一年级体验课程的综合闭环学习分析方案

Munira Syed, Trunojoyo Anggara, Alison Lanski, Xiaojing Duan, G. Ambrose, N. Chawla
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引用次数: 13

摘要

最近的文献表明,两个过程应该更紧密地结合在一起,即识别不健康的学生并进行干预以促进他们的发展。我们在第一年体验(FYE)课程中进行了六个学期的整合,目的是提高学生的成功,通过使用集成的闭环学习分析方案,该方案由多个步骤组成,分为三个主要阶段,如下所述:收集架构(步骤:设计,构建,捕获),分析行动(步骤:识别,通知,促进)和评估改进(步骤:评估,报告)。我们通过允许后期步骤在一个学期和每年迭代中实时通知早期步骤来完成循环,从而从数据驱动的见解中改进课程。这个过程依赖于有目的的集成学习环境的设计,该环境有利于数据的收集、存储和分析。评估我们基于分析的学生干预有效性的方法表明,我们确定成绩不佳学生的标准是令人满意的,成绩不佳的学生从期中到期末的成绩变化比成绩优异的学生更大。最后,我们提出了使用财政年度早期表现作为一年级学生整体表现和保留的指标的案例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrated Closed-loop Learning Analytics Scheme in a First Year Experience Course
Identifying non-thriving students and intervening to boost them are two processes that recent literature suggests should be more tightly integrated. We perform this integration over six semesters in a First Year Experience (FYE) course with the aim of boosting student success, by using an integrated closed-loop learning analytics scheme that consists of multiple steps broken into three main phases, as follows: Architecting for Collection (steps: design, build, capture), Analyzing for Action (steps: identify, notify, boost), and Assessing for Improvement (steps: evaluate, report). We close the loop by allowing later steps to inform earlier ones in real-time during a semester and iteratively year to year, thereby improving the course from data-driven insights. This process depends on the purposeful design of an integrated learning environment that facilitates data collection, storage, and analysis. Methods for evaluating the effectiveness of our analytics-based student interventions show that our criterion for identifying non-thriving students was satisfactory and that non-thriving students demonstrated more substantial changes from mid-term to final course grades than already-thriving students. Lastly, we make a case for using early performance in the FYE as an indicator of overall performance and retention of first-year students.
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