Agency attributions of mental effort during self-regulated learning

Dissociations between data-driven and goal-driven effort reports: Performance, metacognition, and affect

Measuring effort has long been a challenge and this seems particularly true in the case of subjective effort. Koriat et al. compared two types of effort frames, what they call data-driven effort, the amount of effort perceived to be required by a task, and goal-driven effort, the amount of effort one chooses to invest in a task. This study investigates whether self-reports of data- and goal-driven effort are differentially associated with test performance, metacognition, and affect in a complex learning task. Results demonstrate that data- and goal-driven effort have qualitatively different relations with many of these variables. For example, partial correlations revealed data-driven effort was negatively associated with prospective and retrospective performance estimates, but the opposite pattern emerged for goal-driven effort. These results demonstrate that how subjective measures of effort are framed (and interpreted by the respondent) can drastically influence how they relate to other variables of interest.

Building Bridges Between Self-Regulation and Cognitive Load—an Invitation for a Broad and Differentiated Attempt

Building bridges between two of the most influential research fields in educational psychology, self-regulation and cognitive load theory, is highly relevant but also challenging. The collection of papers in this special issue reflects this interplay by reviewing the still scarce base of empirical data in an impressively elaborated and profound way. The papers offer different perspectives on how to improve learning by stimulating both activities for self-regulation as well as for reflecting the mental effort which can be used in return for monitoring and regulation. They provide arguments for the two sidedness of the relationship of self-regulation and cognitive load: that cognitive load can cause self-regulation and that self-regulation can cause cognitive load. The common understanding of self-regulation in this issue is very much focused on monitoring and could benefit from a broader view by including the whole cycle of self-regulation and moderating motivational factors like self-efficacy, as proposed in many self-regulation models. The conceptualization of effort, as it is referred to in most of the papers, could also profit from a more differentiated view, which takes into account the origin of required or invested mental effort. Overall, what learners actually decide to do when dealing with self-regulation as well as with cognitive load highly depends on their resources. In an integrative model, the role of potential resources is discussed as a starting point for future research. This discussion invites for an even broader, more individualized, and differentiated view to add to the bridge-building attempts of this impressive collection of research.

The Relation Between Students’ 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 likely results in a positive correlation. The current study used a meta-analytic approach to synthesize the results from several studies on the relationship between effort and monitoring judgments. The results showed that there was a negative association between effort and monitoring judgments (r = − .355). Furthermore, an exploration of possible moderators of this association between effort and monitoring was made. The negative association was no longer significant when goal-driven regulation was manipulated. Furthermore, it was found that the type of monitoring judgment (i.e., a weaker association for prospective judgments) and type of task (stronger association for problem-solving tasks relative to paired associates) moderated the relation between effort and monitoring. These results have important implications for future research on the use of effort as a cue for monitoring in self-regulated learning.

Looking at Mental Effort Appraisals through a Metacognitive Lens: Are they Biased?

A central factor in research guided by the Cognitive Load Theory (CLT) is the mental effort people invest in performing a task. Mental effort is commonly assessed by asking people to report their effort throughout performing, learning, or problem-solving tasks. Although this measurement is considered reliable and valid in CLT research, metacognitive research provides robust evidence that self-appraisals of performance are often biased. In this review, we consider the possibility that mental effort appraisals may also be biased. In particular, we review signs for covariations and mismatches between subjective and objective measures of effort. Our review suggests that subjective and most objective effort measures appear reliable and valid when evaluated in isolation, because they discriminate among tasks of varying complexity. However, not much is known about their mutual correspondence—that is, whether subjective measures covariate with objective measures. Moreover, there is evidence that people utilize heuristic cues when appraising their effort, similar to utilization of heuristic cues underlying metacognitive appraisals of performance. These cues are identified by exposing biases—mismatch in effects of cue variations on appraisals and performance. The review concludes with a research agenda in which we suggest applying the well-established methodologies for studying biases in self-appraisals of performance in metacognitive research to investigating effort appraisals. One promising method could be to determine the covariation of effort appraisals and objective effort measures as an indicator of the resolution of effort appraisals.