Simulation-based Learning Environment Research Articles

Improving Learners’ Understanding of Environmental Accumulations through Simulation

Background. Poor public understanding of accumulation principles obstructs public engagement in environmental decision-making. Simulation-based learning environments (SBLEs) show promise for improving understanding. Purpose. This study tested the hypothesis that use of stand-alone, self-contained, online SBLEs can improve user understanding of and ability to apply basic principles of accumulation. Method. We used pretest/posttest measures to examine (a) whether user understanding of accumulations principles increased after exposure to a set of two SBLEs in an undergraduate Environmental Science course ( n =126), and (b) how the extent of simulation use affected depth of user understanding. Results.Understanding improved significantly after simulation use. Further, extent of simulation use affected performance although it is not a simple linear relationship. Those who ran the simulation a moderate amount of times (total of 14-24 runs) scored better than the low-range group (0-13 runs, p < .05) and better than the high-range users (25+ runs, p < .10). These results hold when controlled for prior systems understanding, graphing ability, motivation, and science background. Conclusion. The results support the value of simulations for building operational understanding of accumulations and suggest design considerations that may further increase the effectiveness of such SBLEs.

Simulation-Based Learning Environments to Teach Complexity: The Missing Link in Teaching Sustainable Public Management

While public-sector management problems are steeped in positivistic and socially constructed complexity, public management education in the management of complexity lags behind that of business schools, particularly in the application of simulation-based learning. This paper describes a Simulation-Based Learning Environment for public management education that includes a coupled case study and System Dynamics simulation surrounding flood protection, a domain where stewardship decisions regarding public infrastructure and investment have direct and indirect effects on businesses and the public. The Pointe Claire case and CoastalProtectSIM simulation provide a platform for policy experimentation under conditions of exogenous uncertainty (weather and climate change) as well as endogenous effects generated by structure. We discuss the model in some detail, and present teaching materials developed to date to support the use of our work in public administration curricula. Our experience with this case demonstrates the potential of this approach to motivate sustainable learning about complexity in public management settings and enhance learners’ competency to deal with complex dynamic problems.

Toward a mathematical model of motivation, volition, and performance

The goal of this study was to (1) empirically examine factors that affect human performance in a simulation-based learning environment, employing the framework of the integrative theory of Motivation, Volition, and Performance (MVP) (Keller, 2008a) and (2) develop and statistically evaluate a mathematical MVP model that can be applied to other digital learning environments. The development of a mathematical MVP model can provide empirical support for the elements included in the MVP theory and serve as a tool for designing effective digital learning environments. A regression analysis of motivational, volitional, and performance data of 62 graduate students that interacted with an online simulation revealed a significant model that explained approximately 70% of the variation in student satisfaction through motivational and volitional processing variables suggested by the MVP theory. Students' interest and curiosity toward the learning environment had the highest positive predicting power on students' satisfaction, while the volition processing variable had the lowest predicting power. Implications for the digital learning environments design and directions for future research are discussed.

Yes we can: Improving performance in dynamic tasks

Facilitation, a special kind of decisional aid, is all forms of information, strategies, and heuristics delivered by a facilitator to aid decision makers in dynamic decision environments. It is assumed that facilitation has profound effects on decision making, but these effects are understudied and empirically unproven. By incorporating the three levels of facilitation, pre-task, combined pre-task and in-task, and combined pre-task, in-task, and post-task, in the design of a simulation-based interactive learning environment (ILE), this study provides an empirical, laboratory-experiment-based evaluation of the effectiveness of facilitation on performance in dynamic tasks. We develop and use a comprehensive model consisting of four evaluation criteria: task performance, structural knowledge, heuristics knowledge, and transfer learning. We find that the subjects provided with combined pre-task, in-task, and post-task facilitation performed the best, followed by those provided with combined pre-task and in-task facilitation. Contrary to the hypothesis, subjects provided with pre-task facilitation performed poorly.

Effect of Prior Exploration as an Instructional Strategy for System Dynamics

In complex simulation-based learning environments, participants’ learning and performance may suffer due to demands on their cognitive processing, their struggle to develop adequate mental models, failure to transfer what is learned to subsequent learning or activities, and fear of failure. This study investigates an instructional strategy addressing those four problems, which we call prior exploration strategy. It was implemented in a simulation requiring participants to optimize a developing nation’s per capita income. The prior exploration strategy allows participants to manipulate and see the results of a simulation model in practice mode before they manage a similar simulation in a more final mode. The strategy was assessed in an experiment comparing participants using the prior exploration strategy with participants studying equivalent content in a non-exploratory fashion. The dependent variables were performance within the simulation and improvement of participants’ understanding. The prior exploration strategy significantly improved participants’ performance, as measured by per capita income. It also significantly improved some aspects of the participants’ understanding (e.g., their understanding of the nation’s debt accumulation), but not others (e.g., their understanding of the need to balance the nation’s health, education, and infrastructure investments; those that appear to have complex interrelations).

Towards meaningful simulation-based learning with medical students and junior physicians

Background: This research provides an educational perspective on simulation-based medical education by implementing both the characteristics of meaningful learning and the concepts of facilitating, training, and learning processes.Aims: This study aims to evaluate, from the perspectives of both facilitators and students, the meaningfulness of five different simulation-based courses.Methods: The courses were implemented in the spring of 2010. The data were collected from facilitators (n = 9) and students (n = 25) using group interviews (one individual interview), observations, video recordings, and pre- and post-questionnaires. The research analyzes qualitative data using the qualitative content analysis method to answer the following research question: From facilitators’ and students’ perspectives, how does the facilitating and training in simulation-based learning environments (SBLEs) foster the meaningful learning of students?Results: It seems that simulation-based learning is, at its foundation, meaningful since it inherently supports the many characteristics of meaningful learning. However, characteristics also exist that simulation-based learning does not inherently support. In this study, the goal-oriented, self-directed, and individual training characteristics were only somewhat supported during the facilitation and training in SBLEs.Conclusions: In running these courses in the future, facilitators should concentrate on those characteristics that were only somewhat supported.

Healthcare facilitators’ and students’ conceptions of teaching and learning – An international case study

Conceptions of teaching and learning affect approaches to teaching, learning and learning outcomes, and they therefore need to be made explicit. This study addresses how healthcare simulation facilitators and students view teaching and learning. Qualitative data from 43 participants in the field of healthcare was analyzed using the qualitative-content analysis method. The results revealed three distinct categories of conceptions of teaching and learning as well as two categories of conceptions of teaching within the context of a simulation-based learning environment. This knowledge can be used to understand and modify healthcare facilitators’ and students’ conceptions in order to enhance the learning experience.

Simulation Based Learning Environment (SimBLE) Architecture: The Fidelity Continuum

Simulation Based Learning Environment (SimBLE) Architecture: The Fidelity Continuum

Students' expectations of the learning process in virtual reality and simulation-based learning environments

<span>Expectations for simulations in healthcare education are high; however, little is known about healthcare students' expectations of the learning process in virtual reality (VR) and simulation-based learning environments (SBLEs). This research aims to describe first-year healthcare students' (N=97) expectations regarding teaching, studying, and learning in such environments. In addition, it measures students' expectations of instructors, their academic self-perception, and atmosphere, as well as whether there are differences between the expectations of adult and young students. Data was collected through a questionnaire from two different universities of applied sciences in Finland in spring 2009, and analysed using statistical and qualitative methods. Overall, students have quite high expectations of the activities that take place in VR and SBLEs. Adult learners in particular seem to have high expectations compared to younger students.</span>

Goals, Success Factors, and Barriers for Simulation-Based Learning

Introduction: This study describes (a) process goals, (b) success factors, and (c) barriers for optimizing simulation-based learning environments within the simulation setting model developed by Dieckmann. Methods: Seven simulation educators of different experience levels were interviewed using the Critical Incident Technique. Results: (a) The main process goals were to enhance learning, engage participants, and aid the application of what was learned during the course. (b) As success factors, educators stated their own competencies and attitudes, motivation and openness of participants, and a functional environment. (c) As barriers, educators stated a lack of willingness to actively engage in simulation by the participants and time pressure. The results emphasize the need to consider jointly the interrelated elements of simulation-based learning environments to optimize the use of educational simulation. Discussion: The results support the applicability of Dieckmann’s setting model to describe simulation-based courses and emphasize the diversity of factors that need to be considered in optimizing simulation practice. This article can serve as a practical aid for educators within health care simulation settings and in other domains.

An approach to automatic learning assessment based on the computational theory of perceptions

E-learning systems output a huge quantity of data on a learning process. However, it takes a lot of specialist human resources to manually process these data and generate an assessment report. Additionally, for formative assessment, the report should state the attainment level of the learning goals defined by the instructor.This paper describes the use of the granular linguistic model of a phenomenon (GLMP) to model the assessment of the learning process and implement the automated generation of an assessment report. GLMP is based on fuzzy logic and the computational theory of perceptions. This technique is useful for implementing complex assessment criteria using inference systems based on linguistic rules. Apart from the grade, the model also generates a detailed natural language progress report on the achieved proficiency level, based exclusively on the objective data gathered from correct and incorrect responses. This is illustrated by applying the model to the assessment of Dijkstra’s algorithm learning using a visual simulation-based graph algorithm learning environment, called GRAPHs.

Simulation Based Learning Environment (SimBLE) Architecture: Tailoring to the Budget

Simulation Based Learning Environment (SimBLE) Architecture: Tailoring to the Budget

Investigation of the impact of two verbal instruction formats and prior knowledge on student learning in a simulation-based learning environment

This study investigated how the format of verbal instructions in computer simulations and prior knowledge (PK) affected 8th graders' cognitive load (CL) level and achievement in a multimedia learning environment. Although PK was not found to significantly affect student performance and CL level, instruction format was found to impact both. Students who used narrative simulations were found to have a greater CL but also to perform better than those using simulations with on-screen text instructions. However, no significant differences were found between the cognitive efficiency of the two groups. The difficulty of the subject matter and limitations in students' prior content-related knowledge may have increased the intrinsic CL, such that students had difficulty in interpreting the content even if their PK was relatively high. The narrative instructions were more likely than the on-screen text information to reduce the extraneous CL and promote understanding of content. A new measure of cognitive processing is needed to identify the types of CL involved in e-learning and determine the properties of adequate e-learning materials. Finally, the findings of the study are discussed and suggestions for future studies and instructional design are provided.

The effects of directive self-explanation prompts to support active processing of multiple representations in a simulation-based learning environment

Processing of multiple representations in multimedia learning environments is considered to help learners obtain a more complete overview of the domain and gain deeper knowledge. This is based on the idea that relating and translating different representations leads to reflection beyond the boundaries and details of the separate representations. To achieve this, the design of a learning environment should support learners in adequately processing multiple representations. In this study, we compared a scientific inquiry learning environment providing instructional support with directive self-explanation prompts to relate and translate between representations with a scientific inquiry learning environment providing instructional support with general self-explanation prompts. Learners who received the directive prompts outperformed the learners who received general prompts on test items assessing domain knowledge. These positive results did not stretch to transfer items and items measuring learners' capabilities to relate and translate representations in general. The results suggest that learner support should promote the active relation of representations and translation between them to foster domain knowledge, and that other forms of support (e.g. extended training) might be necessary to make learners more expert processors of multiple representations.

Comparing the effects of representational tools in collaborative and individual inquiry learning

Constructing a representation in which students express their domain understanding can help them improve their knowledge. Many different representational formats can be used to express one’s domain understanding (e.g., concept maps, textual summaries, mathematical equations). The format can direct students’ attention to specific aspects of the subject matter. For example, creating a concept map can emphasize domain concepts and forming equations can stress arithmetical aspects. The focus of the current study was to examine the role of tools for constructing domain representations in collaborative inquiry learning. The study was driven by three questions. First, what are the effects of collaborative inquiry learning with representational tools on learning outcomes? Second, does format have differential effects on domain understanding? And third, does format have differential effects on students’ inclination to construct a representation? A pre-test post-test design was applied with 61 dyads in a (face-to-face) collaborative learning setting and 95 students in an individual setting. The participants worked on a learning task in a simulation-based learning environment equipped with a representational tool. The format of the tool was either conceptual or arithmetical or textual. Our results show that collaborative learners outperform individuals, in particular with regard to intuitive knowledge and situational knowledge. In the case of individuals a positive relation was observed between constructing a representation and learning outcomes, in particular situational knowledge. In general, the effects of format could not be linked directly to learning outcomes, but marked differences were found regarding students’ inclination to use or not use specific formats.

Design Patterns for Learning and Assessment: Facilitating the Introduction of a Complex Simulation-Based Learning Environment into a Community of Instructors

Simulation environments make it possible for science and engineering students to learn to interact with complex systems. Putting these capabilities to effective use for learning, and assessing learning, requires more than a simulation environment alone. It requires a conceptual framework for the knowledge, skills, and ways of thinking that are meant to be developed, in order to design activities that target these capabilities. The challenges of using simulation environments effectively are especially daunting in dispersed social systems. This article describes how these challenges were addressed in the context of the Cisco Networking Academies with a simulation tool for computer networks called Packet Tracer. The focus is on a conceptual support framework for instructors in over 9,000 institutions around the world for using Packet Tracer in instruction and assessment, by learning to create problem-solving scenarios that are at once tuned to the local needs of their students and consistent with the epistemic frame of “thinking like a network engineer.” We describe a layered framework of tools and interfaces above the network simulator that supports the use of Packet Tracer in the distributed community of instructors and students.

The influence of learner-generated domain representations on learning combinatorics and probability theory

The aim of the current study was to examine the effects of providing support in the form of tools for constructing representations, and in particular the differential effects of the representational format of these tools (conceptual, arithmetical, or textual) in terms of perceived affordances and learning outcomes. The domain involved was combinatorics and probability theory. A between-subjects pre-test–post-test design was applied with secondary education students randomly distributed over four conditions. Participants completed the same tasks in a simulation-based learning environment. Participants in three experimental conditions were provided with a representational tool that could be used to construct a domain representation. The experimental manipulation concerned the format of the tool (conceptual, arithmetical, or textual). Participants in a control condition did not have access to a representational tool. Data from 127 students were analyzed. It was found that the construction of a domain representation significantly improved learning outcomes. The format in which students constructed a representation did not directly affect learning outcomes or the quality of the created domain representations. The arithmetical format, however, was the least stimulating for students to engage in externalizing their knowledge.

Debriefing can reduce misperceptions of feedback: The case of renewable resource management

According to the hypothesis of misperception of feedback, people's poor performance in renewable resource management tasks can be attributed to their general tendency to systematically misperceive the dynamics of bioeconomic systems. The thesis of this article is that dynamic decision performance can be improved by helping individuals develop more accurate mental models of renewable resource systems through training using computer simulation-based interactive learning environments (CSBILEs) that include debriefing. A laboratory experiment is reported in which participants managed a dynamic task by playing the roles of fishing fleet managers. One group of participants used a CSBILE with debriefing, and another group used the same CSBILE but without debriefing. A comprehensive model consisting of four evaluation criteria was developed and used. The evaluation criteria were task performance, structural knowledge, heuristics, and decision time. It was found that debriefing was effective on all four criteria: Debriefing improved task performance, helped users learn more about the decision domain and develop heuristics, and reduced decision time in dynamic decision making.

Experiential Learning Methods, Simulation Complexity and Their Effects on Different Target Groups

This article empirically supports the thesis that there is no clear and unequivocal argument in favor of simulations and experiential learning. Instead the effectiveness of simulation-based learning methods depends strongly on the target group's characteristics. Two methods of supporting experiential learning are compared in two different complex simulations with students and apprentices as the target groups, and with knowledge acquisition and control performance as the learning criteria. As a main result, students outperform apprentices in knowledge acquisition and transfer performance in general. And whereas students feel most self-efficient in the guided exploration learning setting, for apprentices it is just the opposite: apprentices feel less self-efficient after the guided exploration learning phase. Also in respect to knowledge acquisition, students benefit from both support methods equally well in the low complex simulation, whereas for apprentices the difference is great. Transfer performance and processing time is also affected by the target group, although the simulation complexity plays a greater role here. Finally, after the experiment both target groups differed largely in their motivation to use simulation-based learning environments again. Psychologically relevant differences in target groups are discussed.

An integrated e-learning system for simulation-based instruction of anaesthesia machines

Not all learners learn in the same way and at the same rate. Learners' learning styles that reflect their cognitive abilities vary in known ways; some prefer the traditional text-based or oral presentation of content, while others learn more easily in a visual or kinaesthetic instruction style. Simulation has been shown to be an effective way of teaching abstract concept, principle and process in many application domains. In this paper, we have developed reusable, interactive, simulation-based learning objects and designed and implemented an e-learning system called Gator E-learning System (GELS), to deliver the learning objects to learners in an interactive, adaptive and flexible manner. GELS integrates a web-based Virtual Anaesthesia Machine (VAM) Simulation System and a Collaborative and Dynamic E-learning Service System (CoDESS) for the purpose of teaching medical personnel the functions and operations of anaesthesia machines as well the preuse check of anaesthesia machines in a simulation-based learning environment.