Influence of Students’ Learning Style,Sense of Presence,and Cognitive Load on Learning Outcomes in an Immersive Virtual Reality Learning Environment

Abstract

The purpose of this study was to investigate the relationship among Taiwanese high school students’ learning style, sense of presence, cognitive load, and affective and cognitive learning outcomes in an immersive virtual reality-based learning environment. This study used a teaching experiment intervention method. Seventy-seven students participated in the virtual reality-based learning environment and completed related scales and a test. This study found that although the students’ learning style does not influence learning outcomes, it may influence the subjective sense of presence and cognitive load in the learning process. Regarding the affective learning outcome, involvement/immersion, sensory fidelity, and mental effort are positive predictors. In addition, involvement/immersion, interface quality, mental load, and mental effort are negative predictors of cognitive learning outcomes. The conclusion from this study is that students with some learning style preferences must bear a greater cognitive load to achieve the same learning outcomes as other students. This study also points out that the components of sense of presence and cognitive load generate inconsistent predictive effects on affective and cognitive learning outcomes, respectively. Therefore, it is important to deeply explore the influence of sense of presence and cognitive load structure on learning in virtual environments.

Keywords

learning style sense of presence cognitive load immersive virtual reality

Introduction

Virtual reality (VR) technology seems to have become a powerful and promising educational tool because of its unique technical characteristics that distinguish it from other information and communication technology (ICT) applications (Mikropoulos & Natsis, 2011). VR is a computer simulation technology that uses three-dimensional (3D) graphics and devices to provide a highly interactive experience. This unique experience is known as a virtual experience, which is defined as the psychological and emotional state that users experience when interacting with products in a 3D environment. Among them, the sense of presence is regarded as an important subjective feeling in the virtual experience (Yoon, Choi, & Oh, 2015). Sense of presence refers to a sense of spatial immersion in a mediated environment (Weibel & Wissmath, 2011) and can be used to describe the intensity of emotional involvement. Sense of presence is an important predictor of various positive user responses (i.e., satisfaction, motivation, positive attitude, or positive performance) in VR-based learning environments (Weibel & Wissmath, 2011; Yoon et al., 2015). Bachen, Hernández-Ramos, Raphael, and Waldron (2016) found that the sense of presence can promote learning outcomes if users are not overwhelmed by the game environment. In other words, cognitive load is the key to changing the relationship between sense of presence and learning outcomes. Learners build new knowledge with limited working memory. When the game is integrated into learning, if the learner lacks the ability to effectively explore the game learning task, it may cause the learner to encounter cognitive load, which has a negative impact on learning (Hsu, Wang, & Zhang, 2017). In addition, different cognitive load sources lead to different cognitive load types, such as intrinsic load, extraneous load, and germane load (Sweller, 1988), which may have different effects on learning. Specifically, when intrinsic load is optimal and extraneous load is low, learners can take part in knowledge elaboration processes that impose germane load and further enhance the effect of learning (Leppink, Paas, Van der Vleuten, Van Gog, & Van Merriënboer, 2013). However, few studies have explored the relationship among sense of presence, cognitive load, and learning outcomes.

In addition, user characteristics also influence how well people use technology (Hu, Hu, & Fang, 2017). From the perspective of constructivist learning, to create an effective learning environment (e.g., successful integration of VR in an educational setting), students’ characteristics should be taken into consideration because the learning outcome and the subjective feelings of the students with regard to the VR learning context are associated with their characteristics (Lee, Wong, & Fung 2010; Yoon et al., 2015).

In the field of education, learning style is a key learner characteristic that influences learning outcomes. Therefore, to more effectively use VR-based learning, it is necessary to conduct a comprehensive and thorough study of the interconnections between virtual environment (VE) and learning style (Hauptman & Cohen, 2011). In summary, the purpose of this study is to explore the impact of sense of presence and cognitive load on affective and cognitive learning outcomes and the role of learning styles in this relationship in a VR-based learning environment.

Literature Review

Witmer and Singer (1998) explain the sense of presence as “the subjective experience of being in one place or environment, even when one is physically situated in another. (p.225)” Therefore, taking the VE as an example, the sense of presence means that a user is in some way displaced from the real world to a virtual world. Sense of presence is a cognitive illusion of nonmediation through which users recognize situations that are created by various media; however, when interacting with such media, users fail to perceive or be aware of the existence of that media (O’Neill, 2005).

Regarding the connotation of sense of presence, Witmer, Jerome, and Singer (2005) argue that the sense of presence consists of four elements. The first is involvement, which refers to a natural interface for the user that immediately promotes his or her ability to control activities in the environment, thereby increasing participation. The second is sensory fidelity, which means that the VE allows the user to visually actively search or inspect objects in the VE. Third, adaptation/immersion means the perceived proficiency of interaction and operation with the VE and the speed at which the user accommodates to the VE experience. Finally, interface quality means the user’s perceived quality of the VE interface and its interference with activities in the VE.

Sylaiou, Mania, Karoulis, and White (2010) found that highly immersive VEs help learners achieve a high sense of presence and increase learners’ positive feelings during task execution. Furthermore, this type of immersive presence improves learners’ learning outcomes (Weibel & Wissmath, 2011; Yoon et al., 2015). Traphagan et al. (2010) also indicated that increasing the sense of presence in a virtual world environment has the potential to promote student learning. Accordingly, we propose the following hypothesis:

H1: In a VR-based learning environment, the sense of presence is positively related to learning outcomes.

In using VR technology as a learning device, in addition to focusing on the sense of presence generated by human-computer interaction, an issue that should be noted is cognitive load. Cognitive load is the amount of cognitive processing a person incurs to find information; it is the cost of a cognitive task (Hu et al., 2017). Whitelock, Romano, Jelfs, and Brna (2000) found that VR generates learner cognitive load. The authors explain that a high sense of presence in a VR-based learning environment can cause learners’ motivation; however, it may distract learners’ attention and generate cognitive overload. According to Paas (1992), the cognitive load consists of a task-based aspect and a learner-based aspect. The cognitive load of the task-based aspect is generated according to the task requirements, which means the effort required to complete the task, which is called mental load. The cognitive load of the learner-based aspect is the cognitive ability and the resources demanded of the learner in the performance of tasks, which is called mental effort (Brunken, Plass, & Leutner, 2003; S. F. Chen, 2016). Mental load and mental effort are two different but, in most cases, positively correlated constructs (Krell, 2017). Due to the limited cognitive resources of human beings, when mental effort, which is defined as nonautomatic mental elaborations that are applied to information processing or learning (Bradford, 2011; Nguyen & Zeng, 2014), exceeds the individual’s ability for cognitive processing, cognitive overload occurs, which in turn negatively affects satisfaction and hinders learning (Hu et al., 2017).

Paas (1992) adopted a macroscopic perspective to interpret the concept of cognitive load and did not distinguish between the cognitive load categories. However, his scale could easily be used to successfully measure extraneous and intrinsic cognitive load (Sweller, 2018), and both his explanation and scale are still widely applied today. Furthermore, few cognitive load instruments have been designed for the intrinsic load, extraneous load and germane load (Leppink et al., 2013). Recently, S. F. Chen (2016) integrated Paas’s concept and the above three types of cognitive load to design a new instrument. Chen used Sweller’s structures as a framework and took the perspectives of task/environment, learner characteristics (cognitive competence, cognitive style, predictive knowledge, and experience), and interaction between environment and learner characteristics to develop item content and then divided the developed items into mental effort and mental load based on Paas’s concept. We adapted S. F. Chen’s (2016) instrument in our study as this instrument has the advantages of Sweller and Paas combined. Accordingly, we propose the following hypothesis:

H2: In a VR-based learning environment, cognitive load is negatively related to learning outcomes.

The existing studies rarely explore the relationship between sense of presence and cognitive load, and the results of these studies are inconsistent. Some studies suggest that in the virtual learning environment, the sense of presence takes up the user’s attention, resulting in an increase in cognitive load. Therefore, sense of presence is positively related to cognitive load (Schrader & Bastiaens, 2012a; Whitelock et al., 2000). Other studies have shown that the technology-supported learning environment not only seems to enhance learning outcomes by preventing cognitive overload, but it also strengthens the relationship between learners’ experience of virtual presence and their learning (Schrader & Bastiaens, 2012b). Adequate game design can reduce cognitive load and increase the sense of presence and its positive impact on learning (Schrader & Bastiaens, 2012c). From this perspective, cognitive load and sense of presence show a relationship between growth and decline. Due to the inconsistency between cognitive load and sense of presence, this study attempts to identify and explain the relationship among sense of presence, cognitive load, and learning outcomes in an immersive VR-based learning environment. Therefore, we address the following research question:

RQ1: What is the impact of the relationship between sense of presence and cognitive load on learning outcomes?

Educational researchers are always interested in studying the influence of learners’ aptitudes and characteristics on the learning outcomes based on different forms of instruction, including digital game learning. This kind of study primarily assumes that it is possible and expected that the nature of instruction can be adjusted to accommodate individual differences such as ability, style, or preference to improve learning outcomes (C. J. Chen, Toh, & Ismail, 2005; Lee et al., 2010; Yoon et al., 2015). Learning style is a relatively stable indicator of learners’ perceptions, interactions and responses to the learning environment (Scott, Rodríguez, Soria, & Campo, 2014). The previous studies have shown that VR-based learning outcomes are not affected by learning style, and learners benefit most from VR-based learning, irrespective of their learning styles (C. J. Chen et al., 2005; Lee et al., 2010). However, in the VR-based learning environment, learning outcomes are also affected by other factors such as the aforementioned sense of presence and cognitive load. As suggested by Hauptman and Cohen (2011), it is necessary to thoroughly study the relationship between VE and learning style. Therefore, this study also attempts to explore the impact of learning style on sense of presence, cognitive load, and learning outcomes in a VR-based learning environment. Accordingly, we address the following research question:

RQ2: Do students with different learning styles have dissimilar senses of presence, cognitive loads and learning outcomes under a VR learning context?

Notably, there are abundant learning style constructs, and researchers and educational practitioners apply the learning style model according to the actual situation; there is currently no single commonly used architecture (Romanelli, Bird, & Ryan, 2009). In our study, we adopted the Felder–Silverman learning style model (FSLSM) to examine students’ learning preferences for two reasons. First, De Bello (1990) indicated that the multidimensional learning style provides a more encompassing and responsive approach to the diversity and complexity that are present in the actual participation, absorption, and retention of student learning, and FSLSM is a multidimensional model. Second, FSLSM has received much attention regarding computer-assisted instructional applications (Huang, Lin, & Huang, 2012).

In terms of content, FSLSM is divided into four dimensions based on the reception and processing of information. The dimensions of FSLSM are processing, perception, reception, and understanding of information, which are in line with active/reflective, sensing/intuitive, visual/verbal, and sequential/global learning styles, respectively. In the dimension of processing, active learners tend to understand knowledge engagement through physical activity or discussion, whereas reflective learners tend to view and manipulate information through introspection. In the dimension of perception, sensing learners prefer to observe data through the senses, and they prefer facts and experimentation whereas intuiting learners prefer to indirectly perceive by way of the unconscious, and they like principles and theories. In the dimension of reception, visual learners prefer information in the form of diagrams, flow charts, pictures, or films, whereas verbal learners prefer information that is presented in writing. In the dimension of understanding, sequential learners use a logically linear approach to gain understanding, whereas global learners need a comprehensive understanding of the topic before mastering details (Felder & Silverman, 1988; Wang & Mendori, 2015). Today, FSLSM is widely used in technology-enhanced learning systems (Latham, Crockett, & McLean, 2014). Although the extant studies do not demonstrate which learning style of the FSLSM model is particularly beneficial for VR learning, it nevertheless merits investigation.

Learning outcomes can be divided into psychomotor outcomes, including efficiency, accuracy, and response magnitude; cognitive outcomes, including comprehension, knowledge, application and analysis; and affective outcomes, including satisfaction, attitude, and appreciation for the learning experience (Sharda et al., 2004). In fact, the previous literature indicates that a technology-enhanced learning environment may contribute to students’ achievements, attitudes toward learning, and evaluation of learning (Lee et al., 2010). This study focuses on two domains: the cognitive learning outcome, which refers to the result of learning knowledge test, and the affective learning outcome, which refers to learning satisfaction with a VR-based learning environment.

Method

Research Design

This study used a teaching experiment intervention method. Seventy-seven Grade 11 students (22 boys and 55 girls) from three classes of the same senior high school in Taiwan participated in the study. The students were between 15 and 18 years old (M = 16.10, SD = 0.45).

The students used a VR head-mounted display, handheld sticks, and other virtual devices to conduct immersive learning of blood cells and organelles through The Body Cell software. The Body Cell is an immersive VR learning software for the study of human blood flow and blood cells developed by The Body VR LLC, a New York-based company that designs custom VR content for medical education.

This study treated each student separately. Before the treatment with the VR software program, the students responded to the index of learning styles (ILS). During the treatment, the instructor first explained the precautions and operating instructions, and, subsequently, the participant used a VR head-mounted display, handheld sticks, and other virtual devices to learn a lesson on blood cells and organelles that took approximately 30 minutes to complete (Figure 1). Figure 2 presents a virtual image of the use of the handheld sticks to reach out and pick up blood cells. After the treatment, the students received a knowledge test and answered a set of questionnaires that included questions on sense of presence, cognitive load, and learning satisfaction.

Figure 1.

VR device operating instruction and student's actual operation of the VR device.

Figure 2.

Screenshots of learning content.

Instruments

Felder–Silverman ILS

We used the ILS (Felder & Silverman, 1988) to categorize students’ learning style. The ILS contains four learning style dimensions: information processing, perception, reception, and understanding. Each dimension is associated with 11 forced choice items for each option (a or b). A meta-analysis by Felder and Spurlin (2005) indicates that the internal consistency reliability coefficient of the ILS scale is between .41 and .76 (only one study has a reliability of less than .50 in the indicator of sequential/global learning). The ILS has been translated into a Mandarin version and shows acceptable internal consistency reliability that is within the range of .51 to .64 (Wang & Mendori, 2015).

The coefficients of item response theory (IRT) reliability obtained by using the sample in this study were between .52 and .84. The individual reliability coefficients are greater than .50, which is acceptable for attitude or preference assessments suggested by Tuckman (1999). In addition, 49 students in this study prefer active learning, 28 students prefer reflective learning in information processing, 45 students prefer sensing learning, 28 students prefer intuitive learning in information perception, 67 students prefer visual learning, 10 students prefer verbal learning in information reception, 43 students prefer sequential learning, and 34 students prefer global learning in information understanding.

The sense of presence questionnaire

We adopted the framework of presence by Witmer et al. (2005) and developed a questionnaire concerning the sense of presence following their presence questionnaire. The items were answered using a 7-point Likert-type scale, and the high scores indicated a high sense of presence. After an exploratory factor analysis (EFA) that adopted the principal component method with varimax rotation, we retained 11 items for use in this study. The Kaiser–Meyer–Olkin (KMO) test value was .76 (χ²= 470.87, p < .001), the factor loadings ranged from .65 to .84, and the total explained variance was 72.90% (α = .84). In this questionnaire, the sense of presence was divided into involvement/immersion (five items, e.g., “How natural did your interactions with the environment seem?”), sensory fidelity (three items, e.g., “How well could you examine objects from multiple viewpoints?”), and interface quality (three reversed items, e.g., “How much did the visual display quality interfere with or distract from your learning”). The coefficients of IRT reliability for involvement/immersion, sensory fidelity, and interface quality were .86, .81, and .92, respectively.

The cognitive load questionnaire

We revised S. F. Chen’s (2016) scale of digital reading cognitive load for the cognitive load in the VR learning environment. The items were answered using a 6-point Likert-type scale, with scores ranging from 1 (strongly agree) to 6 (strongly disagree). The high scores indicate that the students feel that learning is difficult and requires much effort. The scale contained the two dimensions of mental load (six items, e.g., “It’s very difficult for me to learn in a VE”) and mental effort (four reversed items, e.g., “I actually used very little effort in the process of learning in virtual reality”). An EFA (principal component method with varimax rotation) result revealed that the KMO test value was .78 (χ²= 356.62, p < .001), the factor loadings ranged from .52 to .73, and the total explained variance was 61.29%. The coefficients of IRT reliability for mental effort and mental load were .76, and .85, respectively.

The learning satisfaction questionnaire

We developed a learning satisfaction questionnaire based on the definition of learning satisfaction referring to the learners’ perceptions and attitudes toward the educational process and the level of fulfillment they perceive, which is further related to their motivation to learn (Yang, Hsu, & Chen, 2016). The items were answered using a 7-point Likert-type scale, and the high scores indicated high learning satisfaction. The final questionnaire contained five items (e.g., “Willingness to learn with VR devices in the future”) attributed to one factor. An EFA (principal component method with varimax rotation) result revealed that the KMO test value was .75 (χ²= 259.66, p < .001), the factor loadings ranged from .62 to .74, and the total explained variance was 67.65% (the coefficient of IRT reliability was .88).

The knowledge test

We developed the knowledge test (12 items, total 120 points) based on the learning content of the study such as cell structure and blood cell function. This is a summative assessment with a short-answer form to evaluate the extent of understanding of the materials learned by students. The content of this test focused on the declarative knowledge (item example: “What is the function of the cytoskeleton in a cell?”). Those items were determined by the two experts who were asked to review and correct the test questions, and the reliability was .88.

Statistical Analyses

First, we used EFA (principal component method with varimax rotation) and IRT (multidimensional random coefficients multinomial logistic model, MRCMLM) to assess the validity and reliability of the questionnaires on the sense of presence, cognitive load, learning satisfaction, and the knowledge test. Furthermore, we used quantile regression analysis to explore RQ1 and verify H1 and H2. Finally, to respond to RQ2, we used a Mann−Whitney U test with Bonferroni correction to analyze differences in cognitive load and learning outcomes across the different learning styles of each FSLSM aspect.

Results

The Interplay of Sense of Presence, Cognitive Load, and Learning Outcomes

Quantile multiple regression was employed to examine the predictors of sense of presence and cognitive load at the median (.50 quantile) level of the learning satisfaction and knowledge test performance score distribution. First, the collinearity diagnosis results showed that the variance inflation factor was from 1.16 to 1.69. This finding means that explanatory variables in this multiple regression model were poorly linearly related (Dormann et al., 2013). Table 1 presents the quantile regression analyses of the prediction effects of the aspects of sense of presence and cognitive load on learning satisfaction and the knowledge test. The results show that the regression coefficients of involvement/immersion, sensory fidelity, and mental effort significantly positively predicted learning satisfaction. This finding indicates that when students highly perceived engagement, interaction, and visually active search in VE, and the exerted more cognitive effort and resources for the VR task, they were more satisfied with their learning. However, the prediction effects of interface quality and mental load on learning satisfaction were not significant.

Table 1.

Quantile Regression Analysis of the Presence and Cognitive Load of Learning Outcomes.

Variable	Learning satisfaction				Knowledge test
Variable	Coefficient	SE	t	p	Coefficient	SE	t	p
Presence
Involvement/immersion	0.44	0.06	7.91	<.001	−2.01	0.88	−2.30	.025
Sensory fidelity	0.27	0.10	2.66	.0098	−0.27	1.63	−0.17	.087
Interface quality	0.06	0.07	0.89	.377	−2.89	1.03	−2.80	.007
Cognitive load
Mental effort	0.29	0.11	2.57	.012	−3.65	1.79	−2.05	.045
Mental load	−0.09	0.12	−0.76	.450	−20.39	1.93	−10.57	<.001

Regarding the prediction effect of these aspects on the knowledge test, the results show that the regression coefficients of involvement/immersion, interface quality, mental effort, and mental load significantly negatively predicted the performance on the knowledge test. This finding indicates that when the students had a greater sense of immersion, perceived the quality of the VE interface to be higher and its interference with VE activity to be lower, perceived the task to be more complicated, and exerted more cognitive effort and resources on the VR task, they performed poorly on the knowledge test. However, the prediction effect of sensory fidelity on performance on the knowledge test was not significant.

Differences in Sense of Presence, Cognitive Load, and Learning Outcomes Across Learning Styles

The Mann–Whitney test of learning style, in terms of sense of presence, cognitive load, and learning outcome, showed only two significant group differences, as demonstrated in Table 2. First, the students who tend to use the reflective method to process information experienced significantly greater mental effort in the VR learning environment than the students who tend to use the active method to process information (U = 440.50, p = .009, r = .30). Second, the students who prefer visual information had significantly higher involvement/immersion in the VR learning environment than the students who prefer verbal information (U = 177.50, p = .017, r = .27). No significant group differences were found for learning outcomes.

Table 2.

Mann−Whitney U Test for the Presence, Cognitive Load, and Learning Outcomes by Learning Style.

Variable	Processing		Perception		Reception		Understanding
	Active	Reflective	Sensing	Intuitive	Visual	Verbal	Sequential	Global
	Mdn	Mdn	Mdn	Mdn	Mdn	Mdn	Mdn	Mdn
Presence^a
Involvement/ immersion	8.67	8.00	8.33	8.33	8.67	7.00	8.33	8.33
Involvement/ immersion	U = 573.50		U = 666.50		U = 177.50*		U = 631.50
Sensory fidelity	5.67	5.50	5.67	5.67	5.67	5.50	5.67	5.67
Sensory fidelity	U = 575.00		U = 659.50		U = 279.50		U = 709.00
Interface quality	4.67	4.33	4.33	4.67	4.67	4.50	4.67	4.67
Interface quality	U = 617.50		U = 650.00		U = 324.50		U = 699.00
Cognitive load^b
Mental effort	2.25	3.00	2.75	2.50	2.50	2.88	2.75	2.25
Mental effort	U = 440.50*		U = 644.00		U = 259.50		U = 558.00
Mental load	2.17	2.25	2.17	2.33	2.17	2.92	2.33	2.17
Mental load	U = 636.50		U = 701.00		U = 204.50		U = 693.00
Learning satisfaction	5.00	4.60	4.80	4.80	5.00	4.30	4.80	4.90
Learning satisfaction	U = 580.50		U = 658.00		U = 234.50		U = 659.00
Knowledge test	85.00	89.50	88.00	87.50	91.00	81.50	85.00	90.50
Knowledge test	U = 654.50		U = 677.50		U = 305.00		U = 729.50

^aSignificant results with Bonferonni correction (adjusted significance of p = .017 with *).

^bSignificant results with Bonferonni correction (adjusted significance of p = .017 with *).

Discussion

This study examines the learning outcomes in an immersive VR learning environment through affective (i.e., learning satisfaction) and cognitive (i.e., knowledge test performance) perspectives. In addition, the relationship among learning style, sense of presence, cognitive load, and the above two learning outcomes is discussed. The results of this study show that the sense of presence of involvement/immersion and sensory fidelity, as well as the mental effort of cognitive load, significantly positively predict learning satisfaction. Knowledge test performance is significantly negatively predicted by involvement/immersion, interface quality, mental effort, and mental load.

These results imply that different variables affect different aspects of learning outcomes and can be used to explain the problem, as indicated in the previous literature (Schrader & Bastiaens, 2012a), that immersive learning is inconsistent with learning outcomes. Therefore, it is important to explore the various aspects of the influence on learning outcomes under sense of presence and cognitive load. Furthermore, this study has the practical value of educational applications, which can help educators understand how to focus on appropriate priorities based on their own teaching goals through VR-assisted instruction. For example, if a teacher emphasizes the effectiveness of cognitive learning, he or she should pay attention to and choose a VR device with a suitable user interface (interface quality is a negative predictor of knowledge test performance in this study).

In VR learning satisfaction, this study found that involvement/immersion and sensory fidelity is the key predictor, rather than interface quality. This result indicates that when students gain a sense of participation and immersion in a VR learning environment, the system interface design seems less important to their learning satisfaction. Compared with the interface quality of advanced technology, the participation and interactivity provided by advanced technology are found to be the main features that affect users’ positive attitudes (Shin, 2019).

In addition, the finding that mental effort, that is, the cognitive load of the learner-based aspect, positively predicts learning satisfaction means that students’ satisfaction with learning is closely related to their effort to complete learning tasks. Darabi, Nelson, and Paas (2007) pointed out that mental effort is related to the learner’s engagement, and increased mental effort will enhance the learner’s sustained, effortful and enthusiastic participation and positive attitude.

In cognitive learning outcomes, knowledge test performance has a negative predictive relationship with the sense of presence (including involvement/immersion and interface quality) and cognitive load (including mental effort and mental load).

The research has found that high plausibility in a VR learning environment can cause learner motivation; however, when learning the concepts that must be understood to engage in such an environment, that highly plausible VR learning environment may require too much of the learner’s attention, increase the load on the learner’s working memory (Whitelock et al., 2000), and reduce learning performance (Hembrooke & Gay, 2003; Karpinski, Kirschner, Ozer, Mellott, & Ochwo, 2013). Moisala et al. (2016) showed that the use of media would cause people to decline in their ability to judge the correctness of written or spoken sentences. The acquisition of declarative knowledge depends on the effective functioning of working memory (Maxwell, Masters, & Eves, 2003). In information processing, if learners adopt distracting cognitive processing (parallel and shallow processing), their speed and correctness will be affected as they must pay attention to two or more simultaneous information streams and make appropriate responses (Matlin, 2013). In this regard, Kalyuga and Liu (2015) noted that learners may need to distract their attention to different sources of information in a highly technology-supported learning environment; thus, their working memory must process different information at the same time, which may increase the cognitive load and reduce the cognitive learning performance.

Regarding the analysis of learner characteristics, in contrast with preferences for other dimensions of learning style, the numbers of students who prefer visual and verbal learning styles vary greatly; preference for the visual type is nearly seven times higher than that for the verbal type. This result may be because such students with digital native or I-generation characteristics, who have grown up in an environment surrounded by technology, are more attracted to visuals, pictures, and graphics than reading texts (Akçayır, Dündar, & Akçayır, 2016). The use of visual communication-based media has greatly affected students’ preferences (Thompson, 2013), making them more inclined toward visual learning. The picture superiority effect may also be a cause for students’ preference for visual information. According to the dual code theory, image stimuli can generate image and language codes; however, text stimuli can only generate language code. Image code obtains relatively strong memory tracking, which can generate better memory effects (H. C. Chen, 2006). In addition, when understanding an article, images can provide assistance in characterization, organization, interpretation, and transformation in cognitive processes (Levin, 1981). Students may have experienced the benefits of images in learning and are therefore more inclined to choose visual information to acquire new knowledge. As a result, teaching or learning that adapts to students’ learning styles and preferences is one of the factors that influences learning success (Thompson, 2013); understanding the learning style of I-generation students not only supports the successful integration of VR into teaching situations, but it also has considerable reference value for other types of ICT in teaching.

The previous research (C. J. Chen et al., 2005; Lee et al., 2010; Loureiro & Bettencourt, 2014) has shown that VE learning is suitable for learners of every learning style. The results of this study also show that learning style does not influence learning outcomes including learning satisfaction and knowledge test performance. Although students’ learning style does not influence learning outcomes, it may influence the subjective psychological experience in the learning process (Lee et al., 2010). The results of this study indicate that visual students’ involvement/immersion is significantly higher than that of verbal students. In addition, reflective students must exert more cognitive effort and resources (i.e., mental effort) in the learning process than active students.

From the point of view of fit, when an individual’s preferences are consistent with the task requirements, performance will be improved and vice versa, which is referred to as misfit (Capdeferro, Romero, & Barberàa, 2014; Hecht & Allen, 2005). Since VR is a vision-based learning method, learners understand and assimilate the knowledge learned through interactive dynamic visualizations (Lee et al., 2010). People who prefer visual learning process information and will learn more effectively under imagery guidance (Chang, Lin, & Chen, 2018), thus becoming more engaged or immersed in the VR-based learning environment due to the familiarity of the environment (Virvou & Katsionis, 2008). Similarly, compared with reflective students who prefer to manipulate and examine information introspectively, active students who prefer to complete tasks (Scott et al., 2014) seem to generate less cognitive load in a VR-based learning environment due to the fit between VR learning characteristics and active students’ learning style.

Conclusion

The purpose of this study was to investigate the relationship among students’ learning style, sense of presence, cognitive load, and affective and cognitive learning outcomes in an immersive VR-based learning environment. The students with different learning styles achieved similar affective and cognitive learning outcomes in the VR-based learning environment. However, the difference in cognitive load during the learning process shows that students with particular learning style preferences must bear a greater cognitive load to achieve the same learning outcomes as other students. In addition, cognitive load is negatively related to cognitive learning outcome, and this result partly supports our second assumption. In addition, our results revealed that the components of sense of presence and cognitive load generate inconsistent predictive effects on affective and cognitive learning outcomes, respectively. The literature shows that sense of presence and cognitive load consist of several components and not only a single concept. Therefore, it is important to deeply explore the influence of the sense of presence and cognitive load aspects on VE learning.

Finally, this study confirms that visual processing has become the main and common way of learning for I-generation students. It is therefore worth noting whether the current educational environment meets the needs of these students. Educators and policy makers should pay attention to the learning characteristics of these digital students when designing teaching curricula and materials and determining the most appropriate teaching methods.

With regard to the limitations of this study, it was designed using a one-shot case study, which has the advantage of being simple and easy to implement, which is commonly used in the evaluation of school curricula (Chiou, 2008). However, due to the lack of experimental control and pretesting as a benchmark reference for change scores, the existence of causal relationships obtained from this study data is less convincing. As a preliminary study, this one-shot case study provides a level of information in a simple and convenient way to facilitate an understanding and generate an overview of Taiwanese students’ immersive VR learning. However, it is still a preexperimental design, which suggests that future research may adopt quasi-experimental designs or true-experimental designs, providing more sufficient test evidence for the causal argument of this study. Using a fully immersive VR learning environment requires high research costs and time. Desktop VR may be another research choice. Future research can even compare the differences in learning style, sense of presence, cognitive load, and affective and cognitive learning outcomes in different immersive environments. Another limitation of this study is the relatively small number of participants; therefore, the generalizability may be limited, and caution should be used in the interpretation of results. Future research should use larger samples. In addition, pretests can be added to determine the impact of learners’ prior knowledge on cognitive learning outcomes.

Footnotes

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: This research was financially supported by the Intelligent Electronic Commerce Research Center from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education in Taiwan.

Chiao Ling Huang, an assistant professor at East China Normal University, Shanghai, China. Her research interest involves a wide range of students' behaviors, such as behaviors in online social networking site, online gaming context and educational environment, and has several papers published in SSCI-listed journals.

Yi Fang Luo, received her PhD degree in Education from National Sun Yat-sen University in Taiwan in 2019. Her research interests include technology teaching, digital media use, online psychology, and electronic health literacy. She has authored several related articles in peer-reviewed journals, including SSCI-listed journals.

Shu Ching Yang, a professor of Graduate Institute of Education, National Sun Yat-sen University, Taiwan. She has a wide range of research interests in the field of education, such as educational systems engineering, educational psychology, cognitive psychology, and online teaching. She has authored numerous articles in SSCI-listed journals and is a member of the editorial board of many peer-reviewed journals.

Chia Mei Lu, an associate professor at Cheng Shiu University, Taiwan. Her research interests focus on interactive multimedia design, such as social dynamics in interactive media communities and interactive media tips for collaborative behavior. She has written articles on the motivation and application of media learning, including the Internet, mobile, digital games and new media.

An-Sing Chen, a professor at National Chung Cheng University, Taiwan. He received his PhD degree in Business from Indiana University Bloomington. His research interests focus on artificial intelligence neural network and financial forecasting. He also published several articles related to computer usage behavior.

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