Introduction
Almost a century of research on the brain and cognition has yielded a wealth of insights into how people learn – insights that can inform how we teach and train learners [Reference Bransford1,2]. With a deeper understanding of the factors that affect learning, from the mechanics of memory to the conditions that spark and sustain motivation to the role of emotion in cognition, one would expect educators to be better positioned than ever before to teach effectively. Yet, it is disconcerting how inconsistently learning research makes its way into educational practice [Reference Mintzes and Walter3–Reference Stanovich and Stanovich10].
Disconcerting, perhaps, but is it surprising? As academics involved in translational research, we know all too well that the process of moving ideas from basic research in controlled conditions to application in the complex, messy real world is far from easy or automatic [Reference Austin11–Reference Garcia, Polegato and Zornoff13]. To do so effectively, bridges need to be built: key operational principles must be identified [Reference Austin11], complexity must be grappled with [Reference Seyhan12], technical language must be deciphered and made comprehensible to stakeholders [Reference DeWitt14], facilitators to implementation must be identified and barriers removed [Reference Brownson, Proctor and Luke15,Reference Emmons and Colditz16]. We also understand the critical importance and high stakes of translational pursuits and know that, without these bridges, important findings and insights from basic research languish in technical journals and are never used to improve practice or policy [Reference Jiang, Zhang, Wang and Shen17].
What’s more, because of our familiarity with the processes of translation and implementation, we may be especially well-suited to the work of bringing learning research into educational practice. We are evidence-based in our orientation, accustomed to working across disciplines to find effective approaches to complex problems, trained to find bridges between research and application, and firmly committed to educating and training the next generation of researchers. Moreover, because we are facing seismic shifts in the educational environment – a sudden move to remote and hybrid modalities, changing student populations, and an ever-broadening range of educational technologies – that demand constant innovation and adaptation [Reference Sharp, Norman, Spagnoletti and Miller18–Reference Rose20], we understand the need to build new educational practices on a solid, evidence-based foundation.
Our goal in this article is to provide a primer on the learning sciences – new for some; a review for others – to guide us in this fundamentally translational process. We will (a) describe the teaching and learning environment in which translational researchers are trained, and explain why it necessitates an understanding of learning research, (b) explain what the learning sciences are and why they matter, (c) distill the complex science of learning into a set of basic principles, (d) situate recent developments in the field within these principles, and (e) explain, in practical terms, how these principles and insights can inform teaching and learning in our unique educational environment.
Our Unique Educational Environment
Since their creation in 2006, the Clinical and Translational Science Institutes (CTSIs) have played a vital role in training and supporting the next generation of clinical and translational researchers [Reference Leshner, Terry, Schultz and Liverman21–Reference Dilmore, Moore and Bjork24]. Although surprisingly little has been written about learners in the CTSIs and in other organizations that train the translational workforce, we know that learners include graduate students, residents, fellows, faculty, research staff, and community collaborators [Reference Brishke, Evans and Shenkman25]. They bring with them considerable prior education (bachelor’s degrees at minimum and often medical, master’s, and doctoral degrees) as well as deep expertise in their fields [Reference Oster, Devick and Thurston26,Reference Yin, Gabrilove and Jackson27]. They tend to have concrete goals for their learning and seek the development of specific, practical skills. Educators also come from a wide variety of fields and departments, from surgery to social work to engineering. The same people who occupy the roles of teacher and student in one context may be colleagues and collaborators in another context, creating a somewhat flattened hierarchy atypical of academic medicine [Reference Whitelaw, Kalra and Van Spall28,Reference Conrad, Carr, Knight, Renfrew, Dunn and Pololi29]. This promotes a high degree of collegiality among instructors and learners.
Translational science is taught in a range of contexts, including credit-bearing courses in master’s, PhD, and certificate programs; training programs for early career investigators; professional development workshops and seminars in areas such as mentorship [Reference Nearing, Nuechterlein, Tan, Zerzan, Libby and Austin30,Reference Pfund, House and Asquith31], leadership [Reference Vaughan, Romanick, Schlesinger, Kost, Drassil and Coller32–Reference Straus, Soobiah and Levinson34], equity and inclusion [Reference Rubio, Mayowski and Norman35,Reference Estape, Quarshie and Segarra36], teamwork and team science [Reference Hall, Feng, Moser, Stokols and Taylor37–Reference Mayowski, Norman, Schenker, Proulx and Kapoor39]; and in the informal space of mentor–mentee relationships and interdisciplinary collaborations [Reference David, Kochan, Lunsford, Dominquez and Haddock-Millar40]. Both formal and informal curricula in the CTSIs tend to be practical rather than theoretical, focused on skill-building in discrete competency areas (e.g., statistical knowledge, grantsmanship, qualitative research skills [Reference Robinson, Moore, McTigue, Rubio and Kapoor41,Reference Mayowski, Norman and Kapoor42]). Since the COVID-19 pandemic, we – along with the rest of higher education – have seen a shift in learning modalities toward online and hybrid programming that may become more permanent [Reference Sharp, Norman, Spagnoletti and Miller18,Reference Lau and Dasgupta43].
These unique elements of the teaching and learning environment, in particular the focus on teaching adult learners in diverse and generally interdisciplinary contexts, should be foremost in our minds as we consider how to apply the rich science of learning to our educational pursuits.
What Are the Learning Sciences and Why Do They Matter?
For such a widely used term, “learning” has proven remarkably difficult to pin down [Reference Barron, Hebets, Cleland, Fitzpatrick and Hauber44,Reference De Houwer, Barnes-Holmes and Moors45]. Most definitions describe a process of change, prompted by experience, that increases knowledge [Reference Garvin46]. It is not a change that happens to learners passively but rather something that learners must make happen by reflecting on the experience and forming and testing mental models [Reference Dewey47,Reference Vygotsky and Kozulin48]. Learning is understood to be an interior process that cannot be measured directly but must be inferred through behavior [Reference Soderstrom and Bjork49]. Performance, in other words, serves as a proxy for learning. Many researchers situate the locus of learning within individuals; however, others locate learning in social interactions [Reference Bransford1,Reference Vygotsky and Kozulin48,Reference Rogoff50–Reference Roth and Jornet52], a formulation that has been extended to describe learning at the level of teams and organizations [Reference Argyris53,Reference Senge54].
The term “learning sciences” emerged in the 1990s to describe an interdisciplinary field of research that seeks to understand the mechanisms by which learning occurs in real-world situations and to identify and encourage practices that facilitate learning [Reference Sommerhoff, Szameitat, Vogel, Chernikova, Loderer and Fischer55,Reference Sawyer56]. The learning sciences are inherently interdisciplinary, drawing on a diverse array of fields including cognitive and developmental psychology, neuroscience, computer science, sociology, and anthropology [Reference Ambrose, Bridges, DiPietro, Lovett and Norman57].
Among other things, the learning sciences have challenged long-standing myths about teaching and learning [Reference Holmes and Richmond58–Reference Christodoulou61]. Among these myths is the belief that subject matter expertise is sufficient to make one an effective teacher [Reference Hattie and Yates62], that increasing content increases learning [Reference Luckie, Aubry, Marengo, Rivkin and Foos63,Reference Deslauriers, McCarty, Miller, Callaghan and Kestin64], that lecturing by itself is an effective teaching strategy [Reference Freeman and Eddy4,65], and that it is important to diagnose and teach to specific learning styles [Reference Pashler, McDaniel, Rohrer and Bjork59,Reference Willingham66,Reference Royal and Stockdale67]. None of these beliefs is supported by evidence. Teaching requires knowledge and skills entirely distinct from subject matter expertise. Less content, accompanied by opportunities for active engagement, contributes to deeper learning and longer retention [Reference Freeman and Eddy4]. Similarly, lectures yield poor learning results relative to active learning, and should be used advisedly [Reference Freeman and Eddy4,Reference Mallin68]. Moreover, although many educators tout the importance of adjusting their teaching strategies to students’ individual learning styles, there is little in the research literature to support that approach. Indeed, “learning styles” are generally little more than context-dependent preferences and not stable states; thus, researchers agree that instructors are better off adjusting their teaching strategies to the content rather than to students’ professed learning styles [Reference Pashler, McDaniel, Rohrer and Bjork59,Reference Galagan69–Reference Kirschner72].
In addition to expanding research on learning and debunking myths, the learning sciences have sought to distill existing research (often highly technical in its original form) into core principles and practical strategies to guide teaching practice. These distillations have yielded principles of adult learning [Reference Knowles73], principles to promote deeper learning and knowledge retention [Reference Brown, Roediger and McDaniel74–Reference Willingham76], multimedia design principles [Reference Mayer77], principles of social learning [Reference Bandura78], and theories of applied intelligence [Reference Sternberg, Kaufman and Grigorenko79], among others. Each framework organizes the complex literature in somewhat different ways, with different foci and intended audiences, and all are valuable. For the purposes of this article, we have loosely adapted the framework set out in Ambrose et a l[Reference Ambrose, Bridges, DiPietro, Lovett and Norman57]. This framework, which synthesizes half a century of literature on learning, identifies a basic set of principles to help educators understand how learning works, as well as how to use that understanding to teach more effectively. The principles are not specific to any discipline or student level, and thus apply across learning contexts and modalities. Moreover, they are sufficiently broad to encompass new discoveries and formulations, such as work in the areas of cognitive load and social presence, which we have also included.
For simplicity, we have organized these principles into three categories: acquisition and integration of knowledge, social and emotional components of learning, and elements of skill-building. In the following sections, we describe the research that informs each area and explain how it relates to the specific learning environments in which translational researchers are educated.
Acquisition and Integration of Knowledge
Four areas of the learning sciences shed light on how knowledge is acquired and integrated. They concern the role of prior knowledge, knowledge organizations, cognitive load, and metacognition.
Prior Knowledge
All learning builds on prior knowledge [Reference Ausubel80–Reference Fyfe and Rittle-Johnson82]. Indeed, learning only occurs when learners connect what they are learning to what they already know or have experienced. In the case of adult learners, who bring significant academic, professional, and life experience into new learning situations, there is a strong knowledge foundation on which to build and one that educators should not neglect. However, gaps and deficits in prior knowledge can also impede learners’ ability to integrate new knowledge and may be particularly important to recognize and address in interdisciplinary learning environments, where students and trainees come from different academic and professional backgrounds and do not all possess the same baseline knowledge. The interdisciplinarity of institutions and departments focused on translational education may also create other learning challenges, including the inappropriate application of prior knowledge. Specifically, learners may apply knowledge gained in one context (e.g., prior degree programs) in contexts where it is not relevant or applicable [Reference Ambrose, Bridges, DiPietro, Lovett and Norman57]. (One example, for instance, is importing concepts of bias and generalizability from quantitative fields into qualitative research, which operates on very different terms.) Both knowledge gaps and misapplied prior knowledge are issues that educators should be aware of and look to remediate.
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Advice for educators: Help learners connect what they are learning to what they already know and have experienced, but also pay close attention to – and address – what they do not know, apply in the wrong context, and believe in error.
Organization of Knowledge
Learning involves not only what learners know but how they organize what they know. The ways that knowledge is organized determines how easily it can be retrieved and how effectively it can be used [Reference Ambrose, Bridges, DiPietro, Lovett and Norman57,Reference Chi, Feltovich and Glaser83]. However, the organizational frameworks of experts and novices differ markedly [Reference Chi, Feltovich and Glaser84,Reference Bradshaw and Anderson85]. Expert knowledge is richly connected [Reference Bradshaw and Anderson85], making it possible for experts (including teachers) to readily see how ideas are linked. Moreover, experts organize what they know around the deep structures and underlying principles of problems and cases, rather than superficial similarities [Reference Chi, Feltovich and Glaser83]. Experts also possess multiple organizational frameworks, which allow them to sort information in different ways for different purposes and facilitates the transfer of that knowledge to new situations [Reference Ambrose, Bridges, DiPietro, Lovett and Norman57]. Expert/novice differences are important to recognize in the context of teaching and learning. As experts in their fields, educators – even at the graduate level – cannot assume their learners naturally possess these organizational structures. Rather, part of the task of educators is to help learners develop similarly meaningful and flexible knowledge organizations [Reference Gentner, Loewenstein, Thompson and Forbus86,Reference Chi and VanLehn87].
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Advice for educators: In addition to imparting information, provide organizational frameworks and schemas to help learners organize their growing knowledge in meaningful and practical ways. Also, ask questions that require learners to make and articulate connections, thus growing their neural networks [Reference Elio and Scharf88].
Cognitive Load
Recognition of the limitations of working memory has been one of the most important discoveries to come out of the learning sciences [Reference Klingberg89]. Working memory is the cognitive system responsible for manipulating, encoding, and organizing new information before it is ultimately moved into long-term memory. While long-term memory is capacious, with almost limitless space (think: the Library of Congress), the cognitive resources available for processing information in working memory are highly limited (think: your physical desktop) and must be husbanded carefully. Cognitive load theory focuses on ways to make optimal use of working memory for learning [Reference Sweller, Ayres and Kalyuga90,Reference Kalyuga and Sweller91]. Scholars in this area have differentiated between intrinsic, germane, and extraneous cognitive load. Intrinsic cognitive load refers to the cognitive resources required by a task itself (e.g., reading a journal article). Germane cognitive load refers to the cognitive resources required to generate meaningful connections or develop a schema (e.g., connecting the content of one journal article to others). Extraneous cognitive load refers to cognitive resources eaten up by incidental or unnecessary factors (e.g., the confusing directions of an instructor) [Reference Sweller92,Reference Klepsch and Seufert93]. Learning scientists agree that instructors should minimize extraneous cognitive load while maximizing germane cognitive load [Reference Sweller92], in other words, to make sure the difficulty in a task advances learning without draining cognitive resources unnecessarily. Cognitive load theory is particularly applicable in the context of online learning, where poorly organized platforms and unfamiliar technologies can add extraneous cognitive load, potentially eroding motivation and impeding learning [Reference Jochems, JJGv and Koper94–Reference Schweppe and Rummer96].
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Advice for educators: Increase germane cognitive load by assigning tasks and asking questions that compel learners to think harder about the material you are teaching. At the same time, decrease the extraneous cognitive load by making written directions clear and succinct and employing good visual design.
Metacognition
Another critical facet of knowledge acquisition is metacognition or the process by which learners understand, monitor, and refine their own cognitive processes [Reference Rhodes97–Reference Osman and Hannafin99]. Ambrose et al represent metacognition as a set of five abilities [Reference Ambrose, Bridges, DiPietro, Lovett and Norman57]: first, the ability to realistically and accurately assess the requirements of a task (e.g., the time, resources, and skills required); second, the ability to evaluate one’s own skills and competencies relative to the task requirements; third, the ability to plan appropriately; fourth, the ability to monitor and assess performance as one acts; and fifth, the ability to reflect back on one’s performance after the fact and make adjustments for the future. While one would think that accomplished graduate-level learners typical of the CTSIs and other translational educational contexts have already developed strong metacognitive skills, research indicates that, in fact, adult learners often fail to monitor their own thinking and fall back into familiar patterns and biases that limit their intellectual growth [Reference Kleka, Brycz, Fanslau and Pilarska100]. Research also shows that metacognitive skills can be strengthened considerably and with very positive outcomes for learning if instructors provide structured opportunities for self-evaluation, planning, and reflection on past performance [Reference Perry, Lundie and Golder101,Reference Kramarski and Mevarech102].
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Advice for educators: Allocate ample time for learners to reflect on their strengths and weaknesses in relation to complex tasks, to assess the demands of those tasks, and to plan their strategy. Allow time at the mid-point of projects for learners to stop, monitor progress, and adjust their approach, and leave time at the end of such tasks for learners to reflect on their performance and plan.
Social and Emotional Components of Learning
Learning is an intensely communal activity that cannot be divorced from the social and interactive contexts in which it occurs [Reference Vygotsky and Kozulin48,Reference Bandura51,Reference Lave and Wenger103]. Indeed, there is increasing recognition that learning is heavily influenced by social and emotional – and not simply cognitive – factors [Reference Cavanagh104], a fact that is even more apparent since the advent of online education [Reference Marmon105,Reference Garrison, Anderson and Archer106], where social connection and community can become attenuated, with detrimental impacts on learning. There is far more to say about the social elements of learning than space here allows. However, four important principles concern the factors that influence motivation, the importance of learners’ developmental stage, the ways in which climate affects learning, and the role of presence, particularly online.
Motivation
Motivation drives the behaviors that result in learning and is thus a critical ingredient in all learning contexts. There are two high-level factors that, taken together, increase learner motivation: value and expectancy [Reference Ambrose, Bridges, DiPietro, Lovett and Norman57,Reference Wildman and Bedwell107]. Value stems from learners’ perceptions that the material they are learning and the tasks they are engaged in are relevant, meaningful, and of practical value. According to the tenets of self-determination theory, three elements increase perceived value: competence (awareness of increasing mastery), relatedness (connection and accountability to other people), and autonomy (a sense of agency and control) [Reference Ryan and Deci108]. Daniel Pink adds to that a sense of purpose [Reference Pink109].
The other factor in motivation is expectancy. Expectancy concerns learners’ beliefs that success is possible: that their efforts are connected to desired goals [Reference Carver and Scheier110], that they are personally capable of achieving those goals [Reference Bandura111], and that the environment will support and not thwart their efforts [Reference Ambrose, Bridges, DiPietro, Lovett and Norman57]. Learners who believe that a task is unreasonably difficult, that they are personally incapable, or that they do not have adequate support will lose motivation. Both value and expectancy must be present for motivation to be high. If learners value an outcome but do not feel capable of achieving it (high value, low expectancy) they will lose motivation. By the same token, if learners feel capable of achieving a goal but do not value it (low value, high expectancy), motivation will suffer. Notice that both value and expectancy are issues of perception, not objective reality: learners must believe that what they are learning has value and that successful learning is possible. While graduate-level learners often possess a fair degree of intrinsic motivation, instructors should not assume that their motivation will be high for all tasks and activities or that motivation cannot be eroded even when initially high. Considering ways to increase value and expectancy is thus a wise course of action for all educators.
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Advice for educators: Seek to increase learners’ motivation by highlighting the practical value of what they are learning and reducing factors that erode expectations of success, without compromising high standards. Provide opportunities for learners to exercise autonomy, demonstrate increasing competence, and connect with one another.
Developmental Stage
While the factors that affect learning (e.g., prior knowledge, motivation, metacognition) are the same for students at all life stages, learners themselves differ, as do their learning needs [Reference Ambrose, Bridges, DiPietro, Lovett and Norman57]. Various stage models have been offered to help educators understand learners at different phases of life. These include Perry’s model of intellectual development, which describes four stages in learners’ ability to tolerate ambiguity and countenance different perspectives on complex issues yet, ultimately, commit to action [Reference Perry112]. Perry’s model has been refined and extended by Baxter-Magolda, who has explored the issue of “self-authorship” across cognitive, interpersonal, and intrapersonal domains of development [Reference Baxter Magolda and King113]. Stage models also include theories of racial identity development [Reference Watson114–Reference Boykins117]. While many such models focus on the developmental tasks of traditional college-aged learners [Reference Chickering and Reisser118,Reference Jones and Stewart119], the paradigm with perhaps the most relevance to the educational context of the CTSIs is Knowles’ theory of andragogy [Reference Knowles120–Reference Knowles122]: teaching adult learners. Theories of adult learning vary but the primary components are these: Adult learners want to know how the material they are learning serves concrete personal or professional goals [Reference Merriam123,Reference Merriam124]. They learn best by doing, i.e., through practice and participation, preferably through problem-solving [Reference Hagen and Park125]. They bring experiences to the learning encounter that can facilitate learning but also at times cause mental rigidity [Reference Bransford1,Reference Knowles120]. Finally, adult learners do best in informal environments, in which they have a degree of self-direction and control, and where the relationship between instructor and learner is more collaborative than directive [Reference Schank126]. It should be noted that developmental theories, many of which took individual psychology as their starting point and neglected structural issues of power and inequity, have been reexamined in recent years through the lens of critical theories about race, ethnicity, gender, and disability [Reference Jones and Stewart119,Reference Torres, Jones and Renn127,Reference Abes, Jones and Stewart128].
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Advice for educators: Assign tasks with obvious practical relevance to learners’ professional and/or personal lives, focus on allowing students to learn by doing, allow ample opportunities for learners to bring their experiences to bear in discussion, and approach the learning situation in a collegial and collaborative manner.
Climate
Equity and inclusion are and should be an increasing focus within higher education [Reference Newton5,Reference Willingham66]. A critical issue for educators to consider is whether the learning climate they foster in courses and training seminars is genuinely inclusive, welcoming, and supportive of diverse learners [Reference DeSurra and Church129,Reference Longerbeam130]. We know that when the climate of a classroom or training is overtly or subtly marginalizing toward learners, whether on the basis of race, gender, age, sexual orientation, disability, or any other factor, it exacts a high toll on learning, performance, motivation, and persistence [Reference Lee and McCabe131–Reference Gregg135]. Powerful messages about inclusion and exclusion can be conveyed to learners simply by the choice of authors and topics to include (or not include) on a course reading list [Reference Ambrose, Bridges, DiPietro, Lovett and Norman57]. Assumptions and biases about ethnic and racial groups can be embedded in case studies [Reference Allotey, Allotey-Reidpath and Reidpath136]. Choices in instructional materials (e.g., the use of videos without subtitles or podcasts without transcripts) can marginalize and disadvantage students with visual or auditory disabilities [Reference Cohn, Molinero and Stoehr137]. Microaggressions can be prevalent in classrooms and detrimental to students’ learning and persistence in the field [Reference Sue138–Reference Murray140]. A fascinating body of research on stereotype threat demonstrates that, when stereotypes are triggered even in the subtlest ways, members of stereotyped groups can experience a disruptive cognitive state that undermines learning and performance [Reference Inzlicht and Schmader141,Reference Goff, Steele and Davies142].
Fortunately, there is much that instructors can do to create inclusive learning environments, including employing simple strategies to reduce stereotype threat, such as communicating high expectations for all learners [Reference Good, Aronson and Inzlicht143–Reference Alter, Aronson, Darley, Rodriguez and Ruble145]. Other factors that create a positive learning climate are the demonstration of “instructor immediacy” – verbal and nonverbal instructor behaviors that convey approachability to students [Reference Creasey, Jarvis and Gadke146–Reference Witt, Wheeless and Allen149]. The communication of immediacy is particularly important online, where learners can easily feel isolated [Reference D’Agustino150]. Universal Design for Learning (UDL), a set of guidelines that grew out of disability research, seeks to make learning accessible to all through the design of flexible learning environments in which learners have a range of choices in how they engage with instructional materials and demonstrate learning [Reference Nelson151,Reference Rose and Meyer152]. While the impact of UDL has yet to be empirically assessed, it is grounded in well-established learning research and early studies look promising [Reference Schreffler, Vasquez, Chini and James153].
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Advice for educators: Work to create a learning environment that is intellectually challenging yet welcoming to every learner. Use content that reflects diverse voices and conveys approachability. Design for accessibility and inclusion.
Presence
Online learning has distinct benefits when it comes to convenience, access, and self-pacing; however, it also has challenges, principally the attenuation of social connection that comes when people are not physically “present” with one another. Scholarship coming out of the Community of Inquiry framework [Reference Arbaugh, Cleveland-Innes and Diaz154,Reference Garrison, Cleveland-Innes and Fung155] has emphasized the importance of creating three types of “presence” in online courses: social presence: the ability of learners to project their identities and connect with one another effectively through technologically mediated means [Reference Whiteside, Dikkers and Swan156,Reference Armellini and De Stefani157]; cognitive presence: the ability of learners to connect deeply to course content [Reference Shea and Bidjerano158,Reference Darabi, Arrastia, Nelson, Cornille and Liang159]; and teaching presence: the instructor’s ability to reach across the distance, seem real and genuine, and connect meaningfully with learners [Reference D’Agustino150]. This body of research points to the fact that social connection and community building cannot be taken for granted but must be developed deliberately and cultivated carefully online [Reference Cuthbertson and Falcone160,Reference Whiteside, Dikkers and Swan161]. As the CTSIs expand their online programming, this research is critically important to consider. However, the Community of Inquiry framework is equally applicable to face-to-face and hybrid educational environments and speaks to the powerful social and emotional components of learning.
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Advice for educators: In all courses, but especially online, be deliberate about projecting your own personality and presence while working to build community and encourage meaningful interaction among learners.
Elements of Skill-Building
Considerable scholarship in the learning sciences has attended to the processes and stages by which learners acquire skills, gain fluency and automaticity using those skills, and develop expertise within a particular domain [Reference Alexander162]. Much of the research in this area explores differences in the ways experts and novices organize, access, and use information [Reference Chi, Feltovich and Glaser84,Reference Charness, Reingold, Pomplin and Stampe163] and is informed by research on artificial intelligence and machine learning. Two relevant principles relate to the development of mastery and the role of practice and feedback in that process.
Mastery
To develop expertise in a given domain (say, clinical research), learners must master complex skills. According to Ambrose et al., this requires first that they acquire the component skills that make up the complex skill (consider, for example, how many sub-skills are required to perform a task like writing a grant proposal!). In addition to acquiring these sub-skills, learners must integrate them successfully, developing speed and fluency at executing these skills in combination. Finally, they must understand when and where to apply what they have learned [Reference Bransford1,Reference Ambrose, Bridges, DiPietro, Lovett and Norman57]. This final element of mastery is also known as transfer and is, arguably, the central point of learning [Reference Billing164]. When should you employ a particular research design? When are specific statistical methods appropriate? Skill gaps at any of these levels can inhibit the development of mastery and interfere with performance. Ironically, one factor that complicates learning for relative novices is the expertise of their teachers, whether in formal or informal learning contexts. Because experts have gained mastery to the point of unconscious fluency [Reference Sprague and Stuart165], they tend not to see all the steps and component skills involved in learning complex tasks, and thus often do not scaffold tasks appropriately for learners. Researchers call this “expert blind spot” [Reference Nathan and Petrosino166,Reference Nathan and Koedinger167]. It is a hazard that educators in translational science programs should watch for, because their own expertise can sometimes blind them to the learning needs of students.
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Advice for educators: Recognize that mastery takes time to develop and allocate sufficient time for students to learn skills in isolation, practice them in combination, and use them in diverse contexts to develop transfer. Also, watch out for your own expert blind spot when teaching others!
Practice
Practice and feedback are both essential for developing competence in any domain [Reference Ambrose, Bridges, DiPietro, Lovett and Norman57]. Practice without feedback is not only demotivating; it also reinforces mistakes [Reference Bangert-Drowns, Kulik, Kulik and Morgan168–Reference Metcalfe170]. Feedback without practice, on the other hand, is pointless: without opportunities to address mistakes, learners do not improve. They also lose motivation [Reference Gnepp, Klayman, Williamson and Barlas171]. However, not all practice and feedback are useful. Ideally, the practice should be focused on specific performance goals [Reference Ericsson and Charness172,Reference Ericsson and Lehmann173]. The task, moreover, should be appropriately challenging: too easy and the learner is not pushed to improve; too difficult and both performance and motivation suffer [Reference Ambrose, Bridges, DiPietro, Lovett and Norman57,Reference Vygotsky and Cole174,Reference Sanders and Welk175]. Finally, the practice should involve sufficient time on task [Reference Martin, Klein and Sullivan176]. A fascinating area of research has focused on what Bjork and Bjork have called “desirable difficulties” [Reference Bjork and Kroll177–Reference Bjork, Soderstrom and Little179]. As it turns out, when learners struggle to learn something, they encode the information more deeply and remember it longer. Thus, there is an optimal level of difficulty (challenging but not discouraging) that facilitates learning. A related area of research is on “retrieval practice,” also called the testing effect [Reference Bjork and Kroll177,Reference Birnbaum, Kornell, Bjork and Bjork178,Reference Pyc and Rawson180,Reference Karpicke181]. This scholarship has found that the act of retrieving information from long-term memory, whether through testing or simply by being asked questions, helps to create stronger mental paths back to that information and ultimately leads to deeper learning [Reference Brown, Roediger and McDaniel60]. In addition, spacing practice sessions farther apart (the “spacing effect”) aids learning by compelling learners to engage in more effortful retrieval [Reference Cepeda, Vul, Rohrer, Wixted and Pashler182]. The research on retrieval practice and spacing has had particular resonance in medical education, where learners are expected to integrate and remember vast amounts of information [Reference Dobson, Linderholm and Perez183].
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Advice for educators: Make sure learners have ample and repeated opportunities to practice key skills, ensuring that tasks are sufficiently difficult to be effortful but not so difficult as to be discouraging. Give learners retrieval practice by asking frequent questions or giving low-stakes assessments.
Feedback
Feedback, or information provided to learners to help them improve their understanding or performance, is one of the most powerful factors affecting learning [Reference Hattie and Yates62,Reference Hattie and Timperley184]. Feedback helps learners identify gaps between current and desired knowledge and skills while helping to identify specific actions they can take to close the gaps. Feedback helps learners develop stronger skills at self-evaluation [Reference Chou and Zou185] and also plays a key role in motivation [Reference Nicol and Macfarlane-Dick186]. Research shows that feedback is most effective when it focuses on specific areas for improvement [Reference Balzer, Doherty and O’Connor187], is prioritized so as to differentiate high-importance items from low-importance items [Reference Lamburg188], and is delivered soon after performance [Reference Hattie and Timperley184]. The collegial, mentorship-focused nature of education in the CTSIs makes feedback a particularly important tool for helping learners improve.
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Advice for educators: Provide learners with feedback that identifies specific, actionable, and prioritized areas for improvement, make sure the feedback is delivered in a timely fashion, and ensure there are immediate opportunities for learners to incorporate your feedback into practice.
Recommendations
While the summary of research and advice for educators provided in the earlier sections may seem somewhat daunting, the lessons are actually simple and intuitive. Taken together, they suggest the types of strategies for teaching and training outlined in Table 1.
Table 1. Strategies educators can use to incorporate research-based learning principles
Conclusion
Teaching and learning are ubiquitous in the CTSIs and other institutions focused on training the translational workforce. Thus, there is much for educators in the CTSIs to gain by cultivating a deep understanding of the mechanisms of learning and the attributes of high-quality teaching. In this article, we have made a case for bringing the learning sciences more systematically into our educational practices. We have argued, moreover, that educators in the field of translational science may be particularly well-equipped to translate the rich, varied, and interdisciplinary research on learning into practice because of their appreciation for the importance and complexity of translational pursuits, and their commitment both to evidence-based practices and to educational excellence.
We have offered this distillation of key principles from the learning sciences and contextualized it within our unique educational environment in the hope that this framework can provide helpful guidance and a shared vocabulary for educators at our institutions, regardless of the specific contexts in which they teach. We believe that, armed with these principles, educators will be better able to discern why effective practices are effective, identify and address teaching problems, adapt strategies successfully to new teaching contexts and modalities, and innovate from a solid foundation of understanding.
Acknowledgements
The authors would like to thank Ethan Lennox, for his invaluable editorial assistance. Research reported in this publication was supported by The University of Pittsburgh Clinical and Translational Science Institute (CTSI) NIH/NCATS: 1UL1TR001857 and the National Center for Advancing Translational Sciences of the National Institutes of Health.
Disclosures
The authors have no conflicts of interest to declare.
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