2021-2022. Over a Senior Thesis in addition to some research beforehand, this BKT Explainable was built using our Cognitive Task Analysis Results, using American Sign Language as the example domain. [Catherine Yeh '22, Mira Sneirson '22]
2019-2021. During a multi-semester project, research assistants conducted interviews and Cognitive Task Analysis to identify the knowledge components of experts of Bayesian Knowledge Tracing. [Catherine Yeh '22, Noah Cowit '20]
Summer 2018. This overview of our Summer 2018 BKT Explainables was accepted as a poster to the 2018 IEEE Workshop on Visualization for AI Explainability and provides an overview of our process.
Summer 2019. The post-hoc explainable for Bayesian Knowledge Tracing was accepted to the 2019 IEEE Workshop on Visualization for AI Explainability. [Noah Cowit '20]
2019-2020. This BKT Explainable was built in Unity as part of a 2-semester, 2-person team during the academic year [Amelia Chen '22, Catherine Yeh '22].
Summer 2019. Using a hot air balloon metaphor, you can interact with the parameters of Bayesian Knowledge Tracing and see how it influences the model's predictions! [Catherine Yeh '22]
2018-2020. In our paper in AAAI AIES 2020 "Assessing Post-hoc Explainability of the BKT Algorithm", we compare the difference in learning gains of Mechanical Turk workers using a static or interactive version of the Alchemy BKT Explainable from Summer 2018. [Haoyu Sheng '20, Tongyu Zhou '20]
Summer 2020. In an autumnal twist on the topic, this project uses knowledge about apples and apple-picking to portray essential BKT concepts. [Minh Phan '23]
Summer 2018. This project uses a story about learning to cook to explain the various parts of the BKT algorithm. [Grace Mazzarella '19]
Summer 2018. This is an early prototype of the Alchemy BKT Explainable featured in our paper in AAAI AIES 2020 "Assessing Post-hoc Explainability of the BKT Algorithm". Later, a static and interactive version was created and evaluated on Mechanical Turk. [Kelvin Tejeda '20, Tongyu Zhou '20]
Summer 2018. This is a paper prototype of a driving analogy to explain the various components of the Bayesian Knowledge Tracing Algorithm. [Young Cho '19]
Summer 2018. The EEP is a Bayesian Knowledge Tracing System to provide context in experiments for participants of our BKT Explainability studies. It is written in Javascript and uses a node.js server for persistent data. [Kelvin Tejeda '20]