大学课程中学生的变化概况:一个复杂动力系统的视角

Oleksandra Poquet, J. Jovanović, Abelardo Pardo
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引用次数: 2

摘要

基于学生学习行为的学习分析方法在如何整合暂时性和变化方面仍然有限。为了推进这一领域的工作,目前的研究考察了混合本科工程课程中学生学习行为的变化概况。该研究通过复杂动力系统理论及其在心理和认知科学研究中的应用来概念化。根据点击流数据中观察到的学生行为变化,对他们进行了分析。学生行为重现的熵值被用来表示学生状态的变化,这与认知科学的证据一致。每周熵值的学生轨迹聚类,以确定不同的概况。确定了三种模式:稳定的每周研究,每周研究的急剧变化,每周研究的适度变化。每周学习活动变化剧烈的学生考试成绩较低,并且在课程早期表现出每周行为的不稳定。该研究调查了这些变化概况、学生表现和其他学习者概况方法之间的关系,如自我报告的自我调节学习措施,以及基于学习行动序列的概况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Student Profiles of Change in a University Course: A Complex Dynamical Systems Perspective
Learning analytics approaches to profiling students based on their study behaviour remain limited in how they integrate temporality and change. To advance this area of work, the current study examines profiles of change in student study behaviour in a blended undergraduate engineering course. The study is conceptualised through complex dynamical systems theory and its applications in psychological and cognitive science research. Students were profiled based on the changes in their behaviour as observed in clickstream data. Measure of entropy in the recurrence of student behaviour was used to indicate the change of a student state, consistent with the evidence from cognitive sciences. Student trajectories of weekly entropy values were clustered to identify distinct profiles. Three patterns were identified: stable weekly study, steep changes in weekly study, and moderate changes in weekly study. The students with steep changes in their weekly study activity had lower exam grades and showed destabilisation of weekly behaviour earlier in the course. The study investigated the relationships between these profiles of change, student performance, and other approaches to learner profiling, such as self-reported measures of self-regulated learning, and profiles based on the sequences of learning actions.
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