Martine Baars

Effects of problem solving after worked example study on secondary school children’s monitoring accuracy

Abstract Monitoring accuracy, measured by judgements of learning (JOLs), has generally been found to be low to moderate, with students often displaying overconfidence, and JOLs of problem solving are no exception. Recently, primary school children’s overconfidence was shown to diminish when they practised problem solving after studying worked examples. The current study aimed to extend this research by investigating whether practising problem solving after worked example study would also improve JOL accuracy in secondary education. Adolescents of 14–15 years old (N = 143) were randomly assigned to one of five conditions that differed in timing of JOLs, whether practice problems were provided, and timing of the practice problems provided: (1) worked examples – JOL, (2) worked examples – delay – JOL, (3) worked examples – practice problems – JOL, (4) worked examples – practice problems – delay – JOL or (5) worked examples – delay – practice problems – JOLs. Results showed that practice problems improved absolute accuracy of JOLs as well as regulation accuracy. No differences in final test performance were found.

Self-explaining steps in problem-solving tasks to improve self-regulation in secondary education

The ability to learn in a self-regulated way is important for adolescents’ academic achievements. Monitoring one’s own learning is a prerequisite skill for successful self-regulated learning. However, accurate monitoring has been found to be difficult for adolescents, especially for learning problem-solving tasks such as can be found in math and biology. This study investigated whether a self-explaining strategy, which has been found effective for improving monitoring accuracy in learning from text, can improve monitoring and regulation-choice effectiveness, and problem-solving performance in secondary biology education. In 2 experiments, one half of the participants learned to solve biology problems by studying video-modeling examples, and the other one half learned by giving step-by-step self-explanations following the video-modeling examples (Experiment 1) or by following the posttest problem-solving tasks (Experiment 2). Results showed that in contrast to earlier studies, self-explaining did not improve monitoring and regulation-choice effectiveness. However, the quality of self-explanations was found to be related to monitoring accuracy and performance. Interestingly, the complexity of the problem-solving tasks affected monitoring and regulation-choice effectiveness, and problem-solving performance. These results are discussed in relation to the cognitive demands that monitoring and regulating learning to solve problems combined with self-explaining pose on learners.

The association between motivation, affect, and self-regulated learning when solving problems

Self-regulated learning (SRL) skills are essential for learning during school years, particularly in complex problem-solving domains, such as biology and math. Although a lot of studies have focused on the cognitive resources that are needed for learning to solve problems in a self-regulated way, affective and motivational resources have received much less research attention. The current study investigated the relation between affect (i.e., Positive Affect and Negative Affect Scale), motivation (i.e., autonomous and controlled motivation), mental effort, SRL skills, and problem-solving performance when learning to solve biology problems in a self-regulated online learning environment. In the learning phase, secondary education students studied video-modeling examples of how to solve hereditary problems, solved hereditary problems which they chose themselves from a set of problems with different complexity levels (i.e., five levels). In the posttest, students solved hereditary problems, self-assessed their performance, and chose a next problem from the set of problems but did not solve these problems. The results from this study showed that negative affect, inaccurate self-assessments during the posttest, and higher perceptions of mental effort during the posttest were negatively associated with problem-solving performance after learning in a self-regulated way.