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---
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title: "Causeway: Scaling Experiential Learning Through Micro-Roles"
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authors: [dlee105]
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author_notes: ["Assistant Professor, UC Santa Cruz"]
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tags: ["osre25", "uc", "web development", "educational technologies"]
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date: 2025-01-30
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lastmod: 2025-01-30
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---
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[Causeway](https://causeway.web.app) is a platform for learning to develop web applications using an Angular, RxJS, NgRx, and Firebase stack. Most online coding tutorials focus on covering the technical syntax or features of a language or framework, which means that new developers don’t have great resources for building a holistic picture of how everything they learn connects to actually developing a complex web application. Causeway breaks down the process of developing a web application into a hierarchy of micro-roles which provides learners with a clear pathway for learning that also translates to a clear process for developing an application. In the longer future, this would also enable learners to easily contribute to projects as they learn through taking on micro-roles for yet-to-be-developed projects. The platform uses the [Stackblitz WebContainer API](https://developer.stackblitz.com/platform/api/webcontainer-api) to run full applications in the browser for interactive learning.
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Thus far, we have developed a version of the platform that walks learners through the process of developing UI components of a web application as well as containers that contain multiple UI components and are responsible for fetching data from the backend and handling events and updates to the database. We'd like to extend the content to cover defining the database schema and entire applications, and to other topics beyond web development like AI/ML. We'd like to add quizzes to the experience and explore ways to use Generative AI to augment the learning experience, e.g. to support planning, reflection, and assessment. Finally, we'd like to instrument the application with logs and analytics so we can better measure impact and learning outcomes, and develop a stronger CI/CD pipeline.
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### Causeway / Improving the Core Infrastructure
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The proposed work includes adding logging, analytics, and a production-level CI/CD pipeline, adding a robust testing framework, and refactoring some of our code into seperate modules. Both roles will also contribute to running usability studies and documenting the platform.
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- **Topics:** Web Development, Educational Technologies, Angular
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- **Skills:** Web development experience, HTML, CSS, Javascript, Angular, RxJS, NgRx, Firebase
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- **Difficulty:** Medium to Hard
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- **Size:** Large (350 hours)
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- **Mentors:** {{% mention "dlee105" %}}
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### Causeway / Quizzes and Generative AI
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The proposed work includes extending the application to support quizzes, adding quizzes for the existing tasks, and exploring the use of generative AI to support the quizzes feature. Both roles will also contribute to running usability studies and documenting the platform.
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- **Topics:** Web Development, Educational Technologies, Angular
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- **Skills:** Web development experience, HTML, CSS, Javascript, Angular, RxJS, NgRx, Firebase, Generative AI
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- **Difficulty:** Medium to Hard
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- **Size:** Large (350 hours)
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- **Mentors:** {{% mention "dlee105" %}}
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---
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title: "Final Blog: SS_Bench - Benchmarking SciStream"
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subtitle: "SciStream Artifact on FABRIC Testbed"
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summary: ""
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authors: [kraislaik]
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tags: ["osre24", reproducibility]
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categories: []
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date: 2025-01-31
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lastmod: 2024-01-31
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featured: false
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draft: false
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image:
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caption: ""
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focal_point: ""
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preview_only: false
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---
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## Introduction
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Hello! My name is Acheme, and I'm thrilled to have collaborated with my mentors [Joaquin Chung](https://github.com/ucsc-ospo/ucsc-ospo.github.io/blob/main/content/authors/chungmiranda/_index.md) and [Flavio Castro](https://github.com/ucsc-ospo/ucsc-ospo.github.io/blob/main/content/authors/fcastro/_index.md) under the [SciStream](/project/osre24/anl/scistream/) project. This project aims to develop SciStream-bench, a set of benchmarks and artifacts designed to precisely evaluate the performance of scientific streaming applications across diverse traffic patterns when running over the SciStream framework.
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In the first half of the project, I focused on describing scientific streaming profiles based on use-cases experienced at Argonne National Lab. The necessary python scripts were developed to generate bursty and constant rate streaming traffic profiles.
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In the second half, I built upon this foundation by conducting experiments with the traffic profiles and measuring performance through metrics of latency, jitter and throughput. These experiments were conducted with different message sizes across LAN and WAN network topology.
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## Key Achievements
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1. **Streaming Traffic Profile:**
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- Developed scripts to generate streaming traffic profiles with configurable parameters.
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2. **Created an Artifact:**
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- I created an artifact using a Jupyter notebook to document an easy to follow integration of SciStream with FABRIC testbed for future experimenters.
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## Conclusion and Future Work
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The work demonstrated that SciStream offers tolerable overhead for secure data streaming and experimentation with this middlebox is possible in publicly available testbed like FABRIC.
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Future work would be to look into the comparative analysis of the performance of SciStream with or without hardware acceleration or offloading.
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## Deliverables
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- **SciStream on FABRIC Demo:** A demo can be found here on how to integrate SciStream on the FABRIC testbed [SciStream on FABRIC](https://www.youtube.com/watch?v=2NNAWPAreU8).
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- **Jupyter Notebook:** An Artifact on FABRIC portal: [FABRIC Artifact](https://artifacts.fabric-testbed.net/artifacts/1d604943-b5c0-4046-9971-ffb8f2535e42).

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