Why Students Should Be Taught Differently: Learner Characteristics,Learning Styles and Simulation Performance

Abstract

Background

Business simulation as an instructional tool helps in developing integrative thinking and decision making skills. It is being taught to audiences who differ considerably in age, work experience (learner characteristics) and learning styles. The use of simulations is likely to grow further with advancements in internet technology and the fact that simulations are very amenable to remote modes of instruction.

Aim

This study aims to assess how learner characteristics and learning styles impact business simulation performance. It further assesses the combined effect of learner characteristics and learning styles on performance in business simulations, we specifically consider the manner in which learning styles moderate the impact of learner characteristics (age) on simulation performance.

Method

The study was conducted with 605 students of full time MBA and executive MBA programs with age group varying from 21 years to 53 years. They were taught using the same business simulation by CAPSIM. The learning styles were measured using Felder-Solomon’s instrument ‘Index of learning style’. Regression analysis was conducted with predictor variables of learner characteristics and learning styles and outcome variable of simulation performance. The moderating effect of specific learning styles on learner characteristics was identified.

Results

The findings indicate that age is a significant predictor of simulation performance (younger, tech savvy students do better). Also, the use of reflective learning style enables better performance in business simulations. Older students are able to draw on experience and benefit more from reflective learning, for business simulations which involve integration across functions.

Conclusion

The study enhances our conceptual understanding of the factors enabling performance in business simulations and provides specific direction on how instructors must adapt facilitation approaches for different age groups of participants. Reflection is important for learning with business simulations; hence, the reflective learning style should be encouraged particularly among older students.

Keywords

business simulation management education learner characteristics learning styles reflective learning style

One of the goals of management education is the enhancement of the decision making skills of prospective and current managers. Business simulations have emerged as an useful pedagogy to achieve this goal as they aid participants to understand the dynamic decision making process (Faria et al., 2009; Goi, 2019; Salas et al., 2009). This happens as players formulate strategies, take decisions, experience the consequence of their decisions, and then adapt strategies and decisions to improve performance (Goi, 2019). The learning through multiple rounds of decision making with quick feedback enables reflection and learning (Faria et al., 2009). The performative experience and emotive involvement helps cognitive and affective engagement which in return helps in developing critical thinking skills (Gatti et al., 2019). Business simulations enable strategic decision making and provide an integrated view of business functions (Crookall, 2010; Goi, 2019; Wolfe, 1976; Zantow et al., 2005). The use of business simulations is likely to grow further due to the fact that simulations are very amenable to remote modes of instruction (Kim & Bonk, 2006; Rogmans & Abaza, 2019). The corona virus pandemic has forced many academic institutions to quickly adapt to online teaching. In the context of online education, the adoption of simulations as a teaching tool is likely to grow (Vlachopoulos & Makri, 2017), largely because simulations can allow for team and individual play with multiple participants at various locations.

The popularity of simulations implies that simulations will often be taught to different audiences which will vary systematically by demographic characteristics (age, gender, work experience, etcetera) and learning styles. A critical question which will be of interest to both academicians and practitioners is: How should different audiences be taught differently? As audiences may differ systematically in terms of both profiles and orientations to learning, this implies the following question in the simulation context: How do learner characteristics and learning styles impact learner performance in business simulations?

While there have been a number of studies which look at the impact of specific learner characteristics on simulation performance (Garber et al., 2017; Lynch & Michael, 1989; Towler et al., 2009), and a few studies (Golden & Smith, 1989; Shellman & Turan, 2006) which explore the impact of learning styles on simulation performance, there are limited studies which explore the joint effect of learner characteristics and learning styles on simulation performance. This is the gap that this study intends to fill. We believe that this will be of great interest to academicians in business schools, as it will provide them with nuanced direction on how they may adapt the teaching of business simulations to different audiences.

The effectiveness of simulations can be affected by multiple factors such as the design of the simulation, instructor capability, characteristics of the learner and preferred learning style of the participant (Vlachopoulos & Makri, 2017). Substantive empirical work on the joint effect of characteristics of the learner and the preferred learning style on performance in simulations is difficult to do, largely because in many real academic settings, there may be significant variation in the complexity of the simulation, along with differences in teaching approach of the instructor. Such exploration is further complicated by the fact that simulations often involve team play, and this can make the link between learner characteristics and learner performance tenuous. Our empirical context allows us to take care of many of these potential roadblocks. Our context allows us to explore the joint effect of learner characteristics and learning styles on simulation performance, while controlling for instructor effects and simulation characteristics. Our study reveals that while some aspects of learner characteristics and learning styles do impact simulation performance, there are interesting joint effects when these two dimensions operate in tandem.

In order to understand the way in which learning styles and learner characteristics impact performance in business simulations, we build an understanding of the nature of business simulations, and the kind of individual learning characteristics and approaches that may impact performance. We then elaborate on the underlying model and instrument used to measure learning styles in this study, and its application to simulations.

Background

Business Simulations

In a typical business simulation, participants take decisions on behalf of a firm. Competitive elements are built into the simulation and there is an element of decision making under uncertainty and time pressure. There are several rounds of decision making and if multiple teams or individuals are playing the same simulation, the situation faced in subsequent rounds by participants will be different from the opening round due to the impact of their decisions and that of competition. The simulation is often run in a technology rich, computerized environment and thus is easily amenable to both in-classroom and remote play (Rogmans & Abaza, 2019). Business simulations allow participants to experience dynamic decision making under complexity, uncertainty and time pressure, reflect on the linkage between decisions and consequences, see underlying patterns and discover/abstract conceptual foundations which can enhance performance (Palia, 2019). Learners are required to study and make connections between different reports and graphs, link qualitative and quantitative information, work with each other to build understanding, and look for alignment across different functional areas (this is particularly true of strategy simulations). Within a simulation, there are three levels of learning through participating, debriefing and writing. While taking part in a simulation, participants learn through the route of discovery. During debriefing, participants learn by listening and sharing of experiences and further clarifying the link between data, inferences, decisions and consequences (Stainton et al., 2010). Reflection can thus be an important enabler of business simulation performance. Khenissi et al. (2013) investigated the relationship between the active/reflective learning and action games and their study confirmed a positive relationship between active learning style and preference for action games. Reflective learners prefer thinking and introspecting and may prefer games based on strategy which require higher reflection. Hence they identified a need for further research which could look into the impact of reflective learning style for strategy games where reflection plays an important role.

Learner Characteristic: Age and Simulations

The millennial generation (those born between 1982–2000) represents a majority of the current students in higher education (Desy et al., 2017). Millennials learn differently and hence need different teaching methods than those used for earlier generations (Martin, 2007). Traditional teaching methods often do not match the student’s changing needs (Jain & Dutta, 2019; Skiba & Barton, 2006). To cater to the changing needs and expectations of the millennials many academicians are changing their instructional approach and creating new learning environments (Jain & Dutta, 2019; Zamfir et al., 2009). As simulations are technologically intensive, designed to be fairly intuitive, and involve elements of gamification, it is likely that younger, technology savvy. ‘digital natives’ will find it easier to participate and perform in business simulations (Black, 2010; Schofield & Honoré, 2009). Gosenpud (1982) found that younger students are more tolerant of ambiguity in a course than older students and hence are likely to gain more than older students in an experiential learning course. While there is a lot of anecdotal assertion about the technological familiarity of digital natives, empirical examination of the link between age and approaches to learning suggests that the case may be overstated (Margaryan et al., 2011). There is thus a need to further explore this aspect.

There are many aspects of learner upbringing and experience that cannot be captured by a limited set of personal characteristics. However, these experiences may manifest in different approaches to specific pedagogies. Accordingly, we turn our attention to the literature on learning styles, and their potential impact on business simulation performance.

Learning Styles

In last few decades, an appreciation that students learn and study in different ways has emerged as an important pedagogical discussion (Hawk & Shah, 2007). Many learning models (Dunn, 1990; Felder & Silverman, 1988) have emerged offering different explanations ranging from a relatively fixed student natural deposition to modifiable preferences towards learning. According to Kolb (1984), a learning style is an aggregate construct of cognitive, affective, and psychological factors that provide insight into how an individual can respond to a specific pedagogy. The process of acquisition of knowledge is the core of Kolb’s experiential learning model. Felder (1996) states that students process information depending on their preferences, learning styles and characteristics. Felder–Silverman learning style model (FSLSM) has elements from earlier models of learning and is formulated with synthesised findings from many studies (Felder & Spurlin, 2005; Khenissi et al., 2016).

It is important to note here that while learning styles are used quite extensively by educators, the empirical evidence for the effectiveness of this approach can, at best, be described as mixed. Coffield et al. (2004) looked at 13 different approaches to the measurement of learning styles, and pointed out that a majority of the instruments did not do well on predictive validity. Garner (2000) highlights inconsistencies in Kolb’s learning styles approach, The idea that instructors can be aware of learning styles of students and tailor their teaching approach to a specific class’ needs and thereby achieve better learning outcomes has not received much empirical support (An & Carr, 2017; Kirschner, 2017; Papadatou-Pastou et al., 2018). Despite this strong criticism, we believe that learning styles deserve greater empirical exploration because:

• The learning styles approach continues to be used by over 90% of teachers worldwide (Papadatou-Pastou et al., 2018).

• There is still an agreement that different audiences need to be taught differently, and the learning styles approach represents a relatively structured and developed approach towards this tailoring (Dede, 2005).

• The Felder and Silverman model that we are using in this study has addressed some of the drawbacks identified in earlier studies and integrates the sensing aspects and cognitive aspects of learning styles. The instrument (Index of learning style) based on this model is seen to be strong on construct and discriminant validity (Platsidou & Metallidou, 2008).

Business simulations represent a pedagogical approach which creates higher student engagement (Prensky, 2001). Simulations also transfer the learning responsibility from the instructor to the learner as they require the learner to be actively involved in the process (Adobor & Daneshfar, 2006). Hence, it is useful to understand how the teaching of simulations should be adapted to diverse audiences. Given that there is limited research which looks at the joint impact of learner characteristics and learning styles in a simulation context, this still remains a fertile area for exploration.

A feature of the Felder and Silverman model which is different from other models is that while others classify students into fixed types, this model is based on the idea of students having a tendency towards one style in each of the four dimensions of ‘Processing’, ‘Perception’, ‘Input’ and ‘Understanding’ (Dorça et al., 2016). This model serves as the basis for measurement in our study. The dichotomous learning style dimensions of this model are in continua and not either/or categories Based on their learning model Felder and Silverman (1988) developed a learning style instrument called ‘Index of learning style (ILS).This instrument has been extensively used across contexts in education to understand pedagogical needs of different learning styles and to design effective learning environments (Baldwin & Sabry, 2003; De Vita, 2001; Sandman, 2014).

The four learning dimensions of FSLSM are described in the subsequent sections

Active and Reflective – The ‘Processing’ Dimension

Active learners will benefit more from active experimentation while reflective learners will learn better if they have time to introspect individually. While simulations do involve active experimentation, a lot of learning comes through by reviewing past decisions, analysing outcomes and trying to understand the link between them. This involves understanding the decision making process, and often making connections to scenarios from their own experience. Reflective learning can thus be a significant enabler of simulation performance.

Sensing and Intuitive – The ‘Perception’ Dimension

According to Tee et al. (2015) sensing learners are those who like to learn through facts and information. The advantage of being a sensing learner is that one is a hands-on learner who works best with factual knowledge. Intuitive learners are conceptual, innovative and orientated towards theory. They often look for reasons, possibilities, relevance and are able to learn new concepts with ease.

Visual and Verbal – The ‘Input’ Dimension

De Vita (2001) through his research in cultural studies brings out the fact that preference for visual rather than verbal mode for absorbing information cuts across cultures. Visual learners prefer graphic, visual representation and videos as teaching approaches. Conversely, explanation, discussions, lectures and dialogues is what verbal learners like.

Sequential and Global – The ‘Understanding’ Dimension

Graf et al. (2007) explain that sequential learners like to learn in increments, stepwise and prefer a linear learning process. On the other hand, global learners use a holistic thinking process and learn in large leaps. They are capable of making connections till the full picture dawns on them. As the big picture is important to them, they are interested in overviews and broad knowledge while sequential learners are interested in details.

Methods

This study aims to look at the joint effect of learning styles and learner characteristics on performance in business simulations. We used a combination of survey methodologies and a pedagogical intervention to test this relationship. We separately measured learner characteristics and learning styles through surveys and a structured instrument. We then administered a simulation based course to separate student cohorts with differing characteristics. We measured performance on the simulation at the end of the course. Multiple regression analysis with moderation analysis was used to test the hypothesized effects. Suitable methods were used to avoid potential multi-collinearity issues.

Research Questions and Hypothesis

As the aim of this study is to look at the potential linkage between learner characteristics, learning styles and performance in business simulations, we first explore the potential impact of learning styles on simulation performance. Participants will need both the ability to draw on facts and hands on learning, as well as the ability to integrate information and make intuitive links. It is thus difficult to predict unambiguously whether sensing or intuitive learners will perform better at business simulations. In a similar vein, the description of the simulation context is textual (and hence amenable to a verbal learning style), but is often supplemented by the ability to create tailored graphical and tabular analysis, which is amenable to a visual learning style.

A similar analysis suggests that a combination of sequential and global learning styles may be useful in business simulations. Sequential learning may be needed to link facts in a hierarchical order while drilling down to specific areas of business. Global learning styles may help to link inferences and create a business level picture. However, when talking about business simulations, the ability to make connections across functions and create a business level picture suggests that global learning may give participants an advantage in business simulations. While business simulations themselves require active involvement, actual performance in the simulation implies the ability to analyse the effects of prior decisions, and then revise strategies and draw generalizations. The ability to link decision inputs and outcomes for better understanding of the decision process involves reflection, and hence reflective learning should enable better simulation performance.

An integrated view of the impact of learner characteristics and learning styles would lead to the following testable hypotheses:

H1: Age of the learner will negatively impact performance on business simulations (due to aspects of comfort with technology and similar interfaces).

H2: The presence of reflective learning style will positively impact performance on business simulations.

H3: The presence of global learning style will positively impact performance on business simulations.

Finally, older learners will frequently have a greater base of life and business experience to reflect on, and thus will benefit more from being able to adopt a reflective learning style. This gives rise to our final hypothesis.

H4: The presence of reflective learning styles moderates the impact of age on simulation performance.

In order to test these hypotheses, we need diverse student cohorts with variations in learner characteristics and learning styles. We will also ideally need to eliminate variation due to differences in the simulation used and instructor level differences. Finally, the measurement of learning styles could itself create a salience of specific styles and behaviours, and we would ideally need to temporally separate the style instrument administration from simulation use. Our empirical setting satisfies these requirements, and we describe it in the next section.

Sample

In order to design a valid study to test our hypotheses, we needed a setting such that there was adequate variation in respondent profiles and the business simulation was conducted under similar conditions including same instructor. Our empirical setting satisfied these conditions. Two cohorts of a two-year full time MBA program and multiple cohorts of a modular executive MBA program were administered an instrument on learning styles early on in the program. Later on in the same program, they participated in a strategy course where the CAPSIM simulation was conducted. Simulations by CAPSIM are leading business simulations (Adobor & Daneshfar, 2006; Chasteen et al., 2018) which are well received by the academic and the business world (Dickinson & Dickinson, 2012). Comp-XM by CAPSIM (www.capsim.com) was used in this study. The Comp-XM is an individual business simulation based assessment where a participant manages a firm individually by taking decisions across functional areas of R&D, marketing, manufacturing, finance and human resources. The scores generated by the CAPSIM system for each participant is based on a balanced scorecard.

Individual performance was recorded for each participant at the end of the simulation. The number of rounds and the metric adopted was the same across participants. Table 1 provides data on the participant profile and learner characteristics. The sample consisted of 605 participants. The average age of the participants was 27.1 years (range=21-53, SD=5.98) with average work experience of 45.9 months (range= 0-312, SD=57.2). Males constituted 67% and females 33% of the sample.

Table 1.

Demographic Data.

Characteristics	No. of Participants	% of Total
Gender
Male	403	67
Female	202	33
Program type
2 year full time	470	78
Executive/part time	135	22
Educational background
Engineers	449	74
Non-engineers	156	26
Age (years)
≤30	489	81
>30	116	19
Work- experience
No experience	72	12
≤5 years	412	68
5≤10 years	64	11
>10 years	57	9

We can see that the participant sample shows dispersion in terms of age and work experience. There is also adequate gender representation to explore the impact of gender as a learner characteristic

Instruments

We now elaborate on the measurement of learning styles in our study. The instrument we used is the Index of Learning Styles(ILS), based on the model developed by Felder and Silverman. The ILS scale is a bipolar instrument with mutually exclusive answers i.e. either (a) or (b). There are an odd number of items on each scale. If items are scored as +1 and -1 respectively, the scores will be in the range of +11 to -11. The dichotomous nature of the scale makes it difficult for use as a standard statistical test. Therefore, only one scale (a) or (b) for each dimension needs to be used. In this study the responses were scored on Active, Sensing, Visual & Sequential scales for use of statistical tests. Scores of respective opposite polarities Reflective, Intuitive, Verbal & Global could be found as complement of respective scale scores in each dimension (e.g. a score of 7 on Active scale would be a score of 4 on a reflective scale). Thus, a higher score an Active or Sensing will imply a lower score on the corresponding opposite. Table 2 gives a picture of the distribution of learning styles within the respondent base of 605 participants.

Table 2.

Overview of Learning style Preference.

Learning Dimensions	Learning Styles	N	%
Processing	Active	331	55
Processing	Reflective	274	45
Perception	Sensing	381	63
Perception	Intuitive	224	37
Input	Visual	526	87
Input	Verbal	79	13
Understanding	Sequential	353	58
Understanding	Global	252	42

Having understood the context for our empirical study, we proceed to describe our empirical model, operationalize our hypotheses and present our results.

Statistical Analysis

We can see the broad econometric model underlying our study as:

Simulation performance = f (learner characteristics, learning styles, control variables)

Specifically, we write this as

\begin{array}{l} {Performance}_{i} = (Constant) + α_{1} * (Age) + α_{2} * (Gender) + α_{3} * (Active) + α_{4} * (Sensing) + \\ α_{5} * (Visual) + α_{6} * (Sequential) + α_{7} * (Full - time) + α_{8} * (Active * Age) + ε_{i} \end{array}

where

ε_{i}

is assumed to be an independent and identically distributed for all i with mean 0 and variance σ²

Note that in the above equation

$α_{1} - α_{2}$ refer to the coefficients on learner characteristics.

$α_{3} - α_{6}$ refer to the coefficients on learning styles.

$α_{7}$ refers to the coefficient on a control variable for students of the full time program.

$α_{8}$ refers to the coefficient on the interaction variable of interest.

Given the coefficients in the equation above, this implies that in the context of our model, the hypotheses can be operationalized as follows:

H1: $α_{1}$ < 0, younger students will perform better on business simulations.

H2: $α_{3}$ < 0, note that the sign of the coefficient is expected to be negative because reflective learning styles are expected to positively impact simulation performance. The variable is coded as active learning, and as explained earlier, the nature of the scale implies that a higher score on active learning implies a lower score on reflective learning and vice versa.

H3: $α_{6}$ < 0, note that the sign of the coefficient is expected to be negative because global learning styles are expected to positively impact simulation performance. The variable is coded as sequential learning, and as explained earlier, the nature of the scale implies that a higher score on sequential learning implies a lower score on global learning and vice versa.

H4: $α_{8}$ < 0, Essentially the effect of age on performance will be moderated when the learner has a reflective learning style. Put differently, older learners will draw proportionately greater benefit from a reflective learning style.

Results

Having described our variables and our empirical model, we proceed with our findings.

Note that our model did not include separate variables for age and work experience, because of collinearity issues. As can be seen, the two variables are highly correlated, and we cannot meaningfully test hypotheses around both of these variables separately. We are also constrained by the fact that we do not have the data to judge the quality of work experience. Our data has age in years, and the number of years of work experience. Given that this is a strategy simulation; the nature of work experience would be an important filter which we are not able to use in our study. The Table 3 also indicates that the correlations between different learning styles are low, giving intuitive support to the learning styles instrument and its ability to identify four distinct dimensions of learning styles. This is also supportive of earlier studies which support the construct and discriminant validity of the ILS (Platsidou & Metallidou, 2008).

Table 3.

Correlations Among the Key Variables of Interest.

	Age	Work-exp.	Male	Full time program	Active learning	Sensing learning	Visual learning	Sequential learning
Age	1	.966**	.235**	−.857**	.108**	0.053	0.03	0.003
Work exp.		1	.220**	−.826**	.096*	0.05	0.023	0.018
Male			1	−.245**	0.013	0.066	.158**	-−.102*
Full time program				1	−.129**	−0.048	-.090*	0.01
Active learning					1	0.019	.278**	−0.076
Sensing learning						1	0.057	.318**
Visual learning							1	−.147**
Sequential learning								1

**Correlation is significant at the 0.01 level (2-tailed).

*Correlation is significant at the 0.05 level (2-tailed).

Male- Dummy variable set at 1, if participant is male otherwise it is 0.

Full time Program(FTP)-Dummy variable set at 1, if participant is from 2-year full time program otherwise 0.

Table 4 presents our results. We present three models, the first with only learner characteristics, the second with learner characteristics and learning styles, and the final model is the full model. The models are presented in this sequence because there are past studies which look at the main effects of learner characteristics on simulation performance and our first model looks at these effects and confirms hypothesized effects of age on simulation performance. The second model looks at learner characteristics along with learning styles. We demonstrate the main effects of learning styles. The third full model represents a nuanced contribution, because it involves assessing the joint impact of learner characteristics and learning styles. For the full model, there are potential multicollinearity and scaling issues when main effects and interaction effects are considered in the same regression. This could cause spurious results (this was found to be true in our case as the original interaction variable for active learning and age was found to be highly correlated to the active learning variable). We normalize the learning style and age variables before computing the interaction to get rid of this potential issue, and use these normalised values in all three models in Table 4.

Table 4.

Regression of Performance Against Learner Characteristics, Learning Styles and Interaction.

	Model 1		Model 2		Model 3
Predictors	Coeff (s.d.)	T	Coeff (s.d.)	T	Coeff (s.d.)	T
Constant	309.18 (10.48)	29.48**	311.11 (10.41)	29.89***	314.71 (10.26)	30.67**
Full-time program	38.39 (11.81)	3.25**	39.37 (11.77)	3.35***	36.47 (11.58)	3.15**
Age	−19.18 (4.91)	−3.88**	−17.95 (4.88)	−3.68**	−17.30 (4.80)	−3.60**
Male	32.30 (5.53)	5.84**	28.25 (5.57)	5.09**	28.20 (5.58)	5.15**
Active learning style			−5.63 (2.62)	−2.15*	−6.09 (2.58)	−2.36*
Sensing learning style			3.35 (2.66)	1.26	3.52 (2.82)	1.34
Visual learning style			4.32 (2.68)	1.61	4.45 (2.63)	1.69
Sequential learning style			−9.00 (2.69)	−3.34**	−8.34 (2,65)	−3.15**
Active learning*Age					−12.09 (2.58)	−4.68**
Adjusted R-squared	0.2219		0.2393		0.2651

Dependent Variable – Simulation Performance-Score.

Model 1 – Fixed effects for full time program plus learner characteristics.

Model 2 – Model 1 independent variables plus learning styles.

Model 3 – Model 2 independent variables plus interaction variable (full model).

*p < .05**; p < .01.

Note that in this paper, we use the use the term interaction and moderation interchangeably. Moderation implies that the impact of a variable X on a variable Z is systematically impacted (increased or decreased) by the level of the variable Y. This implies that the product of the two variables X and Y has a significant impact on performance (Figure 1).

Figure 1.

Interaction effect with age and processing learning dimension on performance.

We find support for our main hypotheses. Specifically,

• We find a negative and significant impact of age on simulation performance. This implies that younger students perform better on simulations.

• The hypothesis is that participants who are higher in their use of reflective learning styles will perform better on strategy simulations. Since the variable is reverse coded the value is lower if reflective learning is higher. The coefficient is negative and significant, thus confirming H2.

• The hypothesis is that participants who are higher in their use of global learning styles will perform better on strategy simulations. Since the variable is reverse coded the value is lower if global learning is higher. The coefficient is negative and significant, thus confirming H2.

• The hypothesis is that older participants will benefit more the use of reflective learning styles. Since the reflective learning variable is reverse coded, this implies that the coefficient on the interaction variable age*active should be significant and negative. This could be interpreted as follows: A participant at a certain age will proportionately enhance performance with a greater level of reflective learning styles. A participant will benefit more from a particular level of reflective learning styles if the participant is older.

While we do not find support for other aspects of learning styles affecting performance, we could attribute it to the fact that a strategy simulation may draw on two aspects of a learning ‘Processing’ and ‘Understanding’ dimensions. We were unable to make clear predictions with respect to the Perception (sensing/intuitive) and Input (visual/verbal) dimensions of learning styles. This is also consistent with earlier studies which suggest that the cognitive aspects of learning styles have higher predictive powers. While our results suggest that are systematic differences in simulation performance by gender, a far more robust design would be needed to rule out selection affects.

In our next section, we summarize our key results, draw implications for educators, summarize limitations of our study and suggest avenues for future research.

Discussion

Ours is one of the first studies to systematically study the joint effect of learner characteristics and learning styles on performance in simulations. We find that younger, more technologically savvy participants find it easier to adapt to simulations, and tend to do better in simulations. The older participants (executive classes) may hence need greater time to get comfortable with the simulation environment and to aid this, instructors must build in more practice rounds to help them develop comfort with the simulation in use. We also find that reflective and global learning styles positively impact performance on business simulations. This may be more likely for simulations which involve a lot of analysis, pattern recognition and integration across multiple business functions. It is thus applicable to strategy simulations and other simulations which involve an integrative aspect. Older participants will benefit more from a reflective learning style because it will help them draw learnings and build on their work experience and therefore may be encouraged to develop it for stronger performance in business simulations.

Limitations and Suggestions for Future Research

In our study we demonstrate empirically that age adversely impacts simulation performance and that global and reflective learning styles enable simulation performance. We also show that the presence of reflective learning styles moderates the effect of age on simulation performance (i.e. older participants benefit more from adopting reflective learning styles). While these are important findings, our study does suffer from multiple limitations. We have used performance in the simulation as the end goal. However, our goal should be long term learning from the simulation and performance, is at best, a very noisy measure. Incorporation of a one-year post metric of effectiveness may yield a more complete picture. We do not have enough qualitative insight into the way specific learning styles impact performance and believe that more research is needed to understand the impact of reflective and global learning styles on performance in business simulations. We also suggest that it would be interesting to see whether these trends continued if the simulations chosen were more functional and less integrative. Further, reflective learners tend to stay away from team based work. It would be interesting to extend this study to team and individual based work and explore whether the role of active learning differed. Given that the data on impact of learning styles on learning outcomes is mixed, it would be interesting to explore whether these findings were robust to alternative measurement instruments and conduct a well-designed experiment based on the implications of the current study with a test and a control group.

Another interesting area could relate to the introduction of both process and outcome metrics and the right way to incentivise participant’s behaviour. We believe that these directions can offer significant promise for future researchers to shed more light on the fast growing world of business simulations. We hope that future researchers will build on some of these exciting possibilities in the years to come.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Authors’ Note

This research was approved by our institution’s Institutional Review Board.

ORCID iD

Bindu Kulkarni

Ranjan Banerjee

Rajasekaran Raghunathan

Author Biographies

Bindu Kulkarni is an Associate Professor of Strategy and Design Thinking at S.P. Jain Institute of Management and Research, India. Her research focus is in the area management education, learning and teaching with simulations and Blue Ocean Strategy.

Ranjan Banerjee is the Dean and Professor of Marketing at BITSoM. His research interests lie in the areas of marketing channels and sales force management, design thinking, the interface between technology and innovation, and behavioural economics.

R Raghunathan is an Associate Professor in Strategy and Entrepreneurship at Birla Institute of Technology and Science, Pilani, Hyderabad Campus. His research interests include SoTL, entrepreneurship, strategy, business negotiations and managerial skills and competencies.

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