10.5826/dpc.1204a188
Introduction: Efficient interpretation of dermoscopic images relies on pattern recognition, and the development of expert-level proficiency typically requires extensive training and years of practice. While traditional methods of transferring knowledge have proven effective, technological advances may significantly improve upon these strategies and better equip dermoscopy learners with the pattern recognition skills required for real-world practice.
Objectives: A narrative review of the literature was performed to explore emerging directions in medical image interpretation education that may enhance dermoscopy education. This article represents the first of a two-part review series on this topic.
Methods: To promote innovation in dermoscopy education, the International Skin Imaging Collaboration (ISIC)assembled a 12-member Education Working Group that comprises international dermoscopy experts and educational scientists. Based on a preliminary literature review and their experiences as educators, the group developed and refined a list of innovative approaches through multiple rounds of discussion and feedback. For each approach, literature searches were performed for relevant articles.
Results: Through a consensus-based approach, the group identified a number of emerging directions in image interpretation education. The following theory-based approaches will be discussed in this first part: whole-task learning, microlearning, perceptual learning, and adaptive learning.
Conclusions: Compared to traditional methods, these theory-based approaches may enhance dermoscopy education by making learning more engaging and interactive and reducing the amount of time required to develop expert-level pattern recognition skills. Further exploration is needed to determine how these approaches can be seamlessly and successfully integrated to optimize dermoscopy education.
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Kelly C. Nelson
https://creativecommons.org/licenses/by-nc/4.0
Dermatology Practical & Conceptual
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Theory-Based Approaches to Support Dermoscopic Image Interpretation Education: A Review of the Literature
Text.Serial.Journal
https://dpcj.org/index.php/dpc
Review
2022-10-31
2021-12-10
2022-10-31
2022-12-02
e2022188
Dermatol Pract Concept
Introduction: Efficient interpretation of dermoscopic images relies on pattern recognition, and the development of expert-level proficiency typically requires extensive training and years of practice. While traditional methods of transferring knowledge have proven effective, technological advances may significantly improve upon these strategies and better equip dermoscopy learners with the pattern recognition skills required for real-world practice.
Objectives: A narrative review of the literature was performed to explore emerging directions in medical image interpretation education that may enhance dermoscopy education. This article represents the first of a two-part review series on this topic.
Methods: To promote innovation in dermoscopy education, the International Skin Imaging Collaboration (ISIC)assembled a 12-member Education Working Group that comprises international dermoscopy experts and educational scientists. Based on a preliminary literature review and their experiences as educators, the group developed and refined a list of innovative approaches through multiple rounds of discussion and feedback. For each approach, literature searches were performed for relevant articles.
Results: Through a consensus-based approach, the group identified a number of emerging directions in image interpretation education. The following theory-based approaches will be discussed in this first part: whole-task learning, microlearning, perceptual learning, and adaptive learning.
Conclusions: Compared to traditional methods, these theory-based approaches may enhance dermoscopy education by making learning more engaging and interactive and reducing the amount of time required to develop expert-level pattern recognition skills. Further exploration is needed to determine how these approaches can be seamlessly and successfully integrated to optimize dermoscopy education.
Kelly C. Nelson
2022/10/31
10.5826/dpc.1204a188
e2022188
The University of Texas MD Anderson Cancer Center
2160-9381
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en
e2022188
Möckel T, Beste C, Wascher E. The effects of time on task in response selection: An ERP study of mental fatigue. Sci Rep. 2015;5(1):10113.
Theory-Based Approaches to Support Dermoscopic Image Interpretation Education: A Review of the Literature
https://dpcj.org/index.php/dpc/article/download/2242/1835
           Theory-Based Approaches to Support
        Dermoscopic Image Interpretation Education:
                A Review of the Literature
     Tiffaney Tran1, Niels K Ternov2, Jochen Weber3, Catarina Barata4, Elizabeth G Berry5,
         Hung Q Doan1, Ashfaq A Marghoob3, Elizabeth V Seiverling6, Shelly Sinclair7,
      Jennifer A Stein8, Elizabeth R Stoos5, Martin G Tolsgaard9, Maya Wolfensperger10,
                               Ralph P Braun10, Kelly C Nelson1


 1 Department of Dermatology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
 2 Department of Plastic Surgery, Herlev Hospital, Herlev, Denmark
 3 Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
 4 Institute for Systems and Robotics; Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
 5 Department of Dermatology, Oregon Health & Science University, Portland, OR, USA
 6 Division of Dermatology, Maine Medical Center, Portland, ME, USA; Department of Dermatology, Tufts University School of Medicine,
   Boston, MA,USA
 7 Department of Biology, Davidson College, Davidson, NC, USA
 8 The Ronald O. Perelman Department of Dermatology, New York University School of Medicine, New York, NY, USA
 9 Copenhagen Academy for Medical Education and Simulation; Department of Obstetrics, Copenhagen University Hospital Rigshospitalet,
   Copenhagen, Denmark
10 Department of Dermatology, University Hospital of Zürich, University of Zürich, Zürich, Switzerland



Key words: dermoscopy education, image interpretation education, pattern recognition, educational theory, container model
Citation: Tran T, Ternov NK, Weber J, et al. Theory-Based Approaches to Support Dermoscopic Image Interpretation Education: A Review
of the Literature. Dermatol Pract Concept. 2022;12(4):e2022188. DOI: https://doi.org/10.5826/dpc.1204a188
Accepted: February 1, 2022; Published: October 2022
Copyright: ©2022 Tran et al. This is an open-access article distributed under the terms of the Creative Commons Attribution-
NonCommercial License (BY-NC-4.0), https://creativecommons.org/licenses/by-nc/4.0/, which permits unrestricted noncommercial use,
distribution, and reproduction in any medium, provided the original authors and source are credited.
Funding: None.
Competing Interests: None.
Authorship: All authors have contributed significantly to this publication.
Corresponding Author: Kelly C. Nelson, MD, Department of Dermatology, The University of Texas MD Anderson Cancer Center, 1400
Pressler Street, Unit 1452, Houston, TX 77030, USA, Telephone: 713-745-1113, Fax: 713-745-3597, E-mail: kcnelson1@mdanderson.org




  ABSTRACT             Introduction: Efficient interpretation of dermoscopic images relies on pattern recognition, and the
                       development of expert-level proficiency typically requires extensive training and years of practice.
                       While traditional methods of transferring knowledge have proven effective, technological advances
                       may significantly improve upon these strategies and better equip dermoscopy learners with the pattern
                       recognition skills required for real-world practice.




Review | Dermatol Pract Concept. 2022;12(4):e2022188                                                                                1
                     Objectives: A narrative review of the literature was performed to explore emerging directions in med-
                     ical image interpretation education that may enhance dermoscopy education. This article represents
                     the first of a two-part review series on this topic.
                     Methods: To promote innovation in dermoscopy education, the International Skin Imaging Collabo-
                     rative (ISIC) assembled a 12-member Education Working Group that comprises international dermos-
                     copy experts and educational scientists. Based on a preliminary literature review and their experiences
                     as educators, the group developed and refined a list of innovative approaches through multiple rounds
                     of discussion and feedback. For each approach, literature searches were performed for relevant articles.
                     Results: Through a consensus-based approach, the group identified a number of emerging directions
                     in image interpretation education. The following theory-based approaches will be discussed in this
                     first part: whole-task learning, microlearning, perceptual learning, and adaptive learning.
                     Conclusions: Compared to traditional methods, these theory-based approaches may enhance dermos-
                     copy education by making learning more engaging and interactive and reducing the amount of time
                     required to develop expert-level pattern recognition skills. Further exploration is needed to determine
                     how these approaches can be seamlessly and successfully integrated to optimize dermoscopy education.




Introduction                                                      healthcare providers estimated that at least six years of ex-
                                                                  perience may be necessary to develop a sufficient level of
As a visual specialty, dermatology relies on the recognition      competency [1].
of characteristic features and patterns within clinical and          The container model embodies a traditional instructional
dermoscopic images of skin lesions. Other fields in medi-         approach centered on the idea that the acquisition of knowl-
cine, such as radiology and pathology, also rely on pattern       edge is comparable to filling one mind, or one mental filing
recognition to formulate diagnoses and plans of care. In          cabinet, with as many facts and concepts as possible [2,3].
these fields, experts are distinguished from novices by their     This learning theory was developed on the metaphor of our
speed and accuracy in interpreting medical images and com-        minds acting as containers capable of accumulating and
pleting diagnostic tasks. While analyzing images of skin          retaining different items, whether real or metaphorical [4].
lesions, viewers may perform global interpretation, which         Using this metaphor, knowledge is regarded as a commodity
encompasses holistic processing with immediate pattern rec-       to be transferred from one medium to another [3].
ognition. Viewers may also perform feature search, which in-         The expectation that diagnostic concepts can be placed in
volves the identification of specific features (eg, dermoscopic   a fixed cognitive “container,” where they can then be easily
criteria) associated with normalcy or pathology.                  accessed, is misleading, especially in the setting of medical ed-
    Experts and novices differ in the order that they perform     ucation. The successful recall of knowledge in the real-world
these mental processes during medical image analysis. Nov-        context seems to be strongly influenced by the learning con-
ices usually start by attempting to search deliberately for       text [5]. However, knowledge acquired using the container
features and comparing their findings with their prior, al-       model is usually isolated from its context and thus static and
beit limited, knowledge and experiences. In contrast, experts     inflexible. In practice, knowledge, even if highly case-specific,
typically reach a diagnostic conclusion relatively quickly        is not a readily available object to retrieve but something to
through global interpretation and then seek to justify their      reconstruct and adapt when faced with different situations [2].
conclusion with an efficient feature search.                         Thus, opponents to the container model point out that in
    Visual diagnostic skills at the expert level require the      solving new problems, learners who have diligently absorbed
ability to perform global interpretation—or efficient pattern     declarative knowledge (eg facts and concepts that can be
recognition without deliberate search strategies. Traditional     verbalized) under the container model may still fail to appro-
educational methods that primarily drill declarative knowl-       priately retrieve and apply that knowledge [6]. Alternatively,
edge (e.g., specific features) have been largely ineffective in   learners may be able to successfully adapt their knowledge in
teaching novices the pattern recognition skills required for      the problem-solving process, but this successful retrieval and
real-world practice. With these conventional training mo-         application may require a high cognitive load [6].
dalities, expert-level proficiency in dermoscopy has typically       The cognitive load theory is an instructional the-
required extensive training and years of practice. A diagnos-     ory derived from current understanding of cognitive
tic accuracy study performed among medical students and           architecture [7]. The term cognitive load refers to the learner



2                                                                             Review | Dermatol Pract Concept. 2022;12(4):e2022188
mental bandwidth to complete tasks [7]. It is based on the         Objectives
idea that working memory—where information is stored
temporarily—has limited capacity to store and use new in-          This article represents the first of a two-part review series
formation [7]. As a result, if the amount of new information       on novel instructional approaches in image interpretation
exceeds mental capacity, further learning and accurate deci-       education that could translate to dermoscopic educational
sion making will be impaired [8]. It is important for educa-       interventions. In this first part, we will present a collection
tors to consider cognitive load when designing instructional       of theory-based approaches —such as whole case learning,
materials to maintain the overall load within the optimal          microlearning, perceptual learning, and adaptive learning—
range for learning and performance.                                that could enhance dermoscopic image interpretation edu-
      For difficult tasks, learners may primarily rely on delib-   cation. While these emerging directions may also apply to
erate and purposeful thinking, and this process may strain         general dermatology education, the scope of this series is
their working memory, negatively affecting their ability to        limited to dermoscopy education.
complete the tasks. In cognitive psychology, the dual process
theory recognizes two thought systems: slow ­
                                            (deliberate)
thinking and fast (automatic) thinking [9]. For medical im-        Methods
age interpretation, the dual process theory manifests as a
                                                                   To promote innovation in dermoscopy education, the In-
two-component diagnostic strategy in which the fast system
                                                                   ternational Skin Imaging Collaborative (ISIC) assembled a
facilitates pattern recognition and the slow system facilitates
                                                                   12-member Education Working Group that comprises in-
analytical reasoning [10]. Within a medical simulation, ac-
                                                                   ternational dermoscopy experts and educational scientists.
cessing relevant knowledge and skills while assessing the
                                                                   For this initiative, the group convened virtually on a regular
simulated clinical environment may create a high extraneous
                                                                   basis to discuss novel methods in medical image interpreta-
cognitive load for learners, causing poor performance [8].
                                                                   tion education that could be translate to dermoscopy train-
The high cognitive load experienced by learners in medical
                                                                   ing programs. Based on a preliminary literature review as
simulations may be explained by their extensive use of slow
                                                                   well as their experiences as educators, the group developed
thinking as opposed to fast thinking, the former requiring
                                                                   and refined a list of innovative approaches through multiple
considerable mental effort and use of mental resources.
                                                                   rounds of discussion and feedback.
      Fast thinking, or non-analytical reasoning, is a key com-
                                                                      For each approach, literature searches were performed
ponent of expert performance for diagnostic tasks that rely
                                                                   in the PubMed and Google Scholar databases for relevant
on pattern recognition, such as skin lesion classification in
                                                                   English-language articles. Search strategies included terms
dermatology and X-ray interpretation in radiology [11]. For
                                                                   for concepts of dermoscopy education, image interpretation
image interpretation education, an important goal is for nov-
                                                                   education, and health science education in addition to the
ices to gradually develop a degree of automaticity in pattern
                                                                   instructional approach under investigation. Articles pub-
recognition, which translates to a low cognitive load [10].
                                                                   lished since 2000 were preferred for inclusion, but articles
Traditional teaching methods based on the container model
                                                                   published before 2000 were also considered, especially when
have been generally ineffective in both teaching flexible
                                                                   seeking to understand the historical and theoretical under-
knowledge and training automaticity in novices.
                                                                   pinnings of some approaches. Additional articles were iden-
      In dermoscopy education, educators who use the con-
                                                                   tified among the references of retrieved articles and through
tainer model usually provide instruction to passive learn-
                                                                   discussions with educational scientists.
ers on a defined set of diagnostic features, adding to their
                                                                      Relevant literature findings on the educational theories,
­“containers.” In teaching learners to detect the relative pres-
                                                                   methods, and concepts identified during the consensus pro-
ence or absence of a feature, this approach in effect requires
                                                                   cess are presented in this review series. The theory-based
real-world stimuli to likewise fit a binary interpretation
                                                                   approaches described in the first part of this series include:
(eg     present/absent, melanoma/non-melanoma). However,
                                                                   whole-task learning, microlearning, perceptual learning, and
real-world stimuli frequently present on a continuum, or a
                                                                   adaptive learning.
sliding scale, where concerning features may be completely
non-existent, obviously present, or extremely subtle.
      Dermoscopy education requires instructional approaches       Results
that transfer flexible knowledge on the continuous nature
of features and their clinically relevant contexts. This review    Whole-Task Learning (4-C/ID)
seeks to explore an array of emerging theory-based approaches      Overview
in image interpretation education that may displace the con-       Whole-task learning is a time-efficient instructional design
tainer model and enhance dermoscopy training programs.             in medical education that teaches complex skill development



Review | Dermatol Pract Concept. 2022;12(4):e2022188                                                                            3
through authentic clinical scenarios [12]. For learners, spon-     Applications in Dermoscopy Education
taneous transfer of knowledge from the learning situation          In whole-task learning, authentic scenarios, structured in a
to the clinical environment is challenging [13]. Through           way to facilitate skill transfer to clinical encounters, serve
incorporation of real-life problems and fragmentation              as the framework for learning. This approach contrasts with
of instruction, whole-task learning aims to teach founda-          conventional teaching models that focus on didactic lectures,
tional knowledge in a way that fosters the “transfer out” of       which are then supported by hypothetical scenarios. Whole-
knowledge to actual practice. This approach also seeks to          task learning could be applied to dermoscopy education
facilitate skill transfer in a manner that attends to cognitive    to promote problem-solving skills and foster professional
load [12].                                                         independence. Learning tasks may involve addressing skin
    A specific whole-task learning strategy is four-c­omponent     complaints in hypothetical patient encounters. Learners then
instructional design (4-C/ID). The four components in 4-C/ID       receive instruction on the dermoscopic appearance of com-
are: (1) learning tasks, (2) supportive information, (3) just-     mon dermatologic diagnoses (supportive information).
in-time information, and (4) part-task practice [12]. Learn-           Dermoscopic training programs that involve case-
ing tasks, which function as the backbone of 4-C/ID, are           based learning could be adapted to whole-task learning by
authentic tasks sequenced from simple to complex in terms          re-structuring the curriculum with cases at the forefront and
of difficulty and organized into “task classes.” Supportive in-    re-imagining each case as a series of learning tasks [17]. For
formation may be presented at the beginning of a task class        example, dermoscopic cases are introduced prior to receiv-
and provide foundational knowledge. Just-in-time informa-          ing instruction. As they navigate through cases, learners may
tion may be provided right when the learner needs it for a         receive further information on specific dermoscopic features
specific task. Part-task practice is an optional component in      and management approaches in the form of didactic lec-
which the learner is given the opportunity to practice a spe-      tures, multimedia content, or other teaching materials [18].
cific task in order to develop a degree of automaticity. By        After completing the didactic portion, learners may then en-
shifting the focus of learning from lectures to clinical scenar-   gage in repetitive practice to develop task automaticity and
ios, learners may better appreciate the educational content        efficiency.
and its relevance to their professional roles [14].
    Whole-task learning is similar to case-based learning in
that both emphasize realistic clinical situations in the in-       Microlearning
structional design. In case-based learning, learners engage        Overview
in group-based discussions of authentic patient cases and          Microlearning is an instructional approach that involves
receive guidance and feedback from instructors [15]. Learn-        segmenting the curriculum into short bursts, or small bites,
ers are usually expected to prepare on their own through           of learning [19]. In contrast to traditional training sessions
self-­directed learning in advance of the case-based learning      with “massed” practice, microlearning sessions may involve
sessions [15]. In whole-task learning, learners are presented      spaced review and distributed practice, increasing on-task
with authentic clinical scenarios prior to receiving formal        attention and decreasing mind wandering [20]. According
instruction. As learners navigate the scenarios, they are pro-     to the “forgetting curve,” memory retention declines over
vided further information relevant to the scenarios in a struc-    time as learners tend to forget much of their learned material
tured delivery format.                                             within hours or days [19]. Microlearning seeks to address
                                                                   this trend by introducing and re-introducing lessons in short
Applications in Medical Education                                  bursts. Through distributed practice, microlearning pro-
Task-based learning has been applied in surgical education         motes the transfer of information from short-term to long-
in recent years. A randomized controlled study conducted           term memory storage [19].
among surgical interns implemented task-based learning in              With the microlearning approach, learners experience
an inanimate surgical skills laboratory setting [16]. Com-         low cognitive load since working memory does not become
pared to the control group, the intervention group per-            overstrained, and this maintains learning capacity [21]. In
formed better on post-intervention assessments and required        reducing mental fatigue, this strategy increases learning re-
less time to complete the clinical procedure [16]. Another         tention and efficiency [19]. While microlearning lessons are
qualitative study evaluated the feasibility and efficacy of        usually self-paced, learners tend to complete them faster
whole-task learning in a web-based doctoral-level pharma-          given their high level of engagement [19].
cotherapy course and garnered positive results [14]. Learners
expressed that by posing authentic scenarios, the complex          Applications in Medical Education
delivery format provided them an opportunity to identify           In recent years, microlearning modules have become
with their future health profession [14].                          more readily available to learners with the emergence of



4                                                                                Review | Dermatol Pract Concept. 2022;12(4):e2022188
multimedia content that can be easily accessed via personal     education, a microlearning module in which learners exclu-
devices. In a Dutch non-randomized study involving medical      sively practice diagnosing seborrheic keratosis (SKs) would
and biomedical university students, investigators employed      result in blocked practice, while one requiring a learner to
an open-source mobile application (or “app”) to teach cir-      distinguish between SKs, benign nevi, and melanomas, pre-
culation and respiration using microlearning and spaced         sented in a random order, would result in interleaved prac-
review [22]. For a month before the exam, learners used         tice. In a before-and-after study for a dermoscopy training
the app to complete training modules with practice assess-      program, blocked practice for benign lesions resulted in high
ments that reviewed educational content and provided feed-      specificity for benign lesions but poor sensitivity for malig-
back. Intensive app users performed significantly better on     nant lesions in that participants would frequently categorize
the final exam compared to moderate users and non-users,        melanomas as, for instance, SKs [28]. Sensitivity for malig-
though these results may also be correlated with increased      nant lesions subsequently improved with the adoption of in-
time spent learning [23].                                       terleaved practice [28].
                                                                   By segmenting complex tasks into smaller units, micro-
Applications in Dermoscopy Education                            learning represents a powerful teaching tool for dermoscopy
In dermoscopy education, a real-life example of microle-        education. It may enable an efficient transfer of expert-level
arning can be found in a telementoring framework model          pattern recognition skills to novices, especially when im-
called Project ECHO (Extension for Community Health             plemented through technology tools such as smartphone
Outcomes) [24]. As an effective alternative to on-site men-     apps. In bridging the gap between formal and informal
toring, tele-mentoring allows learners to process the cases     learning, the use of microlearning technology may enhance
with real-time guidance from dermoscopy experts [25]. In        learner engagement and motivation as well as knowledge
Project ECHO, teaching sessions occur on a monthly ba-          ­retention [29]. Microlearning modules may also be suitable
sis and pair a didactic micro-lecture with learner presen-      for gamification in which game design principles are applied
tations of real-life challenging cases encountered during       to enhance the learning experience and activate intrinsic re-
patient care. A before-and-after study among primary care       ward pathways.
providers demonstrated that ECHO attendance increased
participants’ ability to interpret dermoscopic images of skin
cancer [25].                                                    Perceptual Learning
   Another example of microlearning in dermoscopy               Overview
can be found in the educational webcasts posted by the          Perceptual learning is a learning method that challenges the
International Dermoscopy Society (IDS). These web-              container model theory by promoting the idea of experience
casts include short YouTube videos of 5 to 10 minutes           as fundamental to developing expertise [30]. In neuropsy-
in length organized into disease-based learning (Level 1),      chology, perceptual learning refers to the changes that occur
morphology-based learning (Level 2), and context-based          in neural circuitry as a result of experience, resulting in the
learning (Level 3) as well as case-based learning [26]. To      development of sensory discrimination [31]. This phenom-
facilitate conceptual understanding, these webcasts could       enon explains how we learn to discriminate between faces,
be expanded by posting dermoscopic images with practice         speech sounds, and musical pitches. For visual discrimina-
questions plus key points in a microlearning format on a        tion training, this approach relies on repeated exposures to
weekly or monthly basis.                                        numerous stimuli (eg visual features) so that one learns to
   Since microlearning can be applied to drill certain topics   perceive subtle differences between the stimuli. The concept
or specific skills, educators may consider whether to imple-    of perceptual learning may be applied to visual specialties
ment “blocked” or “interleaved” practice. Many programs         in which the educator teaches key diagnostic features and
involve “blocked” practice in which the learner practices       then creates opportunities for learners to practice recogniz-
specific skills (eg A, B, C) one at a time in isolation (eg     ing these features with feedback.
AAA BBB CCC) [27]. An alternative to “blocked”                     For medical image interpretation education, the two
practice is “interleaved” practice in which learners            components of perceptual learning are discovery and
practice multiple different skills in an intermixed order       ­fluency [6]. In the discovery phase, students learn to identify
(eg ABC BCA CAB). In interleaved practice, the amount           new information relevant to the diagnostic task by ignor-
of practice devoted to a specific skill becomes spaced, or      ing less relevant information and extracting the more salient
distributed, across the learn-ing session [27].                 points. Using inattentional selectivity, learners may process
   By continuously exposing learners to multiple relevant       a large amount of information from a case. Fluency comes
topics, interleaved practice may be more effective in pre-      with practice and refers to the student ability to efficiently
paring learners for real-life applications. In dermoscopy       recognize the information needed for diagnostic tasks.



Review | Dermatol Pract Concept. 2022;12(4):e2022188                                                                          5
Applications in Medical Education                                  Students may have different starting points for a given topic,
Through perceptual learning, learners receive exposure to          or they may learn at different paces based on their individual
real-life examples, engage in repetitive practice, and gradu-      abilities and the instructional method being used. Adaptive
ally learn to recognize important diagnostic features quickly      approaches represent a solution to these problems: by re-
and accurately. Perceptual learning has been applied to ra-        sponding to the learner response times and accuracy rates,
diology and electrocardiogram (EKG) image interpretation           adaptive algorithms can repeat content, or adapt content dif-
training, where learners have demonstrated gains in accu-          ficulty, to optimize the learning process [36].
racy and fluency [6,30]. More recently, perceptual learning           Adaptive response time-based sequencing (ARTS) is an
has been applied to dermatology education, where learners          example of an adaptive learning approach that customizes
classify clinical images of rashes and skin lesions by mor-        the learning sequence based on performance data [37]. Once
phology, configuration, and distribution [32]. Through per-        the algorithm has detected mastery of a specific concept ac-
ceptual learning, learners demonstrated the ability to quickly     cording to objective learning criteria, it can retire that con-
and accurately identify skin lesion characteristics at a level     cept and shift to focus on the learner weaker areas. Learning
comparable to that of expert dermatologists.                       criteria should correlate with a given level of proficiency
                                                                   and could involve a number of accurate responses provided
Applications in Dermoscopy Education                               within a specified amount of time, correlating with a degree
In a dermoscopy training program for primary care pro-             of automaticity. Training is considered complete when all cri-
viders, educators applied a heuristic training approach that       teria are met.
resembled the discovery phase of the perceptual learning ap-
                                                                   Applications in Medical Education
proach [33]. In the heuristic strategy, learners are expected
to devise their own heuristics, or mental shortcuts, for future    Adaptive algorithms have been successfully applied in
decision-making based on their experiences. Following an           teaching transesophageal echocardiography (TEE) image
introductory didactic training session on classical dermo-         interpretation. Like dermoscopic image interpretation, TEE
scopic features, learners in the heuristic training arm were       interpretation involves recognition of diagnostic patterns. In
provided the opportunity to view a series of dermoscopic           one teaching method, an algorithm modified the sequence
images with minimal guidance from instructors. Labeled             of and time intervals between different TEE cases to suit
with the diagnosis only, these images did not contain further      each learner needs [38]. It evaluated both response speed
annotation or description, and learners were expected to           and accuracy to determine whether to retire or re-sequence a
discover salient features on their own. On post-intervention       specific concept. This method proved effective in improving
assessments, learners in the heuristic training arm performed      response time and accuracy and optimizing performance for
as well as learners who had received feedback on the salient       TEE learners.
features in those images.                                             For electrocardiography (EKG) interpretation, adap-
                                                                   tive learning has also been successful in promoting content
                                                                   mastery. Reading an EKG, like evaluating a skin lesion in
Adaptive Learning                                                  dermoscopy, requires pattern recognition skills that nov-
Overview                                                           ices are expected to obtain via experiential learning [30].
Adaptive learning is an educational approach that optimizes        With ARTS, the pace of learning was adapted for each EKG
learning for the individual learner through innovative tech-       learner based on response time and accuracy. As with the
nology tools [6]. This approach features an adaptive algo-         previous example, a concept was retired only if the learner
rithm that tailors the individual learning sequence according      achieved the target response time while maintaining accu-
to their strengths and weaknesses. Adaptive algorithms re-         racy. If both measures were not achieved, the concept was
semble an automated form of the deliberate practice strategy       re-sequenced into the learning sequence.
commonly used to achieve expert performance in music and
sports. In deliberate practice, a teacher evaluates student per-   Applications in Dermoscopy Education
formance and recommends practice activities (training tasks)       For dermoscopy education, adaptive algorithms may grad-
and practice objectives (training goals) based on the teacher      ually increase the difficulty of dermoscopic images based
prior experiences and the student needs [34]. Students follow      on learner performance to generate faster improvements in
teachers recommendations, practice with full concentration,        performance. Alternatively, if a learner repeatedly fails to
and receive or self-generate immediate feedback [34].              recognize a specific dermoscopic feature, additional images
    In traditional medical education, pre-determined lecture       containing the feature may be shown until the learner starts
or training schedules could not be easily adapted or mod-          to “see” the feature. Since learners encounter new content
ified to accommodate the individual student needs [35].            according to a personalized training schedule, demotivation



6                                                                             Review | Dermatol Pract Concept. 2022;12(4):e2022188
and mental fatigue may be reduced [39]. Adaptive learning                We envision a hypothetical dermoscopy training pro-
ensures that each student acquires the knowledge needed,           gram that combines the strengths of each approach pre-
in whatever sequence and at whatever pace, to reach their          sented in this article. In this program, learning concepts, such
desired level of mastery. In addition, innovative technolo-        as a specific dermoscopic diagnoses, would be organized as
gies with user interface interactions may enable performance       their own unit. Each unit may be prefaced by a real-world
tracking and personalized modifications for efficient learning.    clinical scenario that promotes whole-task learning. Educa-
                                                                   tional content on the dermoscopic diagnosis (eg clinical pre-
                                                                   sentation, dermoscopic appearance) could then be presented
Conclusions
                                                                   via microlearning modules that deliver instruction in small
A summary of the instructional approaches explored in the          segments to minimize extraneous cognitive load.
first part of this review series is included in Table 1. In gen-         Each microlearning module may also include multiple
eral, training programs that apply microlearning modules,          example images of each diagnostic feature plus new cases
perceptual learning cases, and/or adaptive learning algo-          for perceptual learning. These example images and cases
rithms may enable novices to acquire expert-level knowl-           could be hosted on a user-friendly application that contains
edge in an effective manner. Meanwhile, whole-task learning        elements of game design and provides immediate feedback
equips learners for real-life clinical situations using hypo-      to learners. Adaptive learning algorithms built into the ap-
thetical clinical scenarios.                                       plication would either re-sequence or retire cases according




   Table 1. Summary of the educational theories presented in the first part of this review series plus
               examples of existing or potential applications in dermoscopy education.
  Educational
    Theory                              Description                             Application(s) in Dermoscopy Education
 Container          • Learners receive passive instruction and fill         Existing Applications
 Model                their mental “container” with as many facts and       • didactic lectures
                      concepts as possible.                                 • rules-based algorithms
                    • Acquired knowledge is usually static and
                      inflexible because it is isolated from its
                      context.
 Whole-Task         • Curriculum design is based on authentic clinical      Potential Application
 Learning             scenarios and comprises 4 components: (1)             • curriculum design: structured as a series of
                      learning tasks, (2) supportive information, (3)         clinical scenarios based on real-life cases
                      just-in-time information, and (4) part-time
                      practice.
 Micro-learning • Educational content is segmented into short               Existing Application
                  bursts, or small bites, of learning that may be           • Project ECHO, developed by dermatology
                  spaced apart.                                               faculty at MaineHealth
                • This approach is expected to enhance
                  engagement, increase knowledge retention, and
                  decrease mental fatigue.
 Perceptual         • For medical image interpretation, fine visual         Existing Applications
 Learning             discrimination skills are developed through           • YouDermoscopy, created and developed by
                      repeated exposures to numerous examples of              Meeter Congressi
                      important visual features.                            Potential Applications
                    • With feedback and practice, novices may learn         • library of training cases: learners classify
                      to efficiently extract important features and           hundreds of images and receive feedback on
                      ignore irrelevant ones.                                 performance
 Adaptive           • Adaptive algorithms respond to the individual         Potential Applications
 Learning             performance data and make personalized                • learning modules: learners complete assessments;
                      modifications to the training schedule.                 adaptive algorithms retire specific learning
                    • Each individual training schedule is tailored to        concepts based on objective mastery criteria
                      his/her strengths and weaknesses in order to
                      optimize learning outcomes.

ECHO = Extension for Community Healthcare Outcomes.



Review | Dermatol Pract Concept. 2022;12(4):e2022188                                                                             7
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Review | Dermatol Pract Concept. 2022;12(4):e2022188                                                                                       9
