The study investigated how teenagers (ages 14-16) explained the functionality of various machine learning applications, including commercial products and e-textile wearables. Using a knowledge-in-pieces perspective, the researchers found that the teenagers showed some understanding of key elements of the machine learning pipeline, even if they did not use the same terminology.
Specifically, the teenagers discussed how machine learning applications "learn" from training data or examples, and how these applications recognize patterns in input data to provide different outputs. They talked about different data features that could be used, such as sound frequency or facial structure. However, they did not elaborate on learning algorithms or models.
The teenagers also demonstrated nuanced understandings of the limitations and potential biases of the machine learning applications, recognizing that the performance depended on the training data used. For instance, they observed that a TikTok filter did not work well for people with darker skin tones or certain hair types.
The findings challenge previous research that has characterized teenagers' understanding of machine learning as simplistic or full of misconceptions. Instead, the knowledge-in-pieces approach revealed productive ideas that could be leveraged to support learning about the machine learning pipeline and its complexities.
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by Luis Morales... às arxiv.org 04-02-2024
https://arxiv.org/pdf/2404.00728.pdfPerguntas Mais Profundas