Educational Data Mining for Individualized Instruction in STEM Learning Environments

STEM 学习环境中个性化教学的教育数据挖掘

• 批准号: 1432156
• 负责人: Min Chi
• 金额: $ 63.94万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1432156&HistoricalAwards=false
• 关键词: Educational; Data; Mining; Individualized; Instruction

This project, at North Carolina State University, will explore ways to augment intelligent tutoring systems by using methods that use historical data from student work on assigned exercises, to enhance the tutoring system's ability to decide what to teach and how to teach it. This research will utilize both hint generation and worked examples. The PI team will begin by augmenting three existing learning environments, adding data-driven techniques for the automatic generation of next-step hints and for the automatic selection of learning activities. Subsequent studies will increase understanding of the benefits provided by hint mechanisms by comparing the effectiveness of sub-goal hints with that of next-step hints. This will then lead to empirical evaluations of the learning impact of such data-driven student support. The project team hypothesizes that existing logic and probability tutors will produce significant learning gains when enhanced by data-driven hint generation coupled with data-driven pedagogical strategy induction. The project will compare logic, probability, and programming learning with and without data-driven hints and data-driven pedagogies, measuring quantitative and qualitative impact on student success. The research team will use a variety of measures of learning, such as time to learn, number of errors made, number of hints requested, and engagement, as well as qualitative measures such as student surveys that gauge self-efficacy and motivation. Student performance data will be analyzed using correlation, analysis of variance, regression and significance testing.

这个项目，在北卡罗来纳州州立大学，将探索如何增强智能辅导系统的方法，通过使用的方法，使用历史数据，从学生的工作分配的练习，以提高辅导系统的能力，以决定教什么和如何教它。 PI团队将开始增强三个现有的学习环境，增加数据驱动的技术，用于自动生成下一步提示和自动选择学习活动。 后续的研究将通过比较子目标提示与下一步提示的有效性来增加对提示机制所提供的益处的理解。 这将导致对这种数据驱动的学生支持的学习影响的实证评估。项目团队假设，现有的逻辑和概率导师将产生显着的学习收益时，通过数据驱动的提示生成加上数据驱动的教学策略诱导增强。 该项目将比较逻辑，概率和编程学习，有和没有数据驱动的提示和数据驱动的方法，测量对学生成功的定量和定性影响。研究团队将使用各种学习措施，如学习时间，错误数量，要求的提示数量和参与度，以及定性措施，如衡量自我效能和动机的学生调查。学生的表现数据将使用相关性，方差分析，回归和显著性检验进行分析。