The Learning Process in Live-Action Simulation Games: The Impact of Personality,Motivation,Immersion,and Flow on Learning Outcome in a Simulation Game.

Abstract

Background

Against the backdrop of the cognitive-motivational process model proposed by Vollmeyer and Rheinberg (1998), this study investigates how the personality trait need for cognition in combination with a specific situation (short and long version of a live-action simulation game), current motivation, and immersion and flow (mediators) predict the learning outcome in the simulation game EVERYDAY LIFE IN THE CLASSROOM (Starker & Imhof, 2014).

Methods

N = 138 teacher students played the short 2-hour version of the simulation game, and N = 77 played the long 2-day version. Need for cognition, current motivation, immersion, flow, and learning outcome were measured by self-report questionnaires.

Results

A hierarchical regression analysis was used to determine the impact of the predictor variables (need for cognition, current motivation, immersion, and flow) on the self-evaluated learning outcome. For the 2-hour group, the predictor variables interest, challenge, and immersion (subscale: engagement) impacted self-reported learning outcome significantly positive. For the 2-day group, significant predictors were need for cognition, interest, and immersion (subscale: engrossment). In comparison of the two game versions, students playing the 2-day version reported significantly higher levels of engagement, engrossment, and learning outcome.

Conclusion

The cognitive-motivational process model of learning was partly supported: interest and immersion predict learning outcome in the live-action simulation game. The extended 2-day version of the game leads to higher levels of immersion and higher learning outcome, indicating that a longer timeframe secures the desired effects on learning outcome from simulation games. Further research needs to shed light on the interaction of personality traits and immersion.

Keywords

Need for Cognition Current Motivation Immersion Flow Live-Action Simulation Game Higher Education

Introduction

Simulation Games in Higher Education

Classroom management is a prime example of a complex problem situation which requires multiple skills. As possibilities for teacher students to acquire pertaining skills in the workplace are limited, simulation games seem to be a valid option (Chernikova et al., 2020). Educational simulation games are action-orientated, they reduce complexity and allow for gradual skill building as they “can help engage learners in specific aspects of professional practice” (Chernikova et al., 2020, p. 500; see also Engartner, 2010). They “provide students the opportunity to observe the outcomes of their actions, and take responsibility for decision-making via problem-solving competencies, thus leading to a more active, transformative and experiential reception of knowledge” (Vlachopoulos & Makri, 2017, p. 25). Not surprisingly, many university curricula integrated simulation games in their programs to close the gap between practice and theory and to execute the “shift from teaching to learning” (e.g., Atkins & Brown, 2002; Kriz, 2014).

The positive effects of simulation games have been demonstrated by several studies (e.g., Garris et al., 2002; Kriz, 2011). Students who played a simulation game showed higher interest and enthusiasm toward the topic, more active participation, and spend more time preparing for the lessons (Hensley, 1993; Shellman & Turan, 2006) than those who took traditional classes. Simulation games engage students emotionally, create lasting and easily accessible memories (Clark & Paivio, 1991; Martin, 1993), and foster the development of empathy (Greenblat, 1973).

However, the well-established positive impact of simulation games needs further investigation, especially in the domain of live-action (opposed to digital/virtual or computer based) simulation games, since a lot of studies focus on digital games. Specifically, the learning process and the variables which determine learning outcome need more research (Kriz, 2014; von der Weth et al., 2018). The goal of this study is to identify relevant variables by applying the well-established cognitive-motivational process model of learning (Vollmeyer & Rheinberg, 1998), which explains the interaction of personal, situational, and motivational aspects and their impact on the learning outcome in different learning situations.

The Cognitive-Motivational Process Model of Learning

The cognitive-motivational process model of learning proposed by Vollmeyer and Rheinberg (1998; also, Rheinberg et al., 2000) captures the influence of motivational process variables on learning outcome in a learning situation. The person- and situation-specific current motivation affects mediating process variables, such as time on task, quality of the learning activity (e.g., applied learning strategies), and functional state of the learner, which subsequently affect the learning outcome (see Figure 1). The functional state “refers to the learner’s physiological and psychological activation and concentration” (Rheinberg et al., 2000, p. 84) and is considered an accumulative category including positive or negative activation, tiredness, and flow (Engeser et al., 2005).

Figure 1.

Adapted Version of Rheinberg et al., (2000) cognitive-motivational process model of learning. Author’s adaptions italicized.

Although simulation games differ from traditional learning settings, as students are placed in a problem-based scenario in which they plan their actions, make decisions, and reflect the results (von der Weth et al., 2018), the learning situation can be analyzed along this model (Beierlein et al., 2005; Vollmeyer & Rheinberg, 2003, 2006): As stable personality trait, need for cognition is investigated, describing a person’s volition to engage in complex problem solving. The current motivation remains as the connector between person/situation and the functional state, while the functional state of the learner during the simulation game could be described through the graded construct immersion, which—together with flow—is investigated in this study (see Figure 1). In a previous study, I found that both personality and situational factors impact current motivation and immersion as mediating variables (Preuß, 2020), but the subsequent impact on the learning outcome could not be investigated, upon which this study now focuses. The pertinent constructs immersion, flow, need for cognition, and learning outcome are briefly discussed in the following.

The Functional State of the Learner: Immersion and Flow

The concepts of immersion and flow both describe the state of mind a person can experience when being engaged in an activity such as playing a simulation game. While the concept of immersion was initially coined by digital gaming research, the term has also been linked to non-gaming or non-technology activities, for example, reading, storytelling, or role-play games (e.g., Bowman, 2018; Brooks, 2003; Douglas & Hargadon, 2000; Salar et al., 2020; Weibel et al., 2010). Brown and Cairns (2004) conceptualized immersion as a graduated psychological process which consists of three consecutive levels: engagement, engrossment, and total immersion. A player engaged in a game has access to it (in terms of rules/controls) and is willing to invest time and effort into playing. An engrossed player is attentive toward the game as well as emotionally attached. In total immersion, players reach a sense of presence (a construct defined for virtual environments/augmented reality) and a sense of flow (established both for live-action and digital games; Brown & Cairns, 2004; Georgiou & Kyza, 2017).

Bowman (2018) assumed players to “engage in several modes of immersion simultaneously and with varying levels of intensity” (p. 383), and while not all modes (activity, game, environment, narrative, character, and community) may be available in an educational simulation game, the different levels of intensity may still be attained, therefore supporting the idea of immersion as a graded construct.

Flow is defined as the process of optimal experience when individuals are so involved in an activity that they lose track of time and their surroundings (Csikszentmihalyi, 1975, 1990). The concept of flow is closely related to that of immersion. While Michailidis et al., 2018 suggested that both concepts describe the same mental state, Jennett et al. (2008) distinguished flow and immersion, since a person can be immersed in a game while—not yet or not fully—experiencing flow. However, flow—as optimal “state of mind”—may be evoked through the graduated process of immersion (Jennett et al., 2008).

This study follows the well-established approach by Brown and Cairns (2004) who conceptualized game immersion as a graduated process (e.g., Cheng et al., 2015; Georgiou & Kyza, 2017; Jennett et al., 2008), assuming that in an educational simulation game, the first stages might be easier to achieve, while total immersion/flow might be harder to reach (e.g., because of group effects or performance pressure). Therefore, the levels of engagement and engrossment are investigated as mediating factors for the functional state of the learner as well as flow, which has been established as an element of the functional state (Engeser et al., 2005; Vollmeyer & Rheinberg, 2006). Due to the conceptual overlap between flow and immersion, flow is taken to represent total immersion in this study.

Personality Traits: Need for Cognition

As I have previously discussed (Preuß, 2020), Cacioppo and Petty (1982) defined the concept need for cognition as “an individual’s tendency to engage in and enjoy effortful cognitive endeavors” (Cacioppo et al., 1984, p. 306; Cacioppo & Petty, 1982; Cacioppo et al., 1996). Individuals with a high need for cognition are more likely to choose difficult tasks over easier ones and to successfully solve them, they recall more information and are more influenced by the quality of arguments than individuals with a low need for cognition (Cacioppo et al., 1996; Reinhard & Dickhäuser, 2009). Need for cognition correlates positively with intrinsic motivation (Olson et al., 1984) and with academic performance in tasks that require a high cognitive effort (Leone & Dalton, 1988).

A simulation game creates a cognitively demanding environment in which players must deal with complex problems. As individuals with a high need for cognition are more likely to engage—and perform better—in complex solving tasks (Unnikrishnan Nair, & Ramnarayan, 2000), it can be concluded that need for cognition is a relevant stable personality trait for learning in a simulation game.

Learning Outcome in Simulation Games

Evidence for the positive effects of simulation games on learning outcome on various levels (cognitive, behavioral, and affective) has been reported in numerous meta-studies (e.g., Clark et al., 2016; de Smale et al., 2016; Randel et al., 1992; Vlachopoulos & Makri, 2017; Young et al., 2012). In particular, the sustainable impact on long-term knowledge retention and the motivational aspects (e.g., higher intrinsic motivation) have been well established (e.g., Boyle et al., 2016; Schedelik, 2018). In an extensive meta-study investigating simulation games in higher education, Chernikova et al. (2020, p. 522) stated that “simulation-based learning has large positive overall effects on the advancement of a broad range of complex skills and across a broad range of different domains in higher education.”

While the positive learning outcomes from the simulation game EVERYDAY LIFE IN THE CLASSROOM could be demonstrated in previous studies (see below, Spaude et al., 2016), the focus of the current study lies on the investigation of the person-centered and situation variables which determine learning outcome.

Problem Statement

The literature review shows that in the domain of live-action educational simulation games, the learning process itself and the impacting variables on learning outcome need further investigation. Against the backdrop of the cognitive-motivational process model of learning (Vollmeyer & Rheinberg, 1998), I investigate how need for cognition, the current motivation, and the functional state (immersion and flow) impact the learning outcome in two different versions of the same simulation game EVERYDAY LIFE IN THE CLASSROOM.

Intervention: Everyday Life in the Classroom

The simulation game EVERYDAY LIFE IN THE CLASSROOM (Starker & Imhof, 2014) was played by teaching students in an educational psychology seminar. The game emulates a school day, with randomly assigned roles of pupils and teachers. The teachers plan and execute 20 min lessons; the pupils attend those lessons and interact with the teacher and each other during the lessons and the breaks. The setting is built to help generate a natural school situation, with classrooms, a staff room, and a schoolyard. The teachers and students receive background information on their character and are free to interact with the other players in a way they see fit for their persona (e.g., pay attention to the lessons or purposefully disrupt sessions and interact with pupils in a jovial or authoritarian way). Depending on the chosen seminar, the students either play a 2-day version of the game—with preparation, play phase, and reflection (total of 12 hr)—or a short, 2-hour version during the weekly seminar. In the latter version, the students prepare in the week before the game and reflect in the week after, so that they play two lessons in the actual game time (total of 4 hr).

The game focuses heavily on the teacher–student interaction: The learning goals are the identification of and working with the act of teaching as a complex problem (e.g., realizing and dealing with interconnectedness, polythelia, intransparency, and multi-perspectivity), the broadening of the perception focus, and the regulation of emotions (Imhof et al., 2016; Imhof & Starker, 2020; Spaude et al., 2016; von der Weth et al., 2018). Previous studies showed that students show a better understanding of the complex situation in a classroom after playing the game, for example, less punishment, fewer negative emotions, and better explanation for disruption (Spaude et al., 2016).

Methods

This research explores the effect of personality traits, current motivation, and immersion on the learning outcome during a live-action simulation game, which was played in a 2-hour and a 2-day version by teaching students in seminars on educational psychology in a German university in the summer semester 2019 and the winter semester 19/20.

Research Question

Assumptions based on the research discussed above, which demonstrated how personality traits and situational features interact to activate current motivation which determines immersion and learning, lead to the following question: How do a person’s need for cognition, their current motivation, and the functional state during a simulation game affect the person’s learning outcome?

Following this question, investigating environmental features the game provides, the second question for this study reads: Is the experienced immersion, flow, and the self-evaluated learning outcome determined by the version of the game played here differentiated between a full 2-day version and a shorter 2-hour version?

Hypotheses

Hypothesis 1. A higher need for cognition, higher current motivation (probability of success, interest, challenge, and anxiety (inverse)), higher levels of immersion (engagement, engrossment), and flow will result in a higher self-evaluated learning outcome.

Hypothesis 2. Students playing the long version of the game EVERYDAY LIFE IN THE CLASSROOM reach higher levels of immersion and flow, and report higher self-evaluated learning outcome than the students playing the short version of the game.

Sample

The simulation game was played in the summer semester 2019 by N = 173 students in different groups in the seminar for educational psychology and by N = 42 students in the winter semester 19/20 (Table 1). This research has been conducted following the ethical requirements established by the German national board of ethics.

Table 1.

Sample Description by Gender, Age, and Study Time.

	#^a	#^b	Drop-out	Gender			Age			Study time
	#^a	#^b	Drop-out	Female	Male	Other	Range	M	SD	Range	M	SD
Summer semester
Short	138	96	42	93	45	0	19–30	21.7	1.74	2–8	4.14	0.92
Long	35	32	3	20	15	0	20–45	22.9	4.7	3–8	4.80	1.16
Winter semester
Long	42	42	0	29	13	0	19–31	22.1	2.13	3–10	1.69	0.47

^aNumber of students participating in the pre-test.

^bNumber of students included in the calculations, due to missing data.

Instruments

As in my previous study (Preuß, 2020), the constructs were measured by self-report questionnaires described below (see Table 2 for scale reliabilities reported by the original authors). For all questionnaires, the items were rated on a 5-point Likert scale from 1 (totally disagree) to 5 (totally agree) and averaged for the respective subscales.

Table 2.

Summer Semester: Psychometric Characteristics of Measurements: n, Internal Consistency (Measured & Original Cronbach’s α).

Scale	n items	Cronbach’s α	Original Cronbach’s α	N valid	N total
Pre-test (all)					173
Need for cognition	4	.54	.51–.54	171
Current motivation
Anxiety	5	.87	.72–.85	170
Challenge	4	.67	.66–.81	172
Interest	5	.87	.71–.90	171
Probability of success	4	.70	.68–.88	170
FOLLOW-UP (Group 1)					35
Augmented reality immersion
Engagement	8	.71	.77	31
Interest	4	.74	.80	32
Usability	4	.69	.82	31
Engrossment	6	.64	.88	32
Emotional attachment	3	.58	.76	32
Focus	3	.52	.87	32
Flow short scale	13
Automatism	6	.83	.88	32
Absorption	4	.70	.73	32
Total flow score*	10	.85	.90	32
Self-evaluated learning outcome	5	.71	-	32	35
FOLLOW-UP (Group 2)					138
Augmented reality immersion
Engagement	8	.83	.77	93
Interest	4	.89	.80	95
Usability	4	.62	.82	93
Engrossment	6	.76	.88	95
Emotional attachment	3	.71	.76	95
Focus	3	.47	.87	95
Flow short scale	13
Automatism	6	.82	.88	92
Absorption	4	.60	.73	94
Total flow score*	10	.82	.90	90
Self-evaluated learning outcome	5	.68	-	96

*Total flow score consists of the mean of automatism and absorption.

Learning Outcome (BEvaKomp)

The self-evaluation of the Learning Outcome was measured with the subscale for personal competency of the Berlin Evaluation Instrument for self-evaluated student competences, which was developed to measure the competence gain of students in academic courses (Braun et al., 2008). The subscale consists of six items, three have been specified for the special purpose of the simulation game (e.g., “academic course” substituted with “simulation game”).

Need for Cognition (NfC-K)

The German-language short scale for the measurement of the construct (NfC-K; Beißert et al., 2014) consists of four items to measure the two facets: engagement and joy.

Questionnaire on Current Motivation (QCM)

The German Version of the Questionnaire on Current Motivation (QCM) was used to measure the four subscales anxiety, probability of success, interest, and challenge (Rheinberg et al., 2001). The scale consists of 18 items. I modified the QCM slightly, to better fit the simulation game situation, for example, instead of “I like this kind of riddles.” the item now reads “I like this kind of games.”

Augmented Reality Immersion Questionnaire (ARI)

The Augmented Reality Immersion Questionnaire (ARI) by Georgiou & Kyza, 2017 has been modified and used to measure the first two of the three stages of immersion in the simulation game: engagement and engrossment. As the engagement subscale usability used by Georgiou & Kyza, 2017 refers to AR applications, the items had to be adjusted to measure the comprehensibility and possible disruption by the rule setting of the simulation game (e.g., “I found the AR application confusing” was transformed into “I found the rules of the simulation game confusing”). The scale consists of 14 items, eight for the two engagement subscales and six for the two engrossment subscales.

Flow Short Scale Questionnaire (FKS)

The two subscales absorption and automatism of the German flow short scale questionnaire (FKS) by Rheinberg et al. (2003) were used. The questionnaire consists of 13 items.

Research Protocol

EVERYDAY LIFE IN THE CLASSROOM (Starker & Imhof, 2014) was played by student teachers in their seminar on educational psychology in a German university in two semesters in 2019. In the summer semester, the study was conducted with a student group playing the long, 2-day version of the game and a group playing a short 2-hour version during their weekly seminar. In the winter semester, the study was taken on a group playing the long version. In all cases, the students were informed on the topic, the rules, and the conduction of the game before answering the questions of the first questionnaires (NfC-K and QCM). The students playing the long version of the game answered the questions of the post-test questionnaire (learning outcome, ARI, and FKS) at the end of the second day, before the reflection, and then again 2 weeks later for a follow-up test. The students playing the short version only answered the questions 2 weeks later for the follow-up, due to the short timeframe of the game. Participation in the game was mandatory for the students in the weekly seminar (short version), and the long version was taken by choice. The participation on the questionnaire was optional, and no personal data were recorded. The questionnaires were handled anonymously and coded by the students to match the first and second batch.

Statistical Analysis

The data from the questionnaires were entered into SPSS 23 by two persons (four-eye-principle) and then analyzed using descriptive analyses, correlational analyses, and hierarchical regression to examine the hypotheses.

Results

Psychometric Characteristics of the Measurements

Table 2 (summer semester) and Table 3 (winter semester) provide an overview of the psychometric characteristics of the measurements used in this study. They summarize the number of items for every subscale, the measured and the original Cronbach’s α, as stated in the literature, and the number of total and valid questionnaires.

Table 3.

Winter Semester: Psychometric Characteristics of Measurements: n, Internal Consistency (Measured & Original Cronbach’s α).

Scale	n items	Cronbach’s α	Original Cronbach’s α	N valid	N total
Pretest					42
Need for cognition	4	.75	.51–.54	42
Current motivation
Anxiety	5	.83	.72–.85	40
Challenge	4	.71	.66–.81	41
Interest	5	.88	.71–.90	40
Probability of success	4	.77	.68–.88	40
Post-test					42
Augmented reality immersion
Engagement	8	.80	.77	42
Interest	4	.83	.80	42
Usability	4	.74	.82	42
Engrossment	6	.86	.88	42
Emotional attachment	3	.91	.76	42
Focus	3	.51	.87	42
Flow short scale	13
Automatism	6	.82	.88	39
Absorption	4	.63	.73	42
Total flow score*	10	.82	.90	39
Self-evaluated learning outcome	6	.85	-	42

*Total flow score consists of the mean of automatism and absorption.

Group Differences

T-tests over the pre-test variables revealed no significant differences between the two groups (need for cognition: t(169) = .541, p = .589; anxiety; t(171) = −.239, p = .812; challenge: t(171) = −1.28, p = .203; interest t(171) = 1,90, p = .059; prob. of success: t(171) = −.825, p = .411).

Results for Hypothesis 1

A higher need for cognition, higher current motivation (probability of success, interest, challenge, and anxiety (inverse)), higher levels of immersion (engagement and engrossment), and flow will result in a higher self-evaluated learning outcome.

All variables have been included in the hierarchical regression analysis, as they are all theoretically plausible to impact learning outcome. The personality traits were entered in the first block, the current motivation variables in a second block, and the variable for the functional state (immersion) in the third block of the regression analysis.

For the summer semester group, the hierarchical regression analysis (Table 4) shows that 42% of the variance of the learning outcome a student evaluates after playing the EVERYDAY LIFE IN THE CLASSROOM simulation game can be explained by the three predictor variables interest, challenge, and engagement (Model 3), which relates to a large effect size (Cohen, 1992).

Table 4.

Hierarchical Regression Analysis of Predictors for Learning Outcome in the Summer Semester.

Predictor variables	b	SE b	β	p	F	p	R ²	Δ R ²
Model 1					27.9	.00	.18	.18
Interest	0.33	.06	.43	.00
Model 2					18.1	.00	.23	.04
Interest	0.27	.07	.35	.00
Challenge	0.22	.08	.23	.01
Model 3					29.6	.00	.42	.19
Interest	0.14	.06	.18	.03
Challenge	0.14	.07	.14	.06
Engagement	0.59	.09	.49	.00

Note. The following predictor variables have been excluded from the regression model: competence apperception, need for cognition, anxiety, probability of success, engrossment, and flow [model 2 additional challenge; model 1 additional engagement and challenge].

For the winter semester group, the hierarchical regression analysis (Table 5) shows that 58% of the variance of the learning outcome a student evaluates after playing the EVERYDAY LIFE IN THE CLASSROOM simulation game can be explained by the three predictor variables need for cognition, interest, and engrossment (Model 3), which relates to a large effect size (Cohen, 1992).

Table 5.

Hierarchical Regression Analysis of Predictors for Learning Outcome in the Winter Semester.

Predictor variables	b	SE b	β	p	F	p	R ²	Δ R ²
Model 1					11.1	.002	.25	.25
Need for cognition	0.43	.13	.50	.002
Model 2					8.7	.001	.31	.10
Need for cognition	0.37	.13	.43	.006
Interest	0.24	.01	.32	.034
Model 3					16.7	.000	.58	.27
Need for cognition	0.28	.10	.33	.008
Interest	0.13	.09	.17	.153
Engrossment	0.49	.11	.22	.000

Note. The following predictor variables have been excluded from the regression model: competence apperception, anxiety, probability of success, challenge, engagement, and flow [model 2 additional interest; model 1 additional engrossment and interest].

Results for Hypothesis 2

Students playing the long version of the game EVERYDAY LIFE IN THE CLASSROOM reach higher levels of immersion and flow, and report higher self-evaluated learning outcome than the students playing the short version of the game.

Table 6 shows the results of the independent t-test for the twin data set, comparing the groups of the short and the long-game version. The twin data were composed to control the self-measured level of competence and to focus on the game mode. For this data set, every student playing the long version of the game was matched with a twin playing the short version, who shows the same level of self-reported competence and similar study time and number of internships.

Table 6.

Means, Standard Deviations and Independent T-test: Learning Outcome with Immersion. Twin Data (N = 64, 32 each).

Variable	Full game		Short game		t	df	p*
Variable	M	SD	M	SD	t	df	p*
Engagement	4.42	0.42	4.11	0.72	2.03	62	.047
Engrossment	4.04	0.52	3.73	0.73	1.98	62	.052
Flow	3.12	0.78	3.19	0.67	−0.85	62	.933
Learning outcome	3,99	0.56	3,21	0.88	4.28	62	.000

*sig. (2-sided).

In a one-tailed t-test, the students playing the full version (M = 4.42, SD = 0.42) in comparison to the students playing the short game (M = 4.11, SD = 0.72) show significantly higher levels of engagement (t(62) = 2.03, p = .024), engrossment full game (M = 4.04, SD = 0.52), short game (M = 3.73, SD = 0.73), t(62) = 1.98, p = .026), and learning outcome full game (M = 3.99, SD = 0.55), short game (M = 3.21, SD = 0.88), t(62) = 4.28, p = .000). The level of flow the students reached during the games shows no significant differences.

Discussion

The goal of this study was to investigate the combined impact of personality traits, current motivation, and mediating variables (functional state: immersion and flow) on the learning outcome in an educational simulation game. Furthermore, the inquiry of the differences in the attained stages of immersion and flow, as well as the self-evaluated learning outcome for two different versions of the live-action simulation game EVERYDAY LIFE IN THE CLASSROOM.

The results of the hierarchical regression analysis for the two groups in the long version of the game, both showed the influence of interest as an important predictor on the learning outcome. This result is consistent with numerous studies indicating that either in learning or gaming interest is one of the main indicators influencing mediating factors like motivation, the functional state, and of course the learning outcome (e.g., Cheng & Tsai, 2020; Engeser et al., 2005; Preuß, 2020).

Both groups have an element of immersion as a relevant predictor on learning outcome, but while for the first group (summer semester), engagement was the significant predictor, and for the second group (winter semester), engrossment predicted the learning outcome. Maybe due to the lower number of participants, the results in both groups are not identical, but they point in the same direction, as clearly immersion is relevant for predicting the learning outcome. In the domain of digital games, the impact of immersion on learning outcome is shown in several studies (e.g., Cheng et al., 2015; Huang et al., 2016; Makransky & Lilleholt, 2018); the findings of this study show similar effects in live-action games.

For one group, challenge is a predictor for learning outcome, and for the other group, the stable personality trait need for cognition has a significant impact. Both constructs have emerged as predictors for learning in previous studies: Challenge together with a high need for cognition study has the effect that students engage more frequently and with higher motivation in complex-problem tasks and that they have a higher probability to reach good results (e.g., Hamari et al., 2016; Unnikrishnan Nair, & Ramnarayan, 2000). These results confirm findings from my own study, which showed that need for cognition and challenge influence the experience of immersion (Preuß, 2020). This is also in line with the concept of “immersion into game” for analog games (Bowman, 2018), which is also viewed as challenge-based immersion (Ermi & Märyä, 2005) entailing complex problem solving and strategic thinking. In sum, it is safe to assume that elements of need for cognition, interest, and challenge are the predominant factors that determine the outcome of this type of simulation game.

The second hypothesis was supported as the students playing the long version of the simulation game EVERYDAY IN THE CLASSROOM showed significantly higher results in engagement, engrossment, and learning outcome than the students playing the short version. This can be taken to indicate that the time and intensity of the simulation game influences the experienced immersion and, subsequently, results in a higher learning outcome. This is in line with previous studies (e.g., Chernikova et al., 2020) indicating that a longer play time leads to more effective outcomes. The possibility of enhancing the level of immersion, especially in the short version with a modified setting, for example, the more props or ambient sounds, could be a topic of further investigation. The intensity of the experienced flow was low for both groups and shows no significant differences, which can be explained by the nature of the educational simulation game: the aspects of clear goals, distorted sense of time, and loss of feeling self-consciousness (Csikszentmihalyi, 1990) are difficult to achieve in a structured game with unclear goals (an attribute of complex situations), fixed time frames for the lessons, and a setting in which observing each other is part of the game (and an important element of the learning process). Therefore, it can be argued that an intensive experience of flow might not help the learning in simulation games, while the levels of immersion—easier to achieve and not as consuming—benefit the learning process through interest and focus of attention. This is supported by a recent study of Bekebrede and Freese (2020) which suggests that—especially in live-action simulation games—the emotional experiences and identification with the role played might positively influence engagement and general learning.

This study investigated the influence of need for cognition, current motivation, immersion, and flow on learning in simulation games. The results of the study imply that—next to the more predictable results for need for cognition and current motivation—the construct of immersion has an important value in explaining the learning outcome of a live-action educational simulation games. The factors that foster immersion in simulation games, for example, play time, authenticity, setting, and props—mostly researched in digital games—therefore should be considered carefully in the context of live-action simulation game design and execution.

Limitations and Suggestion for Further Research

The results of this study should be interpreted carefully. Due to the specific situation of this field study, no randomization was possible and no control group(s) (e.g., with different simulation game) were available. All data were self-reported. A mix of self-report, third-party assessments, and assignments for specific learning outcome would be desirable. More specific tests for cognitive, behavioral, and affective learning outcomes, as well as testing for competence gain would offer more detailed insight in the learning process in this (and general) simulation game(s).

The number of participants in the long-game version (N = 35 and N = 42) can lead to inaccuracies in the hierarchical regression analysis, since small chances in the predictors can significantly change the results of the study. The students’ voluntary choice of the long-version simulation game instead of the mandatory one in the weekly seminar could have been a factor that influenced motivation and, therefore, the degree of immersion and, eventually, learning outcome.

Overall, the results of the study increase the understanding of the learning process stimulated by a simulation game. The construct of immersion has turned out to be relevant and needs more careful investigation in live-action simulation games, especially, since this study does not accommodate the possible different layers of the construct of immersion (Bowman, 2018; Ermi & Mäyrä, 2005). As this study points to immersion as being a serious predictor for learning outcome, the question arises of how to facilitate the immersion the students experience during the game. What elements of the simulation create the adequate degree of authenticity that induces the “right” amount of immersion (Chernikova et al., 2020)?

Conclusion

The results of this study shed light on the interaction of personality traits, current motivation, the functional state (immersion), and their combined impact on the learning outcome in live-action educational simulation games and add to the understanding of the learning process from simulation games.

Furthermore, the comparison of the long and short version of the game showed that the intensity and playtime of the long simulation game is important for the students to achieve higher levels of immersion and self-reported learning outcome. However, the results remain ambiguous and could not be perfectly reproduced in the second group. Further investigation of the role of immersion as a mediating factor in the learning process is essential, as it is a substantial, measurable construct for immersion in live-action educational simulation games.

Footnotes

Acknowledgments

I would like to offer my thanks to my supervisor Prof. Dr. Margarete Imhof and my mentors Dr. Christine Eckert and Dr. Myriam Schlag from Johannes Gutenberg University Mainz for their support, guidance, and availability throughout the process of research and writing.

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.

ORCID iD

Anna K. Preuß

Author Biography

Anna Katharina Preuß is a PhD student in educational psychology at the Johannes Gutenberg University in Mainz, Germany, and a high school teacher for physics and social science. Her thesis topic is the exertion of simulation games in social science education and teacher training; this paper is supposed to be part of this thesis.

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