Leveraging complexity frameworks to refine theories of engagement: Advancing self‐regulated learning in the age of artificial intelligence
Leveraging complexity frameworks to refine theories of engagement: Advancing self‐regulated learning in the age of artificial intelligence
复制标题
利用复杂性框架完善参与理论：在人工智能时代推进自我调节学习
DOI：
10.1111/bjet.13340
复制
发表时间：
2023
影响因子：
6.6
通讯作者：
Bernacki, Matthew
中科院分区：
文献类型：
--
作者：
Hilpert, Jonathan C.;Greene, Jeffrey A.;Bernacki, Matthew
对于研究人员来说，捕捉干预导致的自我调节学习(SRL)行为动态变化的证据是具有挑战性的。在目前的研究中，我们确定了哪些学生可能在生物课上表现不佳，哪些学生可能表现良好。然后，我们随机将一部分被预测表现不佳的学生分配到一门以学习为学习干预的科学中，在那里他们被教授SRL学习策略。学习结果和日志数据(2 5 7个 K事件)收集自Mn= 2 2 6名学生。我们使用一个复杂的系统框架来模拟SRL中的差异，包括在数字跟踪数据(即日志)中捕获的参与的数量、相互关联性、密度和规律性。比较了被预测为(1)表现较差(对照组，n= 48)、(2)表现较差并接受干预(治疗，n= 95)和(3)表现良好(未标记，n= 83)的学生之间的差异。结果表明，学生参与的规律性对课程年级有预测作用，干预组在干预后即刻表现出比对照组更有规律性的参与，并在整个学期保持这种增长。我们讨论了这些发现对人工智能的未来的影响以及在在线环境中监控学生学习的潜在用途。实践者注意到关于这个主题的已知情况自我调节学习(SRL)的知识和技能是大专以上STEM学生成功的有力预测因素。SRL是一个动态的、时间的过程，导致有目的的学生参与。需要在学习环境中测量动态SRL行为的方法和度量。本文增加了一个使用日志数据来测量动态SRL过程的马尔可夫过程。证据是动态的，互动--SRL的主要方面可以预测学生的成绩。证据表明，SRL过程可以通过教育干预受到有意义的影响。理论和实践的应用复杂性方法为动态SRL过程的理论和测量提供了信息。动态SRL过程的静态表示是有希望的学习分析度量。LMS使用的工程特征是对人工智能模型的有价值的贡献。
Capturing evidence for dynamic changes in self‐regulated learning (SRL) behaviours resulting from interventions is challenging for researchers. In the current study, we identified students who were likely to do poorly in a biology course and those who were likely to do well. Then, we randomly assigned a portion of the students predicted to perform poorly to a science of learning to learn intervention where they were taught SRL study strategies. Learning outcome and log data (257 K events) were collected fromn= 226 students. We used a complex systems framework to model the differences in SRL including the amount, interrelatedness, density and regularity of engagement captured in digital trace data (ie, logs). Differences were compared between students who were predicted to (1) perform poorly (control,n= 48), (2) perform poorly and received intervention (treatment,n= 95) and (3) perform well (not flagged,n= 83). Results indicated that the regularity of students' engagement was predictive of course grade, and that the intervention group exhibited increased regularity in engagement over the control group immediately after the intervention and maintained that increase over the course of the semester. We discuss the implications of these findings in relation to the future of artificial intelligence and potential uses for monitoring student learning in online environments.Practitioner notesWhat is already known about this topicSelf‐regulated learning (SRL) knowledge and skills are strong predictors of postsecondary STEM student success.SRL is a dynamic, temporal process that leads to purposeful student engagement.Methods and metrics for measuring dynamic SRL behaviours in learning contexts are needed.What this paper addsA Markov process for measuring dynamic SRL processes using log data.Evidence that dynamic, interaction‐dominant aspects of SRL predict student achievement.Evidence that SRL processes can be meaningfully impacted through educational intervention.Implications for theory and practiceComplexity approaches inform theory and measurement of dynamic SRL processes.Static representations of dynamic SRL processes are promising learning analytics metrics.Engineered features of LMS usage are valuable contributions to AI models.