摘要
由一名新闻记者兼机器人与机器学习每日新闻的新闻编辑-一项关于人工智能的新研究现在可用。根据Ne wsRx记者在布雷顿角大学的新闻报道，研究表明，"大学排名是一种衡量高等教育机构(HEIs)表现的技术，通过对学生满意度、支出、研究和教学质量、引文计数、赠款和入学率等各种标准进行评估。"这项研究的资助者包括加拿大社会科学和人文研究委员会；加拿大自然科学和工程研究委员会。我们的新闻记者从Cape Breton Univers的研究中获得了一句话：“排名已经被确定为帮助学生决定就读哪所学校的一个重要因素。因此，大学寻求提高他们的总体排名，在营销传播中使用这些成功的衡量标准，并将他们的排名地位明确地放在学校的网站上。尽管对排名方法进行了大量的研究，少数研究利用公共分析和机器学习对大学进行排名。在本文中，我们收集了49所加拿大大学2017-2021年的数据，并根据麦克莱恩的分类将它们划分为本科、综合和医学/博士生大学。在确定输入和输出成分后，我们使用各种特征工程和机器学习技术来预测大学的排名。我们使用皮尔逊相关性，结果表明，‘师生比例’、‘引文总数’和‘助学金总数’是加拿大大学排名的最重要因素。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on artificial intelligence is now available. According to news reporting from Cape Breton University by Ne wsRx journalists, research stated, “University ranking is a technique of measuri ng the performance of Higher Education Institutions (HEIs) by evaluating them on various criteria like student satisfaction, expenditure, research and teaching quality, citation count, grants, and enrolment.” Funders for this research include Social Sciences And Humanities Research Counci l of Canada; Natural Sciences And Engineering Research Council of Canada. Our news journalists obtained a quote from the research from Cape Breton Univers ity: “Ranking has been determined as a vital factor that helps students decide w hich institution to attend. Hence, universities seek to increase their overall r ank and use these measures of success in their marketing communications and prom inently place their ranked status on their institution’s websites. Despite decad es of research on ranking methods, a limited number of studies have leveraged pr edictive analytics and machine learning to rank universities. In this article, w e collected 49 Canadian universities’ data for 2017-2021 and divided them based on Maclean’s categories into Primarily Undergraduate, Comprehensive, and Medical /Doctoral Universities. After identifying the input and output components, we le veraged various feature engineering and machine learning techniques to predict t he universities’ ranks. We used Pearson Correlation, Feature Importance, and Chi -Square as the feature engineering methods, and the results show that ‘student t o faculty ratio,’ ‘total number of citations’, and ‘total number of Grants’ are the most important factors in ranking Canadian universities.”
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