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Environmental simulation models have been playing a key role in civil and environmental engineering decision making processes for decades. The utility of an environmental model depends on how well the model is structured and calibrated. Model calibration is typically in an automated form where the simulation model is linked to a search mechanism (e.g., an optimization algorithm) such that the search mechanism iteratively generates many parameter sets (e.g., thousands of parameter sets) and evaluates them through running the model in an attempt to minimize differences between observed data and corresponding model outputs. The challenge rises when the environmental model is computationally intensive to run (with run-times of minutes to hours, for example) as then any automatic calibration attempt would impose a large computational burden. Such a challenge may make the model users accept sub-optimal solutions and not achieve the best model performance. The objective of this thesis is to develop innovative strategies to circumvent the computational burden associated with automatic calibration of computationally intensive environmental models. The first main contribution of this thesis is developing a strategy called “deterministic model preemption” which opportunistically evades unnecessary model evaluations in the course of a calibration experiment and can save a significant portion of the computational budget (even as much as 90% in some cases). Model preemption monitors the intermediate simulation results while the model is running and terminates (i.e., pre-empts) the simulation early if it recognizes that further running the model would not guide the search mechanism. This strategy is applicable to a range of automatic calibration algorithms (i.e., search mechanisms) and is deterministic in that it leads to exactly the same calibration results as when preemption is not applied. One other main contribution of this thesis is developing and utilizing the concept of “surrogate data” which is basically a reasonably small but representative proportion of a full set of calibration data. This concept is inspired by the existing surrogate modelling strategies where a surrogate model (also called a metamodel) is developed and utilized as a fast-to-run substitute of an original computationally intensive model. A framework is developed to efficiently calibrate hydrologic models to the full set of calibration data while running the original model only on surrogate data for the majority of candidate parameter sets, a strategy which leads to considerable computational saving. To this end, mapping relationships are developed to approximate the model performance on the full data based on the model performance on surrogate data. This framework can be applicable to the calibration of any environmental model where appropriate surrogate data and mapping relationships can be identified. As another main contribution, this thesis critically reviews and evaluates the large body of literature on surrogate modelling strategies from various disciplines as they are the most commonly used methods to relieve the computational burden associated with computationally intensive simulation models. To reliably evaluate these strategies, a comparative assessment and benchmarking framework is developed which presents a clear computational budget dependent definition for the success/failure of surrogate modelling strategies. Two large families of surrogate modelling strategies are critically scrutinized and evaluated: “response surface surrogate” modelling which involves statistical or data–driven function approximation techniques (e.g., kriging, radial basis functions, and neural networks) and “lower-fidelity physically-based surrogate” modelling strategies which develop and utilize simplified models of the original system (e.g., a groundwater model with a coarse mesh). This thesis raises fundamental concerns about response surface surrogate modelling and demonstrates that, although they might be less efficient, lower-fidelity physically-based surrogates are generally more reliable as they to-some-extent preserve the physics involved in the original model. Five different surface water and groundwater models are used across this thesis to test the performance of the developed strategies and elaborate the discussions. However, the strategies developed are typically simulation-model-independent and can be applied to the calibration of any computationally intensive simulation model that has the required characteristics. This thesis leaves the reader with a suite of strategies for efficient calibration of computationally intensive environmental models while providing some guidance on how to select, implement, and evaluate the appropriate strategy for a given environmental model calibration problem.

Environmental simulation models have been playing a key role in civil and environmental engineering decision making processes for decades. The utility of an environmental model depends on how well the model is structured and calibrated. Model calibration is typically in an automated form where the simulation model is linked to a search mechanism (e.g., an optimization algorithm) such that the search mechanism iteratively generates many parameter sets (e.g., thousands of parameter sets) and evaluates them through running the model in an attempt to minimize differences between observed data and corresponding model outputs. The challenge rises when the environmental model is computationally intensive to run (with run-times of minutes to hours, for example) as then any automatic calibration attempt would impose a large computational burden. Such a challenge may make the model users accept sub-optimal solutions and not achieve the best model performance. The objective of this thesis is to develop innovative strategies to circumvent the computational burden associated with automatic calibration of computationally intensive environmental models. The first main contribution of this thesis is developing a strategy called “deterministic model preemption” which opportunistically evades unnecessary model evaluations in the course of a calibration experiment and can save a significant portion of the computational budget (even as much as 90% in some cases). Model preemption monitors the intermediate simulation results while the model is running and terminates (i.e., pre-empts) the simulation early if it recognizes that further running the model would not guide the search mechanism. This strategy is applicable to a range of automatic calibration algorithms (i.e., search mechanisms) and is deterministic in that it leads to exactly the same calibration results as when preemption is not applied. One other main contribution of this thesis is developing and utilizing the concept of “surrogate data” which is basically a reasonably small but representative proportion of a full set of calibration data. This concept is inspired by the existing surrogate modelling strategies where a surrogate model (also called a metamodel) is developed and utilized as a fast-to-run substitute of an original computationally intensive model. A framework is developed to efficiently calibrate hydrologic models to the full set of calibration data while running the original model only on surrogate data for the majority of candidate parameter sets, a strategy which leads to considerable computational saving. To this end, mapping relationships are developed to approximate the model performance on the full data based on the model performance on surrogate data. This framework can be applicable to the calibration of any environmental model where appropriate surrogate data and mapping relationships can be identified. As another main contribution, this thesis critically reviews and evaluates the large body of literature on surrogate modelling strategies from various disciplines as they are the most commonly used methods to relieve the computational burden associated with computationally intensive simulation models. To reliably evaluate these strategies, a comparative assessment and benchmarking framework is developed which presents a clear computational budget dependent definition for the success/failure of surrogate modelling strategies. Two large families of surrogate modelling strategies are critically scrutinized and evaluated: “response surface surrogate” modelling which involves statistical or data–driven function approximation techniques (e.g., kriging, radial basis functions, and neural networks) and “lower-fidelity physically-based surrogate” modelling strategies which develop and utilize simplified models of the original system (e.g., a groundwater model with a coarse mesh). This thesis raises fundamental concerns about response surface surrogate modelling and demonstrates that, although they might be less efficient, lower-fidelity physically-based surrogates are generally more reliable as they to-some-extent preserve the physics involved in the original model. Five different surface water and groundwater models are used across this thesis to test the performance of the developed strategies and elaborate the discussions. However, the strategies developed are typically simulation-model-independent and can be applied to the calibration of any computationally intensive simulation model that has the required characteristics. This thesis leaves the reader with a suite of strategies for efficient calibration of computationally intensive environmental models while providing some guidance on how to select, implement, and evaluate the appropriate strategy for a given environmental model calibration problem.

Au stade de projet amont, les ingénieurs utilisent souvent des modèles de basse fidélité possédant de larges erreurs. Les approches déterministes prennent implicitement en compte les erreurs par un choix conservatif des paramètres aléatoires et par l'ajout de facteurs de sécurité dans les contraintes de conception. Une fois qu'une solution est proposée, elle est analysée par un modèle haute fidélité (test futur): une re-conception peut s'avérer nécessaire pour restaurer la fiabilité ou améliorer la performance, et le modèle basse fidélité est calibré pour prendre en compte les résultats de l'analyse haute-fidélité. Mais une re-conception possède un coût financier et temporel. Dans ce travail, les effets possibles des tests futurs et des re-conceptions sont intégrés à une procédure de conception avec un modèle basse fidélité. Après les Chapitres 1 et 2 qui donnent le contexte de ce travail et l'état de l'art, le Chapitre 3 analyse le dilemme d'une conception initiale conservatrice en terme de fiabilité ou ambitieuse en termes de performances (avec les re-conceptions associées pour améliorer la performance ou la fiabilité). Le Chapitre 4 propose une méthode de simulation des tests futurs et de re-conception avec des erreurs épistémiques corrélées spatialement. Le Chapitre 5 décrit une application à une fusée sonde avec des erreurs à la fois aléatoires et de modèles. Le Chapitre 6 conclut le travail. / At the initial design stage, engineers often rely on low-fidelity models that have high uncertainty. In a deterministic safety-margin-based design approach, uncertainty is implicitly compensated for by using fixed conservative values in place of aleatory variables and ensuring the design satisfies a safety-margin with respect to design constraints. After an initial design is selected, high-fidelity modeling is performed to reduce epistemic uncertainty and ensure the design achieves the targeted levels of safety. High-fidelity modeling is used to calibrate low-fidelity models and prescribe redesign when tests are not passed. After calibration, reduced epistemic model uncertainty can be leveraged through redesign to restore safety or improve design performance; however, redesign may be associated with substantial costs or delays. In this work, the possible effects of a future test and redesign are considered while the initial design is optimized using only a low-fidelity model. The context of the work and a literature review make Chapters 1 and 2 of this manuscript. Chapter 3 analyzes the dilemma of whether to start with a more conservative initial design and possibly redesign for performance or to start with a less conservative initial design and risk redesigning to restore safety. Chapter 4 develops a generalized method for simulating a future test and possible redesign that accounts for spatial correlations in the epistemic model error. Chapter 5 discusses the application of the method to the design of a sounding rocket under mixed epistemic model uncertainty and aleatory parameter uncertainty. Chapter 6 concludes the work.

Einleitung Studierende der Veterinärmedizin müssen neben umfangreichem theoretischem Wissen zahlreiche praktische Fertigkeiten erlernen. Da jeder Einzelne in seinem eigenen Tempo lernt, besteht ein großer Bedarf an Trainingsmöglichkeiten. Kadaver und lebende Tiere sind selten in ausreichender Menge verfügbar und lebende Tiere sind zudem aus Gründen des Tierwohls nur eingeschränkt zu verwenden. Simulationsmodelle (Modelle von Organismen / Körperteilen) können hier Abhilfe schaffen. Kommerziell erhältliche Modelle sind sehr kostenintensiv und für die Tiermedizin noch nicht flächendeckend erhältlich. Zunehmend werden selbst entwickelte low-fidelity Modelle in der Tiermedizin verwendet. Aufgrund des Mangels an publizierten Daten zu ihrem Einsatz besteht intensiver Forschungsbedarf. Ziele der Untersuchungen In dieser Arbeit sollte untersucht werden, ob einfache, selbst entwickelte Simulationsmodelle (low-fidelity Modelle) erfolgreich in der Lehre eingesetzt werden können. Dazu wurden zwei selbst entwickelte und gebaute Simulationsmodelle evaluiert (Studie 1) und ihr Einsatz in Kombination mit anderen Lehrmedien untersucht (Studie 2). Materialien und Methoden In Studie 1 wurden zwei low-fidelity Modelle zur kaninen Intubation und Katheterisierung entwickelt und evaluiert. Es wurde ein Studiendesign genutzt, das die erworbenen Fertigkeiten zweier Übungsgruppen und einer Kontrollgruppe in einer praktischen Prüfung (OSCE = objective structured clinical examination) am toten Hund vergleicht. Achtundfünfzig Studierende (4. FS) erhielten eine theoretische Einführung zur Intubation und wurden randomisiert auf drei Gruppen aufgeteilt. Gruppe 1 (high-fidelity) übte am kommerziell erhältlichen Intubation Training Manikin, Gruppe 2 (low-fidelity) am entwickelten low-fidelity Modell und die Textgruppe las einen Text, der die Intubation beim Hund beschreibt. Siebenundvierzig Studierende (10. FS) durchliefen dasselbe Studiendesign zum Thema Katheterisierung der Hündin. Sie nutzten das kommerziell erhältliche Female Urinary Catheter Training Manikin, das selbst entwickelte low-fidelity Modell und Lehrtexte. In Studie 2 wurde die Vermittlung zweier spezifischer Fertigkeiten mit Hilfe von Potcasts und Simulationstraining evaluiert. Zwei anleitende Potcasts zu Intubation und Katheterisierung und die oben beschriebenen Modelle wurden innerhalb eines crossover-Studiendesigns genutzt. In dieser Studie sind Potcasts audio-visuell aufbereitete Animationen mit Schritt für Schritt – Anleitungen und Informationen. Die erworbenen praktischen Fertigkeiten zweier Übungsgruppen, die sich in der Art der theoretischen Vorbereitung unterschieden, wurden in einer praktischen Prüfung (OSCE) am toten Hund verglichen. Ein Fragebogen erfasste das Feedback der Teilnehmer. Sechzig Studierende (2. FS) wurden randomisiert auf eine Potcast- und eine Textgruppe aufgeteilt. Die Potcastgruppe sah sich das anleitende Potcast an, die Textgruppe bereitete sich anhand eines Lehrtextes vor. Im Anschluss hatten beide Gruppen separate Übungseinheiten an den low-fidelity Modellen ohne Betreuung durch Lehrende. Ergebnisse In Studie 1 schnitten alle Übungsgruppen signifikant besser ab als die Textgruppen. Gruppe 1 (high-fidelity) und Gruppe 2 (low-fidelity) unterschieden sich weder bei der Intubation noch bei der Katheterisierung signifikant in ihren Leistungen. In Studie 2 schnitt die Potcastgruppe beim Thema Intubation signifikant besser ab als die Textgruppe, beim Thema Katheterisierung ergaben sich keine signifikanten Unterschiede. Insgesamt hatte das Simulationstraining den Studierenden Spaß gemacht, das Lernen ohne Betreuer wurde jedoch als Herausforderung empfunden. Schlussfolgerungen Es ist davon auszugehen, dass low-fidelity Modelle genauso geeignet für das Training klinischer Fertigkeiten sein können wie high-fidelity Modelle. Das Training klinischer Fertigkeiten mit Hilfe von Potcasts und low-fidelity Modellen sollte durch Betreuer ergänzt werden, anstatt als alleiniges Lehrmedium für Studierende des ersten Studienjahres Verwendung zu finden. Eigenständiges Lernen klinischer Fertigkeiten, angeleitet durch Potcasts bietet eine Möglichkeit für vertiefendes und wiederholendes Training höherer Semester. Der Einsatz von Simulationsmodellen in der veterinärmedizinischen Ausbildung wächst seit wenigen Jahren stetig. Diese Arbeit leistet einen zeitgerechten Beitrag bei der Evaluierung von Simulationstraining. / Introduction Students of veterinary medicine are expected to acquire various practical skills in addition to a wide range of theoretical knowledge. There is a strong demand for training opportunities, as every individual learns and acquires practical skills at individual pace. For reasons of animal welfare concerns and availability, live animals and cadavers cannot always be used for clinical skills training. Simulation models, which are models of organisms or body parts can be a considerable alternative for clinical skills training. Models that are commercially produced often have a high price and are not available for all skills. Self-made models are increasingly used in veterinary education. Because there is few published data regarding their use, more scientific research is required. Aims of the Investigation The objective of this study was to determine, if self-made low-fidelity models can be successfully used in veterinary medical education. For this purpose, two self-made low-fidelity models were evaluated (study 1) and their use in combination with other teaching tools was analyzed (study 2). Materials and Methods In study 1, two self-made low-fidelity models for simulation of canine intubation and canine female urinary catheterization were developed and evaluated. We used a study design that compares acquired skills of two intervention groups and one control group in a practical examination (OSCE = objective structured clinical examination). Fifty-eight second-year veterinary medicine students received a theoretical introduction to intubation and were randomly divided into three groups. Group 1 (high-fidelity) was then trained on a commercially available Intubation Training Manikin, group 2 (low-fidelity) was trained on our low-fidelity model, and the text group read a text describing intubation of the dog. Forty-seven fifth-year veterinary medicine students followed the same procedure for training urinary catheterization using the commercially available Female Urinary Catheter Training Manikin, our self-made model, and text. Outcomes were assessed in a practical examination on a cadaver using an OSCE checklist. In study 2 we evaluated the teaching of two specific clinical skills using potcasts and low-fidelity simulation training. Two instructional potcasts describing intubation and catheterization and both low-fidelity models described above were used. In our study, potcasts are audio-visual animations that provide the learner with step by step information and instruction on a clinical skill. We used a crossover study design and compared the acquired practical skills of two intervention groups after a different theoretical preparation. A survey captured the participants’ feedback. Sixty first year veterinary medicine students were randomly allocated to two groups, a potcast group and a text group. The potcast group watched a potcast while the text group read an instructional text for preparation. Then both groups had separate self-directed training sessions on low-fidelity models. Outcomes were assessed in practical examinations on a cadaver using an objective structured clinical examination (OSCE) checklist. Results In study 1 all intervention groups performed significantly better than the text groups. Group I (high-fidelity) and group II (low-fidelity) for both intubation and catheterization showed no significant differences. In study 2 the potcast group performed significantly better than the text group in study intubation but no significant differences were observed in study catheterization. Overall, participants enjoyed clinical skills training but experienced self-directed learning as challenging. Conclusion Low-fidelity models can be as effective as high-fidelity models for clinical skills training. Clinical skills training using potcasts and self-directed low-fidelity simulation training should be complemented by supervisor or peer instruction rather than used as exclusive tool for teaching first year veterinary students. We assume though, that self-directed learning instructed by our potcasts can be a valuable chance for deepening and repetitive training of higher semesters. The use of simulation models in veterinary education has been consistently increasing in the past few years. This study is an important, timely contribution to the evaluation of simulation based education.

Einleitung Studierende der Veterinärmedizin müssen neben umfangreichem theoretischem Wissen zahlreiche praktische Fertigkeiten erlernen. Da jeder Einzelne in seinem eigenen Tempo lernt, besteht ein großer Bedarf an Trainingsmöglichkeiten. Kadaver und lebende Tiere sind selten in ausreichender Menge verfügbar und lebende Tiere sind zudem aus Gründen des Tierwohls nur eingeschränkt zu verwenden. Simulationsmodelle (Modelle von Organismen / Körperteilen) können hier Abhilfe schaffen. Kommerziell erhältliche Modelle sind sehr kostenintensiv und für die Tiermedizin noch nicht flächendeckend erhältlich. Zunehmend werden selbst entwickelte low-fidelity Modelle in der Tiermedizin verwendet. Aufgrund des Mangels an publizierten Daten zu ihrem Einsatz besteht intensiver Forschungsbedarf. Ziele der Untersuchungen In dieser Arbeit sollte untersucht werden, ob einfache, selbst entwickelte Simulationsmodelle (low-fidelity Modelle) erfolgreich in der Lehre eingesetzt werden können. Dazu wurden zwei selbst entwickelte und gebaute Simulationsmodelle evaluiert (Studie 1) und ihr Einsatz in Kombination mit anderen Lehrmedien untersucht (Studie 2). Materialien und Methoden In Studie 1 wurden zwei low-fidelity Modelle zur kaninen Intubation und Katheterisierung entwickelt und evaluiert. Es wurde ein Studiendesign genutzt, das die erworbenen Fertigkeiten zweier Übungsgruppen und einer Kontrollgruppe in einer praktischen Prüfung (OSCE = objective structured clinical examination) am toten Hund vergleicht. Achtundfünfzig Studierende (4. FS) erhielten eine theoretische Einführung zur Intubation und wurden randomisiert auf drei Gruppen aufgeteilt. Gruppe 1 (high-fidelity) übte am kommerziell erhältlichen Intubation Training Manikin, Gruppe 2 (low-fidelity) am entwickelten low-fidelity Modell und die Textgruppe las einen Text, der die Intubation beim Hund beschreibt. Siebenundvierzig Studierende (10. FS) durchliefen dasselbe Studiendesign zum Thema Katheterisierung der Hündin. Sie nutzten das kommerziell erhältliche Female Urinary Catheter Training Manikin, das selbst entwickelte low-fidelity Modell und Lehrtexte. In Studie 2 wurde die Vermittlung zweier spezifischer Fertigkeiten mit Hilfe von Potcasts und Simulationstraining evaluiert. Zwei anleitende Potcasts zu Intubation und Katheterisierung und die oben beschriebenen Modelle wurden innerhalb eines crossover-Studiendesigns genutzt. In dieser Studie sind Potcasts audio-visuell aufbereitete Animationen mit Schritt für Schritt – Anleitungen und Informationen. Die erworbenen praktischen Fertigkeiten zweier Übungsgruppen, die sich in der Art der theoretischen Vorbereitung unterschieden, wurden in einer praktischen Prüfung (OSCE) am toten Hund verglichen. Ein Fragebogen erfasste das Feedback der Teilnehmer. Sechzig Studierende (2. FS) wurden randomisiert auf eine Potcast- und eine Textgruppe aufgeteilt. Die Potcastgruppe sah sich das anleitende Potcast an, die Textgruppe bereitete sich anhand eines Lehrtextes vor. Im Anschluss hatten beide Gruppen separate Übungseinheiten an den low-fidelity Modellen ohne Betreuung durch Lehrende. Ergebnisse In Studie 1 schnitten alle Übungsgruppen signifikant besser ab als die Textgruppen. Gruppe 1 (high-fidelity) und Gruppe 2 (low-fidelity) unterschieden sich weder bei der Intubation noch bei der Katheterisierung signifikant in ihren Leistungen. In Studie 2 schnitt die Potcastgruppe beim Thema Intubation signifikant besser ab als die Textgruppe, beim Thema Katheterisierung ergaben sich keine signifikanten Unterschiede. Insgesamt hatte das Simulationstraining den Studierenden Spaß gemacht, das Lernen ohne Betreuer wurde jedoch als Herausforderung empfunden. Schlussfolgerungen Es ist davon auszugehen, dass low-fidelity Modelle genauso geeignet für das Training klinischer Fertigkeiten sein können wie high-fidelity Modelle. Das Training klinischer Fertigkeiten mit Hilfe von Potcasts und low-fidelity Modellen sollte durch Betreuer ergänzt werden, anstatt als alleiniges Lehrmedium für Studierende des ersten Studienjahres Verwendung zu finden. Eigenständiges Lernen klinischer Fertigkeiten, angeleitet durch Potcasts bietet eine Möglichkeit für vertiefendes und wiederholendes Training höherer Semester. Der Einsatz von Simulationsmodellen in der veterinärmedizinischen Ausbildung wächst seit wenigen Jahren stetig. Diese Arbeit leistet einen zeitgerechten Beitrag bei der Evaluierung von Simulationstraining. / Introduction Students of veterinary medicine are expected to acquire various practical skills in addition to a wide range of theoretical knowledge. There is a strong demand for training opportunities, as every individual learns and acquires practical skills at individual pace. For reasons of animal welfare concerns and availability, live animals and cadavers cannot always be used for clinical skills training. Simulation models, which are models of organisms or body parts can be a considerable alternative for clinical skills training. Models that are commercially produced often have a high price and are not available for all skills. Self-made models are increasingly used in veterinary education. Because there is few published data regarding their use, more scientific research is required. Aims of the Investigation The objective of this study was to determine, if self-made low-fidelity models can be successfully used in veterinary medical education. For this purpose, two self-made low-fidelity models were evaluated (study 1) and their use in combination with other teaching tools was analyzed (study 2). Materials and Methods In study 1, two self-made low-fidelity models for simulation of canine intubation and canine female urinary catheterization were developed and evaluated. We used a study design that compares acquired skills of two intervention groups and one control group in a practical examination (OSCE = objective structured clinical examination). Fifty-eight second-year veterinary medicine students received a theoretical introduction to intubation and were randomly divided into three groups. Group 1 (high-fidelity) was then trained on a commercially available Intubation Training Manikin, group 2 (low-fidelity) was trained on our low-fidelity model, and the text group read a text describing intubation of the dog. Forty-seven fifth-year veterinary medicine students followed the same procedure for training urinary catheterization using the commercially available Female Urinary Catheter Training Manikin, our self-made model, and text. Outcomes were assessed in a practical examination on a cadaver using an OSCE checklist. In study 2 we evaluated the teaching of two specific clinical skills using potcasts and low-fidelity simulation training. Two instructional potcasts describing intubation and catheterization and both low-fidelity models described above were used. In our study, potcasts are audio-visual animations that provide the learner with step by step information and instruction on a clinical skill. We used a crossover study design and compared the acquired practical skills of two intervention groups after a different theoretical preparation. A survey captured the participants’ feedback. Sixty first year veterinary medicine students were randomly allocated to two groups, a potcast group and a text group. The potcast group watched a potcast while the text group read an instructional text for preparation. Then both groups had separate self-directed training sessions on low-fidelity models. Outcomes were assessed in practical examinations on a cadaver using an objective structured clinical examination (OSCE) checklist. Results In study 1 all intervention groups performed significantly better than the text groups. Group I (high-fidelity) and group II (low-fidelity) for both intubation and catheterization showed no significant differences. In study 2 the potcast group performed significantly better than the text group in study intubation but no significant differences were observed in study catheterization. Overall, participants enjoyed clinical skills training but experienced self-directed learning as challenging. Conclusion Low-fidelity models can be as effective as high-fidelity models for clinical skills training. Clinical skills training using potcasts and self-directed low-fidelity simulation training should be complemented by supervisor or peer instruction rather than used as exclusive tool for teaching first year veterinary students. We assume though, that self-directed learning instructed by our potcasts can be a valuable chance for deepening and repetitive training of higher semesters. The use of simulation models in veterinary education has been consistently increasing in the past few years. This study is an important, timely contribution to the evaluation of simulation based education.