The Role of Mental Effort in Fostering Self-Regulated Learning with Problem-Solving Tasks
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The Role of Mental Effort in Fostering Self-Regulated Learning with Problem-Solving Tasks Tamara van Gog 1 & Vincent Hoogerheide 1 & Milou van Harsel 1,2
# The Author(s) 2020
Abstract
Problem-solving tasks form the backbone of STEM (science, technology, engineering, and mathematics) curricula. Yet, how to improve self-monitoring and self-regulation when learning to solve problems has received relatively little attention in the selfregulated learning literature (as compared with, for instance, learning lists of items or learning from expository texts). Here, we review research on fostering self-regulated learning of problem-solving tasks, in which mental effort plays an important role. First, we review research showing that having students engage in effortful, generative learning activities while learning to solve problems can provide them with cues that help them improve self-monitoring and self-regulation at an item level (i.e., determining whether or not a certain type of problem needs further study/practice). Second, we turn to selfmonitoring and self-regulation at the task sequence level (i.e., determining what an appropriate next problem-solving task would be given the current level of understanding/performance). We review research showing that teaching students to regulate their learning process by taking into account not only their performance but also their invested mental effort on a prior task when selecting a new task improves self-regulated learning outcomes (i.e., performance on a knowledge test in the domain of the study). Important directions for future research on the role of mental effort in (improving) self-monitoring and self-regulation at the item and task selection levels are discussed after the respective sections. Keywords Self-regulated learning, . Problem-solving, . Example-based learning, . Mental effort Learning to solve problems constitutes an important part of the curriculum in many school subjects and particularly in STEM domains (science, technology, engineering, and mathematics). Most of the problems students encounter are well-structured problems (for a typology of
* Tamara van Gog [email protected]
1
Department of Education, Utrecht University, P.O. Box 80140, 3508 Utrecht, TC, Netherlands
2
Learning and Innovation Centre, Avans University of Applied Sciences, Breda, Netherlands
Educational Psychology Review
problem variations, see Jonassen 2000), and students have to learn what series of actions they should perform (possibly bounded by rules regarding what actions are or are not allowed) to get from A (given information on an initial state) to B (a described goal state) (Newell and Simon 1972). Learning to solve problems requires the acquisition of both the procedural knowledge of what actions to perform and how to perform them and the conceptual knowledge of why to perform those actions. Decades of research inspired by cognitive load theory (Sweller et al. 2011) have shown that this knowledge is not efficiently acquired by having students mainly solve practice problems. R
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