Instructional Strategies for Improving Self-Monitoring of Learning to Solve Problems

__Abstract__ Being able to regulate their own learning process is becoming increasingly important for students at all levels of education (OECD Programme for International Student Assessment, 2009). From early on in children’s school careers, children are stimulated to be aware of what they are learning and to make choices about their own learning processes. Self-regulated learning can be defined as a self-directive process by which learners are able to improve their learning performance using the capabilities they already have\ud (Zimmerman, 2008). According to the model of self- regulated learning by Winne and Hadwin (1998), monitoring and control are central processes to self-regulated learning. To effectively regulate their own learning process, students must be able to monitor their progress toward learning goals and use this information to regulate (i.e., control) further study (Metcalfe, 2009; Winne & Hadwin, 1998). For example, if students are trying to solve a math problem, it is important for them to keep track of their conceptual understanding of the problem and the steps of its solution procedure (i.e., monitoring), and to use this to determine whether more problems should be studied or practiced in order to grasp the procedure for solving this type of problem (i.e., control). Monitoring is assumed and has been shown to inform control (Kornell & Metcalfe, 2006; Metcalfe, 2009; Serra & Metcalfe, 2009; Thiede, Anderson, & Therriault, 2003; Winne & Hadwin, 1998), and can therefo

Приближенное решение нелинейной системы уравнений для двухфазных сред

Research on expository text has shown that the accuracy of students' judgments of learning (JOLs) can be improved by instructional interventions that allow students to test their knowledge of the text. The present study extends this research, investigating whether allowing students to test the knowledge they acquired from studying a worked example by means of solving an identical problem, either immediately or delayed, would enhance JOL accuracy. Fifth grade children (i) gave an immediate JOL, (ii) a delayed JOL, (iii) solved a problem immediately and then gave a JOL, (iv) solved a problem immediately and gave a delayed JOL, or (v) solved a problem at a delay and then gave a JOL. Results show that problem solving after example study improved children's JOL accuracy (i.e., overestimation decreased). However, no differences in the accuracy of restudy indications were found. Results are discussed in relation to cue utilization when making JOLs

The role of motivational profiles in learning problem-solving and self-assessment skills with video modeling examples

In the current study, we examine the role of situation-specific motivational profiles in the effectiveness of video modeling examples for learning problem-solving and self-assessment accuracy in the domain of biology. A sample of 342 secondary school students participated in our study. Latent profile analysis resulted in four motivational profiles: (a) good-quality profile (high autonomous motivation, moderate introjected and external motivation), (b) moderately positive profile (moderate motivation levels with relatively higher autonomous motivation), (c) moderately negative profile (moderate motivation levels with relatively higher external motivation), and (d) poor-quality profile (moderate external, low autonomous motivation). Findings showed students with good-quality or moderately positive profiles learned more from the video modeling in terms of problem-solving and self-assessment accuracy than students with poor-quality or moderately negative profiles. Furthermore, students with a moderately negative profile outperformed students with a poor-quality profile on problem-solving and self-assessment accuracy. Results further indicated that students with good-quality and moderately positive profiles experienced studying the video modeling examples as less effortful than students with poor-quality or moderately negative profiles. Overall, our results demonstrated that knowing about students’ motivational profiles could help explain differences in how well students learn problem-solving as well as self-assessment skills from watching video modeling examples

The Relation Between Student’s Effort and Monitoring Judgments During Learning: A Meta-analysis

Research has shown a bi-directional association between the (perceived) amount of invested effort to learn or retrieve information (e.g., time, mental effort) and metacognitive monitoring judgments. The direction of this association likely depends on how learners allocate their effort. In self-paced learning, effort allocation is usually data driven, where the ease of memorizing is used as a cue, resulting in a negative correlation between effort and monitoring judgments. Effort allocation is goal driven when it is strategically invested (e.g., based on the importance of items or time pressure) and likel

Establishing a Scientific Consensus on the Cognitive Benefits of Physical Activity

Research suggests that physical activity can be used as an intervention to increase cognitive function. Yet, there are competing views on the cognitive effects of physical activity and it is not clear what level of consensus exists among researchers in the field. The purpose of this study was two-fold: Firstly, to quantify the scientific consensus by focusing on the relationship between physical activity and cognitive function. Secondly, to investigate if there is a gap between the public's and scientists' interpretations of scientific texts on this topic. A two-phase study was performed by including 75 scientists in the first phase and 15 non-scientists in the second phase. Participants were asked to categorize article abstracts in terms of endorsement of the effect of physical activity on cognitive function. Results indicated that there was a 76.1% consensus that physical activity has positive cognitive effects. There was a consistent association between scientists' and non-scientists' categorizations, suggesting that both groups perceived abstracts in a similar fashion. Taken together, this study provides the first analysis of its kind to evaluate the level of consensus in almost two decades of research. The present data can be used to inform further research and practice

Effects of self-assessment feedback on self-assessment and task-selection accuracy

Effective self-regulated learning in settings in which students can decide what tasks to work on, requires accurate self-assessment (i.e., a judgment of own level of performance) as well as accurate task selection (i.e., choosing a subsequent task that fits the current level of performance). Because self-assessment accuracy is often low, task-selection accuracy suffers as well and, consequently, self-regulated learning can lead to suboptimal learning outcomes. Recent studies have shown that a training with video modeling examples enhanced self-assessment accuracy on problem-solving tasks, but the training was not equally effective for every student and, overall, there was room for further improvement in self-assessment accuracy. Therefore, we investigated whether training with video examples followed by feedback focused on selfassessment accuracy would improve subsequent self-assessment and task-selection accuracy in the absence of the feedback. Experiment 1 showed, contrary to our hypothesis, that selfassessment feedback led to less accurate future self-assessments. In Experiment 2, we provided students with feedback focused on self-assessment accuracy plus information on the correct answers, or feedback focused on self-assessment accuracy, plus the correct answers and the opportunity to contrast those with their own answers. Again, however, we found no beneficial effect of feedback on subsequent self-assessment accuracy. In sum, we found no evidence that feedback on self-assessment accuracy improves subsequent accuracy. Therefore, future research should address other ways improving accuracy, for instance by taking into account the cues upon which students base their self-assessments

Synthesizing cognitive load and self-regulation theory: a theoretical framework and research agenda

An exponential increase in the availability of information over the last two decades has asked for novel theoretical frameworks to examine how students optimally learn under these new learning conditions, given the limitations of human processing ability. In this special issue and in the current editorial introduction, we argue that such a novel theoretical framework should integrate (aspects of) cognitive load theory and selfregulated learning theory. We describe the effort monitoring and regulation (EMR) framework, which outlines how monitoring and regulation of effort are neglected but essential aspects of self-regulated learning. Moreover, the EMR framework emphasizes the importance of optimizing cognitive load during self-regulated learning by reducing the unnecessary load on the primary task or distributing load optimally between the primary learning task and metacognitive aspects of the learning task. Three directions for future research that derive from the EMR framework and that are discussed in this editorial introduction are: (1) How do students monitor effort? (2) How do students regulate effort? and (3) How do we optimize cognitive load during self-regulated learning tasks (during and after the primary task)? Finally, the contributions to the current special issue are introduced

Establishing a Scientific Consensus on the Cognitive Benefits of Physical Activity

Research suggests that physical activity can be used as an intervention to increase cognitive function. Yet, there are competing views on the cognitive effects of physical activity and it is not clear what level of consensus exists among researchers in the field. The purpose of this study was two-fold: Firstly, to quantify the scientific consensus by focusing on the relationship between physical activity and cognitive function. Secondly, to investigate if there is a gap between the public’s and scientists’ interpretations of scientific texts on this topic. A two-phase study was performed by including 75 scientists in the first phase and 15 non-scientists in the second phase. Participants were asked to categorize article abstracts in terms of endorsement of the effect of physical acti

Training self-regulated learning skills with video modeling examples: Do task-selection skills transfer?

Self-assessment and task-selection skills are crucial in self-regulated learning situations in which students can choose their own tasks. Prior research suggested that training with 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, is effective for improving adolescents’ problem-solving posttest performance after self-regulated learning. In these examples, the models used a specific task-selection algorithm in which perceived mental effort and self-assessed performance scores were combined to determine the complexity and support level of the next task, selected from a task database. In the present study we aimed to replicate prior findings and to investigate whether transfer of task-selection skills would be facilitated even more by a more general, heuristic task-selection training than the task-specific algorithm. Transfer of task-selection skills was assessed by having students select a new task in another domain for a fictitious peer student. Results showed that both heuristic and algorithmic training of self-assessment and task-selection skills improved problem-solving posttest performance after a self-regulated learning phase, as well as transfer of task-selection skills. Heurist

Educational Theories and Learning Analytics : From Data to Knowledge

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