挖掘教育数据:关注学习分析

Anu A. Gokhale
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引用次数: 0

摘要

数据挖掘是在大型数据集中发现异常、隐含模式和相关性以预测结果的过程,或者换句话说,是在大量数据中搜索存在但“隐藏”的关系和全局模式的过程。当应用于教育领域时,数据挖掘是一个强大的工具,可以更好地理解关系、结构、模式和因果路径,为学生提供批判性思考、做出决策和解决问题的认知策略。本讲座将讨论本研究的方法和结果,展示所提取的知识,并描述其在教与学空间中的重要性。最近的发展旨在捕获和存储非认知的情感领域特征,如兴趣和持久性。目标是以证据为中心的设计,数据挖掘框架承认评估需要不同程度的信心和风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mining educational data: A focus on learning analytics
Data mining is a process of finding anomalies, implicit patterns, and correlations within large data sets to predict outcomes, or in other words, the search for relationships and global patterns that exist, but are `hidden' among the vast amounts of data. When applied to the educational domain, data mining is a powerful tool that enables better understanding of relationships, structure, patterns, and causal pathways which provide students the cognitive strategies to think critically, make decisions, and solve problems. The talk will discuss the methodology and results of this research, present the extracted knowledge, and describe its importance in the teaching-learning space. Recent developments engineered to capture and store non-cognitive affective-domain features, such as interest and persistence will be addressed. The objective is evidence-centered design and the data mining framework acknowledges that assessments entail different levels of confidence and risk.
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