Exploring Machine Teaching with Children

Autor:	Dwivedi, Utkarsh, Gandhi, Jaina, Parikh, Raj, Coenraad, Merijke, Bonsignore, Elizabeth, Kacorri, Hernisa
Rok vydání:	2021
Předmět:	Computer Science - Machine Learning Computer Science - Computers and Society Computer Science - Human-Computer Interaction
Zdroj:	IEEE Symposium on Visual Languages and Human-Centric Computing 2021
Druh dokumentu:	Working Paper
Popis:	Iteratively building and testing machine learning models can help children develop creativity, flexibility, and comfort with machine learning and artificial intelligence. We explore how children use machine teaching interfaces with a team of 14 children (aged 7-13 years) and adult co-designers. Children trained image classifiers and tested each other's models for robustness. Our study illuminates how children reason about ML concepts, offering these insights for designing machine teaching experiences for children: (i) ML metrics (e.g. confidence scores) should be visible for experimentation; (ii) ML activities should enable children to exchange models for promoting reflection and pattern recognition; and (iii) the interface should allow quick data inspection (e.g. images vs. gestures). Comment: 11 pages, 8 images
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2109.11434 Zobrazit plný text záznamu View this record from Arxiv