A systematized review of cognitive load theory in health sciences education and a perspective from cognitive neuroscience
Sahar Ghanbari1, Fariba Haghani2, Majid Barekatain3, Alireza Jamali4
1 Medical Education Research Center, Education Development Center, Isfahan University of Medical Sciences, Isfahan; Department of Occupational Therapy, School of Rehabilitation Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
2 Medical Education Research Center, Education Development Center, Isfahan University of Medical Sciences, Isfahan, Iran
3 Department of Psychiatry, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
4 Department of Occupational Therapy, School of Rehabilitation Sciences, Iran University of Medical Sciences, Tehran, Iran
|Date of Submission||25-Oct-2019|
|Date of Acceptance||12-Dec-2019|
|Date of Web Publication||28-Jul-2020|
Dr. Fariba Haghani
Medical Education Research Center, Education Development Center, Isfahan University of Medical Sciences, Isfahan
Source of Support: None, Conflict of Interest: None
INTRODUCTION: To design instructions in health sciences education, it is highly relevant to heed the working memory and the approaches for managing cognitive load. In this article, we tried to mention the implications of cognitive load theory (CLT) for optimizing teaching-learning in health sciences education and discussing cognitive load from the perspective of cognitive neurosciences as brain-aware medical education.
MATERIALS AND METHODS: We searched databases of Pubmed, Proquest, SCOPUS, and ISI Web of Science for relevant literature in September 1, 2018.
RESULTS: The 27 articles out of a total of 46 records, along with 23 papers from snowballing and hand searching were included in this study. Main items encompassed; “Various types of cognitive loads,” “Aim of cognitive load theory,” “Strategies to managing Cognitive Load,” “Cognitive Load Theory in novice and experienced learners and “expertise reversal effect,” Medical and Health Sciences Curriculums and Cognitive Load Theory,” “Challenges of Cognitive Load Theory.”
CONCLUSIONS: We discussed six important themes for CLT in health sciences education according to the literature. Mental imagery (visualization) as one of the useful techniques to optimize germane load was suggested, as it processes further gain access to neural circuits that are engaged in sensory, motor, executive, and decision-making pathways in the brain.
Keywords: Cognitive load theory, cognitive science, education, medical, neuroscience
|How to cite this article:|
Ghanbari S, Haghani F, Barekatain M, Jamali A. A systematized review of cognitive load theory in health sciences education and a perspective from cognitive neuroscience. J Edu Health Promot 2020;9:176
|How to cite this URL:|
Ghanbari S, Haghani F, Barekatain M, Jamali A. A systematized review of cognitive load theory in health sciences education and a perspective from cognitive neuroscience. J Edu Health Promot [serial online] 2020 [cited 2022 May 26];9:176. Available from: https://www.jehp.net/text.asp?2020/9/1/176/290948
| Introduction|| |
There exist various educational theories for medical and health sciences education from different disciplines such as educational psychology and cognitive theories.,, Cognitive Load Theory (CLT) is an instructional approach on the basis of human cognitive architecture knowledge, including the limitations of the working memory, how information is organized in long-term memory (LTM), and how the two memory structures are correlated. Working memory is constrained regarding duration and capacity, hence able to maintain and process only a limited amount of data at a certain time. Therefore, working memory acts as the main bottleneck for learning. CLT postulates that the extent of working memory load (exerted by cognitive processes) must not surpass its limited capacity during a learning task.
History of CLT dates back to 1950, yet the concept was actually defined 1980–1990 by an Australian educational psychologist named John Sweller, who considered the different sides of human cognitive architecture pertinent to the instructional design and utilized by CLT depending on evolutionary educational psychology. Evolutionary educational psychology more clarified the aspects of knowledge which CLT both did and did not apply to; the same goes for the way in which data were stored, processed, and utilized during and following instruction. In brief, the theory is associated with the attempt to learn new information and how the instructional design is able to positively influence the effort required to fathom the new material. CLT is also the basis of the approaches employed, including formative/summative assessments, micro-learning, and instructing various learning styles.
In medical education, we deal with the fact that the cognitive load on students is extremely high because of the advanced concepts and the workload, and the final objective is to aid instructional designers in communicating with their audience and enhance the educational experience of the students. Sharma holds that “Recognition of the importance of CLT is now being recognized as key in ensuring the development of the master learner.”,
As regards among major topics in the development of master learners in health science education is CLT which is needed to be noticed accurately with a new evidence-based perspective. In this article, we tried to mention the implications of CLT for optimizing teaching-learning in health sciences education and discussing cognitive load from the perspective of cognitive neurosciences, which is a developing field of the study called by neuroeducation studies.
| Materials and Methods|| |
We searched databases of PubMed, Proquest, SCOPUS, and ISI Web of Science for the relevant literature in September the first (2018). The main search strategy was: (cognitive load theory) AND (“medical educat*” OR “health sciences educat*” OR “health professions educat*” OR “health care professions educat*” OR “education in health sciences” OR “education in Health Professions” OR “education in medicine” OR “education in health care professions” OR “educating health professions” OR “educating health care professions” OR “educating medicine” OR “educating health sciences” OR “clinical teaching” OR “clinical educat*” OR “teaching round*”) and it adapted according to different databases [Table 1].
|Table 1: Databases, search strategies, and number of articles found by preliminary searching|
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The inclusion criteria were as follows: (a) document type of article, (c) English language, and (d) Studies published. We did not exclude any article after article inclusion. One author scanned all the titles and then abstracts to identify potentially relevant articles. Then, full-text versions of these articles were obtained and reviewed. A subsequent searching of reference lists of all full-text studies (snowballing) and a hand searching was also performed to get new related articles [Figure 1].
| Results|| |
Preliminary database searching identified 46 articles which 18 duplicate references removed. One item excluded in the screening phase as it was a book and 27 articles were reviewed according to the full texts. Twenty-three articles added by hand searching and cross-referencing, and finally, 50 articles were used in this study according to direct relevancy to medical and health sciences education. Reviewing the 50 articles resulted in six categories of information which were reported here by “Various types of cognitive loads,” “Aim of cognitive load theory,” “Strategies to managing Cognitive Load,” “Cognitive Load Theory in novice and experienced learners and “expertise reversal effect,” “Medical and Health Sciences Curriculums and Cognitive Load Theory,” “Challenges of Cognitive Load Theory [Table 2]: Overview of included papers (the authors' name, year of publication, article type, title, site of study, and results and suggestion).”
|Table 2: Overview of included papers (the authors' name, year of publication, article type, title, site of study, and results and suggestion)|
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Various types of cognitive loads
Intrinsic cognitive load (ICL), extraneous cognitive load (ECL), and germane cognitive load (GCL) are three types of cognitive load. Most educational psychologists believe that intrinsic load is associated with task performance, while the germane load is concerned with task learning and it encompassing the mind activities associated with schema construction and automation. Others hold that the construct of the intrinsic load must include schema acquisition, and the germane load is to be curbed to additional activities that improve learning including consciously applying learning strategies (e.g., compare and contrast). Another point of view is also argued that intrinsic load includes all activities attributable to germane load, and rejecting to have independent germane load construct.,,,, The difference between intrinsic and extraneous load is contextual and significantly depends on the definition of the objective of the learning task. One task's intrinsic load is another's extraneous load.,
Aim of cognitive load theory
Learning is the development and automation of cognitive schemas. Schema is “a cognitive construct organizing the elements of information based on the fashion they will be dealt with.” Instructional methods generated by CLT optimize learning through reducing the extraneous load, fitting the intrinsic load to the learner's developmental stage, and promoting germane load. The absence of retrieval cues associated with the desired item stored in LTM is the bottleneck for its retrieval; hence, the importance of schema construction and automation is clear. As schema is cognitive structure organizing information elements based on the way they are processed, it can decrease elements' interactivity, thereby reducing the ICL. Compared with a simple collection of lower-level components, cognitive construct shows a higher level of organized knowledge.,
Organized knowledge is not able to augment the retrieval cues, but it is capable of reducing the need for such cues. Schema automation results from providing learners the communicating concepts, and also simulated learning, case-study practice, repeated learning chances, and self-directed learning., Schemas-containing automated procedural knowledge is also regarded to be obtained as a whole. Retrieving suitable schemas and producing more suitable solutions can be done by learners with higher knowledge. Controlling germane load is providing learners with content and data, yet providing limited guidance to occur learning transfer to new contexts.,,
The extent to which components in single schemas interact is called element interactivity. If schemas are learned successively, rather than simultaneously, the ICL will be reduced. Optimally, schemas should be also learned simultaneously due to a holistic perception of element interactions. However, it is important to know that in this case, the higher element interactivity existing between schemas comes more ICL. Nonetheless, successive learning can ameliorate the instructional design in medical education.,, In other words, learners' ICL is reduced when instructional design focuses on successively instructing one idea or schema and obtaining mastery before moving on. It is remarkable that stress is one of the essential manifestations of cognitive overload.
It is to be noted that, as schema construction and automation, learning is a longitudinal phenomenon and different sorts of cognitive loads change with time. Moreover, students with shared previous experience, experiencing similar learning tasks in identical formats (equalizing extraneous and intrinsic load), can undergo highly variant stages of germane load which depends on effort, motivation, and metacognitive skills.,
Strategies for managing cognitive load
Simple-to-complex ordering of learning tasks and working from low- to high-fidelity environments, intrinsic load titration to the clinician developmental stage, augmenting the allotted time or decomposing the tasks through simulations, can decrease ICL. Instructional designers cooperate with faculty members to help students avoid cognitive overload. The difficulty of learning tasks should be in line with student progress. More advanced students are to be given access to simulated tasks in computer models, simulation centers, or practice on cadavers in a slightly more realistic, yet still safe, environment. Students should work with real patients at the end of their education. Medical education professionals must sure that students obtain a proper support level at each stage of education. It is important to analyze learners, write measurable goals, and determine the outcomes with attention to CLT principles.,,,, The extraneous load can be decreased through performing completion tasks, goal-free tasks, and worked examples, integrating various data sources, utilizing various modalities, decreasing redundancy, and minimizing interruptions and other such distractions. The instructional design utilizes interactive infographics, including pop-up information, texts, and pictures. When a student puts a mouse cursor on a particular area of a picture, an ancillary video opens to for a more precise clarification. Health professions educator should include more activities that lead students to an unspecified goal to reduce the extraneous load. For instance, designers can devise an activity requiring learners to think of myriad examples as to the reason why members of the community might not be comfortable with the research practices of the institution. Faculty members can incorporate both the worked example and completion principle in the courses. For example, suturing instruction might contain footage of a person finishing a suture followed by guided practice using mannequins. They might also be able to employ a split attention principle where “just in time” materials are provided in the form of user guides and simulation. In order to use the modality principle, faculty can utilize examples and video lectures instead of reading assignments. According to redundancy principle, they can decrease or eliminate excessive information sources and combine them via Learning Management Systems (LMS),,,,, Finally, augmenting variability over tasks, implementing contextual interference, encouraging strategies facilitating the production of a precise patient mental model, including prior knowledge activation, performance monitoring, and understanding and compare/contrast, and evoking self-explanation can all optimize the germane load. A good instructional designing can utilize various case study instances to employ the variability principle and augment the GCL. Laboratories can utilize the contextual interference principle, and as they move through the semester, the faculty can randomize the tasks completed by students. The advantages of addressing underlying pertinent data via prelecture materials before incorporating other novel concepts in formal lecture forms can be also helpful. Further, the amount of effort dedicated to performance and learning is determined by the clinician. Self-regulated learning takes place at various levels such as efforts to fathom the diseases, language, ideas, numbers, and acronyms, or trying to remember the cases or draw from the handover generalizable lessons. Approaches are self-monitoring to specify when insufficient understanding is present, asking elucidating questions, maintaining various representations in the working memory for compare and contrast, and previous knowledge activation on the disease or the patient which is an applied strategy to optimize germane load [Table 3].,,,,,,,,,
An example of utilizing CLT for instruction design in pharmacology was suggested by Kaylor with using a game of true/false, case studies unfolding collaboration, watching videos on YouTube for brand names of medicine, or having large group debates, as opening activities. It was also mentioned that providing students with lecture notes, can be helpful. “Notes Page” presentation view is a feature of PowerPoint, dividing in half a standard 8.5” 11” page, where the presentation slide appears at the top and the notes section at the bottom. Topic categorization can be helpful in this case which focuses on five concepts: (a) drug categorization and action mechanism, (b) adverse effects that are medication specific or worrying, (c) important contraindications, (d) nursing factors such as infusion rates, injection sites, and laboratory tests, (e) patient instruction aspects including dietary limitations, monitoring education, and data regarding safety. Such learning strategies create an active learning environment concentrated on students' needs. The lecture notes provide the learners with the chance to listen to lectures actively with no stress regarding major topics when note-taking; if needed, students were able to directly do note-taking on the lecture notes that were printed, where they identified whether or not the information was “need to know” or “nice to know.” Learners were further involved in reviewing at the start of the class, and as the semester progressed, even began to ask for their favorite activities. It was indicated by the instructor that the learning activities conducted to creating a “fun, engaging, and student-centered environment”.
Cognitive load theory in novice and experienced learners and “expertise reversal effect“
Research has shown, teaching that is properly designed for amateur learners are not the same as instructing learners who are more experienced. Instructional design for novices must assess the audience with regard to their prior knowledge and what they do not know yet. Underlying knowledge is to be taught prior to supplying one or multiple advanced knowledge to the students. Audio-visual presentations are more effective compared with presentations relying on one. Information is to be instructed in a manner that can be integrated by the students. Consider the potential impact of visual representations on different learners. Novice learners spend a lot of their cognitive resources analyzing the graphic, with limited resources remaining to connect and link the representations. Even when attempting to connect the representations, they more often focus on surface features, and not to the underlying pertinent features.
On the other side, learners are considered experienced once they have automated most of their schemas and have deeply fathomed their field of the study., Such learners are able to rapidly generate ideas and maintain a methodical thinking process utilizing schema or pattern. Recognizing the patterns conduces to learners quickly specifying problems and suitable solutions, while analytical skills and the methodical thinking process aid them in identifying specific problems and coming up with solutions. In longer running programs, the instructional design that used to apply to novice learners no longer impacts learners having more advanced prior knowledge, known as “expertise reversal effect.” Such anomaly has led instructional designers to employ a different strategy when designing materials for advanced learners. Instructional design for advanced learners is to be “adaptive, individualized instruction, based on authentic tasks, that gradually allows learners to take control over the process.” Students ought to specify areas with room for improvement and create activities and learning that can address such items. Students become abler to self-monitor and identify more areas for improvement as they start being more active in learning. Instructional design generates activities enabling students to imagine, self-explain, or partake in future states because of such activities aid students in gaining experience from their mistakes and challenges. Jigsaw activities, peer teaching, and other activities provide advanced learners with more opportunities to explore the material. A LMS is useful for recording student progress. Voice thread and other such programs give students the opportunity to finish these activities and utilize instructor and peer feedback and discussion.,,
Medical and health sciences curriculums and cognitive load theory
Task complexity, task fidelity, and instructional support are the three dimensions of devising curricula and materials in a medical setting. Task fidelity is the progression of learning tasks which start with low risk attempts such as material learning through lecture or text, and progression to simulated environments, and finish with actual scenarios and patients. Tasks are to gradually become more complex and skills required to aid learners will have more advanced knowledge and self-regulation skills. Teaching support differs among various stages of complexity and is to be gradually reduced as students workup their ways to becoming experts. At all levels, instructional support ought to provide students with practice chances and observe their cognitive load.
To achieve learning, educators at the University of Calgary's Faculty of Medicine made use of the concept of worked examples. Using developed schemes, these educators provided the medical students with the experience of experts. They evaluated the pros and cons of the old medical curriculum to review and devise a novel one. By 1991, the Clinical Presentation Curriculum (CPC) was prepared comprised of 120 clinical presentations. Worked examples encourage the development of diagnostic knowledge. In one study, amateurs studying the worked examples of electrocardiograms gained better results in the retention test. CPC possibly offers a suitable approach to training, but it rather focuses on clinical education than on basic sciences or preclinical work.
On the other side, a curriculum that establishes problem-based learning (PBL) strategy in the teaching-learning process, is not based on CLT and is suitable for hypothetic deductive clinical reasoning. In comparison, CPC is more appropriate than PBL for novice learners in the case of CLT., It is to be noted that a complete PBL use in medical education is not recommended, but it could be effectively employed as a supplementary strategy to facilitate the research and self-study of senior students. Moreover, minimally or partially guided teaching can positively influence supervised senior students because the formation of schemas (increasing expertise) is able to decrease the ICL., Pretz, on the other hand, holds that holistic intuitive approach better suits amateurs dealing with everyday challenges, while analytical strategies are more suitable for experts. Such arguments encourage health professional educators to make use of novel research where both analytical reasoning (AN) from system 2 of thinking and nonanalytical (schema or script) reasoning (NA) from system one are utilized. As demonstrated by Norman and Eva, interventions with the objective of having sheer amateurs to employ both types of processes over the diagnosis of electrocardiography illustrate slight but persistent effects. Similarly, one studyhas shown that promoting NA reasoning results in slight achievements with facile challenges, while encouraging AN reasoning leads to enhancements with challenging problems, highlighting the correlation between content and process. According to Rencic, a mnemonic checklist like SEA TOW (is a Second idea required? Is this a pattern recognition diagnosis which is “Eureka”/pattern? Is there an Anti-evidence refuting my diagnosis? Did I Think about my thoughts (metacognition)? Am I Overconfident? What else can I be missing?) Can encourage careful reflection on the challenging and complex diagnostic process. This metacognitive strategy possibly conduces to recognizing the need to decelerate and prevent early closure errors. It seems that although interactive factors are increased in this approach by slowing down the mind and thinking analytically (increasing ICL), it can be helpful for medical error reduction in both novice and advanced learners.
Challenges of cognitive load theory
“Measurement” challenges (how to measure different types of cognitive loads), along with “construct” challenges (types of cognitive loads), are associated with CLT studies. Instruments only measuring the overall cognitive load have limited theory testing and application. In the early 1990s, Paas introduced the primary cognitive load rating scale measure. There exist alternatives to the Paas scale such as secondary tasks which necessitate the students to get involved in a task secondary to the primary task. The indication is an increase in working memory load exerted by the first task may reduce the performance on the second task.,
Moreover, based on the qualities shared between the concepts of metacognition and germane load (e.g., monitoring the understanding and adapting action in actual time), future cognitive load measuring instruments should explore the inclusion of metacognition concepts. So far, there has been no literature published on the measurement of cognitive load subtypes during medical tasks. Some studies have indicated that intrinsic and ECLs are interdependent, possibly with a nonlinear and heavily context-dependent relationship. Hence, their measurements are completely context-dependent. Another challenge is that it not possible to measure germane load right after a learning activity as there is little time for schema development or automation; therefore, there is a weak association between learning outcomes and GCL factors. Further, regarding the challenges of emotional states on CLT, it is known that positive emotions are related to more profound processing, yet it is not clear what approaches are able to most efficiently modulate emotional influences on working memory resources. Based on CLT, the extraneous load is associated with external sources including noise in the background or the manner of information presentation. However, it is noteworthy that a learner's self-consciousness caused by teacher's observation in a medical clinic, anxiety, inner thoughts, fatigue or other internal factors possibly conduce to extraneous load, thereby “consuming” working memory resources. Extraneous load approaches have concentrated on decreasing or discarding the sources, some of which may be inevitable (e.g., some kind of interruptions or distraction that is internally generated). The main question is that are there “teachable” skills – possibly, skills associated with concentration maintenance or mindfulness – able to effectively decrease the working memory influence of a certain distraction? or is it necessary to teach the students regarding problem-solving skills?.,,,, Young et al. fostered an idea about second-generation handover practices which is concerned with internal distraction sources and the clinician's capability to deal with the distractions. He mentioned that approaches such as mindfulness, deep breath, or attention control are to be tested, as they may enhance the capability of a clinician to prevent distractions such as inner anxiety or outer noise, thereby reducing extraneous load and improving the performance. This is a new strategy for controlling the extraneous load; instead of managing the external factors, such interventions deal with the clinician's experience that can lessen the effect of distractions such as internal ones.,
Another challenge is that learner goal, teacher objectives, and approaches to human sensory processing should be included in the CLT model. Such augmentations are crucial because of the weak correlation between cognitive load and instructional design concerning self-controlled learning environments.
Cognitive load theory from cognitive sciences view
There are various opinions about the impact of cognitive load on the ability of attention and concentration. Some believe that as cognitive load increases, distraction increases due to limited executive resources of the human brain. Others believe that relevant cognitive load can reduce distractibility by inhibiting peripheral processing. Bottleneck theory emphasize that human information processing shows its limits in multitasking scenarios (doing two or more new tasks at the time). However, CLT can reduce occurrence of such incidences by reducing constrains that create bottleneck for learning.,,
As was already mentioned, CLT is on the basis of human memory model, which is created by Shiffrin and Atkinson, and is associated with sensory, working, and LTM. Sensory memory contains the exact sensory copy of what was briefly presented (i.e., <0.25 s). Working memory, for a short period, maintains an input material which is more processed (i.e., <30 s), only able to process certain pieces of material. Finally, LTM is the storehouse for a learner's whole knowledge for a long time.
Working memory is only able to maintain very few data elements that are independent at one time (7 ± 2), and is not able to process more than 2–4 elements actively at a certain time. This type of memory is regarded as the “bottleneck” for the processing of data, and is the result of the correlation between myriad element processes, such as attention, prospection, perceptual, and LTM representation. The function of working memory is altered over time with a trajectory that is U-shaped and inverted which can be changed for the better by training. An overloaded working memory impairs performance, and leads to errors, and possibly, entails patient harm., However, it is to be borne in mind that capacity is augmented via “chunking” pieces of data into more convoluted units. A well-accepted idea is that the constraints originate from the difficulty associated with maintaining various active representations separated from each other with minimal interference in neural activity. Humans are able to process more elements when they are distributed between the two channels, because working memory contains channels that are half independent for visual and auditory data.
Although neural activity which is constant in tasks of working-memory is considered as resulting from reentry via circuits that are recurrent, its exact mechanism is yet to be understood, but possibly involves rapid plastic changes at synaptic levels. Therefore, despite the fact that working memory possibly involves short-term plasticity, it does not seemingly need structural changes like a synthesis of new protein, because it functions via recruiting already present ion channels and synapses. During working memory, several brain regions interact such as “executive” regions existing in the prefrontal cortex, parietal cortex, and basal ganglia, along with regions specific for the process of especial representations to be held, such as the fusiform face area responsible for visage data maintenance. Human consciousness can monitor the items in the working memory. Findings from functional magnetic resonance imaging have confirmed the frontoparietal circuitry's role in working memory. This coordinated activity is supported by the superior longitudinal fasciculus (III); a white matter tract which connects the Brodmann area 9 / 46 with the supramarginal gyrus (BA40). Recent studies indicate the role of networks and their relationships such as Default Mode Network and task-based networks can have an impact on performance.
Another important issue in CLT which is related to cognitive sciences is visualization. Visualization (creating pictures or images) is one of the useful techniques to optimize germane load. It would be applicable in teaching-learning according to CLT because images can cue memories. Visualizing involves both the early and higher-order visual thalamus-cortical pathways of the human brain, and develops and refines internal demonstrations of solid and convoluted objects and their relative spatial position. The neuronal networks assembling the information and constructing memories do not concern the source (either created from within) of the inducing inputs so long as the necessary cellular and circuit signaling processes are available. Networks of “mirror neurons” in the brain possibly conduce to such procedures. Visualization processes further gain access to neural circuits that are engaged in sensory, motor, executive, and decision-making pathways in the brain.
| Discussion|| |
In different settings of health science education, high levels of cognitive load can influence the performance and learning of trainees in a negative manner. In this systematized review study, we collected and categorized previous studies to five main categories called by “Various types of cognitive loads,” “Aim of CLT,” “Strategies to managing Cognitive Load,” “CLT in novice and experienced learners and “expertise reversal effect,” Medical and Health Sciences Curriculums and CLT,” “Challenges of CLT.”
CLT is specifically pertinent to complicated learning settings, in which there exist high levels of element interactivity, referring to when the components of a task cannot be independently processed, yet, for learning to occur, need to be processed with regards to one another. The workplace environment can conduce extraneous overload and negatively affects the ability to engage in activities that promote germane load and learning. Intrinsic load must not be too high (overwhelming a student's working memory) or too low (inducing boredom or apathy). Common examples of extraneous loads are environmental distractions or suboptimal instructional design (e.g., unnecessarily having to look for information). Concerns about external or internal problems, competing demands, and self-induced time pressure are among the internal distractions also contributing to extraneous load which is always should be considered and minimized. Germane load, the third kind of cognitive load, refers to when learners freely employ cognitive processes so as to generate or modify cognitive schemas (organized patterns of data retrievable as a single unit and maintained in LTM). An important issue is that instructional designing including interleaved practice compared with blocked practice or prompting generative processes such as self-explaining or elaborating are among the approaches to promoting germane load.,,,, In contrast to the intrinsic load which is not typically controlled by the learner, the capability of controlling or modulating the germane load primarily lies with the student. Increased levels of intrinsic and extraneous load decrease the space for the formation of the schema; therefore, the increase in the levels of either can affect the learning and performance in a negative way. It was suggested different cognitive load strategies according to the literature in this article.
Another issue is that as the complex and automatized schemas are forming, instructional designing must consider “expertise reversal effect.” Instructional design for advanced learners is to be “adaptive, individualized instruction, based on authentic tasks, that gradually allows learners to take control over the process.” Peer teaching is such a good technique for advanced learners.,
A major obstacle to completely understanding the implications of CLT for health professions education workplaces are measuring the cognitive load, particularly cognitive load subtypes. Current debate revolves around whether to conceptualize germane load as the subset of intrinsic load or as the third kind of cognitive load different from intrinsic and extraneous loads.
Transferring cognitive patterns (schemas) to learners during a CPC have positive and negative points according to CLT and were compared to problem-based learning in previous literature.
Finally, as CLT mostly concern about human memory architecture, it seems that CLT's effects for workplace learning might be enhanced if considered besides the context of other learning theories.
Among the limitations of this study was including only English language articles which indicates that missing some studies is possible. It was also limitation related to the uncertainty of the quality of articles. Furthermore, we considered four main databases and the nonuse of some other databases can be mentioned as our limitations. On the other hand, we tried to include a proper range of studies by systematic and hand searching and discuss CLT from the lens of cognitive neuroscience.
| Conclusions|| |
CLT was developed according to human memory architecture, evolutionary educational psychology. Although its various subtypes are a challenge in this theory, intrinsic, extraneous, and GCLs are mostly considered. Instructional designing generated by CLT can optimize learning through reducing the extraneous load, fitting the intrinsic load to the learner's developmental stage, and promoting germane load. A variety of strategies are suggested according to CLT to optimize learning. Teaching that is properly designed for amateur learners is not the same as instructing learners who are more experienced. CPC and PBL curriculums were comparing according to CLT. There are some challenges related to CLT, especially in measurement of subtypes. There are various opinions about the impact of cognitive load on the ability of attention and concentration. Some believe that as cognitive load increases, distraction increases due to limited executive resources of the human brain and the other believe that relevant cognitive load can reduce distractibility by inhibiting peripheral processing. Mental imagery (visualization) is one of the useful techniques to optimize germane load as it processes further gain access to neural circuits that are engaged in sensory, motor, executive, and decision-making pathways in the brain.
This study, registered under the code No. 397540 and ID code of (IR.MUI.MED. REC.1397.170) confirmed by the Ethics Committee, was part of a research project conducted to obtain a PhD degree in Medical education from the Isfahan University of Medical Sciences.
Financial support and sponsorship
This study was financially supported by the Research Deputy of the Isfahan University of Medical Sciences.
Conflicts of interest
There are no conflicts of interest.
| References|| |
Gooding HC, Mann K, Armstrong E. Twelve tips for applying the science of learning to health professions education. Med Teach 2017;39:26-31.
Qiao YQ, Shen J, Liang X, Ding S, Chen FY, Shao L, et al
. Using cognitive theory to facilitate medical education. BMC Med Educ 2014;14:79.
Dennick R. Twelve tips for incorporating educational theory into teaching practices. Med Teach 2012;34:618-24.
van Merriënboer JJ, Sweller J. Cognitive load theory in health professional education: Design principles and strategies. Med Educ 2010;44:85-93.
Sewell JL, Maggio LA, Ten Cate O, van Gog T, Young JQ, O'Sullivan PS. Cognitive load theory for training health professionals in the workplace: A BEME review of studies among diverse professions: BEME Guide No. 53. Med Teach 2019;41:256-70.
Kavic MS. Cognitive load theory and learning medicine. Photomed Laser Surg 2013;31:357-9.
Wissman AW. Cognitive Load Theory: Applications in Medical Education. Technical Communication Capstone Course; 2018. p. 25.
Young JQ, van Merrienboer J, Durning S, Ten Cate O. Cognitive load theory: Implications for medical education: AMEE Guide No. 86. Med Teach 2014;36:371-84.
Sharma N. Cognitive load theory and teaching in primary care. Br J Gen Pract 2016;66:430
Schumacher DJ, Englander R, Carraccio C. Developing the master learner: Applying learning theory to the learner, the teacher, and the learning environment. Acad Med 2013;88:1635-45.
Leppink J. Cognitive load theory: Practical implications and an important challenge. J Taibah Univ Med Sci 2017;12:385-91.
Young JQ, Wachter RM, Ten Cate O, O'Sullivan PS, Irby DM. Advancing the next generation of handover research and practice with cognitive load theory. BMJ Qual Saf 2016;25:66-70.
Mulcock PM, Grassley J, Davis M, White K. Beyond the dedicated education unit: Using cognitive load theory to guide clinical placement. J Nurs Educ 2017;56:105-9.
Fraser KL, Ayres P, Sweller J. Cognitive load theory for the design of medical simulations. Simul Healthc 2015;10:295-307.
Hadie SN, Abdul Manan Sulong H, Hassan A, Mohd Ismail ZI, Talip S, Abdul Rahim AF. Creating an engaging and stimulating anatomy lecture environment using the cognitive load theory-based lecture model: Students' experiences. J Taibah Univ Med Sci 2018;13:162-72.
Hazan-Liran B, Miller P. Stroop-like effects in a new-code learning task: A cognitive load theory perspective. Q J Exp Psychol (Hove) 2017;70:1878-91.
Hessler KL, Henderson AM. Interactive learning research: Application of cognitive load theory to nursing education. Int J Nurs Educ Scholarsh 2013;10. pii:/j/ijnes.2013.10.issue-1/ijnes-2012-0029/ijnes-2012-0029.xml.
Schilling J. In respect to the cognitive load theory: Adjusting instructional guidance with student expertise. J Allied Health 2017;46:e25-30.
Khalil MK, Paas F, Johnson TE, Payer AF. Design of interactive and dynamic anatomical visualizations: The implication of cognitive load theory. Anat Rec B New Anat 2005;286:15-20.
Kluge A, Grauel B, Burkolter D. Combining principles of cognitive load theory and diagnostic error analysis for designing job aids: Effects on motivation and diagnostic performance in a process control task. Appl Ergon 2013;44:285-96.
Leppink J, Duvivier R. Twelve tips for medical curriculum design from a cognitive load theory perspective. Med Teach 2016;38:669-74.
Mitra R, McNeal KS, Bondell HD. Pupillary response to complex interdependent tasks: A cognitive-load theory perspective. Behav Res Methods 2017;49:1905-19.
Devolder P, Pynoo B, Voet T, Adang L, Vercruysse J, Duyck P. Optimizing physicians' instruction of PACS through e-learning: Cognitive load theory applied. J Digit Imaging 2009;22:25-33.
Tabbers HK, Martens RL, van Merriënboer JJ. Multimedia instructions and cognitive load theory: Effects of modality and cueing. Br J Educ Psychol 2004;74:71-81.
Sharma N. Colonoscopy adenoma detection rates: Room for cognitive load theory? Gastrointest Endosc 2016;84:547-8.
Kaylor SK. Preventing information overload: Cognitive load theory as an instructional framework for teaching pharmacology. J Nurs Educ 2014;53:108-11.
Young JQ, Ten Cate O, O'Sullivan PS, Irby DM. Unpacking the complexity of patient handoffs through the lens of cognitive load theory. Teach Learn Med 2016;28:88-96.
Rana J, Burgin S. Teaching learning tips 2: Cognitive load theory. Int J Dermatol 2017;56:1438-41.
Reedy GB. Using cognitive load theory to inform simulation design and practice. Clin Simul Nurs 2015;11:355-60.
van Gerven PW, Paas FG, van Merriënboer JJ, Schmidt HG. Cognitive load theory and the acquisition of complex cognitive skills in the elderly: Towards an integrative framework. Educ Gerontol 2000;26:503-21.
Adams T. The application of cognitive load theory to dual-task simulation training. Simul Healthc 2016;11:66-7.
Sun NZ, Anand PA, Snell L. Optimizing the design of high-fidelity simulation-based training activities using cognitive load theory – Lessons learned from a real-life experience. J Simul 2017;11:151-8.
Schilling JF. Cognitive load theory of learning: Underpinnings and model. Int J Athletic Ther Train 2016;21:12-6.
Young JQ, Sewell JL. Applying cognitive load theory to medical education: Construct and measurement challenges. Perspect Med Educ 2015;4:107-9.
Leppink J, Paas F, van Gog T, van Der Vleuten CP, van Merrienboer JJ. Effects of pairs of problems and examples on task performance and different types of cognitive load. Learn Instr 2014;30:32-42.
Ortony A, Rumelhart DE. The representation of knowledge in memory 1. In: Schooling and the Acquisition of Knowledge. Lawrence Erlbaum Associates London: Routledge; 2017. p. 99-135.
Sweller J, van Merrienboer JJ, Paas FG. Cognitive architecture and instructional design. Educ psychol Rev 1998;10:251-96.
Sweller J. Cognitive load theory, learning difficulty, and instructional design. Learn Instr 1994;4:295-312.
Sweller J, Chandler P. Why some material is difficult to learn. Cogn Instr 1994;12:185-233.
Sweller J. Cognitive load during problem solving: Effects on learning. Cogn Sci 1988;12:257-85.
Cook MP. Visual representations in science education: The influence of prior knowledge and cognitive load theory on instructional design principles. Sci Educ 2006;90:1073-91.
Norman GR, Eva KW. Diagnostic error and clinical reasoning. Med Educ 2010;44:94-100.
Pretz JE. Intuition versus analysis: Strategy and experience in complex everyday problem solving. Mem Cognit 2008;36:554-66.
Rencic J. Twelve tips for teaching expertise in clinical reasoning. Med Teach 2011;33:887-92.
Sweller J. Cognitive load theory, evolutionary educational psychology, and instructional design. In: Geary D., Berch D. (eds) Evolutionary Perspectives on Child Development and Education. Evolutionary Psychology. 2016; Springer, Cham291-306.
Sweller J. Measuring cognitive load. Perspect Med Educ 2018;7:1-2.
Chen F, Zhou J, Wang Y, Yu K, Arshad SZ, Khawaji A, et al
. Emotion and cognitive load. In: Robust Multimodal Cognitive Load Measurement. Human–Computer Interaction Series. Springer, Cham; 2016. p. 173-83.
Fraser K, Ma I, Teteris E, Baxter H, Wright B, McLaughlin K. Emotion, cognitive load and learning outcomes during simulation training. Med Educ 2012;46:1055-62.
Pawar S, Jacques T, Deshpande K, Pusapati R, Meguerdichian MJ. Evaluation of cognitive load and emotional states during multidisciplinary critical care simulation sessions. BMJ Simul Technol Enhanc Learn 2018;4:87-91.
Fraser K, McLaughlin K. Temporal pattern of emotions and cognitive load during simulation training and debriefing. Med Teach 2019;41:184-9.
Seufert T. The interplay between self-regulation in learning and cognitive load. Educ Res Rev 2018;24:116-29.
Sörqvist P, Dahlström Ö, Karlsson T, Rönnberg J. Concentration: The neural underpinnings of how cognitive load shields against distraction. Front Hum Neurosci 2016;10:221.
Borst JP, Taatgen NA, van Rijn H. The problem state: A cognitive bottleneck in multitasking. J Exp Psychol Learn Mem Cogn 2010;36:363-82.
Mayer RE. Applying the science of learning to medical education. Med Educ 2010;44:543-9.
Saif AA. Modern Educational Psychology (Psychology of Learning and Instruction). 7th
ed. Tehran: Dowran Publishing Company; 2017. p. 726.
Eriksson J, Vogel EK, Lansner A, Bergström F, Nyberg L. Neurocognitive architecture of working memory. Neuron 2015;88:33-46.
Logie RH, Morris RG. Working Memory and Ageing. Hove, United Kingdom: Psychology Press; 2014.
Dirette DP, Anderson MA. The relationship between learning style preferences and memory strategy use in adults. Occup Ther Health Care 2016;30:245-54.
Karlsgodt KH. Working memory. In: Toga AW, editor. Brain Mapping. Waltham: Academic Press; 2015. p. 319-26.
Brain science, learning and teaching. Clin Teach 2009;6:283-4.
Salvetti F, Bertagni B. e-REAL: Enhanced reality lab. Int J Adv Corp Learn 2014;7:41-9.
Friedlander MJ, Andrews L, Armstrong EG, Aschenbrenner C, Kass JS, Ogden P, et al
. What can medical education learn from the neurobiology of learning? Acad Med 2011;86:415-20.
[Table 1], [Table 2], [Table 3]