Self-explanation prompts in example-based learning are usually directed backwards: Learners are required to self-explain problem-solving steps just presented ("retrospective" prompts). However, it might also help to self-explain upcoming steps ("anticipatory" prompts). The effects of the prompt type may differ for learners with…

Predicting student problem-solving strategies is a complex problem but one that can significantly impact automated instruction systems since they can adapt or personalize the system to suit the learner. While for small datasets, learning experts may be able to manually analyze data to infer student strategies, for large datasets, this approach is…

Use of modeling as a learning strategy in introductory science courses could serve to support the persistence of all students, including those who identify with a traditionally underrepresented ethnic minority (URM). If students find inherent value in working on modeling-type problems then this authentic practice would aid in transforming personal…

Background: In example-based learning, examples are often combined with generative activities, such as comparative self-explanations of example cases. Comparisons induce heavy demands on working memory, especially in complex domains. Hence, only stronger learners may benefit from comparative self-explanations. While static text-based examples can…

This study invested 24 effective and 16 ineffective problem-solving kindergarten children's awareness of metacognitive self-regulated learning (MSRL) while watching other child's problem-solving behaviors. The model in a video performed a task with a trial-and-error approach and finally asked for help. After watching the video, children were asked…

Investigators often rely on the proportion of correct responses in an assessment when describing the impact of early mathematics interventions on child outcomes. Here, we propose a shift in focus to the relative sophistication of problem-solving strategies and offer methodological guidance to researchers interested in working with strategies. We…

Students' solutions of enumerative combinatorial problems may be assessed along two main dimensions: the correctness of the solution and the method of enumeration. This study looks at the second dimension with reference to the Cartesian product of two sets, and at the 'odometer' combinatorial strategy defined by English (1991). Since we are not…

In the era of 21st century learning, many secondary students with learning disabilities continue to struggle with mathematics problem solving. Emerging evidence suggests self-regulated strategy development can be combined with existing evidence-based and promising practices during mathematics instruction. These practices include explicit…

Self-regulated learning (SRL) is a critical component of mathematics problem solving. Students skilled in SRL are more likely to effectively set goals, search for information, and direct their attention and cognitive process so that they align their efforts with their objectives. An influential framework for SRL, the SMART model, proposes that…

Being a cognitive strategy instruction model called 'Solve It!' involves cognitive and metacognitive elements. The model was developed by Montague (1992) as one of the process-based teaching strategies. The purpose of 'Solve It!' strategy is to teach the following seven cognitive strategy steps: read, paraphrase, visualize, hypothesize, predict,…

The strategy choice model (SCM) is a highly influential theory of human problem-solving. One strength of this theory is the allowance for both item and person variance to contribute to problem-solving outcomes, but this central tenet of the model has not been empirically tested. Explanatory item response theory (EIRT) provides an ideal approach to…

Self-regulated learning (SRL) is a critical component of mathematics problem-solving. Students skilled in SRL are more likely to effectively set goals, search for information, and direct their attention and cognitive process so that they align their efforts with their objectives. An influential framework for SRL, the SMART model (Winne, 2017),…

The strategy choice model (SCM) is a highly influential theory of human problem-solving. One strength of this theory is the allowance for both item and person variance to contribute to problem-solving outcomes, but this central tenet of the model has not been empirically tested. Explanatory item response theory (EIRT) provides an ideal approach to…

Recent educational initiatives have emphasised the importance of fostering critical thinking skills in today's students in order to provide strategies for becoming successful problem solvers throughout life. Other scholars advocate the use of critical thinking skills on the grounds that such tools can be used effectively when considering social…

Metacognition is a powerful predictor for learning performance, and for problem-solving. But how metacognition works for cognitive strategies and learning performance is not clear. The present study was designed to explore how metacognition affected the cognition (learning strategies and problem solving strategies) and different kinds of learning…