摘要
由一名新闻记者兼机器人与机器学习的工作人员新闻编辑每日新闻-关于机器学习的详细数据已经呈现。根据NewsR X记者在澳大利亚纽卡斯尔的新闻报道，研究表明：“将机器学习(ML)方法整合到教育研究中，有可能对研究、教学、学习和评估产生巨大影响，因为它能够实现个性化学习、适应性评估，并提供对学生表现、进步和学习模式的洞察力。为了更多地揭示这一概念，我们调查了过去十年中用于教育数据分析的ML方法，并为进一步研究提供了建议。这项研究的财政支持来自纽卡斯尔大学（马达加斯加）。新闻记者从纽卡斯尔大学的研究中获得了一句话，“使用系统的文献回顾(SLR)，”我们使用双气泡映射和评价性回顾分析，对两个大型和高影响力的教育研究数据库中的77篇出版物进行了研究。我们的结果表明，两个数据库中最常用的前五个关键词是相似的。这些出版物的大部分(88%)使用监督的ML方法来预测学生的表现和发现学习模式。这些方法包括决策树、支持向量机、随机森林、半监督学习方法的使用频率较低，但在预测学生成绩方面也取得了良好的效果。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Machine Learning have been presented. According to news reporting from Newcastle, Australia, by NewsR x journalists, research stated, “Integrating machine learning (ML) methods in ed ucational research has the potential to greatly impact upon research, teaching, learning and assessment by enabling personalised learning, adaptive assessment a nd providing insights into student performance, progress and learning patterns. To reveal more about this notion, we investigated ML approaches used for educati onal data analysis in the last decade and provided recommendations for further r esearch.” Financial support for this research came from The University of Newcastle (Austr alia). The news correspondents obtained a quote from the research from the University o f Newcastle, “Using a systematic literature review (SLR), we examined 77 publica tions from two large and high-impact databases for educational research using bi bliometric mapping and evaluative review analysis. Our results suggest that the top five most frequently used keywords were similar in both databases. The major ity of the publications (88%) utilised supervised ML approaches for predicting students’ performances and finding learning patterns. These methods include decision trees, support vector machines, random forests, and logistic re gression. Semi-supervised learning methods were less frequently used, but also d emonstrated promising results in predicting students’ performance.”
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