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Cognitive load theory was introduced in the 1980s as an instructional design theory based on several uncontroversial aspects of human cognitive architecture. Our knowledge of many of the characteristics of working memory, long-term memory and the relations between them had been well-established for many decades prior to the introduction of the theory. Curiously, this knowledge had had a limited impact on the field of instructional design with most instructional design recommendations proceeding as though working memory and long-term memory did not exist. In contrast, cognitive load theory emphasised that all novel information first is processed by a capacity and duration limited working memory and then stored in an unlimited long-term memory for later use. Once information is stored in long-term memory, the capacity and duration limits of working memory disappear transforming our ability to function. By the late 1990s, sufficient data had been collected using the theory to warrant an extended analysis resulting in the publication of Sweller et al. {Educational Psychology Review, 10, 251-296, 1998). Extensive further theoretical and empirical work have been carried out since that time and this paper is an attempt to summarise the last 20 years of cognitive load theory and to sketch directions for future research.

Although self-regulated learning (SRL) is becoming increasingly important in modern educational contexts, disagreements exist regarding its measurement. One particularly important issue is whether self-reports represent valid ways to measure this process. Several researchers have advocated the use of behavioral indicators of SRL instead. An outstanding research debate concerns the extent to which it is possible to compare behavioral measures of SRL to traditional ways of measuring SRL using self-report questionnaire data, and which of these methods provides the most valid and reliable indicator of SRL. The current review investigates this question. It was found that granularity is an important concept in the comparison of SRL measurements, influencing the degree to which students can accurately report on their use of SRL strategies. The results show that self-report questionnaires may give a relatively accurate insight into students’ global level of self-regulation, giving them their own value in educational research and remediation. In contrast, when students are asked to report on specific SRL strategies, behavioral measures give a more accurate account. First and foremost, researchers and practitioners must have a clear idea about their research question or problem statement, before choosing or combining either form of measurement.

In the past 50 years, the original McMaster PBL model has been implemented, experimented, revised, and modified, and is still evolving. Yet, the development of PBL is not a series of success stories, but rather a journey of experiments, failures and lessons learned. In this paper, we analyzed the meta-analyses and systematic reviews on PBL from 1992 to present as they provide a focused lens on the PBL research in the past 5 decades. We identified three major waves in the PBL research development, analyzed their impact on PBL research and practice, and offered suggestions of research gaps and future directions for the field. The first wave of PBL research (polarization: 1990–mid 2000) focused on answering the question “Does PBL work?” and the outcomes. The results were conflicting. The researchers took polarizing positions and debated over the merits of PBL throughout this wave. However, the contradictory results and the debates in fact pushed the researchers to look harder for new directions to solve the puzzle. These efforts resulted in the second wave (from outcomes to process: mid 2000–mid 2010) that focused on the question “How does PBL work?” The second wave of PBL research targeted at investigating the effects of implementation constituents, such as assessment formats or single versus curriculum wide implementations. The third wave (specialization: mid 2010 and onward) of PBL research focused on “How does PBL work in different specific contexts?” These research widened our perspectives by expanding our understanding of how PBL manifests itself in different contexts. Given the diversification of PBL and more hybrid PBL models, we suggest “Why does PBL with particular implementation characteristics for specific outcomes work or not work in the condition where it is implemented?” to be the question to answer in the next wave of PBL research.

In general, researchers attempt to quantify cognitive load using physiologic and psychometric measures. Although the construct measured by both of these metrics is thought to represent overall cognitive load, there is a paucity of studies that compares these techniques to one another. The authors compared data obtained from one physiologic tool (pupillometry) to one psychometric tool (Paas scale) to explore whether they actually measured the construct of cognitive load as purported. Thirty-two participants with a range of resuscitation medicine experience and expertise completed resuscitation-medicine based multiple-choice-questions as well as arithmetic questions. Cognitive load, as measured by both tools, was found to be higher for the more difficult questions as well as for questions that were answered incorrectly ( p  < 0.001). The group with the least medical experience had higher cognitive load than both the intermediate and experienced groups when answering domain-specific questions ( p  = 0.023 and p  = 0.003 respectively for the physiologic tool; p  = 0.006 and p  < 0.001 respectively for the psychometric tool). There was a strong positive correlation (Spearman’s ρ = 0.827, p  < 0.001 for arithmetic questions; Spearman’s ρ = 0.606, p  < 0.001 for medical questions) between the two cognitive load measurement tools. These findings support the validity argument that both physiologic and psychometric metrics measure the construct of cognitive load.

Although the theoretical framework of cognitive load theory has acknowledged a role for the learning environment, the specific characteristics of the physical learning environment that could affect cognitive load have never been considered, neither theoretically nor empirically. In this article, we argue that the physical learning environment, and more specifically its effects on cognitive load, can be regarded as a determinant of the effectiveness of instruction. We present an updated version of the cognitive load model of Paas and Van Merriënboer (Educational Psychology Review, 6:351-371, 1994a), in which the physical learning environment is considered a distinct causal factor that can interact with learner characteristics, learning-task characteristics, or a combination of both. Previous research into effects of the physical learning environment on cognitive performance that could inspire new cognitive load research is discussed, and a future research agenda is sketched.

Radiologists can visually detect abnormalities on radiographs within 2s, a process that resembles holistic visual processing of faces. Interestingly, there is empirical evidence using functional magnetic resonance imaging (fMRI) for the involvement of the right fusiform face area (FFA) in visual-expertise tasks such as radiological image interpretation. The speed by which stimuli ( e . g ., faces, abnormalities) are recognized is an important characteristic of holistic processing. However, evidence for the involvement of the right FFA in holistic processing in radiology comes mostly from short or artificial tasks in which the quick, ‘holistic’ mode of diagnostic processing is not contrasted with the slower ‘search-to-find’ mode. In our fMRI study, we hypothesized that the right FFA responds selectively to the ‘holistic’ mode of diagnostic processing and less so to the ‘search-to-find’ mode. Eleven laypeople and 17 radiologists in training diagnosed 66 radiographs in 2s each (holistic mode) and subsequently checked their diagnosis in an extended (10-s) period (search-to-find mode). During data analysis, we first identified individual regions of interest (ROIs) for the right FFA using a localizer task. Then we employed ROI-based ANOVAs and obtained tentative support for the hypothesis that the right FFA shows more activation for radiologists in training versus laypeople, in particular in the holistic mode ( i . e ., during 2s trials), and less so in the search-to-find mode ( i . e ., during 10-s trials). No significant correlation was found between diagnostic performance (diagnostic accuracy) and brain-activation level within the right FFA for both, short-presentation and long-presentation diagnostic trials. Our results provide tentative evidence from a diagnostic-reasoning task that the FFA supports the holistic processing of visual stimuli in participants’ expertise domain.

Background Feedback is crucial in medical education for developing communication skills and fostering comprehensive learning. Despite its importance, medical students often face challenges in effectively leveraging feedback. This study investigates how students perceive and make sense of internal and external feedback in a 2nd language (L2) medical Dutch course. Methods Sixteen third-year international medical students (mean age = 23) participated in a medical Dutch course that included six structured sessions. Each session encompassed a briefing, simulated patient consultations (SPCs), and a debriefing. The curriculum integrated internal feedback from self-reflections and external feedback from peers, teachers, and simulated patients. Data were gathered through a students’ feedback perception survey and semi-structured interviews and analyzed via inductive thematic analysis. Results Survey data indicated a trend where the preference, satisfaction, and trust in external feedback were higher than those for internal feedback. However, both types of feedback were regarded as equally effective in facilitating learning progress. Through thematic analysis, we identified five crucial themes that show how students perceive and make sense of various forms of feedback: proactive engagement with feedback, critically analyzing and utilizing the exchange in dialogues and discussions, self-reflection and progress tracking, value from diverse perspectives, and moment-specific and actionable feedback. Conclusions This study emphasizes the vital roles of internal and external feedback in enhancing medical Dutch communication skills among medical students. Internal feedback encourages self-reflection and growth, essential for complex medical communications, while external feedback provides clear, specific and supportive guidance and experience from teachers, simulated patients and peers. These feedback mechanisms together improve students’ skills in medical Dutch communication, leading to better doctor-patient interactions. Future research should focus on adapting these feedback strategies across diverse educational settings to further support the development of medical L2 communication skills in global medical contexts.

To prevent radiologists from overlooking lesions, radiology textbooks recommend “systematic viewing,” a technique whereby anatomical areas are inspected in a fixed order. This would ensure complete inspection (full coverage) of the image and, in turn, improve diagnostic performance. To test this assumption, two experiments were performed. Both experiments investigated the relationship between systematic viewing, coverage, and diagnostic performance. Additionally, the first investigated whether systematic viewing increases with expertise; the second investigated whether novices benefit from full-coverage or systematic viewing training. In Experiment 1, 11 students, ten residents, and nine radiologists inspected five chest radiographs. Experiment 2 had 75 students undergo a training in either systematic, full-coverage (without being systematic) or non-systematic viewing. Eye movements and diagnostic performance were measured throughout both experiments. In Experiment 1, no significant correlations were found between systematic viewing and coverage, r  = −.10, p  = .62, and coverage and performance, r  = −.06, p  = .74. Experts were significantly more systematic than students F 2,25  = 4.35, p  = .02. In Experiment 2, significant correlations were found between systematic viewing and coverage, r  = −.35, p  < .01, but not between coverage and performance, r  = .13, p  = .31. Participants in the full-coverage training performed worse compared with both other groups, which did not differ between them, F 2,71  = 3.95, p  = .02. In conclusion, the data question the assumption that systematic viewing leads to increased coverage, and, consequently, to improved performance. Experts inspected cases more systematically, but students did not benefit from systematic viewing training.

When analyzing classroom video, pre-service teachers can improve their professional vision, that is, their ability to notice important events in a classroom and to interpret them based on theoretical knowledge. However, learning with video is especially challenging for novice learners. Thus, video needs to be embedded into an instructional context to be effective. In an experimental study with 89 pre-service biology teachers, we investigated the effect of a short professional vision training and whether two design principles from multimedia learning research—namely segmenting and self-explanation prompts—could additionally increase training effects. In a one-hour training session on small-group tutoring strategies, participants watched practice video examples either as a whole or segmented. After each video or video segment, respectively, they received either open or focused self-explanation prompts to analyze the scene. We assessed participants’ professional vision skills before and after training. Overall, participants’ performance substantially increased from pretest to posttest. Moreover, during training, both segmented video examples and focused self-explanation prompts led to increased noticing of relevant strategies. This advantage during training, however, did not result in higher professional vision improvement in posttest scores compared to participants who worked in the less supported training phase conditions. We discuss possible explanations why additional support increased training performance but not learning gains and suggest an additional fading phase as a means to achieve persistent effects.

Cognitive Load Theory has emerged as an important approach to improving instruction in the health professions workplace, including patient handovers. At the same time, there is growing recognition that emotion influences learning through numerous cognitive processes including motivation, attention, working memory, and long-term memory. This study explores how emotion influences the cognitive load experienced by trainees performing patient handovers. From January to March 2019, 693 (38.7%) of 1807 residents and fellows from a 24-hospital health system in New York city completed a survey after performing a handover. Participants rated their emotional state and cognitive load. The survey included questions about features of the learner, task, and instructional environment. The authors used factor analysis to identify the core dimensions of emotion. Regression analyses explored the relationship between the emotion factors and cognitive load types. Two emotion dimensions were identified representing invigoration and tranquility. In regression analyses, higher levels of invigoration, tranquility, and their interaction were independently associated with lower intrinsic load and extraneous load. The interaction of invigoration and tranquility predicted lower germane load. The addition of the emotion variables to multivariate models including other predictors of cognitive load types significantly increased the amount of variance explained. The study provides a model for measuring emotions in workplace learning. Because emotion appears to have a significant influence on cognitive load types, instructional designers should consider strategies that help trainees regulate emotion in order to reduce cognitive load and improve learning and performance.