Training Self-Assessment and Task-Selection Skills to Foster Self-Regulated Learning: Do Trained Skills Transfer Across Domains?

• In Event: Meaningful Scaffolds to Foster Learning in Interactive Learning Settings

Abstract

Self-assessment and task-selection skills are crucial in interactive electronic learning environments in which students can choose their own learning tasks. When students are not able to accurately evaluate their own performance (self-assessment) and select an appropriate new learning task in response (task-selection), learning outcomes will be suboptimal, as students will end up working on learning tasks that are either too easy or too difficult for them. Because accurate self-assessment and task-selection is notoriously difficult, educational researchers are investigating ways to improve these skills, with the aim of fostering self-regulated learning outcomes (e.g., Azevedo & Cromley, 2004; Thiede, Anderson, & Therriault, 2003).
Prior research in the domain of biology problem solving has shown that video modeling examples, in which another person (the model) demonstrates and explains the cyclical process of problem-solving task performance, self-assessment, and task-selection, were effective for training self-assessment and task-selection skills, and that such training fostered self-regulated learning outcomes (Kostons, Van Gog, & Paas, 2012). An important open question, however, is whether the trained skills would transfer. For example, would students know how to decide what a suitable next learning task would be in mathematics, when they have acquired task-selection skills in the context of biology problems? We present two experiments here in which we aimed to replicate the findings by Kostons et al. (2012), and to investigate whether self-assessment and task-selection skills trained in one domain (biology), would transfer to another domain (math).
In Experiment 1, secondary education students first engaged in training (the control condition observed the problem-solving phase of the modeling examples, but not the self-assessment and task-selection phase). Then, there was a self-regulated learning phase in which they worked on eight self-selected learning tasks (biology problems, cf. training). This was followed by a problem-solving posttest (problems similar to the learning phase) and transfer test. Transfer of task-selection skills was assessed by scenarios, in which students selected a new task for a fictitious peer student based on that student’s performance/effort, in another domain (math problems). Results showed that training of self-assessment and task-selection skills improved problem-solving posttest performance after the self-regulated learning phase (replicating Kostons et al., 2012), as well as transfer of task-selection skills.
In Experiment 2, secondary education students first engaged in training (or control condition) followed by a check on whether they had acquired task-selection skills, both in the domain of biology. Then they engaged in self-regulated learning in math followed by a math posttest. The manipulation check confirmed that training improved task-selection. However, there was no evidence of transfer: there were no differences among conditions in posttest performance, suggesting that students failed to apply the trained skills during self-regulated learning in math.
These findings show that example-based self-assessment and task-selection training can be an effective and relatively easy to implement method for improving students’ self-regulated learning outcomes in interactive e-learning environments. However, they also suggest that these skills do not necessarily transfer beyond the tasks in which they were trained.

Authors

• Steven Raaijmakers, Utrecht University

• Martine Baars, Erasmus University Rotterdam

• Fred Paas, Erasmus University

• Jeroen J.G. Van Merrienboer, Maastricht University

• Tamara van Gog, Utrecht University