Incorporation of simulation features to improve higher order thinking skills

Highlights

• A third order of formative model of simulation features with 3 s-order components—simulation design, simulation interactivity, and simulation realism—and seven first-order dimensions was empirically examined with two samples-one in U.S. and one in Peru.

• Simulation characteristics of design, interactivity, and realism drive simulation features, which has the most substantial impact on reflective thinking followed closely in strength by critical thinking. Simulation design is a stronger dimension of simulation feature followed by realism and interactivity in both studies.

• Simulation involvenment is important for learners in U.S. to develop critical and reflective thinking skills whereas learners in Peru require simulation structure to develop higher order thinking skills.

Abstract

More and more educators are adopting simulations for teaching students and training individuals.A more comprehensive model proposing key simulation features is needed for simulation designers and educators. The aim of the study is to propose a theoretical model for examining simulation features in digital-based learning and the impact of simulation features on higher-order thinking. Data was collected from 301 business management students from two universities. Study One focused on a university in the U.S. (North America) and Study Two focused on a university in Peru (South America). The findings; confirm that the three key components comprising simulation features are: simulation design, simulation; interactivity, and simulation realism. The results also support the operationalization and conceptualization of simulation features as a multi-factor, higher-order construct. Lastly, simulation features have an impact on higher order thinking skills, including critical thinking and reflective thinking. Educators and content developers should ensure they consider the specific simulation features discussed in this research when designing and selecting simulations.

Introduction

The use of simulations as an effective education technique is gaining popularity across different disciplines (Aqel & Ahmad, 2014). Scholars and practitioners are exerting substantial effort in teaching students and training individuals to use simulations. In business education, the theory and measurement of simulation design and features have become critical because institutions increasingly use technology for teaching, learning, and enhancing the analytical skills of individuals (Prado, Arce, Lopez, García, & Pearson, 2020). With the growing sophistication of learning technologies and innovative instructional designs in education management, especially in the rise of digital business-centered simulations, it is necessary to investigate which simulation features best assist learners in developing their skills (Adib-Hajbaghery & Sharifi, 2017).

Scholars have focused on understanding the effect of business simulations on attitudes, learning, and flow experiences (Buil, Catalán, & Martínez, 2018); however, more empirical research is needed for understanding the impact of simulation features. Although some scholarly work on assessing the impact of using simulations has presented noteworthy advancement, scholars explicitly suggest a research stream requiring more robust consideration concerning the testing and analysis of simulation features (Hernández-Lara, Perera-Lluna, & Serradell-López, 2019). Most studies have assessed simulations’ influence on developing necessary business skills, attitudes, self-esteem, and competencies (Buil, Catalán, & Martínez, 2019; Gatti, Ulrich, & Seele, 2019; Hernández-Lara & Serradell-López, 2018). For example, existing studies examine relationships among the use of simulation, individual simulation features, student engagement, learning, and mental processing from the perspective of the application and design.

A more comprehensive model proposing key simulation features is needed for simulation designers and educators. Computer-based simulations in business education have become an innovative teaching strategy and tools for teaching students to anticipate possible business scenarios and take the best business decisions (Goi, 2019). Empirically identifying the underlying main components of simulation features will provide insight into the favorable implications of these features on developing specifically the higher order thinking dimensions of critical thinking and reflective thinking. The aim of the study is to identify the main simulation features that achieve real business decision-making situations and the impact of simulation features on higher order thinking. Therefore, the research questions for the study are:

• What are the critical components comprising simulation features and what are the dimensions representing each component?

• Can the simulation features construct be conceptualized and operationalized as a multi-factor, higher- order construct?

• Do simulation features affect higher order thinking (critical thinking and reflective thinking)?

Digital simulation based learning (DSBL) is often characterized as effective instrument for its capability to motivate and engage students. On the other hand, some researchers continue unconvinced on the learning effectiveness of DSBL (Huang, Johnson, & Han, 2013). Given that existing research used different simulation components when investigating digital simulation based learning. The key components of educational simulations and their dimensions remain largely unanswered presenting a gap in the literature.

A new concept, named simulation features (SF), is conceptualized as the key components that a well-designed simulation must incorporate. This study identifies three components that collectively describe the main elements to consider when designing a simulation for educational purposes. A model of SF is developed based on previous studies on what components are relevant for educational simulations. SF includes three distinct triggers: simulation design, simulation interactivity, and simulation realism. These components jointly describe the key components simulation developers must incorporate. Correct specification of simulation feature model provides the opportunity for educators to focus on the components identified. The components collectively contribute to the development of simulation feature. This study examines the attributes that form simulation features.

Furthermore, adequate specification of simulation features (SF) is of strategic relevance for simulation developers and researchers given that simulation features are the principal ingredients for the adequate configuration of an educational simulation. As such, the authors center the efforts on specifying the components of SF, the underneath dimensions of each component and its impact on higher order thinking. In this manner, this study proposes a theoretical argument for conceptualizing a formative model specification to the simulation features construct and testing the model empirically. Building on previous digital learning and simulation feature models, the authors conceptualized a SF third-order construct that mainly specifies the elements that may be included in an efficient and well-configured educational simulation. Additionally, the present research articulates a formative configuration of the SF construct and presents empirical confirmation to corroborate the model. Lastly, the authors investigate the impact of simulation features construct (SF), defined as a multicomponent formative third-order model, on the development of critical thinking and reflective thinking as main components of higher-order thinking.

The contributions of this research include the need to empirically investigate formative digital simulation based learning models to directly answer to a previous call in the body of knowledge that the postulation of SF and its components being a reflective scale must be re-observed conceptually and tested empirically. Other contributions include that this research expands on the simulation features being examined within digital simulation based learning context by applying our method to observe learner perceptions of simulation features to the educational business context. In doing so, the authors tested the model across two different sample groups (university students in a U.S University and university students in a Peruvian University) which have not been investigated in past educational simulation studies. Additionally, the study presents theoretical and practical implications to improve specific simulation features and its components to advance learners perceptions and its equivalent stimulus on advancing critical thinking and creative thinking abilities. Specifically, the authors embraced a third-order formative design of simulation features and examined its effect on higher thinking multidimensional construct.

Additionally, attention is necessary for studying model specification of simulation features. Except for Huang et al. (2013), prior simulation frameworks were developed from the impact of individual features in simple reflective measurement formations building the foundation for most of the scale development literature in multiples disciplines. Conversely, a substitute for reflective framework development is the assessment of formative conceptual frameworks. As indicated by several ground-breaking studies, research that examines games, technology, and simulated environments, including consumer research, focuses on assessing formative frameworks even when previously proposed frameworks are adequate (Petter, Straub, & Rai, 2007; Jarvis, MacKenzie, & Podsakoff, 2003; Becker, Klein, & Wetzels, 2012). These studies also suggest that formative constructs are often being erroneously confused with reflective constructs. In the last decade, accurate model specification is receiving increasing attention in digital simulation-based learning (Gegenfurtner, Quesada-Pallarès, & Knogler, 2014; Kim, 2011), with scholars noting that simulation design features might be better assessed using formative rather than reflective constructs (Fu, Su, & Yu, 2009; Lee, Wong, & Fung, 2010; Novak, Hoffman, & Yung, 2000). Regardless of this recommendation, few researchers have focused on model specification of simulation features. This limitation in the literature might be a concern for scholars and simulations/games designers, since failure to adequately specify a theoretical framework could lead to distorted estimates of the relationships among model constructs, weakened statistical findings, and misleading implications for theory and practice (Bagozzi & Yi, 2012; Coltman, Devinney, Midgley, & Venaik, 2008; Diamantopoulos & Siguaw, 2006). The value of the study is that it articulates a theory-based framework by implementing a formative model specification of the simulation feature construct and empirically examines the model. The focus on formative model can contribute to the literature with richer simulation feature construct resulting in better simulation design and selection for teaching and learning.

Additional attention is needed on examining model specification in behavioral research (Jarvis et al., 2003). Prior simulation and game features models are mainly centered on reflective measurement configurations (Bell & Loon, 2015). Huang et al., 2013), which builds the foundation of much of the scale development body of knowledge in overall (Fornell & Bookstein, 1982; Jarvis et al., 2003). Conversely, an alternative to reflective where the direction of causality is from the latent construct to its measures is the examination of formative models where the direction of causality is from the measures to the latent variable (Coltman et al., 2008; Theodosiou, Katsikea, Samiee, & Makri, 2019). As stated by Huang et al. (2013) few studies in simulation and game research utilize formative methods, despite the fact such methodologies would appear to theoretically adequate. Therefore, correct model specification is evolving as an important issue in digital simulation-based education and business simulation training research in current years suggesting that simulation features might be better theorized via formative measures instead of reflective measures (Abbasi, Ting, & Hlavacs, 2017; Abbasi, Ting, & Hlavacs, 2016; Shu-Hui, Wann-Yih, & Dennison, 2018).

A comprehensive review of the literature was completed to identify the underlying dimensions of simulation features. First, digital learning research has yet to completely delineate the specific nature of the underlying features of simulations. Building on the existent model of simulation features proposed in the literature (Cook et al., 2013; Fu et al., 2009; Huang et al., 2013; Vos, 2015), this study conceptualizes a simulation feature construct that explicitly determines the fundamental characteristics needed for simulations to serve as effective e-learning tools. Second, this investigation proposes a formative configuration of the simulation feature construct and presents empirical support for the proposed framework. Third, the proposed formative framework is expanded to integrate the impact of the simulation feature construct (theorized as a multi-factor, formative, third-order framework (Van Voorhis & Paris, 2019)) on higher order thinking skills that are critical for learning success.

The study is arranged as follows. First, the theory-driven framework is presented from a review of the literature related to the critical factors considered in the study, followed by developing the proposed relationships and the framework for analysis. Second, the authors discuss the study's context, methodology, findings, and results. This is followed by theoretical and practical implications and future research opportunities.