Let Us Save Lives Using Games! A Study of the Effect of Digital Games for Traffic Education

Abstract

The main purpose of this research is to examine the effect of game-based learning on knowledge acquisition and retention of road rules. A secondary purpose of this study is to investigate possible gender differences related to such an approach. The third purpose is to explore the relationship between beliefs and knowledge acquisition. This quasi-experimental study employed pretest–posttests design involving 42 participants, randomly selected from people in Alberta, Canada. The participants took a pretest, played a game specifically designed to help players learn road rules, and then two posttests. The results show that gaming not only can improve players’ knowledge of road rules and road safety but also can help players retain such knowledge. However, no gender difference was identified in knowledge gain after the gaming intervention.

Keywords

digital games game-based learning traffic education gender difference knowledge acquisition and retention

Introduction

World Health Organization (2018) report states that traffic accidents every year cause roughly 1.35 million people die and over 20 million people suffered from nonfatal injury (Toroyan, 2009). Although the number of traffic accidents had declined in high-income countries over the last four decades, the fatality rates were still high, with more than 10 events per 100,000 people. Previous research (Parker et al., 1995) has shown that a main cause of traffic accidents was drivers’ violation of road rules as indicated by the strong association between traffic incidents and unsafe driving behaviors, highlighting the importance of educating people about road rules and road safety.

Existing research, however, show that conventional approaches to teaching road rules are not very effective. For examples, one meta-analysis of previous studies on high-school driving education found that the existing driving education approaches had no significant effect on reducing traffic incidents (Vernick et al., 1999), while another meta-analysis showed that distributing educational or informational material and traffic incident reduction are not connected (Masten & Peck, 2004).

On the other hand, studies have demonstrated that drivers’ knowledge test scores are correlated to their on-road driving tests, as exemplified by a study of close to 1,800 Swedish drivers (Wolming & Wiberg, 2004). Thus, the efforts of promoting road safety should consider the important factor of driving knowledge of road rules. Yet, Tay (2010) found only a small portion, in fact 11.4%, of licensed drivers were able to pass a practice road rule test. This finding suggested that the conventional approach to prelicensing learning focused mainly on passing the written test and this type of learning implied that the knowledge would be kept mainly in the short-term memory.

Logically, an adequate and updated knowledge of the road rules is essential for drivers to drive safely. The conventional method used, in North America, for example, to acquire this knowledge is by reading the Driver’s Handbook published by the local government. This approach is not attractive to younger drivers, especially after they have obtained their drivers’ license. Research evidence collected to date suggests that more innovative and effective approaches to teaching road safety rules are needed.

This calls for new methods not only to help novice drivers learn the road rules and retain this knowledge after passing their driver tests but also to encourage licensed drivers to update their knowledge. One promising approach in today’s digital environment is the use of gaming. This study, therefore, examines the effect of game-based learning on people’s knowledge acquisition and retention of road rules.

Related Literature

Rapid technological development is profoundly changing our economy, culture, and society. The appeal of gaming has become not only a common phenomenon but also a defining feature of our digital generations of learners. Gaming is reshaping our ways of living and learning. Unfortunately, most of the driving games available today focus on the thrill of speeding and other risky driving behaviors instead of road safety. Nonetheless, the power of contemporary games to provide a more attractive mode of learning and induce deeper learning can be harnessed to address this important social issue.

The value of gaming and its compelling educational potentials are derived mainly from its provision of an authentic and engaging learning in a safe environment (Dede, 2005; Li, 2018; van Eck, 2006). Digital game-based learning helps increase students’ interest in the subject matter and better meet students’ needs (Kiili, 2007; Li et al., 2013). Also, games are “immersive, require the player to make frequent, important decisions, have clear goals, adapt to each player individually, and involve a social network” (Oblinger, 2006, p. 2).

In the field of training, simulation and game can enhance its effectiveness by increasing trainees’ excitement level. For example, a meta-analysis of 32 studies shows that game-based learning results in higher cognitive gains than traditional teaching (Vogel et al., 2006). Another meta-analysis of 65 studies indicates that the use of simulated games on training enhances work-related knowledge and skills (Sitzmann, 2011).

Furthermore, based on different assumptions and containing new sets of features, contemporary games can create learning worlds entirely different from traditional games (Squire, 2006). Such change of assumptions are fundamentally alters gaming, which makes it intriguing as a medium for learning (Aldrich, 2005; Blumberg, 2000; Squire, 2006).

Recent studies showed that learning by game could be considered a preferable option in instruction. Annetta et al. (2009) studied the use of digital games on high school students’ understanding of core concepts in genetics. They found that students with digital game-based learning performed as well as students with traditional learning. However, students with digital game-based learning had higher engagement in learning.

Although gaming had been argued to be generally beneficial to students’ learning, playing some types of commercial games could potentially be harmful (e.g., Beullens et al., 2008; Holtz & Appel, 2011). For instances, Holtz & Appel (2011) found that playing first-person shooting games could predict early adolescents’ problematic behavior such as aggression and delinquency. A Belgium study showed that the level of intensity and physical agreession of adolescents was positively associated with the frequency of their driving-related video game play (Beullens et al., 2008).

However, research work examining the effects of commercial games on players in relation to road safety produced inconsistent results. On the one hand, Fischer et al. (2007) showed that racing game players tended to take higher risks in simulated critical road traffic situations. On the other hand, Backlund et al. (2006, 2010) indicated that gamers were ranked higher on capacity for divided attention and handling situation requiring quick decisions. These researchers found little or no difference in safety performance between gaming and nongaming participants. Nonetheless, gamer participants did better than nongamer participants on lane changing with rear-view mirror check.

A few studies also explored gaming in relation to driver training (Sitzmann, 2011; Vogel et al., 2006). Although limited, these studies covered a range of gaming and simulation (Backlund et al., 2010; Martín-delosReyes et al., 2019). In an early study, Lee et al. (1984) designed a simple simulation to give children practical experience in road safety. The study recruited a sample of 94 students, aged 5 to 10 years old, from 3 primary schools in Edinburgh to participate in a simulation game to cross the street. Each participant had to indicate whether they could cross the street safely in the simulation. Once they had decided, they could see if they would arrive at the designated point safely and received feedback from an adult supervisor. The study suggested that “pretend task could prove a valuable aid to training” (Lee et al., 1984).

A study explored the effects of game-based learning on a particular at-risk population (Coles et al., 2007). The researchers studied 32 children, aged 4 to 10, diagnosed with Fetal Alcohol Syndrome and partial Fetal Alcohol Syndrome in Atlanta. The children participated in either a fire safety game or a street safety game. An animated dog called Buddy conducted the training. The participants showed an improved safety behavior after playing the game. The study indicated that game-based learning could potentially increase children’s knowledge on road rules and road safety. Li and Tay (2014) examined the affects of playing entertainment games designed to impart knowledge of road rules. Their results showed a positive gain in drivers’ knowledge acquisition and retention after they played the game.

A recent literature review article (Martín-delosReyes et al., 2019) provided a good summary of the field of driving education and simulations. They found although numerous studies published using driving simulators, very limited (in fact only five) studies met their inclusion criteria. Their systematic review concluded that the studies in general were with low quality, low power to detect differences, and often using self-reports. Furthermore, the inconsistency of the results identified in their review called for additional research that overcomes the limitations of research design used.

Taking these evidences together, we can see that game-based learning has the potential to enhance and retain drivers’ knowledge about road rules and road safety. Knowledge of road rules acquired through game-based approach will enable learners to associate the rules with contextual information about the driving environment that is lacking in conventional learning sources such as the Driver’s Handbook. It can also provide a safe environment in which to acquire more experience in applying safe driving principles and increasing learners’ hazard perceptions and risk management skills.

Although numerous games such as racing games exist that imitate driving, most of them focus on the thrill of speeding and other risky driving behaviors. Few computer games are designed to help users learn the road rules and the limited studies of instructional gaming for road safety focus mainly children. Within the small number of studies on gaming and road rules, even fewer have explored the effect of this type of approach coherently considering potential contributing factors such as gender, beliefs in relation to knowledge acquisition, and retention.

Theoretical Context

The theoretical framework of this study is enactivism applied in within an educational technology setting, focusing on gaming (Li et al., 2010). This new paradigm directly responds to the call for more encompassing frameworks to meet the current epistemological challenges for education caused by rapid development of technology (Dede, 2008).

Rooted in biology (Varela et al., 1991) and phenomenology (Merleau-Ponty, 1964), enactivism is an emerging philosophical view. At a fundamental level, enactivism rejects dualism and focuses on the importance of embodiment and action to cognition. In stressing embodied action, it finds a middle ground between two extreme views about reality: The objective view assumes that reality exists independent of our experience as opposed to the subjective perspective in which reality is independent on the surrounding environment.

According to Li and colleagues (Li, et al., 2010), enactivism, compatible with elements of Piaget’s and Vygotsky’s psychology, as well as experientialism of Lakoff (Reid, 1995), is based on two important premises: Cognition and environment are inseparable, and the “systems” interact with one another. Consistent with the ontological embodiment view, enactivism argues that “the world is inseparable from the subject, but from a subject which is nothing but a project of the world, and the subject is inseparable from the world, but from a world which the subject itself projects” (Varela et al., 1991, p.7). Enactivism regards the body not only as a living structure to experience but also the setting for cognition. It claims that our mind, body, and the world are inseparable. Cognition is, therefore, a human, social, and biological phenomenon. Learning is achieved through the learners’ acts and is acted upon by the learning world, and understanding is embedded in doing.

This research focuses on two significant aspects of enactivism: (a) an emphasis on doing and (b) knowledge coauthoring. A well-known slogan of enactivism is “all doing is knowing and all knowing is doing” (Varela et al., 1991). Another central idea of enactivism is that learners are coauthors rather than simply consumers of knowledge (Davis et al., 2000; Li, 2008). Accordingly, this study uses a purposefully designed game that allows players to interact, enact in, and with this environment. As players learn through doing, they immerse in a world of action through which they gain situational understanding through experiences. The newness of enactivism implies that many readers may not familiar with this theoretical perspective. However, it is important to realize that enactivism is irreplaceable for it provides the foundation for this study, from the conceptualization of the game to the research design of this work.

Aims

In this study, we explore several factors that contribute to game-based learning and knowledge acquisition, and retention of road safety rules. These factors are selected based on the theoretical framework used or because they have been reported in the literature as valid predictors.

Demographic Variables

As reported in previous research, gender and age play a significant role in safe driving (Lewis et al., 2007; ).For example, Poulter and McKenna (2010) claim that young drivers are overrepresented in road traffic fatalities and collisions. An Organisation for Economic Co-operation and Development(2006) report indicates that in developed countries, young drivers’ collision fatalities rate almost double that of the older drivers. Their report also suggests gender differences in drivers’ behaviors.

Prior Experience and Opinions

Prior driving-related experiences including traffic violation experiences may be related to drivers’ knowledge about road rules and road safety (Parker et al., 1995; Poulter & McKenna, 2010). In addition, people’s opinions and perceptions about road rules and road safety may be related to their knowledge about these areas.

Beliefs About Gaming

Since this study examines the effects of game-based learning, people’s beliefs about their gaming experience may contribute to their learning outcomes. We focus on the following three factors such as engagement, immersion, and doing. Engagement is the first possible contributing factor (Mitchell & Savill-Smith, 2004; Whitton, 2011). Gaming provides fun, captivating, and challenging activities that lead to positive affect and cognition (Hoffman & Nadelson, 2010). Studies on the use of game-based learning across different learning subjects show that learning by gaming increases learning engagement (Annetta et al., 2009; Li, 2014). Since engagement and cognitive achievement correlate with each other across learning subjects, an increase in engagement will likely also increase participants’ learning and knowledge on road rules and road safety.

Another possible contributing factor on game-based learning cognitive achievement is immersion (Huizenga et al., 2009; van Eck, 2006). Dede (2009) defines immersion on gaming as “subjective impression that one is participating in a comprehensive, realistic experience”. Van Eck (2006) argues that gaming embodies the immersive process of balancing cognitive disequilibrium in an experiential setting. In practice, simulation games involving role playing and immersive display provide more learning support (i.e., multiple perspectives, situated learning, and transfer) for the students (Dede & Barab, 2009). The additional learning support and learning context provided in the game can help the learner get a higher level of achievement on cognitive aspects such as knowledge acquisition.

The enactivist perspective of this article suggests the third possible contributing factor: doing. “Doing” means that learning happens through a learner’s act (Li et al., 2010) and therefore knowledge becomes part of knowing. Caracciolo (2012) argues that learning happens when the learner actively builds meaning within the interaction with the learning object. Within the game, learners develop meaning by reflecting on the context in the game in relation to their own situations. Knowing happens during the game. Many studies show that doing relates to knowledge acquisition (e.g. Brown & Coles, 2011; Li et al., 2012).

In particular, this research aims to answer the following questions:
Does game-based learning affect participant knowledge on road rules and road safety?

Are there gender differences in participant knowledge on road rules and road safety?

Are participants’ beliefs and driving experiences correlated with their knowledge on road rules and road safety?

Significance of the Study

This study integrates creative applications of technology that may offer valuable models for research and practice in the social sciences. We explore how gaming can be used for knowledge acquisition and retention of safe driving principles, an under-researched field of study, in an attempt to improve the safety of road users. We apply a contemporary learning theory in road safety to advance current research while reflecting, interpreting, and analyzing games-based learning.

In addition to contributing to the theoretical and scientific literature, this innovative research may add significantly to improving drivers’ knowledge of the road rules. Hence, it has the potential to reduce traffic deaths and injuries, thereby improving people’s health, wellness, and quality of life. It may also help to reduce the huge social cost associated with traffic injuries. A report by the World Health Organization states that the estimated annual global cost of road crashes and injuries is US$518 billion (Peden, 2004). Furthermore, this research may provide policy makers with valuable information on the need for, and the effectiveness of, game-based learning/training of road safety principles. The nature of this research makes the findings directly relevant to key stakeholders and thus, can be used to change practices in the industry.

Methods

Research Design and Sample

This quasi-experimental study employed pretest–posttest design with no control group in an attempt to explore the intervention effect on participants’ knowledge. The participants took the first posttest immediately after learning by gaming and a second posttest 6 to 8 weeks after undertaking the intervention. The first posttest was given in order to see the immediate effect of the intervention. The second posttest was given in order to gauge the participant knowledge retention.

Before taking the game-based learning intervention, the participants completed a questionnaire survey on their beliefs about road safety. The survey covered the participants’ opinions about speeding, traffic signs, and driving and drinking behavior. After taking the game-based learning intervention, the participants took another survey on their beliefs about learning by gaming. This survey covered participants’ perception about their engagement, immersion, and doing on the learning activity they took part in.

This study involved 42 participants consisting of 21 males and 21 females. Among them, 34 (16 males and 18 females) participants completed all the surveys and tests. Since previous research (Blunt, 2007) indicated that the age threshold of effective game-based learning was 41 years old, we intentionally chose our participants to be between 14 and 35 years old.One third of the participants had no driver license, yet at the time they took the game-based learning intervention.

The Game

The game used in this study was designed to help players learn road rules and road safety through an engaging mystical, graphic novel detective story. The learning content of the game covered knowledge on various topics ranging from license classification, to traffic control, to safety driving. The learning content was packaged in an intriguing story line.

In this game, the player followed the story of Jessica and Detective Jones on investigating Jessica’s mother’s car accident. Throughout the game, the player collected information on road rules and road safety and used the information to solve puzzles (see Figure 1 for a screenshot of the game). The game consisted of 10 sections of puzzles that utilized knowledge from the 10 chapters in the Drivers’ Handbook to solve. It took most participants 2 to 3 hours to complete the game.

Figure 1.
A Representative Screenshot of the Game Used in This Study.

Data and Instrumentation

Quantitative data were collected from both the knowledge tests and the surveys. To test the effect of game intervention, the participants took a total of three knowledge tests: a pretest, the first posttest, and the second posttest. Each knowledge test consisted of 30 items. The knowledge tests were modified from the practice tests available on the provincial government’s website. Question format and level of difficulty remained consistent among the three knowledge tests, covering the same materials such as traffic signs, traffic tickets, speeding, drinking and driving, and driving behavior (e.g., changing lanes and entering highway).

In addition, the participants took two surveys: a presurvey and a postsurvey. The presurvey was conducted before the game intervention and collected information on the participants’ opinions about road safety rules and their prior driving experience, using the 5-point Likert-type scale and coded numerically from 0 = strongly disagree to 4 = strongly agree. The postsurvey, conducted immediately after the game intervention, focused on participants’ beliefs about game-based learning. This postsurvey also used a 5-point Likert-type scale to collect participants’ responses.

Data Analysis

To answer the first research question, there are several methods that can be used, including general linear model (Li & Tay, 2014). This study employed paired sample t test, a different approach, to examine the effect of the intervention on participants’ knowledge. A total of three paired sample t tests were conducted with the first paired sample t test on pretest versus first posttest to explore immediate knowledge gain, the second on pretest versus second posttest to investigate long-term knowledge gain and the third on the first posttest versus second posttest to analyze knowledge retention.

The second research question focused on possible gender differences and three independent t tests on participant knowledge test scores were conducted to answer this research question. First, an independent sample t test on pretest to first posttest gain was conducted to test gender difference on immediate knowledge gain. Second, an independent t test on pretest to second posttest gain was conducted to test gender difference on long-term knowledge gain. Third, an independent t test on first posttest to second posttest gain was conducted to test gender difference on knowledge retention.

To address the third research question, correlation analyses between variables were employed to see the relationships between participant beliefs and participant knowledge gain, and between driving experience and knowledge gain. Participant beliefs consisted of measures on participants’ belief on both road safety and game-based learning while participant experience included measures on participants’ age and whether participants had traffic tickets within the last 3 years. Since the belief variable was expressed as ordinal data, and knowledge gains were interval data, Spearman’s rank correlation coefficient was used. The rank bi-serial correlation test was conducted to examine the relationship between participants’ experiences and participants’ knowledge gain because the experience variable was expressed as nominal data, and the knowledge gains were interval data (Huck, 2004).

Results

Effect of Game-Based Learning on Participants’ Knowledge

After learning by gaming, participants’ knowledge on road rules and road safety had increased, as presented in Table 1. Three t tests were conducted on (a) pretest to the first posttest score difference, (b) pretest to the second posttest score difference, and (c) first posttest to the second posttest score difference. The test results were reported in Table 2.

Table 1.
Participants’ Test Scores.

M SD SE

Pretest 21.91 5.54 .95

First posttest 24.41 4.36 .75

Second posttest 24.35 4.07 .70

Table 2.
Participants’ Knowledge Gains.

Mean difference 95% CI of difference
t P

Upper Lower

First posttest–pretest 2.58 4.08 1.06 3.44 .001

Second posttest–pretest 2.44 4.08 0.81 3.04 .005

Second posttest–first posttest –0.06 1.38 –1.5 –0.08 .934

Note. CI = confidence interval.

First, there was a statistically significant difference between the pretest and the first posttest scores, t(41) = 3.44, p = .001; pretest M = 21.91, SD = 5.54; first posttest M = 24.41, SD = 4.36; d = –0.49. Our results showed that the participants achieved significantly higher scores in their first posttest than in their pretest. This result suggested that participants acquired significant knowledge gain after they took the game intervention.

Second, there was also a significant difference between the pretest and the second posttest scores, t(33) = 3.04, p = .005; pretest M = 21.91, SD = 5.54; second posttest M = 24.35, SD = 4.07; d = –0.64. Again, this finding indicated that the participants’ second posttest scores on road rules were significantly higher than their pregame knowledge test scores.

Third, there was no significant difference between first posttest and second posttest scores, t(33) = –0.08, p = .934; first posttest M = 24.41, SD = 4.36; second posttest M = 24.35, SD = 4.07; d = –0.13. Therefore, the participants’ knowledge on road rules remained consistent between the first posttest and the second posttest, indicating good knowledge retention.

In the absence of a control group, the effect sizes were compared with similar studies to get relative standing of the game-based learning effect. The effect sizes of game-based learning on fire safety and street safety instruction for alcohol affected children were –0.2 and –0.05, respectively (Coles et al., 2007). The effect size of video commentary use on hazard perception intervention for adults, aged 18 to 19 years old, was –0.61 (Isler et al., 2009).The effect size of video use on primary school student road safety instruction was –0.12 (Zeedyk & Wallace, 2003). Compared with these prior studies, the effect sizes of the immediate effect on this study, d = –0.49, and knowledge retention, d = –0.64, could be considered as relatively large.

Gender Differences in Participant Knowledge

Three independent t tests were conducted to examine gender differences on knowledge tests. The results of the tests showed that there were no statistically significant gender differences in participants’ knowledge gain on road rules and road safety. The test results were reported in Table 3.

Table 3.
Gender Differences on Knowledge Gain.

N M SD t P

Pretest to first posttest gain

Male 21 1.25 4.75 –1.29 .206

Female 21 3.61 4.85

Pretest to second posttest gain

Male 16 0.94 3.53 –1.87 .077

Female 18 4.23 5.25

First posttest to second posttest gain

Male 16 –0.31 4.53 –0.33 .743

Female 18 0.17 3.84

The participants’ initial knowledge on road rules and road safety was not statistically significant different, t(40) = 1.52, p = .14, between male (M = 23.25, SD = 1.46) and female (M = 20.72, SD = 1.2). After the game, the test scores on both groups increased and showed long-term knowledge gain. Similarly, there were no significant gender differences, t(40) = 0.26, p = .78, in the first posttest and the second posttest between male (M = 24.5, SD = 1.37) and female (M = 24.33, SD = 0.76). Also, there was no statistically significant difference in knowledge between male (M = 24.19, SD = 1.24) and female (M = 24.5, SD = 0.75) 6 to 8 weeks after the intervention. Finally, no gender difference, t(32) = –0.22, p = .83, was identified in participants’ knowledge retention (male: M = –0.31, SD = 4.53 and female: M = 0.17, SD = 3.84).

Beliefs and Experience in Relation to Knowledge

A survey on participants’ opinions about road safety was conducted before they undertook the game-based learning. Positive correlation tests were found between participants’ opinions about road safety and knowledge test scores (r_s = .385; p < .05), indicating that the more positive the participants’ opinions (M = 2.5, SD = 0.71) were about road safety, the more likely they would get higher scores on the pretest. However, there was no significant correlation between participants’ opinions about road safety and their scores on the first posttest (M = 24.41; SD = 4.36, r_s = .296; p = .06), second posttest and knowledge retention (M = 24.35; SD = 4.07, r_s = .22; p = .22). Hence, it could be inferred that the game-based learning intervention remediated participants’ knowledge regardless of their prior opinions about road safety.

Immediately after completing the game-based learning, the participants took another survey about their beliefs regarding game-based learning, which consisted of three major components: (a) engagement, (b) immersion, and (c) doing. This survey yielded average scores between neutral and agree on all components (engagement M = 2.41, SD = 0.70; immersion M = 2.44, SD = 0.64; doing M = 2.56, SD = 0.50). This meant that the participants, in general, moderately agreed that (a) gaming engaged them on learning, (b) they were immersed in learning through the gaming process, and (c) they were actively involved in doing by the game. Correlation tests indicated that participants’ beliefs were not significantly correlated with participants’ knowledge (p > . 05) as presented in Table 4.

Table 4.
Correlations Between Participants’ Beliefs on Game-Based Learning and Knowledge.

1 2 3 4 5 6

1. Engagement –

2. Immersion .848* –

3. Doing .683* .738* –

4. Pretest .085 –.072 –.084 –

5. First posttest .185 .031 .016 .583* –

6. Second posttest .141 .039 .028 .561* .542* –

p < .05.

To examine the relationship between participants’ experience and knowledge, the rank bi-serial correlation analysis of age and test scores was conducted (see Table 5 for details). Participants’ age was significantly correlated with participants’ pretest scores (r = .339; p < .05). Older participants (n = 18) were more likely to have more initial knowledge on road rules and road safety than younger participants (n = 26). However, there was no significant correlation between age and participants’ scores on the first posttest or second posttest (p > .05). This result suggested that game-based learning remediated participants’ knowledge on road rules and road safety regardless of their age.

Table 5.
Correlations Between Age, Road Rules Violation Experiences and Knowledge Tests.

Age Traffic tickets

3. Pretest 0.339 0.18

4. First posttest 0.284 0.34*

5. Second posttest 0.194 0.13

*p < .05.

The rank bi-serial correlation analysis was also conducted to examine the relationship between road rules violation experience (i.e., traffic tickets within 3 years) and test scores. Significant correlation was only found between participants’ experience and the first posttest scores (r = .394; p < .05). There was no significant correlation between participants’ experience and the pretest or second posttest scores (p > .05).

Since there were three categories for the traffic ticket variable (i.e., having traffic ticket, not having traffic ticket, and not applicable because of not having driver license), the analysis of variance and post hoc test were conducted. The analysis of variance result showed that there were differences between the groups. The post hoc test showed that there was no difference between those who had traffic tickets and those who did not. Moreover, there was also no difference between those who did not have traffic tickets and those who did not have a driving license. However, differences were found for the posttest scores between those who had traffic tickets and those who did not have a driving license. Finally, traffic ticket holders were found to have significantly higher posttest scores compared with nonlicensed participants. Therefore, it could be inferred that road rules violation experience might be moderating the immediate knowledge test results after learning by gaming. Table 6 presents the details.

Table 6.
Analysis of Variance Test for Road Rules Violation Experience (TICKETS).

SS df MS F P

TICKETS Between group 149.524 2 74.762 3.62 .036

Within group 805.548 39 20.655

Total 24717.000 42

Tukey-b post hoc test on first posttest
N
Subset

1
2

Traffic tickets within 3 years Not applicable 14 21.36

No 22 24.50 24.50

Yes 6 26.83

Note. SS = sum of squares; MS = mean squares.

Discussion

The most important finding of this study was that gaming could not only improve players’ knowledge on road rules and road safety but also helped players retain such knowledge. Recent research (e.g., Tay, 2010) found that less than 12% of licensed drivers passed a practice road rule test, indicating that prelicensing learning often resulted in short-term memory of the knowledge. After successfully passing driving tests and obtaining their driving licenses, drivers tended to forget much of their knowledge on road rules, which could potentially result in risky driving behaviors and road crashes.

Other studies of traffic education found little or no positive effect of road safety education intervention, both for traditional classroom-based high school programs (e.g., Vernick et al., 1999), and for the use of media products such as videos at home or schools. For example, Poulter and McKenna (2010) studied the use of live show featuring videos for predrivers. They found that the intervention only had a small immediate effect on some, but not all, road safety beliefs in predrivers. Yet, no long-term effect was identified. Similarly, another study (Zeedyk & Wallace, 2003) examined the impact of an entertaining video on children. Their results showed the video had no educational impact on children’s knowledge about road safety.

Contrary to these earlier studies, we found that gaming improved drivers’ knowledge acquisition as well as retention with relatively high effect sizes. The results from this study and our earlier study Li and Tay, (2014) suggested that the game served an effective educational function, both short term and long term, as evidenced by the remarkable effects of gaming on players’ achievement gains.

This study is grounded in enactivism where doing plays a central role in cognition. We intentionally choose gaming because one of its core characteristics is doing. On the one hand, traditionally driving education often adopts learning through text reading or video watching, which place learners in a passive position. On the other hand, previous studies on gaming and driving have focused on the use of commercial racing games. Such work (Beullens et al., 2008; Fischer et al., 2007) demonstrates the negative effects of racing games on driving behavior.

Unlike these conventional approaches, our study is innovative by using a carefully designed game, aimed specifically to impart knowledge on road rules and road safety. In this study, we engage players in active problem-solving in gaming situations. With such a focus on doing, the players gain deep understanding of the concepts and principles of road rules integrated in the game. Such situational problem-solving also facilitates good knowledge retention.

Our finding is consistent with other game-based learning studies which demonstrate that learning by gaming has a positive effect on cognitive skills and knowledge acquisition (Sitzmann, 2011; Vogel et al., 2006). Experiential learning throughout the game moderates meaning and knowledge development (Gee, 2008). Yet, a unique contribution of our study is that it also suggests long-term educational effects of gaming as reflected in the players’ knowledge retention of road rules and road safety. This is significant because it directly addresses the problem of traditional traffic education where learners only have short-term knowledge acquisition (Poulter & McKenna, 2010; Tay, 2010).

Correlation analysis conducted in this study showed that the game intervention might address possible differences in players’ beliefs on road safety and the age differences. Participants who had more positive beliefs about road safety had higher pretest scores, but this relationship disappeared in the first and second posttests. In a similar vein, younger players had worse pretest scores than their older counterparts; nonetheless, no correlation was identified in the subsequent posttests. These results suggested that regardless of the participants’ age or prior knowledge, they all achieved equally well after learning through gaming.

In addition, the participants’ beliefs about game-based learning were not related to their achievement scores, indicating that whether a player held positive or negative attitudes toward gaming, it had no effect on their knowledge acquisition of road rules through game-based learning. These results together offered strong support for the idea that gaming can provide a robust intervention for understanding road rules.

This study also provided evidence that there was no gender difference in knowledge gains arising from the use of the game-based learning intervention. This finding confirms previous results that game-based learning positively affected learners’ knowledge regardless of their gender. For example, Backlund et al.’s (2010) study on the use of simulation game for driving education found that there was no gender difference. A meta-analysis of game-based learning use on various subjects also shows that the effect of game-based learning is positive across people (Vogel et al., 2006).

This study had limitations. First, due to various reasons, this study adopted the single group pre-to-post quasi-experimental design without a control group. To compensate, we compared the effect size with the other studies on road rules and road safety education to illustrate the magnitude of the effect. Future research should add a control group. Second, the sample size was relatively small, with only 42 participants. Future studies with an increased sample size should be conducted. Third, it is suggested that pretest data also collected about participants’ beliefs.

Conclusion

This study explored the implementation of game-based learning on road rules and road safety knowledge. Grounded in enactivism with a focus on doing, this study demonstrated that the gaming intervention on road rules and road safety yielded significant effect on participant knowledge. This study also provided some evidence that game-based learning was applicable across gender, age, and prior beliefs.

Overall, this study found that gaming could be useful in the context of driver education. The results from our study suggested that playing the carefully designed game for road rules and road safety could be an effective tool for road rules and road safety learning. Harnessing the power of games for road safety training, therefore, would have the potential to save lives on the roads.

The results of this study, along with previous research findings, painted a rather compelling picture of the effectiveness of road safety training. Carefully designed games specifically tailored toward road safety rules, seemed to be effective for people to learn and consolidate their knowledge, regardless of their age, gender, or prior experience and opinions. Hence, such gaming interventions could remedy the possible difference in safety knowledge due to players’ prior opinions about road safety, as well as, bridge the gap presented by age and experience. This finding would have substantial road safety implications because traffic knowledge had been found to be directly related to driving behavior (Wolming & Wiberg, 2004). In short, game-based training could provide a cost-effective and evidence-based intervention, which could help reduce road crashes and promote road safety.

	M	SD	SE
Pretest	21.91	5.54	.95
First posttest	24.41	4.36	.75
Second posttest	24.35	4.07	.70

	Mean difference	95% CI of difference	t	P
First posttest–pretest	2.58	4.08	1.06	3.44	.001
Second posttest–pretest	2.44	4.08	0.81	3.04	.005
Second posttest–first posttest	–0.06	1.38	–1.5	–0.08	.934

	N	M	SD	t	P
Pretest to first posttest gain
Male	21	1.25	4.75	–1.29	.206
Female	21	3.61	4.85
Pretest to second posttest gain
Male	16	0.94	3.53	–1.87	.077
Female	18	4.23	5.25
First posttest to second posttest gain
Male	16	–0.31	4.53	–0.33	.743
Female	18	0.17	3.84

	1	2	3	4	5	6
1. Engagement	–
2. Immersion	.848*	–
3. Doing	.683*	.738*	–
4. Pretest	.085	–.072	–.084	–
5. First posttest	.185	.031	.016	.583*	–
6. Second posttest	.141	.039	.028	.561*	.542*	–

	Age	Traffic tickets
3. Pretest	0.339*	0.18
4. First posttest	0.284	0.34*
5. Second posttest	0.194	0.13

	SS	df	MS	F	P
TICKETS	Between group	149.524	2	74.762	3.62	.036
	Within group	805.548	39	20.655
	Total	24717.000	42
Tukey-b post hoc test on first posttest		N	Subset
	1	2
Traffic tickets within 3 years	Not applicable	14	21.36
			No	22	24.50	24.50
			Yes	6		26.83

Footnotes

Acknowledgments

The authors thank Chris Appleton for designing the game, and Robert Louis, Yang Liu for their research assistance. The data set used in this manuscript is also used inin Li, Q. & Tay, R. (2014). Improving drivers’ knowledge of road rules using digital games, Accident Analysis and Preventions, 65, 8–10.

Declaration of Conflicting Interests

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

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Financial support from the Social Science and Humanities Research Council is gratefully acknowledged. However, the views and opinions expressed by the authors do not necessarily reflect those of the council.

ORCID iD

Qing Li

Author Biographies

Qing Li, PhD from University of Toronto (2001), is a professor in College of Education at Towson University, USA. She is the author/co-editor of two books, and has over 75 scholarly publications in refereed journals and books.She was a visiting scholar at MIT in 2009 and at University of Idaho in 2006.

Richard Tay is the vice chancellor's principal research fellow in Economics and Social Development. His research includes transport economic and planning, transport safety and security, and transport and travel demand modelling.

Arkhadi Pustaka is a doctoral candidate in education at the Towson University, USA.

References

Aldrich

(2005). Learning by doing: A comprehensive guide to simulations, computer games, and pedagogy in e-Learning and other educational experiences. Pfeiffer.

Annetta

L. A.

Minogue

Holmes

S. Y.

Cheng

M. T.

(2009). Investigating the impact of video games on high school students’ engagement and learning about genetics. Computers & Education, 53(1), 74–85.

Backlund

Engström

Johannesson

(2006). Computer gaming and driving education [Paper presentation]. Proceedings of the workshop Pedagogical Design of Educational Games affiliated to the 14th International Conference on Computers in Education (ICCE, 2006), Beijing, China.

Backlund

Engström

Johannesson

Lebram

(2010). Games for traffic education: An experimental study of a game-based driving simulator. Simulation & Gaming, 41(2), 145–169.

Beullens

Roe

Van den Bulck

(2008). Video games and adolescents’ intentions to take risks in traffic. Journal of Adolescent Health, 43(1), 87–90.

Blumberg

F. C.

(2000). The effects of children’s goal for learning on video game performance. Journal of Applied Developmental Psychology, 6(21), 641–653.

Blunt, R. (2007) Does game-based learning work? Results from three recent studies. In: Proceedings of Interservice/Industry Training, Simulation & Education Conference (I ITSEC). NTSA, Orlando, pp. 945–954.

Brown

Coles

(2011). Developing expertise: How enactivism re-frames mathematics teacher development. ZDM Mathematics Education, 43(6), 861–873.

Caracciolo

(2012). Narrative, meaning, interpretation: An enactivist approach. Phenomenology and the Cognitive Sciences, 11(3), 367–384.

10.

Coles

C. D.

Strickland

D. C.

Padgett

Bellmoff

(2007). Games that “work”: Using computer games to teach alcohol-affected children about fire and street safety. Research in Developmental Disabilities, 28(5), 518–530.

11.

Davis

Sumara

Luce-Kapler

(2000). Engaging minds: Learning and teaching in a complex world. Lawren Erlbaum Associates.

12.

Dede

(2005). Planning for Neomillennial learning styles: Implications for investment in technology and faculty. In D. Oblinger & J. Oblinger (Eds.), Educating the net generation (pp. 15.11–15.22). EDUCAUSE.

13.

Dede

(2008). A seismic shift in epistemology. EDUCAUSE Review, 43(3), 80–81.

14.

Dede

(2009). Immersive interfaces for engagement and learning. Science, 323(5910), 66–69.

15.

Dede

Barab

(2009). Emerging technologies for learning science: A time of rapid advances. Journal of Science Education and Technology, 18(4), 301–304.

16.

Fischer

Kubitzki

Guter

Frey

(2007). Virtual driving and risk taking: Do racing games increase risk-taking cognitions, affect, and behaviors? Journal of Experimental Psychology Applied, 13(1), 22–31.

17.

Gee

(2008). What video games have to teach us about learning and literacy (2nd ed.). Palgrave Macmillan.

18.

Hoffman

Nadelson

(2010). Motivational engagement and video gaming: A mixed methods study. Educational Technology Research and Development, 58(3), 245–270.

19.

Holtz

A ppel

(2011). Internet use and video gaming predict problem behavior in early adolescence. Journal of Adolescence, 34(1), 49–58.

20.

Huck, S. W. (2004). Reading Statistics and Research (4th ed.). Allyn & Bacon.

21.

Huizenga

Admiraal

Akkerman

Dam

G. T.

(2009). Mobile game‐based learning in secondary education: Engagement, motivation and learning in a mobile city game. Journal of Computer Assisted Learning, 25(4), 332–344.

22.

Kiili

(2007). Foundation for problem-based gaming. British Journal of Educational Technology, 38(3), 394–404.

23.

Isler, R., Starkey, N., & Williamson, A. (2009). Video-based road commentary training improves hazard perception of young drivers in a dual task. Accident; analysis and prevention, 41, 445–452. https://doi.org/10.1016/j.aap.2008.12.016

24.

Lee

D. N.

Young

D. S.

McLaughlin

C. M.

(1984). A roadside simulation of road crossing for children. Ergonomics, 27(12), 1271–1281.

25.

(2008, September). Enactivism and research: Exploration of a new paradigm for gaming [Paper presentation]. Expanding Our Horizons: Canadian Games at Home and Abroad, CGSA Workshop, Vancouver.

26.

Lewis, I., Watson, B., Tay, R., & White, K. (2007). The Role of fear appeals in improving driver safety: A review of the effectiveness of fear-arousing (threat) appeals in road safety advertising. International Journal of Behavioral Consultation and Therapy, 3(2), 203–222. https://dx-doi-org.web.bisu.edu.cn/10.1037/h0100799

27.

(2014). Learning through digital game design and building in a participatory culture: An enactivist approach. Peter Lang.

28.

(2018). Enactivism and teacher instructional game building: An inquiry of theory adoption and design consideration. Educational Technology Research and Development, 66(6), 1339–1358.

29.

Clark

Winchester

(2010). Instructional design and technology grounded in enactivism: A paradigm shift? British Journal of Educational Technology, 41(3), 403–419.

30.

Lemieux

Vandermeiden

Nathoo

(2013). Are you ready to teach secondary mathematics in the 21st century? A study of preservice teachers’ digital game design experience. Journal of Research on Technology in Education, 45(4), 309–337.

31.

Tay

(2014). Improving drivers’ knowledge of road rules using digital games. Accident Analysis & Prevention, 65, 8–10.

32.

Tay

Louis

(2012). Designing digital games to teach road safety: A study of graduate students’ experiences. Loading, 6(9), 17–35.

33.

Martín-delosReyes

L. M.

Jiménez-Mejías

Martínez-Ruiz

Moreno-Roldán

Molina-Soberanes

Lardelli-Claret

(2019). Efficacy of training with driving simulators in improving safety in young novice or learner drivers: A systematic review. Transportation Research Part F: Traffic Psychology and Behaviour, 62, 58–65.

34.

Masten

S. V.

Peck

R. C.

(2004). Problem driver remediation: A meta-analysis of the driver improvement literature. Journal of Safety Research, 35(4), 403–425.

35.

Merleau-Ponty

(1964). The primacy of perception and other essays. Northwestern University Press.

36.

Mitchell

Savill-Smith

(2004). The use of computer and video games for learning: A review of the literature. Learning and Skills Development Agency.

37.

Oblinger

(2006, May). Simulations, games, and learning. Educause.

38.

Organisation for Economic Co-operation and Development. (2006). Young drivers: The road to safety (Vol. OCT.). OECD Policy Brief.

39.

Parker

West

Stradling

Manstead

A. S.

(1995). Behavioural characteristics and involvement in different types of traffic accident. Accident Analysis & Prevention, 27(4), 571–581.

40.

Peden

(2004). World report on road traffic injury prevention: Summary. World Health Organization.

41.

Poulter

McKenna

(2010). Evaluating the effectiveness of a road safety education intervention for pre-drivers: An application of the theory of planned behaviour. British Journal of Educational Psychology, 80, 163–181. https://doi.org/10.1348/014466509X46821

42.

Reid

(1995). The need to prove [PhD doctoral dissertation]. University of Alberta.

43.

Sitzmann

(2011). A meta-analytic examination of the instructional effectiveness of computer-based simulation games. Personnel Psychology, 64(2), 489–528.

44.

Squire , (2006). From content to context: Videogames as designed experience. Educational Researcher, 35(8), 19–29.

45.

Tay

(2010). Alberta drivers’ knowledge of the road rules. Alberta Motor Association Traffic Safety Foundation.

46.

Toroyan

(2009). Global status report on road safety: Time for action. World Health Organization.

47.

van Eck

(2006). Digital game-based learning: It’s not just the digital natives who are restless. EDUCAUSE Review, 41(2), 16–30.

48.

Varela

Thompson

Rosch

(1991). The embodied mind: Cognitive science and human experience. Massachusetts Institute of Technology Press.

49.

Vernick

J. S.

MacKenzie

E. J.

Baker

S. P.

Gielen

A. C.

(1999). Effects of high scholl driver education on motor vehicle crashes, violations and licensure. American Journal of Preventative Medicine, 16(1), 40–46.

50.

Vogel

J. J.

Vogel

D. S.

Cannon-Bowers

Bowers

C. A.

Muse

Wright

(2006). Computer gaming and interactive simulations for learning: A meta-analysis. Journal of Educational Computing Research, 34(3), 229–243.

51.

Whitton

(2011). Game engagement theory and adult learning. Simulation & Gaming, 42(6), 596–609. https://doi.org/10.1177/1046878110378587

52.

Wolming

Wiberg

(2004). The Swedish driver licensure examination: An examination of a two stage model. Journal of Safety Research, 35, 491–495.

53.

World Health Organization. (2018). Road traffic injuries.

54.

Zeedyk

M. S.

Wallace

(2003). Tackling children’s road safety through edutainment: An evaluation of effectiveness. Health Education Research: Theory & Practice, 18(4), 493–505.

	Mean difference	95% CI of difference		t	P
	Mean difference	Upper	Lower	t	P
First posttest–pretest	2.58	4.08	1.06	3.44	.001
Second posttest–pretest	2.44	4.08	0.81	3.04	.005
Second posttest–first posttest	–0.06	1.38	–1.5	–0.08	.934