Self-regulated Learning: Selecting Between Worked Example and Problem Solving for Contents with Different Assigned Values

Abstract

Effective use of learning strategies is crucial for self-regulated learning. Previous research has shown that novice learners benefit more from using worked examples before problem solving. This study incorporated a manipulation from the study time allocation literature to examine how undergraduate students regulate their use of worked examples and problem solving when learning problem-solving procedures with different assigned posttest values, some worth five points in the posttest and others worth one point. Vanderbilt students learned to solve four types of probability problems and selected between the two learning strategies for each of the 24 problems. Results revealed that although students’ overall selection of worked examples did not differ between problem types with different assigned point values, they made more switches between the two learning strategies when they were studying probability problem types with higher assigned point values than problem types with lower assigned point values. After making a mistake while problem-solving, students more often opted to study worked examples than if they solved the problem correctly. Also, students spent more time overall studying problem types that are worth more points and had higher categorization accuracy at posttest than problem types that are worth fewer points. These findings raise a number of questions about students’ reasoning behind each choice of worked example or problem solving and each switch between them. They also suggest some future directions to consider, such as exploring how students combine and leverage different learning strategies to enhance learning outcomes effectively.