Exploring Machine Teaching with Children

• URL: http://arxiv.org/abs/2109.11434v1
• Date: Thu, 23 Sep 2021 15:18:53 GMT
• Title: Exploring Machine Teaching with Children
• Authors: Utkarsh Dwivedi, Jaina Gandhi, Raj Parikh, Merijke Coenraad, Elizabeth Bonsignore, and Hernisa Kacorri
• Abstract summary: 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.
• Score: 9.212643929029403
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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).

Related papers

• Unpacking Approaches to Learning and Teaching Machine Learning in K-12 Education: Transparency, Ethics, and Design Activities [0.0]
  We identify three approaches to how learning and teaching machine learning could be conceptualized. One of them, a data-driven approach, emphasizes providing young people with opportunities to create data sets, train, and test models. A second approach, learning algorithm-driven, prioritizes learning about how the learning algorithms or engines behind how ML models work.
  arXiv  Detail & Related papers  (2024-06-05T17:37:21Z)
• Toward In-Context Teaching: Adapting Examples to Students' Misconceptions [54.82965010592045]
  We introduce a suite of models and evaluation methods we call AdapT. AToM is a new probabilistic model for adaptive teaching that jointly infers students' past beliefs and optimize for the correctness of future beliefs. Our results highlight both the difficulty of the adaptive teaching task and the potential of learned adaptive models for solving it.
  arXiv  Detail & Related papers  (2024-05-07T17:05:27Z)
• Investigating Youths' Everyday Understanding of Machine Learning Applications: a Knowledge-in-Pieces Perspective [0.0]
  Despite recent calls for including artificial intelligence in K-12 education, not enough attention has been paid to studying youths' everyday knowledge about machine learning (ML) We investigate teens' everyday understanding of ML through a knowledge-in-pieces perspective. Our analyses reveal that youths showed some understanding that ML applications learn from training data and that applications recognize patterns in input data and depending on these provide different outputs.
  arXiv  Detail & Related papers  (2024-03-31T16:11:33Z)
• Evaluating and Optimizing Educational Content with Large Language Model Judgments [52.33701672559594]
  We use Language Models (LMs) as educational experts to assess the impact of various instructions on learning outcomes. We introduce an instruction optimization approach in which one LM generates instructional materials using the judgments of another LM as a reward function. Human teachers' evaluations of these LM-generated worksheets show a significant alignment between the LM judgments and human teacher preferences.
  arXiv  Detail & Related papers  (2024-03-05T09:09:15Z)
• A Vision Check-up for Language Models [61.852026871772914]
  We show how a preliminary visual representation learning system can be trained using models of text. Experiments on self-supervised visual representation learning highlight the potential to train vision models capable of making semantic assessments of natural images.
  arXiv  Detail & Related papers  (2024-01-03T18:09:33Z)
• Not Just Training, Also Testing: High School Youths' Perspective-Taking through Peer Testing Machine Learning-Powered Applications [0.0]
  Testing machine learning applications can help creators of applications identify and address failure and edge cases. We analyzed testing worksheets, audio and video recordings collected during a two week workshop in which 11 high school youths created physical computing projects. We found that through peer-testing youths reflected on the size of their training datasets, the diversity of their training data, the design of their classes and the contexts in which they produced training data.
  arXiv  Detail & Related papers  (2023-11-21T17:15:43Z)
• Examining the Values Reflected by Children during AI Problem Formulation [9.516294164912072]
  We find that children's proposed ideas require advanced system intelligence and understanding the social relationships of a user. Children's ideas showed they cared about family and expected machines to understand their social context before making decisions.
  arXiv  Detail & Related papers  (2023-09-27T17:58:30Z)
• Imitation versus Innovation: What children can do that large language and language-and-vision models cannot (yet)? [0.10312968200748115]
  We argue that large language models and language-and-vision models are efficient imitation engines. Our findings suggest that machines may need more than large scale language and images to achieve what a child can do.
  arXiv  Detail & Related papers  (2023-05-08T18:26:39Z)
• Panoramic Learning with A Standardized Machine Learning Formalism [116.34627789412102]
  This paper presents a standardized equation of the learning objective, that offers a unifying understanding of diverse ML algorithms. It also provides guidance for mechanic design of new ML solutions, and serves as a promising vehicle towards panoramic learning with all experiences.
  arXiv  Detail & Related papers  (2021-08-17T17:44:38Z)
• Unadversarial Examples: Designing Objects for Robust Vision [100.4627585672469]
  We develop a framework that exploits the sensitivity of modern machine learning algorithms to input perturbations in order to design "robust objects" We demonstrate the efficacy of the framework on a wide variety of vision-based tasks ranging from standard benchmarks to (in-simulation) robotics.
  arXiv  Detail & Related papers  (2020-12-22T18:26:07Z)
• Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience [76.9910678786031]
  We propose a novel paradigm of explainable active learning (XAL), by introducing techniques from the recently surging field of explainable AI (XAI) into an Active Learning setting. Our study shows benefits of AI explanation as interfaces for machine teaching--supporting trust calibration and enabling rich forms of teaching feedback, and potential drawbacks--anchoring effect with the model judgment and cognitive workload.
  arXiv  Detail & Related papers  (2020-01-24T22:52:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.