Computers & Education 88 (2015) 256e267

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Computers & Education

journal homepage: www.elsevier .com/locate/compedu

An examination of interactions in a three-dimensional virtual world

Rabia M. Yilmaz a, *, Ozlem Baydas b, Turkan Karakus a, Yuksel Goktas a

a Department of Computer Education & Instructional Technology, Ataturk University, 25240, Erzurum, Turkeyb Department of Computer Education & Instructional Technology, Giresun University, 28000, Giresun, Turkey

a r t i c l e i n f o

Article history:Received 10 December 2014Received in revised form4 June 2015Accepted 8 June 2015Available online 17 June 2015

Keywords:Interactive learning environmentsMedia in educationVirtual reality

* Corresponding author. Tel.: þ90 442 231 40 47.E-mail addresses: rkufrevi@atauni.edu.tr, rabia.k

yuksel.goktas@hotmail.com (Y. Goktas).

http://dx.doi.org/10.1016/j.compedu.2015.06.0020360-1315/© 2015 Elsevier Ltd. All rights reserved.

a b s t r a c t

Three-dimensional (3D) virtual worlds hold the users' attention by providing rich interaction in anenvironment similar to the real world. User engagement duration is known to increase in environmentswith intense interaction. However, information in the literature about whether gender, experience, orspatial ability affects interaction in these environments is limited. In this study, these three factors arecompared to users' depth of interaction in a 3D virtual world. In addition, the relationships betweenengagement duration, spatial ability, and depth of interaction are examined to investigate whether thefirst two factors can predict the third. Findings showed that users' depth of interaction was not influ-enced by gender, but experience and spatial ability did affect interaction. A strong relationship wasdetermined between depth of interaction and engagement duration, and a moderate relationship wasfound between depth of interaction and spatial ability. Findings indicated that when designing 3D en-vironments, it is important to consider which kinds of tasks provide more interaction and to what extentspatial abilities affect interaction, as well as to prepare activities that will increase engagement durationand to devise strategies to enhance depth of interaction.

© 2015 Elsevier Ltd. All rights reserved.

1. Introduction

The use of three-dimensional (3D) virtual worlds has become increasingly widespread recently, with much attention paid to theirfeatures. These virtual worlds enable users to interact constantly in enjoyable, realistic environments. Such environments can even bedesigned to reflect the users' own experiences in daily life (Dede, Ketelhut, & Ruess, 2002; Kapp & O'Driscoll, 2010; Messinger et al., 2009).Moreover, these environments provide users with opportunities for deeper interaction, leading to the creation of communities, as well asdecreased social anxiety, enhanced motivation, and increased engagement (Barab, Thomas, Dodge, Carteaux, & Tuzan, 2005; Gee, 2003;McGee, 2007; Prensky, 2006). Wann and Mon-Williams (1996) have asserted that virtual worlds effectively use 3D structures, and thecentral components of these environments are (a) direct intervention in the environment and (b) involvement in interaction.

The use of virtual worlds in education has now also gained the interest of researchers, who have argued that these environments aremore attractive compared to other web environments, offering personal experience, dynamic feedback, and flexibility of time and place(Dickey, 2005a; Firat, 2010; Thompson, 2007). They encourage a high degree of interaction and provide an environment similar to the realworld (Dalgarno & Lee, 2010; Franceschi-Diaz, 2009). Users can employ synchronous and asynchronous communication, interact withobjects in the environment, and engage in text-based and voice discussion (Boulos, Hetherington, & Wheeler, 2007; De Noyelles, 2011;Dickey, 2003; Hollander & Thomas, 2009; Kohler, Matzler, & Füller, 2009; Mancuso, Chlup, & McWhorter, 2010). Furthermore, userscontrol, arrange, delete, and recreate 3D objects in these environments, improving their spatial skills and ability to analyze complexstructures (Dünser, Kaufmann, Steinbügl, & Glück, 2006). The users' need to perceive 3D objects as if they were objects in the real worldraises the question of whether spatial ability is important. Arguments can be found in the literature indicating that the presence of 3Dobjects and rich interactions increase engagement duration because they encourage users to be more active (Ang & Wang, 2006; Johnsonet al., 1998). The more users become involved in interaction, the more active they will be; the more active they are, the more their

ufrevi@gmail.com (R.M. Yilmaz), ozlembaydas@hotmail.com (O. Baydas), turkan.karakus@gmail.com (T. Karakus),

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engagement duration will increase. Both individual and environmental differences might influence this interaction. As individual differ-ences, gender and previous experience have been shown to create significant differences in user interaction (Calisir & Gurel, 2003; Chen,2008; Chen&Wang, 2009; Inal& Cagiltay, 2007) in addition to spatial ability. Shortly, this studywill examine users' depth of interaction andeffects of spatial ability, gender, prior experiences and engagement in a 3D virtual world on interaction.

1.1. Interaction

Interaction has been identified as one of the most important features of the learning experience in both traditional and virtual learningenvironments and a key feature of Internet technology (Holmberg, 1983; Lustria, 2007; Moore, 1993). Communication exchange, activeuser control, and synchronicity interactivity are three main dimensions of interaction (Heeter, 2000; Liu & Shrum, 2002). Interaction hasbeen separated into various categories in the literature. Moore (1993) identified types of user interaction as learner-content, lear-nerelearner, and learnereinstructor. Hillman, Willis, and Gunawardena (1994) added learnereinterface interaction to these classifications.According to Moore (1993), learnerecontent interaction is the process in which students interact with course materials presented during alearning experience. These materials can include multimedia resources such as text, sound, graphics, or video, enhancing interactivity.Learnereinstructor interaction is communication between the student and teacher during a learning experience. It can be asynchronous,such as email and discussion boards, or synchronous, such as real time chat and video conferencing. Learnerelearner interaction iscommunication between two or more students during a learning experience. Similarly, it can be asynchronous or synchronous. Garrison(1990) found that when learners interact with their instructor and other learners they are more motivated and have better learningexperiences. Some studies have shown that these interaction types have different effects on learning (Jung, Choi, Lim, & Leem, 2002).According to Adelsk€old, Aleklett, Axelsson, and Blomgren (1999), learnerelearner interaction has a greater effect in a problem solvingsituation. On the other hand, Gunawardena and Zittle (1997) and Kanuka and Anderson (1998) found that learnerelearner and lear-nereinstructor interaction provide increased learner satisfaction and rich opportunities for interaction. Gulbahar (2009) indicated that bymeans of learnerecontent interaction, learners can improve their knowledge and perspective. In this study, learnerecontent interactioninvolving 3D objects and the virtual environment is examined in detail, contributing to the literature in terms of learnerecontentinteraction and the effects of interaction types.

Multimedia components such as text, video, and sound may increase learnerecontent interaction (Sabry & Baldwin, 2003). Cardak(2012) has demonstrated that writing personal opinions or comments, asking questions, engaging in discussions, sharing academic orscientific knowledge, and having daily conversations are important indicators of learnerecontent interaction in blended learning envi-ronments. Also, messages sent by users demonstrate high levels of interaction. Sajjanhar (2012) described the avatareavatar and ava-tareobject interaction that takes place in 3D virtual worlds, which can be educational and social while encouraging a high level ofinteraction. Dickey (2003) also studied 3D virtual worlds in terms of features such as interactions with avatars and objects. She indicatedthat 3D virtual worlds provide variety that enhances interaction and pointed out features that facilitate conversation and offer guidance foreasy interaction. Users can employ animations; add new objects, sounds, and pictures; and interact with 3D objects and web pages in theseenvironments. For this study, learnerecontent interactionwas examined in 3D virtual worlds in terms of progress and sequence control, useof multimedia, avatar control, written communication control, oral communication control, object control, perspective control, and interfacecontrol (Cardak, 2012; Jung et al., 2002; Teo, Oh, Liu, & Wei, 2003).

Learner interaction is a unique characteristic of 3D virtual environments, making these environments well-designed for situated learning(Dalgarno & Lee, 2010). Some researchers have asserted that interactive learning environments increase students' performance (Pinho,Bowman, & Freitas, 2002). Also, rich interaction in these environments has been shown to affect users' presence positively (Bulu, 2011).Users who spend time in these environments are said to be motivated by their entertaining, interactive, and cooperative features (Allison,Miller, Sturgeon, Nicoll, & Perera, 2010; Arya, Hartwick, Graham, & Nowlan, 2011). Increased interactivity can also provide higher learnersatisfaction (Rafaeli & Sudweeks, 1997).

Many research found relationship between demographic variables user motivations, attitudes, and behaviors of technology acceptanceand use (Zhou, Jin, Vogel, Fang, & Chen, 2011). As a way of using technology, interaction might be associated with individual differences.Chen and Wang (2009) studied game-based practices in terms of gender and experience and concluded that in games where both genderand experience (challenge-interactivity) make interaction difficult, males are more successful, while there is no difference between gendersin terms of mixed-interactivity. Calisir and Gurel (2003) compared males and females with background knowledge and experience. Theyconcluded that males understand what they read more easily compared to females and navigate nonlinear systems more easily. In anotherstudy, Inal and Cagiltay (2007) concluded that males are more active compared to females while playing games. Larson et al. (1999) claimedthat females are better at interacting with objects in a 3D environment, but they are not as good at visualization. Some empirical studieshave revealed individual and environmental factors that influence interaction, such as feedback, the structure of a course, class size, andprior experience (Vrasidas&McIsaac, 1999). Dillon (1991) and Lawless and Kulikowich (1994) found that prior knowledge provides learnersthe ability to comprehend content and manipulate navigation systems. Therefore in the current study, gender and prior experiences invirtual environments were added as individual differences.

When interaction in virtual worlds and games has been examined in terms of gender and experience, significant differences have beenfound (Calisir & Gurel, 2003; Chen&Wang, 2009; Inal& Cagiltay, 2007). Veltri, Krasnova, Baumann, and Kalayamthanam (2014) previouslyexamined gender differences in online environments, and related sections from that and the current study are presented in Table 1. As seenon Table 1, although gender was examined in a broad range of user behavior on virtual environments, their effect on interaction with theenvironment was not handled.

Because the conceptualization of interaction is disputable, it is difficult to make solid conclusions about its role (Lustria, 2007). In thisstudy, interaction is examined empirically, providing an important contribution to the literature. Some systems incorporate interaction butdo not enhance learning because of a poor design that causes disorientation and cognitive overload (Calisir & Gurel, 2003; Lustria, 2007).Interactionmust be properly calibrated in an online environment. Questions remain about what can be done to increase interaction and howto realize interaction in the process of participation (Cardak, 2012; Gulbahar, 2009). The present study reveals which components of virtualworlds increase interaction and clarifies a broad range of interaction types in virtual worlds.

Table 1Gender differences in online environments.

Males Number of Users Males used virtual worlds more than females. Becerra et al. (2008)Hainey et al. (2011)

Level of Activity (Time,Frequency)

Males played more frequently and spent more time playing. Chou and Tsai (2007)Hainey et al. (2011)Chen (2010)

Gaming Skills Males exhibited better spatial skills (navigation and spatialproblem solving efficiency).

Tippett et al. (2009)

Types of Activities in Games Males enjoyed building things, making money, andworking on their own virtual property.

Zhou et al. (2011)Guadagno et al. (2011)

Gaming Performance Males performed a variety of tasks better. Tlaukaa et al. (2005)Females Level of Activity (Time,

Frequency)Females spent more time playing. Kuo et al. (2012)

Williams et al. (2008)Relationship Seeking Females were more active seeking friendships in online games. Guadagno et al. (2011)

Hassouneh and Brengman (2014)Choi et al. (2012)

Group/Social Behavior Females were more social, sought help more, and participatedmore in group activities and discussions.

Hou (2012)Choi et al. (2012)Hong and Hwang (2012)Yee et al. (2007)

Avatar Customizing andVisualAppearance

Females paid more attention to and were more likely tochange their avatar's appearance.

Hou (2012)Guadagno et al. (2011)Dunn and Guadagno (2012)

Types of Activities in Games Females enjoyed socializing, shopping, exploring,and improving their character.

Zhou et al. (2011)Guadagno et al. (2011)Hou (2012)Hassouneh and Brengman (2014)

Both Arousal Both males and females recognized benefits of arousal. Choi et al. (2012)Zhou et al. (2011)Chou and Tsai (2007)

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1.2. Spatial ability

Spatial ability is defined as all of the abilities used to visualize objects in themind, to know them fromdifferent perspectives, and tomovethem. Concepts such as 3D objects, visualization, moving, orientation, transformation, and manipulation are common in all definitions ofspatial ability (Yildiz, 2009). Rafi, Samsudin, and Said (2008) studied interactive virtual environment effects on spatial ability with positiveresults. The relationship between spatial ability in virtual worlds and the ability to move and find one's direction in the environment hasbeen studied bymany researchers, who also found positive results (Brooks, Attree, Rose, Clifford,& Leadbetter, 1999; Wraga, Creem-Regehr,& Proffitt, 2004). Huk (2006) found that prepared 3D objects in a unit about the cell had positive impacts on spatial thinking skills, andBricken and Byrne (1993) have further claimed that it is necessary to use interactive virtual reality tools to develop spatial abilities, whichsupports the case of the present study.

Spatial ability has been studied from many perspectives, such as according to gender, by investigating relationships between usingcomputers and gaming, or by working with concrete objects. Relevant gender studies have generated very different results. Some studiesfound differences in spatial abilities (Nemeth & Hoffman, 2006; Rafi et al., 2008; Turgut & Yilmaz, 2012), while others did not (Battista,Wheatley, & Talsma, 1989; Feng, Spence, & Pratt, 2007; Goldstein, Haldane, & Mitchell, 1990; Turgut, 2007). Computer use, computergame play, and use of 3D virtual environments have been found to have significant relationships with spatial skills (Caissie, Vigneau,& Bors,2009; Olkun& Altun, 2003; Rafi et al., 2008; Turgut, 2007; Yildiz& Tuzun, 2011). Human computer interaction studies have also shown thatspatial ability might have an effect on information retrieval, navigation, retention, and mapping of visual information (Chen, Czerwinski, &Macredie, 2000). For example, spatial ability has been found to have a positive relationship with navigation and mapping the environment(Brooks et al., 1999; Klatsky, Loomis, Beall, Chance, & Golledge, 1998; Wraga et al., 2004).

Many studies have investigated whether spatial skills affect virtual learning andwhether virtual environments affect the development ofspatial skills. In both cases, studies have found a positive relationship, but little information has been established about how spatial skillsaffect learning in virtual worlds. A broad range of variables can clarify why spatial skills influence task performance in virtual worlds, andthis study has illuminated whether spatial skills have positive relationships with depth of interaction (the number of interactional behaviorpatterns).

1.3. Engagement

Student engagement relates to the concept that an individual's learning is affected by levels of participation in learning activities (Coates,2005). In other words, the more an individual practices and is active in the process, the more the person will learn (Carini, Kuh, & Klein,2006). Student engagement is also defined as the time and effort spent by an individual on activities related to intended learning out-comes (Kuh, 2001, 2009). Researchers believe that effective learning occurs when a student is spatially engaged with the environment andbecomes involved in rich and intensive interaction (Dillenbourg& Hong, 2008). It has also been shown that depth of interaction depends onthe duration of engagement (Dickey, 2005b). Along with entertainment, a variety of strategies encourage users to be more active, helping tolengthen engagement (Dickey, 2005b; Prensky, 2001). Gordon and Koo (2008) have asserted that entertaining, social, and absorbing en-vironments that enable effective interaction can also increase engagement.

Sajjanhar (2012) has stated that 3D virtual worlds have the potential to ensure student engagement in accordance with the real world.Jarmon, Traphagan, Mayrath, and Trivedi (2009) observed that these environments allow users in different places to practice social skills

Gender Experience Spatial ability Engagement

Depth of Interaction

Individual differences

Experiential differences

Fig. 1. Framework of the study.

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effectively and encourage longer engagement in learning activities. Meanwhile, Johnson et al. (1998) asserted that the most importantfeatures of virtual world learning environments are interaction and engagement. They observed that learning is directly based onengagement duration, which is dependent on active participation. Users who were less engaged time-wise did not achieve the goals of thevirtual environment. Johnson et al. (1998) and Ang and Wang (2006) have shown that users' engagement duration increases with moreinteraction. In addition, depth of interaction is affected by factors such as motivation and presence in 3D virtual environments (Allison et al.,2010; Arya et al., 2011; Bulu, 2011; Kapp&O'Driscoll, 2010). Engagement is related to presence in the virtual environment (Shea& Bidjerano,2009), and self-movement in a virtual world increases spatial presence and raises interaction (Regenbrecht& Schubert, 2002). Therefore, inthis study spatial engagement indicators were also considered as one of the factors increasing depth of interaction.

Each study in the literature has examined different variables to determine interaction. This study, on the other hand, investigated severalvariables to see a more comprehensive picture of interactional behavior patterns in virtual worlds and the relationship there betweendemographic, individual difference and experiential variables. As seen in the literature related technology use, gender and prior experienceare important individual factors. However, in literature only certain dimensions of interaction have been discussed, overlooking effects ofgender and experience. To fill that gap, this study examined these variables' connections to interaction. Additionally, spatial ability as in-dividual difference and spatial engagement as experiential indicators were examined to understand big picture of depth of interaction invirtual worlds. A clear framework of the study was shown on Fig. 1.

In this study, users' interactions in a 3D virtual world learning platform were measured and compared across gender, prior 3D virtualworld experience, and spatial ability. In addition, relationships between engagement duration, spatial ability, and depth of interactionwereexamined to determine whether the first two factors could predict the third. The following research questions guided this study:

� Do gender, experience, or spatial ability affect depth of interaction in a 3D virtual world?� Is there a relationship between engagement duration, spatial ability, and depth of interaction in a 3D virtual world?

2. Method

2.1. Research design

Non-experimental correlational and comparative methodologies were used together in this study. First, the comparative method wasused to examine differences between the study groups in terms of the selected variables (McMillan & Schumacher, 2010). Depth ofinteraction in the environment was compared across the factors of gender, experience, and spatial ability. Then the correlational methodwas employed to determine and measure the relationships between the variables (McMillan & Schumacher, 2010) without intervening inany way (Fraenkel & Wallen, 2000). The correlational method was used specifically to investigate the relationships between engagementduration, spatial ability, and depth of interaction.

2.2. Participants

A total of 61 university sophomores and seniors from the Computer Education and Instructional Technology (CEIT) department of a largeEast Anatolian university in Turkey participated in the study (see Fig. 2). While the sophomore students did not have experience with 3D

30 Sophomore Students 31 Senior Students

High level: 12 students Medium level: 38 students Low level: 11 students

29 Females

32 Males

After Spatial Ability Test

Fig. 2. Demographics of study participants.

Table 2Depth of interaction behaviors in 3D virtual worlds.

Progress and sequence control Using directions/signboardsProceeding without directionsSitting/standingTeleportation

Use of multimedia Watching videosUsing animationsUsing picturesUsing text/notes

Avatar control Taking off clothesChanging clothesChanging appearance of face or body

Written communication control Writing comments about the environmentAsking questions/engaging in discussionSharing academic/scientific knowledgeHaving daily conversationsSending private messages

Oral communication control Talking about the environmentAsking questions/engaging in discussionSharing academic/scientific knowledgeHaving daily conversationsSending private messages

Object control Clicking on objectsMoving avatars around objectsAnalyzing features of objects

Perspective control Zooming inZooming outAdjusting the perspective

Interface control Using menus

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virtual worlds, the senior students had taken two courses in which they created designs for and used 3D environments actively. Purposefulsamplingwas used due to the experience variation among participants. Students in both groups took the role of practitioner, whose taskwasto navigate the environment freely.

2.3. Data collection instruments

Three data collection instruments were used in accordancewith the aim of the research. The first two instruments were tests to measurespatial visualization and mental rotation skills. The spatial visualization test was taken fromWinter, Lappan, Phillips, and Fitzgerald (1986),

A multiple and parabolic regression model was created to explain the depth of interaction.

Depth of interaction was revealed according to gender, experience, and spatial ability.

During analysis, durations of pauses, movements, and overall screencasts were calculated to determine engagement periods.

Four experts in two groups analyzed each screencast in accordance with the interaction observation form to ensure reliability.

All 61 students spent 20 to 45 minutes in the 3D environment, and screencasts were recorded.

The students' levels of spatial ability were identified before starting the application.

CEIT students were selected and identified in terms of experience with 3D virtual worlds.

The 3D virtual world implementation environment was determined.

Fig. 3. Research process of the study.

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and the mental rotations test was adopted from a study conducted by Peters et al. (1995). Both tests were translated into Turkish and hadtheir validity and reliability examined by Yildiz (2009). The third instrument, the Interaction Observation Form, was developed by theauthors to gather data on progress and sequence; multimedia use; avatar control; written and oral communication; and object, perspective,and interface control. The studies of Cardak (2012), Teo et al. (2003), and Jung et al. (2002) were consulted while creating this form, andfindingswere combinedwith the researchers' own experienceswith 3D virtual worlds to design a tool tomeasure depth of interaction in theenvironment. This formwas examined by seven experts who specialized in and actively used 3D virtual worlds. The validity and reliability ofthe form were also confirmed. The content of the observation form is displayed in Table 2.

2.4. Procedures of the study

A winter sports education area was prepared in the Second Life environment to raise the users' knowledge of and interest in wintersports. In this study, short track and figure skating were the focused sports. An information house provided basic facts using text, pictures,and videos; a dressing section introduced uniforms and equipment; a practice section offered pictures and animations to teach step-by-stepmovements; and an application section prompted students to attempt the movements. Students moved freely in the environment for20e45 min, normally beginning in the information house and finishing in the practice area. While students completed these steps,screencasts were recorded. Detailed information about the research process is provided in Fig. 3 and the 3D winter sports environment ispresented in Fig. 4.

2.5. Data analysis

Depth of interaction was the dependent variable, and gender, experience, and spatial ability were independent variables. Frequencieswere recorded for each item on the Interaction Observation Form; the total frequency gave the depth of interaction score. In addition,engagement duration was determined by subtracting the sum of the durations for pauses and walking or running in the environment fromthe total duration [Engagement Period ¼ Total Duration e (Paused Durations þ Duration of Walking/Running)]. The number of correct

Fig. 4. 3D winter sports environment designed for this study.

Table 3Research questions, variables, and related analysis methods.

Independents Dependent Test

Research Question 1: Do gender, experience, or spatial abilityaffect depth of interaction in a 3D virtual world?

Gender ExperienceSpatial ability

Depth of interaction Multi Way ANOVA

Research Question 2: Is there a relationship betweenengagement duration, spatial ability, and depthof interaction in a 3D virtual world?

Engagement durationSpatial ability

Depth of interaction Simple correlation,multiple regression, parabolic regression

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answers out of 39 on the spatial ability test were calculated, and the Z-scores of this variable were converted to standard scores. Scoresbelow �1 were grouped as low level, scores between �1 and þ1 were medium level, and scores over þ1 were high level. Continuous dataabout spatial ability were used for the second research question. Before comparison tests and correlational analysis were applied, tests fornormality, homogeneity, and equivalence of variances were conducted to ensure internal consistency. All of the data for gender, experience,and depth of interaction had normal distributions except for the factor of interaction. To ensure validity and reliability, the possibility ofmissing data was examined; no missing data were found. For the first research question, depth of interaction was analyzed acrossgender, experience, and spatial ability. As with the second research question, relationships between engagement duration, spatial ability,and depth of interaction were examined, and regression analysis was applied to create a model. The data analysis procedure is shown indetail in Table 3.

Several measures were taken to ensure the validity of the study and data collection process. First, two experts checked the researchprocess regularly. Second, reliable tests were used for spatial skills. Also, data collection tools were developed according to both theliterature and experts' input. Finally, during video analysis, 10 videos were analyzed together and all important points were determined toreduce bias caused by researchers, and a checklist was developed for analysis. For reliability of checklist points, intra-class correlation andindependent t-test were conducted. According to the reliability results, there were high correlation (R¼ .97) and no significance differences(t¼ .324, p > .05) between two raters' scores. After this, two researchers in a group analyzed as two group. In addition, when one researcherwas undecided, he or she asked the others.

3. Findings

3.1. Descriptive analysis

As seen in Table 4, average student engagement durationwas 20.19 min. Students mostly interacted with the environment by controllingobjects (M ¼ 98.30) and using multimedia components (M ¼ 33.08); they used avatar control (M ¼ 2.98) and communication (M ¼ 1.48)much less often. In the spatial ability test, the highest possible score was 39, and students averaged 18.56. Among inexperienced partici-pants, no differences were found between females and males for engagement, depth of interaction, and spatial ability. However, males hadhigher scores when they were experienced with 3D worlds.

3.2. Effects of gender, experience, and spatial ability on interactions in the 3D virtual world

A threshold value was determined for the applied environment to evaluate depth of interaction in the 3D virtual world. To do this, fiveexperts wandered in the environment under the same conditions as the participants; their mean score was calculated to beM¼ 250.33. Theparticipants' depth of interaction scores all fell below this value (M ¼ 187.13, SD ¼ 90.22).

A multi way ANOVAwas applied to investigate the effects of gender, experience, and spatial ability on depth of interaction. According toresults, variances in the variable of interaction were identified as homogenous (p > .05). The participants' depth of interaction changed inaccordance with their experience (F ¼ 4.05, p < .05, R2 ¼ .076). Spatial ability also affected their interaction in the 3D virtual world, as therewere differences between the low, medium, and high-rated groups (F ¼ 4.98, p < .05, R2 ¼ .169). Detailed data are provided in Table 5.According to LSD Post Hoc results, a significant positive difference was discovered between the high spatial ability group and the mediumand low groups (p < .05).

Table 4Participant engagement duration, interactions and spatial ability (N ¼ 61).

Factors M SD Experienced Inexperienced

Males Females Males Females

Engagement Duration (Second) Total duration 1782.52 414.68 1481.85 1231.39 1085.31 1080.14Duration of Walking/Running 324.08 435.35Pause durations 246.70 265.34Total 1211.74 628.07

Depth of Interaction (Frequency) Object control 98.30 49.78 241.08 208.06 157.38 144.14Use of multimedia 33.08 18.77Perspective control 28.84 35.20Progress and sequence control 27.08 8.29Interface control 3.93 5.48Avatar control 2.98 2.04Communication 1.48 4.25Total 187.13 90.22

Spatial Ability (Total Score) Total 18.56 7.10 21.85 17.50 18.44 17.00

Table 5Differences in interaction across gender, experience, and spatial ability.

Independent Variables SS MS df F p R2

Corrected model 177605.79 16145.98 11 2.54 .012 .36Intercept 1353167.90 1353167.89 1 213.35 .000 .81Gender 5147.66 5147.65 1 .81 .372 .02Experience 25734.50 25734.50 1 4.05 .049 .08Level of spatial ability 63187.13 31593.56 2 4.98 .011 .17Gender� experience 18.78 18.77 1 .00 .957 .00Level of spatial ability� experience 3300.60 1650.30 2 .26 .772 .01Level of spatial ability� gender 13005.08 6502.54 2 1.02 .366 .04Level of spatial ability� gender� experience 1024.06 512.03 2 .08 .923 .00

Table 6Relationship between interaction, engagement duration, and spatial ability.

Depth of interaction Engagement duration Spatial ability

Depth of interaction 1Engagement duration .539** 1Spatial ability .345** .066 1

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3.3. Relationships between engagement duration, spatial ability, and depth of interaction in the 3D virtual world

When the relationships between the participants' depth of interaction, engagement durations, and spatial ability were examined, astrong relationship was found between depth of interaction and engagement duration (r ¼ .539, p < .05), and a moderate relationship wasfound between interaction and spatial ability (r ¼ .345, p < .05). Detailed data are provided in Table 6.

A multiple regressionwas applied to determine whether engagement duration (X) or spatial ability (Z) could predict depth of interaction(Y). In this respect, a regression analysiswas conductedwith interaction, engagement period, and spatial ability. In order to conductmultipleregression, the assumptions were first checked, and no relationship was found between the independent variables of engagement durationor spatial ability (r ¼ .066, p > .05) and autocorrelation values (DW ¼ 1.5). After assumptions were verified, a multiple regression analysiswas conducted using the “stepwise” method. A model was developed to explain engagement durations initially, followed by spatial abilitytogether with depth of interaction. In the beginning, the calculations for engagement durationwere found to be significant (F(1,59)¼ 24.22,p < .05, R2 ¼ .279). After adding spatial ability to engagement duration, the model explained 36% of the depth of interaction, and thecalculations were significant (F(1,58) ¼ 9.09, p < .05, R2 ¼ .366). Detailed data are provided in Table 7.

In the model developed in accordance with the information in Table 5, the constant term was zero, since the coefficient of the fixedvariable was not significant. In line with these data, a final model for interactionwas determined: Y¼ 0.075Xþ 3.944Z. In addition, a modelwas needed to determine to what extent engagement duration (X) predicted interaction (Y) by itself. Parabolic regression via a curve es-timatewas themost suitable option, and a significant model was attained (F(2,58)¼ 22.34, p < .05, R2¼ .435). Detailed data are presented inTable 8.

Since the coefficient of the engagement duration was not significant in the developed model, its coefficient was set as zero. In line withthese data, the model for interaction was established: Y ¼ 3.404� 2 þ 0.03X þ 92.483.

4. Discussion

In the present study, users' depth of interaction in a 3D virtual world was determined and compared across the factors of gender,experience regarding 3D virtual worlds, and spatial ability. The relationships between engagement duration, spatial ability, and depth ofinteraction were analyzed, along with whether the first two factors could predict depth of interaction.

Depth of interaction varied according to participant experience and spatial ability. Similar results exist in the literature (Brooks et al.,1999; Wraga et al., 2004). In this case, those users who had more experience and higher-rated levels of spatial ability interacted more,probably because they were able to use features such as progress and sequence control, objects and media in the environment, avatars,written and oral communication, and perspective and interface controls more quickly and effectively. The relationship between spatialability and depth of interaction may have been due to the users' acquisition of experience. Since they spent a long time in the environment,their spatial ability seemed to gradually increase, which allowed them to becomemore involved in interaction. No significant difference was

Table 7Model for the prediction of interaction by engagement duration and spatial ability.

Model B t p F p R2

Step 1(Constant) 93.22 4.34 .000 24.22 .000 .28Engagement duration .08 4.92 .000Step 2(Constant) 23.60 .77 .444 9.09 .004 .37Engagement duration .08 5.04 .000Spatial ability 3.94 3.02 .004

Table 8Model for prediction of interaction by engagement duration.

Model B T p F p R2

(Constant) 92.49 4.79 .000 22.34 .000 .44Engagement duration .03 1.33 .188(Engagement duration)2 3.40E 3.85 .000

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found between genders regarding depth of interaction. However, the mean depth of interaction for males was higher compared to females.Even though some studies in the literature have reported thatmales show superior skills while interacting in virtual environments (Calisir&Gurel, 2003; Chen &Wang, 2009; Inal & Cagiltay, 2007), others have reported that females are better at interacting with 3D objects (Larsonet al., 1999). Observations that males show general superiority over females in terms of 3D virtual world interactive skills have becomecommonplace in recent gender comparisons. Hartmann and Klimmt (2006) examined females' experiences in 3D games and concluded thatfemales prefer games involving social interactions. Although no significant difference was found with regard to gender in this study, the factthat the males' mean value for interaction was higher may be related to the observation that males enjoy playing a wider variety of 3Dcomputer games, whereas females tend to enjoy more limited kinds of games and consequently practice necessary skills less frequently.

Various relationships were revealed between engagement duration, spatial ability, and depth of interaction in the 3D virtual world. Astrong relationshipwas found between depth of interaction and engagement duration; amoderate relationshipwas found between depth ofinteraction and spatial ability; and no relationship was found between spatial ability and engagement duration. Engagement duration wasnot investigated in relation to mental processes in this study, and users did not experience any directed spatial mental processes, likelyexplaining the absence of a relationship between these variables. Regarding the relationship between depth of interaction and engagementduration, studies in the literature have indicated that depth of interaction depends on engagement duration, which is parallel to this study(Ang & Wang, 2006; Dickey, 2005b; Johnson et al., 1998). Also, even learners with low spatial ability skills may achieve better results thanlearners with higher skills in a virtual world when they are guided well (Merchant et al., 2013). Therefore, spatial ability might not berelevant depending on the design and instructional approach in virtual worlds.

Using a variety of strategies (Dickey, 2005b), encouraging users to be active and utilizing entertainment are all important factors thattend to lengthen engagement duration. In this study, diverse strategies were adopted with the intent that users would have fun andtherefore be more active. The inclusion of videos, animations, and activities in the environment encourages users to be more active, andtheir engagement durations increase accordingly. As seen in the results, frequency of interaction with communication tools was very low.Since the students already knew each other and were all in the same class during the implementation, they did not prefer onlinecommunication. However, even in this case, certain activities might have triggered increased communication, such as collaborativeassignments.

Concerning the relationship between spatial ability and depth of interaction, studies in the literature have indicated that 3D objects helpusers to develop spatial abilities (Huk, 2006), and spatial ability affects movement and direction-finding skills (Trindade, Fiolhais, &Almeida, 2002; Wraga et al., 2004). Including activities such as wandering around objects, analyzing features of objects, and zooming inand out as part of object and perspective control helps users to develop spatial abilities. Users with strong spatial abilities will benefit morefully from the environment and becomemore involved in interaction. Sabry and Baldwin (2003) also claimed that interaction is encouragedby the presence of such multimedia elements as text, animation, video, and sound. In this study, the frequency of interacting withmultimedia components paralleled the variety of items presented in the virtual world. It might be claimed that more multimedia com-ponents lead to more interaction with them. The high frequency of interaction with objects in the environment implies that the objectsthemselves mostly determine levels of interaction.

Finally, a model was created to reveal to what extent engagement duration and spatial ability can predict interaction in accordance withthe identified relationships in this study. While engagement duration explains 20% of depth of interaction by itself, spatial ability along withengagement duration explains 36%. Thus, for future research on depth of interaction in a 3D virtual world, a model based upon engagementduration and spatial ability is suggested. While only 36% of the users' interaction was explained in the first model, engagement durationalone explained 43% of the users' interaction in the second model. The conclusion to be drawn here is that activities that enable users to beengaged longer are very important. Likewise, the depth of interaction in 3D virtual worlds can be re-calibrated by using the engagementvariable alongside other variables in future research.

5. Conclusion and suggestions

In this study, gender, experience, and spatial ability were examined to determine how these variables affect depth of interaction in a 3Dvirtual world. The results indicate that depth of interaction is affected by experience and spatial ability but not by gender. We also inves-tigated the potential relationship between engagement duration, spatial ability, and depth of interaction. While a strong relationship wasfound between depth of interaction and engagement duration, only a moderate relationship was found between interaction and spatialability. A model was created to reveal to what extent engagement duration and spatial ability predict interaction. While engagementduration alone explains 20% of depth of interaction, spatial ability together with engagement duration explains 36%. These results arelimited to the data gathered from 61 students studying Computer Education and Instructional Technology in Turkey. The following sug-gestions are provided for future researchers:

� New strategies must be established to make learners interact more with objects. For example, a piece of information should not bepresented in only one place: students could be asked to click multiple objects to acquire more and different information and engagewith the learning environment. Thus, more channels would be used and more flexible learning is provided. On the other hand, toprevent students lost in the environment, cognitively overloaded, and expose similar informationmany times, teleportation tools mightbe used to lead students go to the next required places in the environment.

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� Available communication tools might not be enough to make students use them effectively, so practitioners must develop strategies toincrease spatial collaboration in the environment. To achieve this some tasks might be given students such as collecting data from theirpeers, telling some information to others or doing some tasks together. However, teacher must monitor the tasks since there is noautomatic feedback mechanism.

� Since experience has a crucial role in developing spatial skills, engagement, and interaction, students should be provided with expe-rience in 3D worlds before participating in learning activities. While providing experience, some gamification strategies might beapplied like scavenger hunt, which students find some objects and make list as a team. Thus, their motivation might also be providedbefore they need to go through the environment for learning purposes.

� Since multimedia components are supposed to enhance interaction, they must be added in variety of ways and roles (music, video,videoconference, animation, sound, pictures, 3D objects). Thus, the students who have different needs and preferences would beaddressed.

� The same study could be replicated with a larger and richer sample in terms of user differences and longer observations.� Whether spatial abilities are the result of unchangeable personal differences or are a skill set that can be developed should beinvestigated.

� A more comprehensive definition of engagement duration is needed, and it should be informed by qualitative studies. Engagementduration is a concept that involves social and cognitive processes; it is difficult to measure only behaviorally.

� New studies about user differences can be conducted. For instance, it has been predicted that experience playing computer games,interest in content, areas where activities take place (school, home), whether activities are compulsory or voluntary, duration limita-tions, and whether users wander freely or take a pre-set role all may affect interaction and engagement duration.

� Although the effects of some user differences have been revealed in this study, effects on interaction and engagement duration that arecaused by the design of the environment cannot be ignored. The best way to increase depth of interaction is to give users roles thatinvolve them cognitively and socially, as well as to provide resources within the environment. Moreover, the presence of guiding avatarsin the environment can increase interaction. A similar study could be conducted with different variables that influence interaction.

Acknowledgments

This study was conducted as part of project number 111K516, the Effects of Virtual and Multimedia Environments on Interest andAwareness towards Winter Sports, and supported by the Scientific and Technological Research Council of Turkey.

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