Exploratory Analysis in Learning Analytics

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• 作者：David Gibson ; Sara de Freitas
• 关键词：Learning analytics ; Computationally intensive mixed methods research ; Game ; based learning ; Virtual performance assessment
• 刊名：International Journal of Computers for Mathematical Learning
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：21
• 期：1
• 页码：5-19
• 全文大小：961 KB
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• 作者单位：David Gibson (1)
  Sara de Freitas (2)
  
  1. Curtin University, Bentley, Perth, WA, Australia
  2. Murdoch University, Perth, Australia
  
• 刊物类别：Humanities, Social Sciences and Law
• 刊物主题：Education
  Mathematics Education
  Interdisciplinary Studies
  Mathematics
  
• 出版者：Springer Netherlands
• ISSN：2211-1670

文摘

This article summarizes the methods, observations, challenges and implications for exploratory analysis drawn from two learning analytics research projects. The cases include an analysis of a games-based virtual performance assessment and an analysis of data from 52,000 students over a 5-year period at a large Australian university. The complex datasets were analyzed and iteratively modeled with a variety of computationally intensive methods to provide the most effective outcomes for learning assessment, performance management and learner tracking. The article presents the research contexts, the tools and methods used in the exploratory phases of analysis, the major findings and the implications for learning analytics research methods.