About CSAssess

CSAssess is a CSForAll:RPP project related to the NSF grant titled Seeding an Assessments Hub and Catalyzing a Community of Educators for Student Success in CS. The project entails exploratory research and development activities to (1) seed a sustainable technology hub (for and by teachers along with researchers and technologists) that push the boundaries on rich, innovative assessments organized by CS standards, grade, concepts, curriculum and other relevant criteria for intuitive use by teachers of all experience levels, (2) create a framework for helping make formative assessment a critical part of K-12 CS classroom practice, and (3) support professional development and a Community of Practice (CoP) for K-12 CS teachers in the US centered on a shared need for quality assessments and building CS teachers’ assessment capabilities. A strong interdisciplinary leadership team from Looking Glass Ventures, LLC (creator of an NSF-SBIR funded assessment authoring and delivery platform-‘Edfinity‘) and Computer Science Teachers Association (CSTA), will collaborate with K-12 CS teachers, curriculum developers, the CSForAll community, and experts in the field to execute this vision.

This project is funded by the CSForAll program of the National Science Foundation (CNS #1943530).

Resources

In this project, we are exploring and innovating on how we can teach AI and Machine Learning (ML) to 13-15 year olds through situations/issues set in the context of cybersecurity in ways that “lift the hood” on how ML models are designed, how they they work, and the impact of human decisions in this process.
The goal of our exploratory research is to innovate on learning design and pedagogy to bring together AI and cybersecurity topics, and integrate them in systematic and cogent ways that are accessible to early teen learners. We hope to push the boundaries of AI education in K-12 through developing code examples, abstractions, and coding experiences that help make fundamental ML concepts accessible without requiring mastery of the underlying (often complex) mathematical concepts. Instead of simply playing with AI models, we want early teen learners to really get a sense for the sauce in the ML models and be able to examine the underlying algorithms in order to build deeper understandings and intuitions of how AI/ML works.

This project is funded by the CSForAll program of the National Science Foundation (CNS #1943530).

CSAssess Activities

In this project, we are exploring and innovating on how we can teach AI and Machine Learning (ML) to 13-15 year olds through situations/issues set in the context of cybersecurity in ways that “lift the hood” on how ML models are designed, how they they work, and the impact of human decisions in this process.
The goal of our exploratory research is to innovate on learning design and pedagogy to bring together AI and cybersecurity topics, and integrate them in systematic and cogent ways that are accessible to early teen learners. We hope to push the boundaries of AI education in K-12 through developing code examples, abstractions, and coding experiences that help make fundamental ML concepts accessible without requiring mastery of the underlying (often complex) mathematical concepts. Instead of simply playing with AI models, we want early teen learners to really get a sense for the sauce in the ML models and be able to examine the underlying algorithms in order to build deeper understandings and intuitions of how AI/ML works.

This project is funded by the CSForAll program of the National Science Foundation (CNS #1943530).

Teacher PD Module