Robotics & Machine Learning Daily News2024,Issue(Jun.7) :116-116.

Study Findings on Machine Learning Are Outlined in Reports from University of Ca lifornia (On the paradigms of learning analytics: Machine learning meets epistem ology)

关于机器学习的研究结果在加州大学的报告中概述(关于学习分析的范式:机器学习与认识论相遇)

Robotics & Machine Learning Daily News2024,Issue(Jun.7) :116-116.

Study Findings on Machine Learning Are Outlined in Reports from University of Ca lifornia (On the paradigms of learning analytics: Machine learning meets epistem ology)

关于机器学习的研究结果在加州大学的报告中概述(关于学习分析的范式:机器学习与认识论相遇)

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摘要

由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-调查人员发布了关于人工智能的新报告。根据来自美国欧文的新闻报道,B y NewsRx记者,研究表明,"贝克等人"。我们的新闻记者从加州大学的研究中获得了一句话:“(2021年)最近提议使用哲学框架,将学习分析研究分为四种范式。在这里,我以他们的主题为基础,反思不同学习分析方法的哲学差异。首先,我介绍了它们分类的两个局限性,为了解决这些问题,我借鉴了机器学习的偏差-方差权衡,并展示了如何根据不同的学习分析方法在权衡中的立场来看待不同的学习分析方法。因为我们还必须适应不同方法背后的基本认识论。我认为学习分析学的建构主义认识论缺失,这在一定程度上可以解释贝克等人(2021年)的观点,即建构主义工作在已建立的学习分析研究社区中相对缺乏。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting from Irvine, United States, b y NewsRx journalists, research stated, “Baker et al.” Our news reporters obtained a quote from the research from University of Califor nia: “(2021) recently proposed using a philosophical framework to classify learn ing analytics research in terms of four paradigms. Here I build on their theme o f reflecting on philosophical differences in different approaches to learning an alytics. I first present two limitations of their classification, which raise qu estions for how to best classify different approaches in learning analytics. In an attempt to resolve these questions, I draw upon the bias-variance tradeoff fr om machine learning and show how different learning analytics approaches can be viewed in terms of their positions on the tradeoff. However, I claim that this i s not enough, as we must also be attuned to the underlying epistemologies behind different approaches. I claim a constructivist epistemology for learning analyt ics has been missing, which could, in part, explain Baker et al.’s (2021) observ ation that constructivist work has been relatively absent in established learnin g analytics research communities.”

Key words

University of California/Irvine/United States/North and Central America/Cyborgs/Emerging Technologies/Machine Lear ning

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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