Training self-assessment and task-selection skills to foster self-regulated learning: Do trained skills transfer across domains?

Supplementing Intraoperative Mastoidectomy Teaching With Video-Based Coaching

OBJECTIVES

Video-based coaching might complement general surgery education, but little is known of its applicability for otologic microsurgical teaching. Our purpose was thus to evaluate the content and resident-perceived benefit of video-based coaching for mastoidectomy education.

METHODS

In this mixed-methods pilot design, mastoidectomies were recorded from operative microscopes and reviewed during 30-minute video-based coaching sessions at 2 tertiary care centers. Eight residents and 3 attendings participated. Ten-point Likert-type questionnaires on the extent to which attendings taught 12 topics through 8 techniques were completed by residents after surgical and coaching sessions. Coaching sessions and structured interviews with residents were audio-recorded, transcribed and iteratively coded.

RESULTS

Seven audio-recordings were available for coaching sessions, during which a mean of 2.22 ± 0.5 topics per minute were discussed. Of the 12 teaching topics, technique was discussed most frequently (32%, 0.71 ± 0.2 topics/min), followed by anatomy (16%, 0.31 ± 0.16 topics/min). Of all 8 ratings between coaching and operative sessions, residents indicated a greater extent of discussion of anatomy (median difference, [95% confidence interval (CI)] of 3 [1-4]), progress (2.25 [95% CI, 0.5-4]), technique (3.5 [95% CI, 1.5-5.5]), pitfalls (2.5 [95% CI, 1-3.5]), and summarizing (3 [95% CI, 1-5]). In structured interviews, residents reported improved self-confidence and global perspective.

CONCLUSIONS

Video-based coaching is educationally dense and characterized by perceived richer teaching and promotion of a deeper surgical understanding. It requires no additional resources, can be completed in a short period of time and can be implemented programmatically for any otolaryngologic subspecialty utilizing video-recording capable equipment.

Do Video Modeling and Metacognitive Prompts Improve Self-Regulated Scientific Inquiry?

Guided inquiry learning is an effective method for learning about scientific concepts. The present study investigated the effects of combining video modeling (VM) examples and metacognitive prompts on university students’ (N = 127) scientific reasoning and self-regulation during inquiry learning. We compared the effects of watching VM examples combined with prompts (VMP) to watching VM examples only, and to unguided inquiry (control) in a training and a transfer task. Dependent variables were scientific reasoning ability, hypothesis and argumentation quality, and scientific reasoning and self-regulation processes. Participants in the VMP and VM conditions had higher hypothesis and argumentation quality in the training task and higher hypothesis quality in the transfer task compared to the control group. There was no added benefit of the prompts. Screen captures and think aloud protocols during the two tasks served to obtain insights into students’ scientific reasoning and self-regulation processes. Epistemic network analysis (ENA) and process mining were used to model the co-occurrence and sequences of these processes. The ENA identified stronger co-occurrences between scientific reasoning and self-regulation processes in the two VM conditions compared to the control condition. Process mining revealed that in the VM conditions these processes occurred in unique sequences and that self-regulation processes had many self-loops. Our findings show that video modeling examples are a promising instructional method for supporting inquiry learning on both the process and the learning outcomes level.

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 self-regulated 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.

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.