Simulation Learning

Chapter 25

Simulation Learning Jan Breckwoldt, Hans Gruber, and Andreas Wittmann

Abstract An overview is presented of the strengths and limitations of simulation learning, with a particular focus on simulation learning in medicine and health care. We present what simulation learning is about and what the main components of simulations are. The most important theoretical approaches are reviewed which were developed in order to explain why simulation learning is effective. The most prominent best-practice examples of simulation learning applications are presented, and a short overview on research findings concerning simulation learning is given. Keywords Simulation learning • High-fidelity full scale environment • Deliberate practice • Experience • Interprofessional education • Learning outcomes • Practicebased learning • Professional learning • Skill acquisition

25.1

25.1.1

Simulation Learning: Practice-Based Authentic Activity in Educational Safety What Is Simulation Learning About?

Simulation learning denotes learning within a safe educational environment, in which some form of reality is simulated. Learners have to learn and act within this environment. They usually have to fulfill quite complex tasks, which often are close

J. Breckwoldt, MME Vice-Deanery of Education, Faculty of Medicine, University of Zurich, Pestalozzistr 3-5, CH-8091 Zurich, Switzerland e-mail: [email protected] H. Gruber (*) • A. Wittmann, M.A. Institute of Educational Science, University of Regensburg, D-93040 Regensburg, Germany e-mail: [email protected]; [email protected] S. Billett et al. (eds.), International Handbook of Research in Professional and Practice-based Learning, Springer International Handbooks of Education, DOI 10.1007/978-94-017-8902-8_25, © Springer Science+Business Media Dordrecht 2014

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to real-life tasks. Simulation learning, thus, is a form of experiential learning that is learner-centred, integrates many facets of learning (e.g. cognitive, motivational, affective, psychomotor, social) and has a high degree of authenticity. Many forms of simulation learning, in particular those which shall be deemed to be most typical, use computer simulations. Computer simulations feature complex systems and provide learning environments which resemble reality in a number of aspects. They are dynamic and change over time (whether the learner reacts or not), and the interplay of variables usually is complex and not completely transparent (i.e. the learner has to understand side effects). First and foremost, however, they provide educational safety and illustrative clarity. Computer simulations can model situations that in reality are too dangerous to be used for learning (e.g. aviation, surgery, nuclear reactions), which are either too large or too small to be observed (e.g. seismotectonic processes, molecular processes), or cannot easily be repeated for didactical reasons (e.g. earthquakes, traffic accidents). In many domains, professional acting requires the handling of such situations, however. In order to prepare learners, simulations, in particular computer simulations, can help to design complex learning environments which come close to reality and feature a high degree of authenticity. Thus, they allow experiential learning, on the one hand, and the reliable and reproducible construction of adequate mental models, on the other hand. Simulation learning is a practice-based, close-to-authentic kind of learning within a learning environment which permits the design of systematic instructional efforts.

25.1.2

The Development of Different Kinds of Simulation Learning

Simulation can be used at various fidelity levels, from low to high-fidelity (Dunn 2001). Low fidelity simulations often are based on written case studies (e.g. in problem-based medical curricula), or in non-technical role plays. High-fidelity simulations (Lupien and George-Gay 2001), as the contrasting opposite of simulations, use complex scenarios, often including tremendously complex computer models (e.g. aviation, medicine) and artifacts that are close to reality (e.g. manikins in medicine or nursing; Medley and Horne 2005). Simulation learning has been used in many professions since more than 80 years, but rapid developments were achieved with the advent of more powerful technological tools (Gaba 2004). In particular, within the domains of medicine, health care and nursing, simulation learning has considerably grown in importance. In this chapter, therefore, a focus is on simulation learning in medicine, in particular in subdomains like emergency care, in which typical professional action cannot easily be introduced in learning settings without simulations. Many developments of simulations for learning were mainly intended either for practical purposes within particular professions, or they were mainly technologydriven. As a consequence, the educational perspective often has been neglected or,

25 Simulation Learning

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at best, been a side product. From an educational perspective, there are many good reasons to promote simulation learning, but there is not a unique “educational theory of simulation learning”. Rather, aspects of simulation learning can be found in many different theories. We consider these multiple explanatory bases a strength of simulation learning, rather than an argument to deplore a theoretical under-development. The theoretical diversity, or even eclecticism, in the field is mirrored, however, in a similarly diverse state of the art concerning firm empirical evidence. In this chapter it is not aimed to invent, or design, a “unified theory of simulation learning and instruction”, but rather to present the diversity, and richness, of evidence. The most promising avenues, both from a practice-based and a theory-based perspective, are outlined in some detail. Simulation learning allows learners of all kinds of professions and of all performance levels to gain knowledge, to acquire skills or to learn complex procedures in a controlled and safe environment. Simulation aims to provide close-to-authentic learning experiences to prepare learners for real future situations in particular in hazardous, even life-threatening situations without having to fear any serious consequences. In contrast to many other learning activities, simulation learning is intrinsically practice-based. It allows and challenges learners to experience authentic situations and processes, and to actively apply newly acquired practical skills and knowledge to solve problems they might face in future challenges. Simulation learning sometimes is described as a “dress rehearsal”, for instance prior to confronting medical students with real patients. Acting in a simulated safe environment allows for testing existing skills and knowledge and to make sure whether learners are ready to act in the real world. Evidence exists that learning from simulations is efficient and effective for acquiring both technical and non-technical skills. For example, Hallikainen et al. (2009) found that learning from a full-scale simulation in anaesthesia outperformed learning from supervised teaching in the operating theatre, even when time-on-task and amount of training personnel was controlled for. Besides making easier initial experiences in a field, simulation can also serve lifelong learning by facilitating deliberate practice of experts. This usage of simulation is still rare, however; it mainly can be found in aviation and in most potentially dangerous professions. In fact, the first applications of simulation learning and simulation training were developed in high risk industry sectors such as the military, nuclear power industry, or aviation (Gaba 2004). Although serious incidents occur extremely seldom in these areas, they can happen, as the Fukushima disaster dramatically demonstrated. When the potential harm of an incident is high, then even burdensome effort is worth to be invested in prior training. When, for instance, an airliner ditches in the open sea (which occurs very rarely), hundreds of people may die if the pilot and cabin crew are not trained to handle the situation. To enable the pilots to collect experience how to act adequately (apart from the knowledge how they should act), flight simulators provide a safe and authentic environment, allowing pilots to practice critical incidents (Hays et al. 1992). Similarly, cabin crews practice in mock fuselages to organise quick evacuations.

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The potential and need of simulation learning has also been realised in health care in the last decades (Gaba et al. 2001). One reason for this trend is the fact that “abusing” real patients to train practical skills is unethical and dangerous because errors in treatment may cause serious harm. A second reason is that (similar to aviation) emergency situations are not frequently encountered, leading to a potential lack of situational experience. Consequently, simulation learning has become an essential part of medical education which allows not only for acquiring all kinds of technical skills, but also communication skills and teamwork (Leonard et al. 2004). Medical simulation learning experiences a rapid development. Many studies in the field focus on methods and effectiveness of such trainings. In the following sections of this chapter, we thus mainly focus on simulation learning in the medical area. Analogous arguments and conclusions could be drawn from other fields, however. In Sect. 25.2, we present the main components of simulations in medicine. In Sect. 25.3, the most important theoretical approaches are reviewed which were developed to explain why simulation learning is effective. Section 25.4 comprises the most prominent best-practice examples of simulation learning applications. Each of them considers simulation learning as a kind of practice-based learning, but the foci are different. In Sect. 25.5, we present a short overview on research findings concerning simulation learning. In the conclusion (Sect. 25.6), we finally discuss in-how-far the findings and the theoretical explanations are already well-developed and how future research and practice might develop. For this reason we summarise strengths and limitations of simulation learning.

25.2

Components of Simulations in Medicine

Simulation aims at mimicking reality and thereby giving learners the feeling as if they were acting in the real world (Gaba 2004). The creation of authentic, complex learning environments can be supported by actors, devices used in daily practice, and the construction of an environment resembling real world situations. According to the tasks posed in the simulation, levels of difficulty can vary from extremely simple to extremely difficult. Simulation learning can have many different foci; it can address practical, cognitive or social skills, but often it integrates all of them, thus simulating reality (Issenberg et al. 2005). According to prespecified learning goals, simulation can take many forms (Issenberg et al. 1999). An often-used form of simulation learning is computer assisted learning (CAL), using special software that creates a virtual world where learners have to solve a problem, sometimes by interacting with computer-generated persons. In general, the main focus of CAL is to solve cognitive tasks, but it is also used to support the acquisition of practical skills. Rogers et al. (1998) investigated the effectiveness of CAL to teach a technique how to tie surgical knots. CAL proved effective compared to a lecture in combination with a feedback seminar. To train isolated practical skills in anatomy, however, lectures using partial-task trainers were superior to CAL (Evans et al. 2010). Obviously, simulation learning is not effective per se, its usage has to be carefully planned.


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Another form of simulation learning, which makes significant use of real social situations, are high-fidelity full scale environments. Such simulations provide multifaceted and extremely serious scenarios requiring a high degree of teamwork. They are often used to train social and practical skills, but also aim at the development of strategic skills, including priority setting, organising multi-parted processes, and anticipatory planning. Good examples for such stress- and anxiety-evoking simulations are professional trauma resuscitation trainings, usually performed in settings which resemble clinical reality (Perkins 2007). If during a polytraumatised patient scenario an additional cardiac arrest happens, emergency physicians have to make immediate decisions how to proceed, weighing up alternatives and prioritising specific organ survival. To train these skills within such courses, high-fidelity manikin simulators offer a wide range of features, like displaying blood pressure, heart rate, breath, heart and respiratory sounds, voice, and pupils’ diameter, all with physiological responses to the trainee’s interventions (Okuda and Quinones 2008). The ultimate goal of simulation learning is facilitation of transfer to daily practice and avoidance of inaccurate behaviour in future situations. To reach this goal necessitates the provision of opportunities to learn from mistakes and to acquire “negative knowledge” (Gartmeier et al. 2008a). The process of learning from mistakes requires providing learners with extensive feedback on their performance including gaps in that performance (Ziv et al. 2005). These requirements are implemented in an essential component of simulation learning, the debriefing session, which normally takes part prompt to the simulation scenario and may be supported by video recordings. During the debriefing, learners reflect on and discuss their performance, thoughts, and feelings, weigh up alternative courses of actions and identify gaps in knowledge or practical skills. The learning results may then be used to develop concrete plans how to overcome the deficits (Rall et al. 2000). The two key components of a debriefing session are feedback and self-reflection. To maximise the value of a debriefing session, feedback and self-reflection need to be facilitated by an instructor, who should have both expert knowledge and didactic skills (Rall et al. 2000). The instructor should encourage self-reflection through targeted and meaningful questioning, e.g. “What do you think went well in your scenario?”, “What do you specifically mean by this?” or “Can you explain that to us?” (Seropian 2003, p. 1702). Asking for the reasons of decision-making provokes inspiring discussions with other learners, which frequently lead to the emergence of divergent perspectives that provoke attempts to develop alternative solutions of the experienced problem. Instructors may provide specific strategies how to resolve certain performance gaps. In many simulation settings, video recordings facilitate feedback. In this case, learners review the tapes of their sessions either individually or in small groups. The recordings aid memory to intensively discuss the individuals’ performance, including decision-making processes, feelings, and observable gaps in knowledge and skills. This video-based self-observation leads to additional facilitation of the learners’ reflection, thereby enhancing learning outcome (Cauraugh et al. 1999; Hill et al. 2000; Hoyt et al. 1988; Scherer et al. 2003). The preparation of debriefing sessions normally starts at the beginning of the simulation learning – the briefing. Besides clarifying the learning objectives and making learners familiar


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with the learning environment and its devices, it is of prime importance that the instructor creates an atmosphere of trust and respect. There should be no fear of making mistakes or being judged by the instructor or other members of the group (Fanning and Gaba 2007). The instructor should be aware that every learner has his or her own previous life experiences and individual frames that should be considered, as these prerequisites have an impact on learners’ processing and assimilation of new information (Rudolph et al. 2006). To sum up, simulation learning allows for the acquisition of practical skills, experiential knowledge or other cognitive abilities, e.g. problem solving skills that often cannot easily be acquired in real world situations. In the last decades, several simple as well as very sophisticated simulation devices were developed to facilitate learning, especially in high-risk domains like medicine or aviation industry. An important keystone for learning success after simulation learning is effective debriefing, in which feedback is provided by an experienced instructor to facilitate learners’ self-reflection. The existing variety of components of typical simulation learning is one of the reasons, why a number of different theoretical perspectives have been used to explain why simulation learning is effective. Many of these theories have a much broader scope in different fields of learning and instruction, none of them can be considered to be a specific theory of simulation learning. An overview of the most often cited theoretical perspectives concerning simulation learning is presented in the next section.

25.3

Theoretical Explanations: Why Simulation Learning Is Effective

Simulation learning is a broad concept that describes a complex learning activity in a learning environment that is designed to resemble in many respects authentic real-life situations. Consequently, simulation learning comprises many different components, as was shown in the previous section. Accordingly, many theories of learning and instruction offer plausible explanations why particular forms of simulation learning are effective. They do not form a coherent, specific theory of simulation learning, however. Rather, theoretical accounts of simulation learning tend to be eclectic. As foreshadowed, we do not deplore this situation but consider it supports the strength of simulation learning. It might be deplored, however, that the lack of a unified theory of simulation learning is responsible for a lack of coordination of research on simulation learning. A common element of the theoretical perspectives that are sketched in this section is that the power of simulation learning comes from the practice-based approach, on the one hand, and from the large degree of authenticity and, thus, transferability, on the other hand. Theoretical explanations of simulation learning’s efficacy can be found in different approaches which focus particular aspects of learning that are inherent to the


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process of simulation learning. The theories outlined in this section are: (1) adult learning principles, (2) experiential learning, (3) constructivist and situated learning theories, and (4) deliberate practice.

25.3.1

Adult Learning Principles

Simulation learning includes all elements which are mentioned as parts of appropriate adult learning. Adult learning strategies and motivations typically differ considerably from those in school education (Okuda et al. 2009). Five essential principles of effective adult learning strategies and motivation were identified by Bryan et al. (2009, p. 558): (1) (2) (3) (4) (5)

Adults need to know why they are learning, adults are motivated to learn by the need to solve problems, adults’ previous experience must be respected and built upon, adults need learning approaches that match their background and diversity, and adults need to be actively involved in the learning process.

Typical simulation learning pays attention to each of these principles. Before entering a simulation scenario, learning goals and their practical meanings are commonly illustrated by an instructor within the briefing session (first principle). During a simulation session specific problems need to be solved (second principle) and learners actively participate (fifth principle). The opportunity to vary the scenarios’ complexity and difficulty makes it possible to adapt to learners’ level of skills, knowledge, or individual experiences (third principle). Because simulation learning is usually performed in small groups, attention can be paid to diversity of the learners (fourth principle) by individual mentoring if necessary (Okuda et al. 2009; Ziv et al. 2000).

25.3.2

Experiential Learning

With its focus on practice during learning, simulation training meets the important characteristics of experiential learning. A much cited model of experiential learning is Kolb’s (1984) notion of a learning cycle in which four recurrent stages are distinguished: (1) concrete experience, (2) reflective observation, (3) abstract conceptualisation, and (4) active experimentation. This model presents as a cycle, because in the fourth stage generalisations are created, and new hypotheses are developed to be tested in future practice. So, when entering the next relevant situation, learners reenter the first stage, according to this model. During professional work, activity usually occurs simultaneously at each of the stages. For specific work episodes, and in particular in identifying learning associated with that episode, it proved helpful to explicitly separate the stages. Kolb’s model of experiential learning easily fits the learning processes in simulation learning: “The simulation experience affords

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an excellent opportunity to expand on this model [of experiential learning]. Learners are thrown into a simulated concrete experience that allows them to progress through the cycle, ideally developing skills and knowledge to be applied in future simulated or actual concrete experiences. One of the most important parts of the experiential learning cycle is debriefing, a process that is often difficult to perform in the typical clinical learning experience. In a simulated environment, debriefing can be successfully accomplished.” (Okuda et al. 2009, p. 334). In this quotation, Okuda et al. (2009) describe how well the model of experiential learning fits to simulation learning, and also explicitly stress the importance of debriefing. The quality of debriefing plays a crucial role for the efficacy of simulation learning. If the debriefing is missed, learners most likely do not significantly improve their skills (Morgan et al. 2009; Savoldelli et al. 2006). The positive effect of debriefing arises from two different causes. First, learners receive feedback on their performance including what they still have to learn. (Notably, this comes along with behaviourist theories that highlight the importance of direct feedback to gain certain levels of expertise; Bradley and Postlethwaite 2003.) Secondly, learners can engage in self-reflection on their actions and experience. During reflection on their own performance, decision-making processes may be reassessed, and learners can engage introspectively about whether alternative lines of action would have been more effective (Gartmeier et al. 2008b). The analysis of critical incidents and their triggers is also consistent with theories about learning from mistakes (BangertDrowns et al. 1991; Ziv et al. 2005).

25.3.3

Constructivist and Situated Learning Theories

Cognitive processes like self-reflection or introspection and meaningful feedback processing are also important elements of constructivist theories. Constructivism assumes that learners’ new experience either fits to their existing cognitive structure or produces a conflict, if it does not match expectations (Perkins 2007). For instance, when learners acquired deficient knowledge or skills, e.g. an energy-snapping posture during cardiac massage, but nevertheless believe they were doing it correctly, appropriate feedback may trigger a cognitive and behavioural conflict. There are two ways how the learner can resolve this conflict. Either the existing deficient knowledge is modified or the new experience is ignored or considered to be flawed, thereby leading to disengagement (without any positive learning effect). It is part of instructor’s responsibilities to encourage learners to reflect and assimilate the received (and unexpected) feedback in a constructive way, so that effective learning can take place. Simulation learning is learning in a close-to-authenticity environment. It thus creates a form of “in situ” or “on site” learning, which is analogous to the ideals of situated learning theories, in particular with a focus on communities of practice (Lave and Wenger 1991). From this learning perspective, teams are trained within their everyday routine environment in which they can use their own familiar


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equipment. The concept of situated learning focuses on learning through the interaction with other persons within professional teams. Actively participating in such a group is what Lave and Wenger (1991) call legitimate (peripheral) participation in a community of practice. In situated learning environments, teachers’ roles are to stay in the background and instead work to facilitate learners’ independence which is an important principle in simulation learning. This capacity is important because simulation learning allows learners of different levels of expertise to participate in shared activities and to take over the adequate respective roles, some being more peripheral, some being more central. The involvement in communities is a tool to ensure the emergence of feedback and reflection, as the whole community’s outcome is affected by individual actions.

25.3.4

Deliberate Practice

Practice, in particular large amounts of practice, helps a lot to improve professional performance (Ericsson 2004). Examples from professional sport show that top-level competitors immediately suffer from performance drawbacks when they interrupt practicing. However, it is not only the quantitative amount of practice that counts, but rather it is the quality of practice. The “right” things have to be trained. Professional communities often indicate which sort of practice is required, although the persons who decide about the appropriate direction of training activities, often remain invisible (they remain “in the shadow”, as Gruber et al. 2008, refer to it). “Practicing the right things” is at the core of the theory of deliberate practice. This theory explains how expertise is acquired in complex domains (Ericsson 2004). In their studies on music expertise, Ericsson et al. (1993) demonstrated that qualitative differences in musical performance could be attributed to vast amounts of deliberate practice over extended periods of the musicians’ careers. Musicians who had spent more time with deliberate practice activities showed higher instrumental achievements than their colleagues who had engaged in equal amounts of musical activities, but at a lower level of deliberately focused practice. Experts were more involved in laborious learning activities over a long period of time that only aimed at improving performance. The engagement in deliberate practice focuses explicitly on problems which the learner is not yet able to perform correctly. Deliberate practice focuses – usually with support of a trainer or skilled teacher (Degner and Gruber 2011) – on the identified lacks of current performance. It introduces a definition of adequately designed step-by-step practice units and monitors the degree of improvement. The role of teachers or trainers is extremely important as teachers function not only as domain experts, but also as teaching experts. As domain expert, the teacher provides knowledge about typical requirements of the domain; as teaching expert, the teacher functions as personated accumulation of knowledge about appropriate teaching methods for domain-specific contents and for the development of skills (Lehmann 2002). It is not the execution of deliberate practice per se that explains an increased level of performance. Rather, mediating cognitive processes and reflective analyses

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are constitutive for performance enhancement, because they guide subsequent corresponding cognitive adaptations (Lehmann and Ericsson 2003). Deliberate practice thus includes (1) repetitive execution of cognitive or psychomotor skills in a special domain, combined with (2) a rigorous assessment that gives (3) an informative feedback to the learners, and leading to (4) significant improvement in skills in a safe environment (Issenberg et al. 2005; Van de Wiel et al. 2011). Analogous to the notion of deliberate practice, simulation learning often manifests features that facilitate learning progress through transfer of knowledge (Domuracki et al. 2008) and thus contribute to the acquisition of higher levels of expertise (Wayne et al. 2005, 2006). Notably, deliberate practice may even be a prerequisite for the effectiveness of simulation learning, as has been argued in a recent metaanalysis (McGaghie et al. 2011). The role of teachers in the design of effective deliberate practice is therefore salient. The quality of instruction plays a major role in the design of simulation learning. Skilled facilitators necessarily have to create authentic problems, to choose the adequate level of difficulty, to connect learning to prior experience and to link the debriefing session to meaningful further development.

25.3.5

Summary of Theoretical Explanations

In summary, there is a variety of theoretical arguments why simulation learning can be effective. Simulation learning addresses essential principles of adult education and thus seems to be appropriate to foster adult learning. It employs a genuine learner-centred approach and leads to learning as described in the cycle of experiential learning. Simulation learning makes extensive use of feedback and provokes learners’ self-reflection (Driessen et al. 2008; Hattie and Timperley 2007). Constructivist models stress the importance of curricular immersion, which is typical for simulation learning activities. These conclusions highlight the relevance of practice-based learning, whilst also stressing the importance of adequate training of teachers and instructors. These persons-in-the-shadow are responsible to appropriately direct the learners’ practice, to provide them with adequate roles in communities of practice – either peripheral roles or central roles – and thus to match simulation learning with pre-existing experience in order to facilitate learners’ reflection. The role of simulation fidelity can be seen differently from particular theoretical perspectives and, thus, is extensively discussed in the literature (Norman et al. 2012; Teteris et al. 2012). Authenticity in the eyes of the learner seems to be the most important mediating factor. Yet, it is an educational challenge for teachers and instructors to construct simulation scenarios in a manner, which meets authenticity in the eyes of learners. In standardised resuscitation courses for emergency and intensive care physicians, for instance, the main learning focus is placed on the algorithm of action rather than on specific actions. In this way, the courses are based on very simple simulators. The courses rely on the “movie in one’s head” of the learners, who should connect the given problem to their real life experiences (Maran and Glavin 2003). There is little empirical evidence, however, concerning the effects of such didactical decisions. The


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lack of a unified theory of simulation learning is closely related with a lack of a coherent body of empirical research on the topic. Nevertheless, the practical value of simulation learning is rarely doubted, which may result from the availability of a number of well-established best-practice examples of simulation learning applications. A sketch of the most relevant examples is presented in the next section.

25.4

Best-Practice Examples: Simulation Learning Applications

In this section we describe in some detail both typical and unusual applications of simulation learning to demonstrate its wide possible use. These examples are (1) twodimensional CAL simulation, (2) communication training, (3) resuscitation training, (4) clinical simulation workplace, and (5) simulation as quality assurance tool.

25.4.1

Two-Dimensional CAL Simulation

Early CAL simulation attempts typically adopted two-dimensional simulation formats. The programme “Dermatology 2000” may serve as an example of an interactive two-dimensional CAL programme. It was developed for undergraduate medical education in dermatology (Roesch et al. 2003) and was for some time the most demanded programme across all domains in the Virtual University of Bavaria (Germany). It was awarded a “prize summa cum laude” at the competition “Medikinale 2000” for innovative learning environments in medicine. In Dermatology 2000, students were confronted with virtual patients suffering a large variety of skin diseases, whose appearances were illustrated through real clinical photographs. Apart from the acquisition of dermatological knowledge, students learned how to ascertain case history and how to develop diagnostic reasoning skills as well as self-control in learning. The interaction of learners was supported by the use of video-based cases which are difficult to encounter in clinical reality in a scheduled fashion. Examples used were emergencies (which need immediate treatment), or transient symptoms and signs. In general, despite some lack of authenticity two-dimensional simulations offer opportunities to interact with the scenario and produce variable outcomes after interventions. More broadly, such learning formats can be qualified as “serious games”. Sostmann et al. (2008) developed a simulation for learning in pediatrics within an undergraduate medical curriculum. In their programme, an interactive touch screen of 90 × 180 cm on a table surface was used, on which a real size child in her/his bed was displayed. The virtual pediatric patient displays typical physical signs (as a skin rash) and even moves her/his body while lying in the virtual bed. The students’ task is to diagnose the child and to initiate a treatment. For diagnosing they had to use virtual examination tools such as a stethoscope or a blood pressure cuff. They had to perceive physical signs and findings which were presented as audio

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files, visual displays, or a screen of a haemodynamic monitor, similar to real life conditions. Beyond learning medical knowledge, the programme also fostered students’ team work and provided opportunities for expert feedback (SIMMED n.d.). Two-dimensional CAL simulations were considered useful to provide opportunities to collect first experiences in close-to-authentic job-related issues. They have a limited potential, however, to mimic the real world. For example, it proved difficult to simulate authentic forms of interaction, in which empathic features are displayed. Other forms of simulation learning, for example communication trainings, are more appropriate to train such aspects.

25.4.2

Communication Training

Teaching and learning of communication skills in a structured way may be supported by professional actors who are trained to play specific roles within a defined setting. A number of ‘best-practice’ examples have been developed focussing on the simulation of interpersonal relationships. Professional actors are trained to provide a highly authentic learning experience through which learners try to build an atmosphere of trust while interacting with subordinates or other professionals and through which they learn how to interact with a difficult patient. The use of professional actors in simulations offers opportunities for delivering immediate feedback, because the actors are close-to-authentic interaction partners on the one side, but professional observers of interaction processes on the other side. They easily get used to provide extensive feedback how they felt during the encounter. In medicine, it is increasingly often requested to implement extensive training phases with actors as “simulated patients” before starting with to work with real patients. Learning with simulated patients leads to a better structured patient-physician interaction (Kiessling et al. 2010). In medicine it also frequently occurs that situations are distressing for patients, for instance informing them of ‘bad news’. It is ethically appropriate that novice doctors are comprehensively prepared in these processes before they encounter and enact such distressing interaction situations with real patients. It has been argued, that simulated patients provide a more authentic feedback, if they lack backgrounds in a medical profession, because then they are not blind to the “bad habits” of the professional field (“deformation professionelle”). In medicine, a substantial body of literature exists concerning standardisation of the training of standardised patients and their effectiveness (Bokken et al. 2009; Kiessling et al. 2010; Lingemann et al. 2012; Wind et al. 2004). It is difficult to empirically substantiate the effects in terms of outcome changes at patient level. Nevertheless, it can be concluded, that simulating social encounters using professional actors is a frequently used method to train communication or negotiation skills, to learn how to build interpersonal relationships and how to acquire other skills, for instance empathic behaviour or confident body language. Besides such “soft skills”,


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specific practical skills can be taught using simulation manikins instead of actors. A very popular form of such trainings using simulation manikins is resuscitation training in emergency medicine.

25.4.3

Resuscitation Training

A prototypical context for simulation is the training of non-routine events with severe consequences, among which resuscitation is one of the most widely used (Wayne et al. 2008). It is important to know that basic forms of resuscitation are required from virtually everyone, not only from medical experts. In resuscitation trainings, thus, the level of expertise of learners can range from pre-school children to specialist emergency medical physicians. Very simple simulation devices are used for mass trainings, the most basic one being an inflatable cushion-like torso to perform chest compressions in combination with a self instructing video. In one study, 35,000 children at Danish schools were trained by this strategy and they then in turn took the “simulator” home to train their parents and playmates (Isbye et al. 2007; Lorem et al. 2008). The other extreme of resuscitation training is aimed to display scenarios of very rare incidents to emergency medicine specialists utilising high-fidelity full scale simulators at substantially high costs for equipment, staff and maintenance. These systems even react to virtual physiological or pharmacological stimuli with an underlying physiological model that integrate up to 20 different substance effects (Akaike et al. 2012). Such advanced settings are useful to train on the highest levels of expertise, i.e. for situations that are so rare that an average physician will never encounter them, before being required to respond decisively. An example is the case of malignant hyperthermia during general anesthesia, which has an incidence of 1 in 50,000 anesthesias. At present, a number of university centres operate full scale simulators; in Germany, currently about 20–30 such systems are in use. Some authors claim that practicing anesthetists should train every second year on such a simulator, but outcome relevant effects have not yet been demonstrated at the patient level. To sum up, simulation training regarding technical skills like in resuscitation has to be adaptively based on subject-specific skill level and thus can vary from very simple to high-fidelity devices. Apart from single devices or even completely equipped rooms, simulation environments can take very different forms according to the individual purpose, as the following paragraph will illustrate in which clinical simulation workplaces are described.

25.4.4

Clinical Simulation Workplace

Recently, much energy has been invested in designing and arranging clinical simulation workplaces which allow to promote simulation learning in a large variety of complexity, including the most complex, close-to-authentic environments (Lamb 2007).

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Such workplaces may be single rooms, as for example intensive care medicine workplaces, operating theatres, or emergency medical vehicles. To go beyond that, some institutions began to construct whole hospital tracts with various settings for training (Studienhospital Münster n.d.). A simulation hospital contains all relevant components like patient rooms, sanitary areas, disinfection supplies, oxygen supplies, communication and alerting technology, etc. Such environments are especially suitable for interprofessional learning, because they resemble an everyday routine context, in which (for example) medical doctors and paramedics have to interact (Hallikainen et al. 2007). Simulation hospitals thus provide excellent conditions for situated interprofessional working in professional communities. Again, it is difficult to find strong evidence concerning effectiveness at the patient level, but there are clues of the effectivity (Hallikainen et al. 2007). In any case, training hospitals are recognisable for students as learning centres and thus help to overcome a problem that is often reported by medical students: the feeling of being unprepared for responding to serious incidents. Learning to effectively respond to highly unpredictable courses of action is a core activity in another form of simulation learning: the disaster management. Disaster scenarios have been used for learning and coordination of teams of leading officers from fire departments, civil protection, police and emergency medical services. During the team trainings, the divisions between different responsibilities are particularly important to be understood. The German government, for example, operates a training centre with two simulated coordination centres for the training of fire department leaders, who are involved in a disaster scenario over the course of a week. The virtual scenario is located in a “real” German district, and the available resources to manage the scenario closely resemble those which are available in reality. The scene gradually builds up, for example starting with a severe thunderstorm during an open air music festival and then involving more and more complex incidents, such as a concomitant fire in a chemical industry site, which is caused from lightning during the thunderstorm. Toxic gases are produced and are being moved by the wind towards a hospital. After every half a day of training the scenario may be interrupted to give specific input with respect to the main new problem which occurred in the scenario. In the eyes of trainees, this kind of simulation is perceived as (dramatically) authentic, although the disaster effects are not directly experienced. However, it is generally acknowledged that the operating staff would also be apart from the direct disaster effects if such an accident occurred in reality. The examples presented in this paragraph show that there are almost no limits to mimicking reality. The most complex simulations involve such a variety of influences and effects, that it is difficult to empirically find proofs of effectiveness. Many of the simulations do not have any serious alternative, however, so that bestpractice evidence is considered to be acceptable. It is evident that conclusions about effectiveness depend on the sort of simulation used for particular kinds of simulation learning. For at least some kinds of simulations learning, efforts have been made to use simulations as tools of quality control and quality assurance.


25.4.5

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Simulation as Quality Assurance Tool

As shown in the previous best-practice examples, simulations are useful for multifaceted purposes. Simulations as a quality assurance tool are focussing on the assessment of the current state of individuals’ skills and knowledge as well as functioning of internal organisational processes, e.g. in patient resuscitation within hospitals (Ziv et al. 2000). There is some evidence of positive aspects with regard to the assessment of skills for quality assurance (Gaba et al. 1998; Høyer et al. 2009). Simulation of scenarios allows for observing the quality of practical skills rather than testing knowledge, e.g. through oral or written tests. The concept of “practical knowledge” (or, almost synonymously used, “tacit knowledge” or “practical intelligence”: Sternberg et al. 2000) thus is often used to describe the outcomes of simulation learning. Some sorts of practical knowledge or practical skills are impossible to be tested in real life, for example the management of an engine failure during a helicopter take off. Therefore, pilots have to pass “check flights” on flight simulators on regular time base. Accordingly, the most frequent forms of quality assurance through simulation are applied during the examination of trainees, for instance for graduation at the end of a curriculum. In medicine, practical examinations were designed as “Objective Structured Clinical Examinations” (OSCE) and were implemented into the curricula of many medical schools all over the world (Federation of State Medical Boards (FSMB) and National Board of Medical Examiners (NBME) n.d.; Swiss Confederation 2012). OSCEs usually make extensive use of simulated patients (Adamo 2003). In conclusion, simulations serve well issues of quality control, certification, and re-certification because of their property to specifically tailor standardised settings to assess performance. Simulations offer a large degree of standardisation in the assessment of complex skills and practices and thus usually are perceived from examinees as being fairer than unstandardised assessments. The use of simulations as quality assurance tool is based on the recognition of some “objective” outcomes. As mentioned above, the overall body of research evidence about simulation learning is still comparably small and inhomogenous. The complexity of simulation learning, on the one hand, and the wealth of potential theoretical explanations for the effectiveness of simulation learning, on the other side, contribute to this state. However, there is now an increasing emphasis on activities associated with establishing reliable research findings about simulation learning. An overview of that research is presented in the following section.

25.5

Research Findings

Although there still is a lack of a coherent body of research on simulation learning, single research results have been developed since long. More than 30 years ago, Dekkers and Donatti (1981) pled to integrate research studies in educational attempts

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including simulations. Simulations have been used to measure the processing of complex or rarely happening processes (Dooley 2002). Simulations offer options for research by creating standardised settings for observation, i.e. laboratory like conditions. Fischer et al. (2011) analysed the effects of applying a mechanical resuscitation device (MRD) on the quality of resuscitation provided by flight attendants in a cabin simulator. The use of MRD resulted in less effective ventilation. Such an investigation could hardly be carried out under real life conditions, e.g. in a flying airplane with a real patient suffering from cardiac arrest. Apart from testing new devices, simulation permits the evaluation of new procedures and processes by different approaches to determine the most appropriate alternative. One focus of simulation studies is the investigation of complex interactions or team development processes which are either hard to observe in the real world because of rareness or because they are critical from an ethical perspective. Hunziker et al. (2010) observed the early phase of team development processes in the context of managing unforeseen critical incidents in resuscitation. Simulations can be used to replicate such processes and to systematically vary experimental conditions. In a similar vein, simulations were used to investigate human error, like deficits in communication. Bogenstätter et al. (2009) examined the accuracy of shared information between nurses and physicians joining a medical emergency situation. Using simulated cardiac arrests, the authors demonstrated that only 18 % of the given information was inaccurate. Skorning et al. (2012) investigated the adherence of emergency physicians to a professional telemedical support by analysing complex full scale scenarios. Physicians who performed better in managing the scenario were shown to make more use of the telemedical advice given by the dispatch centre. Marsch et al. (2004) studied the relations between physicians’ non-technical skills and the outcome of simulated resuscitations. They found that teams were more successful in the simulation which showed a higher quality of leadership behaviour and a more explicit individual assignment of tasks. In these examples of studies aiming at investigating the role of non-technical skills and human errors it was preferred to collect simulation data rather than data in the daily clinical routine. The investigation of these issues would have been hardly feasible in clinical practice, as the relevant events occur uncommonly, if at all. Simulations have also long been used to investigate teaching effects. Zausig et al. (2009) examined the effects of applying a standardised simulator-based training to teach anesthesiologists’ non-technical skills. Follow-up analyses of video recordings of emergency scenarios failed to show an improvement in learners’ non-technical skills. As a consequence, they concluded that the simulations have to be modified, mainly concerning the authenticity. In contrast to the study of Zausig et al. (2009), Cooper (2001) found that a leadership training within a simulated cardiac arrest scenario significantly improved clinical performance. The study of Morrison et al. (2004) revealed that a teacher training intervention within a simulation led to an increase in teaching quality as measured by an OSTE (objective structured teaching examination).


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Simulations were successfully used as a research tool to evaluate variations in the retention of knowledge and skills over time (Smith et al. 2008), in the appropriate time intervals for refresher trainings (Woollard et al. 2006), and in alternative training devices to enhance retention (Spooner et al. 2007). Arriaga et al. (2013) investigated operating room teams working in a series of surgical crisis scenarios in a simulated operating room. Each team was randomly assigned to manage half the scenarios with a set of crisis checklists and the remaining scenarios from memory alone. The primary outcome measure was failure to adhere to critical processes of care. Such failure was less common during simulations when checklists were available, even when controlling for clustering within teams, the teams’ institution, scenario, and learning and fatigue effects: Every team performed better when the crisis checklists were available than when they were not. This suggests that checklists for use during operating room crises have the potential to improve surgical care. Weller et al. (2011) developed an instrument to evaluate the effectiveness of teamwork trainings in healthcare by the use of simulation. A similar approach was used by Cooper et al. (2010) to develop a valid, reliable and feasible instrument to measure teamwork in medical emergencies. To sum up, simulation allows for the creation of laboratory conditions that are close to reality, but without putting other persons at risk. Simulations can be used to systematically implement and investigate effect of relatively rare events under standardised conditions. Collecting data is much easier and especially faster than if it was being gathered in clinical practice. On the one hand, researchers do not have to work on the compliance of real patients, on the other hand, critical ethical issues in working with real patients may be avoided by using manikins. The possibility to design simulations in detail makes it possible to investigate complex dynamic processes that are impossible to be observed in the real world.

25.6

Conclusions

This chapter presented an overview of the many aspects related with simulation learning. It was concluded that a wide variety exists of simulation applications to design practice-based learning activities, but there is still a lack of an unified approach to theory-building and, as a consequence, still a lack of supporting empirical evidence. To some degree these lacks do result from the nature of simulation learning, because simulation learning focuses on complex, multifaceted forms of learning in complex and dynamic learning environments. It is often difficult to compare simulation learning outcomes with real-life learning outcomes, because the relevant professional situations are rare, dangerous, inaccessible, etc. The overview revealed that simulations may serve various purposes, ranging from skill acquisition over training of rare events to assessment, quality control, to research. It is remarkable that supporting theoretical arguments can be found in many different approaches, each of which contributes to some degree to a rationale how to explain the effectiveness of simulation learning. The theories sketched in this

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chapter include principles of adult learning, which easily can be addressed to simulation learning. Kolb’s (1984) model of a cycle of experiential learning can be transferred to simulation learning, as learners pass through the steps of experiencing, reflecting, conceptualising and active experimenting. Other theoretical approaches supporting the benefit of simulation learning are learning by mistakes, behaviourist and constructivist theories referring to the importance of feedback and reflection, and the theory of situated learning, which focuses on learning through the interaction with other persons within professional teams. The theory of deliberate practice explains how expertise is acquired in complex domains. The basic mechanisms of deliberate practice, an explicit focus on training issues which are not yet mastered, and a strong direction of learning provided by significant others, are typical for simulation learning. Therefore, it can be concluded that there is a robust theoretical basis to support the use of simulation. It has to be admitted, however, that the arguments have yet to come together in an integrated theoretical model of simulation learning. The diverse theoretical background is related with a considerable diversity of empirical evidence. In order to explore the strengths and limitations of simulation learning, this chapter presented some of the most typical and some unusual bestpractice examples of the use of simulation learning. These examples were used to identify particularly reliable strengths and weaknesses of simulation learning, some of which were investigated in laboratory research. As simulation learning can aim at acquiring through practice-based learning many different kinds of experiential knowledge and simple as well as very complex skills, a wide range of simulation devices and environments is in use. Partial-task trainers like anatomical models are used to teach isolated practical skills, whereas high-fidelity full scale environments aim at the development of strategic skills, including prioritising, organising multi-parted processes, and anticipatory planning, as well as communication and teamwork skills. The question about strong empirical evidence concerning the effects of simulation learning is not easy to answer. The available empirical evidence is not as solid as one might expect. At present, most studies report consistently high satisfaction levels of learners and significant improvements at cognitive and psychomotor levels. However, the studies often lack a rigorous methodology. For example, when self assessment of physicians is used as a measurement instrument, results are flawed by extensive data on limited self assessment abilities of physicians (Davis et al. 2006). The main point, however, is that only few studies explicitly tested the transfer into clinical practice (Camp et al. 1997; Makker et al. 1995; Pottle and Brant 2000; Sanders et al. 1994), although enhancing clinical practice and patient safety is the ultimate purpose of simulation learning in medicine (Perkins 2007).

25.6.1

Strengths of Simulation Learning

One of the main advantages of simulation learning is that no persons are harmed during the learning process. Thus, learners can fully concentrate on their own learning progress without fearing any negative consequences when making a


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mistake – quite the contrary: In simulation learning, learning from mistakes is a basic premise (Ziv et al. 2005). As learning from mistakes requires explicit feedback by others, it is a key element of simulation learning’s effectiveness. Evidence exists that learning from mistakes under many circumstances has a greater effect than encouraging flawless behaviour (Bangert-Drowns et al. 1991). Individuals’ ability to identify the gaps in their own knowledge and skills is a prerequisite to overcome individual own deficits, thereby allowing for the development of further learning activities and learning strategies. One of the major strengths of simulation learning is that it permits practice in the context of non-routine or even very rarely occurring events (Hunziker et al. 2010), which is necessary for many particularly critical fields in domains like medicine or aviation. Learners can gain experience how to manage incidents which statistically (almost) never occur during a single individual’s working life time. Simulation learning allows that such events can be repeated arbitrarily often (Arthur et al. 1998). Because scenarios can be varied in their level of complexity and speed, many different didactical approaches can be implemented. Many simulations rely on a didactical sequence which resembles the apprenticeship model. Basic skills are taught first, followed by more and more ambitious skills and an increasing central role the learners take in professional communities (Issenberg et al. 2005). The option to slow down or even stop a simulated process can be useful for novices who require more time to learn complex and stressful tasks or to understand decisionmaking processes. Interrupting the simulation at certain points of interest to reflect of some particular issues is a powerful feature. The option to interrupt the simulated process, finally, helps to escape from disaster without dramatic real-life consequences (McFetrich 2006). Reflection is at the core of many theories of practice-based learning. To enable learners to reflect about the simulation learning experience, systematic feedback plays a crucial role. One of the most important components of many simulation trainings, thus, is the debriefing session, in which learners can consider and evaluate the whole scenario. These processes provide opportunities for a detailed analysis of the individual learners’ actions, decision-making, feelings, and thoughts. Identifying individual gaps in knowledge and skills can be used to modify training contents or methods to overcome these weaknesses (CannonBowers and Salas 1997). For example, Marsch et al. (2005) showed that learners within a resuscitation scenario were not aware of their multiple needless interruptions of chest compressions (which substantially lower patient outcome). Extensive feedback during debriefing helped to realise the deficits and to find effective ways for improvement. Obviously, simulation learning offers particular advantages, most prominently the possibility to make mistakes without having to fear real-life consequences, the opportunity to engage in the reflective practice of rare events, and the option to vary complexity according to the interindividual differences in learning. Naturally, simulation learning also has its own limitations.

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25.6.2


Limitations of Simulation Learning

Despite all efforts to date, there is not yet clear evidence that simulation learning has a significant impact at the institutional level (Perkins 2007). For example, it is not yet convincingly evidenced that training of leadership ability in a simulated environment has a significant impact on business success. Neither do we have undoubted evidence that team training in health care improves patient safety or the mortality rate. Accordingly, Issenberg et al. (2005) cautioned against being overly optimistic concerning the generalisability of outcomes of simulation learning studies. Most studies addressing effects of simulation learning in medicine describe improvements in knowledge and skills, and the reported satisfaction levels are consistently high. However, many of these studies lack a rigorous methodology (Nestel et al. 2011). However, at least some conditions have been identified, under which simulation effects are more reliable, such as deliberate practice or extensive feedback (McGaghie et al. 2010, 2011). From an economical perspective, simulation learning often is highly costintensive, especially if expensive technological equipment and technicians are needed to operate the scenarios. Technical maintenance in addition usually is time consuming, and instructors have to be trained how to use the equipment (Graf and Grube 2004; Grube et al. 2001). This expense is often not compensated by financial earnings, but contributes rather to non-measurable financial benefits like knowledge or an improvement in skills (McFetrich 2006). Such immaterial gains might prevent future costs which result from defective goods or medical malpractice, but such causal consequences are difficult to assess (Ziv et al. 2000). Another critique of simulation learning is that knowing about the fact that it is “only a simulation” may influence learners’ attitudes and behaviour, e.g. not taking it sufficiently seriously. Additional effort of instructors might be necessary to enhance the learners’ motivation (Fanning and Gaba 2007). On the other hand, when learners perceive being observed, they might act in a more reflected manner, leading to superior performance compared to exposure in daily routines. There is a lack of systematic investigations of such effects, however. There is evidence that, in particular, high-fidelity full scale environments and most complex simulated scenarios are generally perceived as extremely realistic (Perkins 2007). Learners often report a high emotional and motivational involvement (Hunziker et al. 2010), so that the emotional vulnerability of learners has been reported to be a serious problem associated with simulation learning. Simulated scenarios are often perceived as exhausting and daunting, and learners report fear of being judged by the instructor or by other learners (Nilsen and Baerheim 2005; Savoldelli et al. 2005). Therefore, it is a key role of the instructor to create an atmosphere of trust and respect (Fanning and Gaba 2007).

25.6.3

Outlook

To finally sum up, in weighing up the pros and cons of simulation learning, it can be concluded that the advantages seem to outweigh the disadvantages. The increasing complexity of many modern professions, like medicine, provokes new challenges


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for adequate practice-based learning. The use of simulation learning might gain in importance, as it provides learning environments close to reality in which students can collect experiences without fearing any negative consequences. Prior to the implementation of simulation learning, however, clear learning objectives have to be defined, which are in accordance with the curriculum. Curricular immersion probably is of considerably more importance than variations in the level of simulation fidelity. Therefore, the role of the instructor is crucial: Instructors have to be experienced in the use of technical devices, and they have to be able to provide adequate feedback to learners to facilitate self-reflection. As always, “further research is needed” to answer the complex question of effects of simulation learning on an institutional level and to decide in didactically sensible ways about appropriate support of learners during simulation learning. Acknowledgment This paper was written during a sabbatical stay of the second author as Visiting Professor at the Centre for Learning Research, University of Turku, Finland.

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