively impact students by explicitly addressing barriers to their engagement in the classroom. During each workshop, one member of the research team and one undergraduate research assistant observed the interactions among cogen participants and documented these in the form of ethnographic field notes. Another undergraduate research assistant took detailed notes during the workshop to record the content of small and large group discussions, presentations, and questions/responses throughout the workshops. A grounded theory approach was used to analyze the field notes. Additionally, at the conclusion of each workshop, participants completed a Cogen Feedback Survey (CFS) to gather additional information. The CFS were analyzed through open thematic coding, memos, and code frequencies. Our preliminary results demonstrate high levels of engagement from teacher and student participants during the workshops. Students identified that the cogen structure allowed them to participate comfortably, openly, and honestly. Further, students described feeling valued and heard. Students’ ideas and experiences were frequently affirmed, which served as an important step toward dismantling traditional teacher-student boundaries that might otherwise prevent them from sharing freely. Another result from the use of cogens was the shared experience of participants comprehending views from the other group’s perspective in the classroom. Students appreciated the opportunity to learn from teachers about their struggles in keeping students engaged. Teachers appreciated the opportunity to better understand students’ schooling experiences and how these may affirm or deny aspects of their identity. Finally, all participants shared meaningful suggestions and strategies for future workshops and for the collective betterment of the group. Initial findings shared here are important for several reasons. First, our findings suggest that cogens are an effective approach for fostering participants’ commitment to creating the conditions for students’ success in the classroom. Within the context of the workshops, cogens provided teachers, students, and faculty with opportunities to engage in authentic conversations for addressing the recruitment and retention problems in computer science for underrepresented students. These conversations often resulted in the development of tangible pedagogical approaches, examples, metaphors, and other strategies to directly address the recruitment and retention of underrepresented students in computer science. Finally, while we are still developing the CRPG-CSCT, cogens provided us with the opportunity to ensure the voices of teachers and students are well represented in and central to the document. 

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2. Understanding Context: Propagation and Effectiveness of the Concept Warehouse in Mechanical Engineering at Five Diverse Institutions and Beyond – Results from Year 2

   https://doi.org/10.18260/1-2--35419  

   Self, B. P. ; Koretsky, M. D. ; Papadopoulos, C. ; Widmann, J. M. ( July 2021 , ASEE annual conference exposition)

   It has been well-established that concept-based active learning strategies increase student retention, improve engagement and student achievement, and reduce the performance gap of underrepresented students. Despite the evidence supporting concept-based instruction, many faculty continue to stress algorithmic problem solving. In fact, the biggest challenge to improving STEM education is not the need to develop more effective instructional practices, but to find ways to get faculty to adopt the evidence-based pedagogies that already exist. Our project aims to propagate the Concept Warehouse (CW), an online innovation tool that was developed in the Chemical Engineering community, into Mechanical Engineering (ME). A portion of our work focuses on content development in mechanics, and includes statics, dynamics, and to a lesser extent strength of materials. Our content development teams had created 170 statics and 253 dynamics questions. Additionally, we have developed four different simulations to be embedded in online Instructional Tools – these are interactive modules that provided different physical scenarios to help students understand important concepts in mechanics. During initial interviews, we found that potential adopters needed coaching on the benefits of concept-based instruction, training on how to use the CW, and support on how to best implement the different affordances offered by the CW. This caused a slight shift in our initial research plans, and much of our recent work has concentrated on using faculty development activities to help us advertise the CW and encourage evidence-based practices. From these activities, we are recruiting participants for surveys and interviews to help us investigate how different contexts affect the adoption of educational innovations. A set of two summer workshops attracted over 270 applicants, and over 60 participants attended each synchronous offering. Other applicants were provided links to recordings of the workshop. From these participants, we recruited 20 participants to join our Community of Practice (CoP). These members are sharing how they use the CW in their classes, especially in the virtual environment. Community members discuss using evidence-based practices, different things that the CW can do, and suggest potential improvements to the tool. They will also be interviewed to help us determine barriers to adoption, how their institutional contexts and individual epistemologies affect adoption, and how they have used the CW in their classes. Our research will help us formulate strategies that others can use when attempting to propagate pedagogical innovations. 

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3. CSDMS: a community platform for numerical modeling of Earth surface processes

   https://doi.org/10.5194/gmd-15-1413-2022  

   Tucker, Gregory E. ; Hutton, Eric W. ; Piper, Mark D. ; Campforts, Benjamin ; Gan, Tian ; Barnhart, Katherine R. ; Kettner, Albert J. ; Overeem, Irina ; Peckham, Scott D. ; McCready, Lynn ; et al ( January 2022 , Geoscientific Model Development)

   Abstract. Computational modeling occupies a unique niche in Earth and environmental sciences. Models serve not just as scientific technology and infrastructure but also as digital containers of the scientific community's understanding of the natural world. As this understanding improves, so too must the associated software. This dual nature – models as both infrastructure and hypotheses – means that modeling software must be designed to evolve continually as geoscientific knowledge itself evolves. Here we describe design principles, protocols, and tools developed by the Community Surface Dynamics Modeling System (CSDMS) to promote a flexible, interoperable, and ever-improving research software ecosystem. These include a community repository for model sharing and metadata, interface and ontology standards for model interoperability, language-bridging tools, a modular programming library for model construction, modular software components for data access, and a Python-based execution and model-coupling framework. Methods of community support and engagement that help create a community-centered software ecosystem are also discussed. 

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4. Engagement in Practice: Lessons Learned Partnering with Science Educators and Local Engineers in Rural Schools

   Lesko, H. L. ; Grohs, J. R. ; Matusovich, H. M. ; Kirk, G. R. ; Carrico, C. ; van Montfrans, V. ; Gillen, A. L. ; Paradise, T. ; Blackowski, S. A. ; Baum, L. M. ( June 2018 , ASEE Annual Conference proceedings)

   Our NSF-funded ITEST project focuses on the collaborative design, implementation, and study of recurrent hands-on engineering activities with middle school youth in three rural communities in or near Appalachia. To achieve this aim, our team of faculty and graduate students partner with school educators and industry experts embedded in students’ local communities to collectively develop curriculum to aim at teacher-identified science standard and facilitate regular in-class interventions throughout the academic year. Leveraging local expertise is especially critical in this project because family pressures, cultural milieu, and preference for local, stable jobs play considerable roles in how Appalachian youth choose possible careers. Our partner communities have voluntarily opted to participate with us in a shared implementation-research program and as our project unfolds we are responsive to community-identified needs and preferences while maintaining the research program’s integrity. Our primary focus has been working to incorporate hands-on activities into science classrooms aimed at state science standards in recognition of the demands placed on teachers to align classroom time with state standards and associated standardized achievement tests. Our focus on serving diverse communities while being attentive to relevant research such as the preference for local, stable jobs attention to cultural relevance led us to reach out to advanced manufacturing facilities based in the target communities in order to enhance the connection students and teachers feel to local engineers. Each manufacturer has committed to designating several employees (engineers) to co-facilitate interventions six times each academic year. Launching our project has involved coordination across stakeholder groups to understand distinct values, goals, strengths and needs. In the first academic year, we are working with 9 different 6th grade science teachers across 7 schools in 3 counties. Co-facilitating in the classroom are representatives from our project team, graduate student volunteers from across the college of engineering, and volunteering engineers from our three industry partners. Developing this multi-stakeholder partnership has involved discussions and approvals across both school systems (e.g., superintendents, STEM coordinators, teachers) and our industry partners (e.g., managers, HR staff, volunteering engineers). The aim of this engagement-in-practice paper is to explore our lessons learned in navigating the day-to-day challenges of (1) developing and facilitating curriculum at the intersection of science standards, hands-on activities, cultural relevancy, and engineering thinking, (2) collaborating with volunteers from our industry partners and within our own college of engineering in order to deliver content in every science class of our 9 6th grade teachers one full school day/month, and (3) adapting to emergent needs that arise due to school and division differences (e.g., logistics of scheduling and curriculum pacing), community differences across our three counties (e.g., available resources in schools), and partner constraints. 

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5. Recent Advances in the Temple University Digital Pathology Corpus

   https://doi.org/10.1109/SPMB47826.2019.9037859  

   Hunt, I. ; Husain, S. ; Simon, J. ; Obeid, I. ; Picone, J. ( December 2019 , IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, Iyad ; Picone, Joseph ; Selesnick, Ivan (Ed.)

   The Neural Engineering Data Consortium (NEDC) is developing a large open source database of high-resolution digital pathology images known as the Temple University Digital Pathology Corpus (TUDP) [1]. Our long-term goal is to release one million images. We expect to release the first 100,000 image corpus by December 2020. The data is being acquired at the Department of Pathology at Temple University Hospital (TUH) using a Leica Biosystems Aperio AT2 scanner [2] and consists entirely of clinical pathology images. More information about the data and the project can be found in Shawki et al. [3]. We currently have a National Science Foundation (NSF) planning grant [4] to explore how best the community can leverage this resource. One goal of this poster presentation is to stimulate community-wide discussions about this project and determine how this valuable resource can best meet the needs of the public. The computing infrastructure required to support this database is extensive [5] and includes two HIPAA-secure computer networks, dual petabyte file servers, and Aperio’s eSlide Manager (eSM) software [6]. We currently have digitized over 50,000 slides from 2,846 patients and 2,942 clinical cases. There is an average of 12.4 slides per patient and 10.5 slides per case with one report per case. The data is organized by tissue type as shown below: Filenames: tudp/v1.0.0/svs/gastro/000001/00123456/2015_03_05/0s15_12345/0s15_12345_0a001_00123456_lvl0001_s000.svs tudp/v1.0.0/svs/gastro/000001/00123456/2015_03_05/0s15_12345/0s15_12345_00123456.docx Explanation: tudp: root directory of the corpus v1.0.0: version number of the release svs: the image data type gastro: the type of tissue 000001: six-digit sequence number used to control directory complexity 00123456: 8-digit patient MRN 2015_03_05: the date the specimen was captured 0s15_12345: the clinical case name 0s15_12345_0a001_00123456_lvl0001_s000.svs: the actual image filename consisting of a repeat of the case name, a site code (e.g., 0a001), the type and depth of the cut (e.g., lvl0001) and a token number (e.g., s000) 0s15_12345_00123456.docx: the filename for the corresponding case report We currently recognize fifteen tissue types in the first installment of the corpus. The raw image data is stored in Aperio’s “.svs” format, which is a multi-layered compressed JPEG format [3,7]. Pathology reports containing a summary of how a pathologist interpreted the slide are also provided in a flat text file format. A more complete summary of the demographics of this pilot corpus will be presented at the conference. Another goal of this poster presentation is to share our experiences with the larger community since many of these details have not been adequately documented in scientific publications. There are quite a few obstacles in collecting this data that have slowed down the process and need to be discussed publicly. Our backlog of slides dates back to 1997, meaning there are a lot that need to be sifted through and discarded for peeling or cracking. Additionally, during scanning a slide can get stuck, stalling a scan session for hours, resulting in a significant loss of productivity. Over the past two years, we have accumulated significant experience with how to scan a diverse inventory of slides using the Aperio AT2 high-volume scanner. We have been working closely with the vendor to resolve many problems associated with the use of this scanner for research purposes. This scanning project began in January of 2018 when the scanner was first installed. The scanning process was slow at first since there was a learning curve with how the scanner worked and how to obtain samples from the hospital. From its start date until May of 2019 ~20,000 slides we scanned. In the past 6 months from May to November we have tripled that number and how hold ~60,000 slides in our database. This dramatic increase in productivity was due to additional undergraduate staff members and an emphasis on efficient workflow. The Aperio AT2 scans 400 slides a day, requiring at least eight hours of scan time. The efficiency of these scans can vary greatly. When our team first started, approximately 5% of slides failed the scanning process due to focal point errors. We have been able to reduce that to 1% through a variety of means: (1) best practices regarding daily and monthly recalibrations, (2) tweaking the software such as the tissue finder parameter settings, and (3) experience with how to clean and prep slides so they scan properly. Nevertheless, this is not a completely automated process, making it very difficult to reach our production targets. With a staff of three undergraduate workers spending a total of 30 hours per week, we find it difficult to scan more than 2,000 slides per week using a single scanner (400 slides per night x 5 nights per week). The main limitation in achieving this level of production is the lack of a completely automated scanning process, it takes a couple of hours to sort, clean and load slides. We have streamlined all other aspects of the workflow required to database the scanned slides so that there are no additional bottlenecks. To bridge the gap between hospital operations and research, we are using Aperio’s eSM software. Our goal is to provide pathologists access to high quality digital images of their patients’ slides. eSM is a secure website that holds the images with their metadata labels, patient report, and path to where the image is located on our file server. Although eSM includes significant infrastructure to import slides into the database using barcodes, TUH does not currently support barcode use. Therefore, we manage the data using a mixture of Python scripts and manual import functions available in eSM. The database and associated tools are based on proprietary formats developed by Aperio, making this another important point of community-wide discussion on how best to disseminate such information. Our near-term goal for the TUDP Corpus is to release 100,000 slides by December 2020. We hope to continue data collection over the next decade until we reach one million slides. We are creating two pilot corpora using the first 50,000 slides we have collected. The first corpus consists of 500 slides with a marker stain and another 500 without it. This set was designed to let people debug their basic deep learning processing flow on these high-resolution images. We discuss our preliminary experiments on this corpus and the challenges in processing these high-resolution images using deep learning in [3]. We are able to achieve a mean sensitivity of 99.0% for slides with pen marks, and 98.9% for slides without marks, using a multistage deep learning algorithm. While this dataset was very useful in initial debugging, we are in the midst of creating a new, more challenging pilot corpus using actual tissue samples annotated by experts. The task will be to detect ductal carcinoma (DCIS) or invasive breast cancer tissue. There will be approximately 1,000 images per class in this corpus. Based on the number of features annotated, we can train on a two class problem of DCIS or benign, or increase the difficulty by increasing the classes to include DCIS, benign, stroma, pink tissue, non-neoplastic etc. Those interested in the corpus or in participating in community-wide discussions should join our listserv, nedc_tuh_dpath@googlegroups.com, to be kept informed of the latest developments in this project. You can learn more from our project website: https://www.isip.piconepress.com/projects/nsf_dpath. 

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