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1. Some Bridges Span More than Water: Engaging High School Java Learners with Data Structure Visualizations and Real-World Data

   https://doi.org/10.1145/3408877.3439558  

   Perry, Kathryn ; Subramanian, Kalpathi ; Saule, Erik ( March 2021 , Proceedings of the 52nd ACM Technical Symposium on Computer Science Education)

   Many high school mathematics teachers have stepped up to the charge of learning computer science and offering CS courses to their students. As CS grows in popularity, more students are completing AP CS A as sophomores or juniors, and looking for advanced opportunities while still in high school. Our project seeks to support high school teachers in their quests to meet students' needs for advanced CS coursework. I am one such teacher who faced that need, and was relieved to find the BRIDGES libraries and projects repository website for CS college professors. I began the work of adapting their data structures related projects for use in my courses. Solving Java programming challenges using BRIDGES libraries has helped my students visualize and program with one- and two-dimensional arrays and linked lists. In this talk, we encourage/recruit high school teachers to try our adapted-for-high-school BRIDGES materials, and share in the joy of cool visualizations that make data structures come alive. Using one sample project, I will show how an engaging problem, scaffolded learning materials, and dynamic visualizations converge to facilitate student understanding of, and programming agility with, two-dimensional arrays. 

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2. 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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3. The RED Teams Start-Up Session: Leveraging Research with Practice for Success in Academic Change

   https://doi.org/10.18260/1-2--35360  

   Williams, Julia ; Mohan, Sriram ; Andrijcic, Eva ; Margherio, Cara ; Litzler, Elizabeth ; Doten-Snitker, Kerice ( June 2020 , 2020 ASEE Virtual Annual Conference Content Access)

   null (Ed.)

   At the start of their work for the National Science Foundation’s Revolutionizing Engineering Departments (RED) Program (IUSE/Professional Formation of Engineers, NSF 19-614), RED teams face a variety of challenges. Not only must they craft a shared vision for their projects and create strategic partnerships across their campuses to move the project forward, they must also form a new team and communicate effectively within the team. Our work with RED teams over the past 5 years has highlighted the common challenges these teams face at the start, and for that reason, we have developed the RED Start Up Session, a ½ day workshop that establishes best practices for RED teams’ work and allows for early successes in these five year projects. As the RED Participatory Action Research team (REDPAR)--comprised of individuals from Rose-Hulman Institute of Technology and the University of Washington--we have taken the research data collected as we work with RED teams and translated it into practical strategies that can benefit RED teams as they embark on their projects. This presentation will focus on the content and organization of the Start Up Session and how these lessons learned can contribute to the furthering of the goals of the RED program: to design “revolutionary new approaches to engineering education,” focusing on “organizational and cultural change within the departments, involving students, faculty, staff, and industry in rethinking what it means to provide an engineering program.” We see the Start Up Session as an important first step in the RED team establishing an identity as a team and learning how to work effectively together. We also encourage new RED teams to learn from the past, through a panel discussion with current RED team members who fill various roles on the teams: engineering education researcher, project manager, project PI, disciplinary faculty, social scientist, and others. By presenting our findings from the Start Up Session at ASEE, we believe we can contribute to the national conversation regarding change in engineering education as it is evidenced in the RED team’s work. 

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4. Real-World Problems, Data, and Visualizations Using BRIDGES (PreConference Workshop)

   Kalpathi Subramanian, Erik Saule ( January 2022 , Journal of Computer Science in Colleges (Central Plains))

   The rise in CS enrollments in the past few years has also resulted in a more diverse population of learners that have different expectations, motivations and interests, making it important to provide relevant learning materials in early foundational courses. Grounding Computer Science concepts in reality by solving important real-world or fun problems are keys to increasing students’ motivation and engagement in computing, which may help improve student retention and success. This workshop provides instructors with a hands-on introduction to BRIDGES, a software infrastructure for programming assignments in early computer science courses, such as CS1, CS2, data structures, and algorithm analysis. BRIDGES provides the tools for creating engaging programming assignments, including: (1) a simplified API for accessing real-world data, such as those from social networks, entertainment (songs, movies), science, engineering (USGIS Earthquakes, elevation maps), geography (OpenStreet maps), and literature (Project Gutenberg), (2) creating visualizations of the data, (3) an easy to use API for game-based assignments, and, (4) algorithm benchmarking. Workshop attendees will engage in hands-on experience using BRIDGES with multiple datasets, have the opportunity to discuss the challenges they face in their own courses, and how BRIDGES can be used in their own courses. Using BRIDGES in data structures, algorithms, and other courses have shown improved retention of CS knowledge and better student performance in follow-on courses, when compared to students from other sections of the same course. BRIDGES has impacted nearly 2000 students across 20 institutions since its inception 5 years ago. A repository of BRIDGES assignments is now maintained for instructors using BRIDGES in their classes. 

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5. BRIDGES: Real world data, assignments and visualizations to engage and motivate CS majors

   https://doi.org/10.1007/s10639-023-11958-4  

   Burlinson, David ; Mcquaigue, Matthew ; Goncharow, Alec ; Subramanian, Kalpathi ; Saule, Erik ; Payton, Jamie ; Goolkasian, Paula ( October 2023 , Education and Information Technologies)

   BRIDGES is a software framework for creating engaging assignments for required courses such as data structures and algorithms. It provides students with a simplified API that populates their own data structure implementations with live and real-world data, and provides the ability for students to easily visualize the data structures they create as part of routine classroom exercises. The objective is to use the infrastructure to promote a better understanding of the data structure and its underlying algorithms. This report describes the BRIDGES infrastructure and provides evaluation data collected over the first five years of the project. In the first 2 years, as we were developing the BRIDGES projects, our focus was on gathering data to assess whether the addition of the BRIDGES exercises had an effect on student retention of core concepts in data structures; and throughout the 5-year duration of the project, student interest and faculty feedback were collected online and anonymously. A mixed method design was used to evaluate the project impact. A quasiexperimental design compared student cohorts who were enrolled in comparable course sections that used BRIDGES with those that did not. Qualitative and quantitative measures were developed and used together with course grades and grade point averages. Interest and relevance in BRIDGES programming assignments was assessed with additional survey data from students and instructors. Results showed that students involved in BRIDGES projects demonstrated larger gains in knowledge of data structures compared to students enrolled in comparable course sections, as well as long-term benefits in their performance in four follow-on required courses. Survey responses indicated that some investment of time was needed to use BRIDGES, but the extra efforts were associated with several notable outcomes. Students and instructors had positive perceptions of the value of engaging in BRIDGES projects. BRIDGES can become a tool to get students more engaged in critical foundational courses, demonstrating relevance and context to today’s computational challenges. 

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