通过应用跟踪、调查和评估数据的学习分析实现精准教育

Dirk T. Tempelaar, B. Rienties, Quan Nguyen
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引用次数: 1

摘要

准确和及时地测量学习参与度对于精确教育的应用至关重要。与此同时,无论是在学习分析界还是在更广泛的教育研究领域,它仍然是一个中心研究主题。“参与是教育心理学领域最热门的研究课题之一”,这是最近一期特刊的开头句,理由很充分。在我们的贡献中,我们提出了一种测量敬业度的整体方法,通过从日志文件中捕获的学习过程的痕迹,将行为类型的数据整合到敬业度的情感、行为和认知测量中,这些测量是通过调查和学习评估的认知测量收集的。我们将这种整体方法应用于性格学习分析的实证分析中。从两步聚类创建的四种不同的参与概况开始,我们发现这些概况在参与学习的时间上主要不同。接下来,我们开发了基于回归的预测模型,该模型清楚地表明,跟踪、调查和评估数据在向有失败风险的学生发出信号方面具有互补作用,并且它们都是预测方程的三个关键组成部分,它们在学习反馈的时间上有所不同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enabling Precision Education by Learning Analytics Applying Trace, Survey and Assessment Data
Accurate and timely measurement of learning engagement is crucial for the application of precision education. At the same time, it is still a central research theme, both in the learning analytics community as in the broader area of educational research. 'Engagement is one of the hottest research topics in the field of educational psychology' is for a good reason the opening sentence of a recent special issue. In our contribution, we propose a holistic approach to the measurement of engagement by integrating data of behavioral type through traces of learning processes captured from log files into affective, behavioral, and cognitive measures of engagement collected with surveys and cognitive measures from assessments for and as learning. We apply this holistic approach in an empirical analysis of dispositional learning analytics. Starting from four different engagement profiles created by two-step clustering, we find that these profiles primarily differ in their timing of engagement with learning. Next, we develop regression-based prediction models that make clear that trace, survey, and assessment data have complementary roles in signaling students at risk for failure and are all three crucial constituents of prediction equations that differ in the timing of learning feedback.
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