ABSTRACT

The ability to self-regulate learning (SRL) is a skill theorized to transfer across learning environments. Students with this ability can consider a learning task, identify a goal, develop a plan to achieve it, execute that plan, and monitor and adapt learning until the goal is met. This paper examines the educational implications of developing the SRL expertise of high and typical-ability students, as operationalized by high school performance, who entered college and struggled with mathematics in their 1st year. Students who initially failed a 6-week intensive college math course completed a 3-h SRL training mid-semester and re-engaged in math learning with an adaptive problem-solving program and resources hosted on a course website. Students trained to evaluate tasks, plan, employ cognitive strategies, and monitor learning behaved distinctly from those who completed a math refresher course. Non-parametric, comparative analyses revealed that SRL-trained students more efficiently mastered math topics during digital problem-solving, demonstrating superior learning efficiency. Under a sequential explanatory mixed methods design, a follow-up multiple case study approach aligned to the Situated Model of SRL traced adaptive learning processes employed by multiple high-ability self-regulators and contrasted them with learning processes of exemplar learners from the untrained group.

摘要

自我调节学习（SRL）的能力是一种理论上可以跨学习环境转移的技能。具有这种能力的学生可以考虑学习任务，确定目标，制定实现该目标的计划，执行该计划，并监控和调整学习直至达到目标。本文研究了发展高中和典型能力学生的SRL专门知识的教育意义，这是通过高中表现来实现的，他们进入大学并在第一年就与数学作斗争。最初未能通过为期6周的密集型大学数学课程的学生在学期中完成了3小时的SRL培训，并通过自适应的问题解决程序和课程网站上的资源重新投入了数学学习。经过培训的学生可以评估任务，计划，采用认知策略，和监控学习的行为与完成数学进修课程的学生截然不同。非参数的比较分析表明，受SRL培训的学生在数字化解决问题过程中可以更有效地掌握数学主题，显示出卓越的学习效率。在顺序说明性混合方法设计下，遵循与SRL的定位模型一致的后续多案例研究方法，追踪了多个高能自我调节器采用的自适应学习过程，并将其与未经训练的样本学习者的学习过程进行了对比。