Study Data from Federal University of Santa Catarina Update Knowledge of Machine Learning (Reliability and Validity of an Automated Model for Assessing the Lear ning of Machine Learning in Middle and High School: Experiences from the 'ML for ...)
圣卡塔琳娜联邦大学的研究数据更新了机器学习的知识（评估初中和高中机器学习的自动化模型的可靠性和有效性：来自'ML for ...的经验）
Study Data from Federal University of Santa Catarina Update Knowledge of Machine Learning (Reliability and Validity of an Automated Model for Assessing the Lear ning of Machine Learning in Middle and High School: Experiences from the 'ML for ...)
圣卡塔琳娜联邦大学的研究数据更新了机器学习的知识（评估初中和高中机器学习的自动化模型的可靠性和有效性：来自'ML for ...的经验）
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摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑-每日新闻-调查人员发布了关于人工智能的新报告。根据NewsRx记者来自圣卡塔琳娜联邦大学的新闻报道，研究表明：“机器学习(ML)在日常生活中的插入表明了在学校中普及学习语言的重要性。”我们的新闻记者从圣卡塔琳娜联邦大学的研究中得到一句话:“随之而来的是评估学生学习的需要。然而,到目前为止,很少有人提出评估,大多数缺乏E评估。因此,”我们评估了学生学习图像分类模型的自动化评估的可靠性和有效性，该评估是作为“ML for All!”课程的学习结果创建的。基于240名学生收集的数据的结果表明，评估可以被认为是可靠的(系数Omega=0.834/Cronbach的αa=0.83)。我们还根据多色相关确定了中等到St rong收敛和判别有效性因素分析表明两个基本因素'数据管理和模型培训'和'绩效解释'，相互补充。
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 originating from Federal Univ ersity of Santa Catarina by NewsRx correspondents, research stated, "The inserti on of Machine Learning (ML) in everyday life demonstrates the importance of popu larizing an understanding of ML already in school." Our news journalists obtained a quote from the research from Federal University of Santa Catarina: "Accompanying this trend arises the need to assess the studen ts' learning. Yet, so far, few assessments have been proposed, most lacking an e valuation. Therefore, we evaluate the reliability and validity of an automated a ssessment of the students' learning of an image classification model created as a learning outcome of the ‘ML for All!' course. Results based on data collected from 240 students indicate that the assessment can be considered reliable (coeff icient Omega = 0.834/Cronbach's alpha a=0.83). We also identified moderate to st rong convergent and discriminant validity based on the polychoric correlation ma trix. Factor analyses indicate two underlying factors ‘Data Management and Model Training' and ‘Performance Interpretation', completing each other."
Key words
Federal University of Santa Catarina/Cy borgs/Emerging Technologies/Machine Learning
NETL
NSTL