Raaijmakers Steven F, Baars Martine, Paas Fred, van Merriënboer Jeroen J G, van Gog Tamara
Department of Education Utrecht University Utrecht The Netherlands.
Department of Psychology, Education and Child Studies Erasmus University Rotterdam Rotterdam The Netherlands.
Appl Cogn Psychol. 2018 Mar-Apr;32(2):270-277. doi: 10.1002/acp.3392. Epub 2018 Feb 9.
Students' ability to accurately self-assess their performance and select a suitable subsequent learning task in response is imperative for effective self-regulated learning. Video modeling examples have proven effective for training self-assessment and task-selection skills, and-importantly-such training fostered self-regulated learning outcomes. It is unclear, however, whether trained skills would transfer across domains. We investigated whether skills acquired from training with either a specific, algorithmic task-selection rule or a more general heuristic task-selection rule in biology would transfer to self-regulated learning in math. A manipulation check performed after the training confirmed that both algorithmic and heuristic training improved task-selection skills on the biology problems compared with the control condition. However, we found no evidence that students subsequently applied the acquired skills during self-regulated learning in math. Future research should investigate how to support transfer of task-selection skills across domains.
学生准确自我评估其表现并相应选择合适后续学习任务的能力对于有效的自我调节学习至关重要。视频建模示例已被证明对训练自我评估和任务选择技能有效，而且重要的是，这种训练促进了自我调节学习成果。然而，尚不清楚经过训练的技能是否会跨领域迁移。我们研究了从生物学中使用特定算法任务选择规则或更一般启发式任务选择规则进行训练所获得的技能是否会迁移到数学中的自我调节学习。训练后进行的操作检查证实，与控制条件相比，算法训练和启发式训练都提高了生物学问题上的任务选择技能。然而，我们没有发现证据表明学生随后在数学的自我调节学习中应用了所获得的技能。未来的研究应该调查如何支持任务选择技能跨领域的迁移。
