Engineering Lab in Immersive VR—An Embodied Approach to Training Wafer Preparation

Abstract

This paper reports an immersive virtual reality lab (iVRLab) training environment that offers college students an immersive and embodied experience in engineering lab work. The iVRLab provides a simple and safe environment for students to learn the complex lab operations to process and prepare silicon wafers. It features highly embodied interactions congruent to the actual body movements to manipulate the lab devices and materials in the physical world. Fourteen college students participate the lab training and the study results reveal positive learning effects, confidence levels of accomplishment, and embodied experiences. Students’ cognitive load is also measured, and its relationship with the embodied experiences is examined and discussed. The study offers a reference for peer researchers and practitioners for the design and implementation of immersive VR systems for engineering lab training. It also sketches a complementary approach during the time like the pandemic to practicing authentic lab work without having to be in a real classroom or laboratory.

Keywords

immersive virtual reality embodied interactions education engineering lab training

People normally use their body gestures and movements to interact with the environment – to convey ideas, to facilitate communication, to operate machinery, and to trigger performances. In the discussion of this paper, we regard such body gestures and movements as embodied interactions. Embodied interactions and learning, or embodied learning, has been a popular research topic for years, in which learning activities are triggered, facilitated, or manipulated by embodied interactions. Numerous studies have shown that body movements and gestures would facilitate learning and cognition (e.g., Broaders et al., 2007; Chao et al., 2013; Lindgren et al., 2016; Rumme et al., 2008; Sauter et al., 2012; Yoon et al., 2011). Information and communication technologies were not involved in early research studies on embodied learning, and the body movements related to learning in these studies were either performed by the instructors (Alibali & Nathan, 2007) or the learners themselves (Broaders et al., 2007). As body sensing and tracking technologies evolve, people apply such technologies to enable body movements in computer-supported learning environments (e.g., Chang et al., 2013; Hung et al., 2015; Lan et al., 2018; Lindgren et al., 2016). Such technologies made it possible for the users to use their body movements to interact with the computers affording learning with embodied interactions. In recent years, consumer-level virtual reality (VR) technologies have brought embodied features into the game and provide the possibilities to implement embodied interactions for learning within VR settings and to examine learners’ learning performance. In a series of studies, Johnson-Glenberg et al. examine embodiment and learning in VR and MR (mixed reality) settings and compared the learning effects from varied levels of embodiment and non-embodiment scenarios (Johnson-Glenberg et al., 2014; Johnson-Glenberg & Megowan-Romanowicz, 2017). Existing studies acknowledged the positive impact of embodiment on learning within the virtual reality or mixed reality settings, although mixed results were found compared with non-embodied approaches regarding ‘better’ learning performances or not (e.g., Hung et al., 2018; Johnson-Glenberg & Megowan-Romanowicz, 2017; Lan et al., 2018; Merkouris et al., 2019; Nathan & Walkington, 2017).

Researchers and practitioners have been using computer-generated scenarios or simulations to instruct and to train. Generally, simulation involves any imitation or replica of a target system or process featuring its structural elements and dynamic characteristics (Frasson & Blanchard, 2012). In educational area, simulation plays an important role. A simulation can be lively played by real people in a real environment so that learners can practice and rehearse; it can also be constructed by computer systems for users to practice and to try-and-error as needed. In the context of training, VR-enabled simulations have been used to offer trainees an immersive training experience. For example, Larkin (2020) applies the latest VR technology to train the staff and caregivers to the old and the disabled for them to boost their empathy, and their understandings of their clients. In the STEM (science, technology, engineering, and mathematics) fields, simulations generated by computer systems have been largely used to complement teaching activities. In a review on software-based virtual laboratories concentrating on the engineering field (Robotics in particular), the authors summarized various virtual labs pertaining to the technologies used and the scales involved while highlighting the trend to use virtual reality for lab simulations and trainings (Potkonjak et al., 2016). An engineering virtual lab training has notable advantages over traditional lab training that is often hampered by limited access to resources, high lab maintenance cost, and inability to deliver the content in a distance learning setting (Potkonjak et al., 2016).

Virtual Reality and Learning

Virtual Reality (VR) remains one of the most popular buzzwords in recent years. 3 D virtual environments have been studied and utilized in instructions and trainings across disciplines (e.g., Barkand & Kush, 2009; Pivec, 2012; Roussou et al., 2006; Tolentino et al., 2009; Virvou & Katsionis, 2008; Vogel et al., 2006; Young et al., 2012). Chang and Weiner (2016), and Smith and Hamilton (2015) introduced VR applications in simulating medical emergencies and in training medical and nursing students. In Steuer (1992), the author noticed that a 3 D VR environment could provide learners a sense of being in the real world. Such an environment could motivate and engage its learners (Bouta & Retalis, 2013; Pan et al., 2006; Tiala, 2006), and could facilitate learner understanding, rehearsing, and training by offering audio, visual and even tangible experiences (Tolentino et al., 2009). People are conducting VR-based instruction and training to a variety of learners and trainees (Adamovich et al., 2009; Nagendran et al., 2013; Sugden et al., 2012).

Constructionists believe that learning is a process of knowledge construction, and effective learning happens when people actively make meaningful products through their interaction with the environment around them (Papert & Harel, 1991). Over the past decade, plentiful research in VR-based learning was based on desktop 3 D environments. Examples are SecondLife and OpenSimulator. In such environments, learners use keyboards and mice to control avatars and interact with the VR environments of different themes. For example, Le et al. (2015) created a construction safety education setting in SecondLife and developed learning scenarios to increase the learners’ awareness of safety, accident, and hazard in the construction industry. These studies provide empirical evidence that such VR environments may motivate and engage learners, foster learning cooperation, provide a sense of immersion and presence, and help develop collaboration skills, which in turn would enhance learning effects (Andreas et al., 2010; Blas & Paolini, 2014; Cho & Lim, 2017; Wang et al., 2017). Recently, affordable immersive virtual reality technology, enabled by VR headsets and embodied devices (e.g., HTC Vive and Oculus Rift), is transforming the way institutions and organizations train their students and employees. Some studies have shown that people may learn faster and better in a virtual reality environment as it allows greater interaction with the contents that are perceived as being really there (Adamovich et al., 2009; Cho & Lee, 2013).

Situated learning suggests that learning occurs if authentic and contextual environment is provided (Lave & Wenger, 1991; Stein, 1998), where content knowledge is embedded in real-world activities. In such an environment, learners interact with the in-situ scenarios applying both their knowledge and kinesthetic capabilities. Based on situated learning theory, Maher et al. (2018) helped pre-professionals in STEM field to practice communication with the general public about scientific and engineering content. A group of college STEM students practiced and responded in situated scenarios in a planetarium. The participating students gained increased confidence of what they have successfully learned when talking with people, and better prepared their future careers (Maher et al., 2018). Immersive virtual technologies present the affordance to building learning and training situations at low cost that resemble the real-world counterparts. Rather than having to rely on actual physical places and human resources needed (like a planetarium), users may get access to carefully-designed virtual environments that resemble the real ones around the clock for theoretically endless practices and rehearsals without substantial consequences. Particularly, with the help of VR technology, users will immerse themselves in computer-generated environments and participate learning- or training-related activities. In such activities, users may play one or more designated roles, such as a machine operator, a lab researcher, and a problem solver, who directly engage with the dynamic elements in the environment. Within the context of our study, we regard such an environment as a participatory lab simulation. In a latest review study, Xie et al. (2021) provided abundant examples about VR-based training studies and practices from the sectors of first responding, medical and military practices, transportation and workforce, as well as interpersonal skills training. This review introduced main technologies used in and the overview of those trainings in different domains, and found that such works “often use a simulator that is either controlled by a real instructor or driven by a set of training tasks to improve the trainee’s performance on certain tasks or skills” (Xie et al., 2021, p. 2). However, it should be noted that the VR-based trainings are dominantly within the professional and workforce settings, rather than being in the postsecondary curricula context.

Potkonjak et al. (2016) listed the four-plus-one criteria to evaluate virtual laboratories which are, 1) identical lab equipment and user interfaces, 2) equivalent virtual and physical system performances, 3) authentic visualizations, 4) possibility to communicate and collaborate with peers and/or supervisors, and 5) haptic responses and interfaces (the plus). Such criteria can also be regarded as design requirements for people to construct virtual labs for engineering practice and learning uses.

Embodied Interactive Learning in VR

There is an abundance of literature on embodied interactions and learning studying the roles of body movements and gestures as either the instructional methods or the learning approaches. Research has shown the effectiveness of embodied interactions in learning processes across disciplines (e.g., Alibali & Nathan, 2012; Arzarello et al., 2009; Bautista et al., 2011; Glenberg & Kaschak, 2002; Hostetter & Alibali, 2008; Lee et al., 2012; B. Vogel et al., 2012). Thanks to the new human-computer interactive technologies, leaners may use their body movements to control and interact with the learning materials in a computer-mediated virtual setting. In educational technology field, researchers have been trying to use technologies like the Kinect to facilitate learning and instructional activities (Hung et al., 2015; Johnson et al., 2013; Pasfield-Neofitou et al., 2015; Repetto et al., 2015). From the embodied perspective, researchers advocate that cognition may actually arise from our body, may complement other perceptions as channels to learning, and may serve as a bridge between the abstract and the concrete, and the environment and mental activities (Gallagher & Lindgren, 2015; Weisberg & Newcombe, 2017). Xu and Ke (2014) advocate that body movements may attract learners’ attention, facilitate information coding and concretization, offer an extra modality, and help instructional communication. Modern consumer-level immersive virtual reality (VR) devices, such as Oculus Rift and HTC Vive, provide digital trackers that capture and record users’ body movements so that users may control and interact seamlessly with the virtual environment they are in, which will provide and enrich an embodied learning experience.

Alibali and Nathan (2012) talked about body gestures in three types: pointing, representational, and metaphoric and argued that such gestures manifested learning and cognition by directing to a space of interest or a selected part of learning materials, and by concretizing ideas and externalizing concepts with limb- or body-made illustrations or metaphors. In most existing studies on embodiment and learning in VR settings, the researchers had the users apply body movements to learn some certain concepts (Lindgren et al., 2019), calculations (Johnson-Glenberg & Megowan-Romanowich, 2017), or languages (Lan et al., 2018). Something in common among the body movements or gestures involved in these studies is that such movements are directions, representations, and/or metaphors which link the content knowledge together and contribute the germane cognitive load for the learners to process and acquire the necessary information. Nevertheless, the movements that glue learning and content together by adding these directions, representations, and metaphors in between may also more or less impose extraneous cognitive load during the learning process. As a consequence, instructions with embodied features do not necessarily bring anticipated learning results especially for learners with varied abilities (Lan et al., 2018; Post et al., 2013). In the recent pieces from Johnson-Glenberg, the researcher proposed a ‘Necessary Nine’ – the nine principles in guiding the design and analysis of embodied learning in virtual settings ( Johnson-Glenberg, 2018, 2019; see Table 1). Until recently, very few VR-based embodied learning studies have possessed all these nine principles during both the design and implementation phases of their study interventions.

Table 1.

The Nine Design Principles and iVRLab.

The Necessary Nine (Johnson-Glenberg, 2018, 2019)	iVRLab features
Scaffold cognitive effort (and the interface) - one step at a time	• Highlights on objects guiding next step to do• In-VR audio effects (voice messages + sound effects)
Use guided exploration
Give immediate, actionable feedback	• Real-time scoring system• In-VR text feedback on things done
Playtest often - with correct group	• Multiple training sessions
Build in opportunities for reflection	• Scoring and feedback system• Opportunity for multiple training sessions
Use the hand controls for active, body-based learning	• Most hands and arms usages to interact
Integrate gestures that map to the content to be learned	• Body movements well mapped to the lab content
Gestures are worth the time - they promote learning, agency, and attenuate simulator sickness	• Plenty of training time offered without any timer setting
Embed gesture as a form of assessment, both during and after the lesson.	• In-VR instant scoring system• In-VR test session requiring gestural controls

As a summary of the literature and research background review above, we may find that although simulations and virtual labs have been largely applied and studied, few of them deliberately bring embodiment onboard for research purposes, although most lab work and operations, especially those in the STEM fields, require the users to use their bodies to manipulate. And also, as VR-based simulations and trainings are widely applied and studied in the professional and workforce sectors, there seems to be a lack of study on the application of VR-based training within the postsecondary curriculum lab setting. At last, a study on VR-based embodied training scenario implementing the Necessary Nine (Johnson-Glenberg, 2018, 2019) while upholding the four-plus-one criteria by Potkonjak et al. (2016).

Consequently, in this paper, we are reporting a latest study on a participatory lab simulation which applied the state-of-the-art VR technologies to offer an immersive and embodied engineering lab training simulation – the immersive VR lab (iVRLab), developed to serve as a complement to an existing undergraduate level lab curriculum in a large land-granted university. We constructed the lab system which enables engineering students to practice the silicon wafer preparation lab. The major designing features of this VR lab are, (a) a highly immersive VR learning/training environment, (b) abundant body movements involved in order to operate the lab equipment, and (c) direct link between embodied interactions and lab operations. We aim at answering the following research questions,

RQ1 – Will the immersive VR lab with embodied features bring positive learning effects in a college engineering lab training?

RQ2 – How do college students perceive their embodied experience and cognitive load when participating the iVRLab training?

RQ3 – What kind of relationship exists between the students’ embodied experience and their perceived cognitive?

By addressing the above research questions, we hope to add empirical evidence in the research field of embodied interactive learning in immersive VR settings, to bring more possibilities for future research in this area, and to offer reference for peer researchers and practitioners for the design and implementation of similar immersive VR systems.

Methodology

iVRLab System Design and Lab Content

The iVRLab system was developed by the researchers’ team from scratch. 3 D modeling tools like Maya, the Unity3D game engine, and the corresponding Virtual Reality Tool Kit (VRTK) (https://vrtoolkit.readme.io/) were used. A Dell Alienware VR-ready desktop computer powered the virtual environment and the Oculus Quest. The iVRLab training took place in a multimedia room where the participating students did the work one at a time. Wearing the Oculus headset and holding its corresponding sensors, a student utilized body movements, such as walking, bending, touching, grabbing, holding, etc., to interact with the system directly. Since the real lab operations are kinetic in nature and require intensive body, especially hands and arms, movements, our VR lab enabled the students to use their embodied interactions to fully control the virtual body in the lab and to operate the equipment. We designed the embodied interactions to be highly congruent to what a student was supposed to do in the real lab in order to manipulate the devices in an immersive setting (see Figure 1).

Figure 1.

iVRLab in Action.

We introduced the Necessary Nine in the above section, and the iVRLab embraced all the nice design principles for embodied VR environment proposed in Johnson-Glenberg (2018, 2019) in the following way (see Table 1).

The learning content in this lab was about photolithography. In this iVRLab project, we designed an immersive VR training scenario for microfabrication photolithography. Photolithography is a process used in microfabrication to define and transfer a pattern onto a thin film layer on the wafer, and is an essential technology in fabricating integrated circuits. The process occurs several times during the fabrication of a micro device as layers build upon layers. Basically, there is a light source to transfer a geometric pattern from a photomask (also called an optical mask) to a photosensitive (light-sensitive) chemical photoresist on the substrate (wafer). A series of chemical treatments then either etches the exposure pattern into the material or enables deposition of a new material in the desired pattern upon the material underneath the photoresist. In large and complex integrated circuits, a CMOS (Complementary Metal Oxide Semiconductor) wafer may go through the photolithographic cycle as many as 50 times. Photolithography is a complex process, including the operations of mask aligner, spin coater, hotplates, oven, fume hood and microscope, and the handling of photoresist, developers, solvents and other types of chemicals. Particularly, the iVRLab was designed to train students the following lab operations:

Clean the sample wafer inside the fume hood by flushing with acetone, isopropanol, and blow it dry with a nitrogen gun.

Place the wafer in a spin coater, and operate the spin coater to coat photoresist onto the wafer.

Place the wafer on hot plates for baking.

Use the mask aligner to perform the alignment and the exposure processes to pattern the photoresist by the UV light.

Transfer the wafer to a hot plate with set temperatures for post bake.

In the iVRLab, we also import haptic features – when a student touches, grabs, and holds some lab objects, he/she will receive haptic feedback from the system. Meanwhile, we add instant scaffolding mechanism such as highlighted objects and in-VR text hints, as well as a scoring system to inform a student’s real-time performance (see Figure 2).

Figure 2.

In-VR Scaffolding and Scoring System.

Accordingly, when designing the iVRLab, we addressed the four-plus-one criteria in Potkonjak et al. (2016) in these ways, 1) we applied state-of-the-art software to make ‘identical lab equipment and user interfaces’, 2) the virtual lab operations through the embodied interactions afforded ‘equivalent virtual and physical system performances’, 3) what the student saw in front of their eyes featured ‘authentic visualizations’, 4) the instant directions and feedback, and the voice hint based on how a student performed offered the ‘possibility to communicate and collaborate with peers and/or supervisors’, and 5) the vibrotactile force feedback of the system provided the “haptic responses and interfaces”.

Research Methods

The research design of this current study featured a single group pre-and-post-test method, although a more comprehensive mixed-method design had been planned before the pandemic. We implemented convenient sampling method sending out recruiting mails to our undergraduate students and their participation was on a voluntary basis without any incentives provided. By analyzing the tests results, we examined how much the students learned about the lab operations through the intervention, how the students perceived their embodied experience and cognitive load, and what relationship existed between their embodied experience and perceived cognitive load.

Participants

We recruited fourteen undergraduate and graduate students as participants for iVRLab in a land-granted American university. Because of an unanticipated power outage, one participant did not finish the lab intervention and we collected data from 13 students. More participants were planned to be recruited but the recruitment was halted by the pandemic starting March 2020. Among the 13 participants, there were 9 males and 4 females with an average age of 23. Most of the students were majoring in information technologies, while one was in journalism and one in education. Three students had processed no VR experience and the others had some basic VR experience before. And for the lab work itself, most of them (85%) were not familiar with the operations related to microfabrication cleanroom.

Procedure and Test Instruments

Arriving at the study venue, a participant was asked to complete a pre-survey regarding the pre-knowledge and confidence level about the accomplishment of lab operations. The items about the lab knowledge were on the recognition and application of corresponding lab equipment, as well as the sequence of the lab operations. Such items were produced and validated by three lab experts independently, as shown in the Appendix. The survey items on the confidence level of accomplishment (see Table 2) about the lab were task-specific on whether and how much the participants believed that they were able to accomplish the lab tasks. The 5 items were results of multiple interviews and consultancies from expert lab operators. The Cronbach’s α for these items in the pretest and posttest were .89 and .83, which indicated good reliability (George & Mallery, 2003). During the training, a participant wore an Oculus Quest headset and held the corresponding sensor wands in both hands. Being immersed in the virtual lab, the participant would firstly arrive at a preparation room where some warm-up practices took place for gestural operations of the Oculus device. The participant would then be going through the lab training intervention for two consecutive times, which we referred to as training sessions. During these sessions, the screen hints, directions, and scores on each required operation (shown in above Figures 1 and 2) were available to the participant, who was also able to hear instant voice messages over the headset. After the two training sessions, the student had an option for a short break for 5 minutes, and would then complete a final test about the lab operations within the virtual reality. The test content was exactly the same as that in the two training sessions, except for the absence of the on-screen hints, voice messages, and scoring reminders. After the intervention, the participant was asked to complete a post-survey regarding the VR experience and the knowledge about the lab content. The post-survey composed of the same content-knowledge and confidence-level items, with added questions concerning the embodied experience and cognitive load in this iVRLab (see Tables 4 and 5 in Results section). The added questions about embodied experience were chosen and adapted from Lewis and Lloyd (2010), and Asai et al. (2016). The questions about cognitive load were adapted from Chertoff et al. (2010), Hart (2006), and similar studies. Necessary customizations were made to these questions to fit the iVRLab context. After calculations, the Cronbach’s α for the embodied experience items was .82 and the one for the perceived cognitive load items was .75, which indicate good test reliability.

Table 2.

Confidence in Accomplishing Lab Operations.

	Pre		Post		t	df	p-value
	M	SD	M	SD	t	df	p-value
Operating the lab equipment	3.23	.832	4.00	.707	−2.540	12	*.026
Identifying necessary chemicals	3.23	1.166	4.08	.862	−2.085	12	.059
Sensing directions in the environment	3.85	.899	4.46	.660	−2.309	12	*.040
Knowing the steps of the lab	3.15	1.068	4.15	.801	−2.360	12	*.036
Completing the training activities	3.46	.776	4.38	.650	−3.860	12	*.002

Note. *α value set at .05.

Data Analyses

All quantitative data were collected and stored through the Qualtrics online service. With the data available to us in this study, we would provide the descriptive statistics for all the variables of interest. For pairwise comparisons, we would apply both parametric (t-test for example) and non-parametric (Wilcoxon signed-rank for example) tests considering the small number of samples and possible normality-compliance issues. The results from both parametric and non-parametric tests were expected to compensate and endorse each other. More information is to be reported in the following Results section.

Results

For the lab content to be trained, paired t-tests informed statistically significant improvement (t = −5.28, p < .001) in recognizing the lab equipment, applying a device to the right situation, and ordering the lab steps between the pre-test score (n = 13, M = 3.69, SD = 2.75) and post-test score (n = 13, M = 7.77, SD = 2.20). For the limited number of participants, a non-parameter Wilcoxon signed-rank test was conducted and the test result (Z = −3.07, p < .01) endorsed the paired t-tests (see Figure 3).

Figure 3.

Pretest Versus Posttest.

And for how the students perceived they were confident in accomplishing the lab operations, as shown in Table 2, all paired t comparisons showed significant changes (with one marginal p value of .059) after the iVRLab intervention. Such results were also endorsed by the Wilcoxon signed-rank test (Z = −1.97., p < .05; Z = −1.81, p = .07; Z = −2.00, p < .05; Z = −2.10, p < .05; Z = −2.59, p < .05)

Table 3 and Figure 4 illustrate the average time the students spent in the training sessions and the test within the VR lab, as well as the corresponding scores received. The recorded numbers showed an obvious decline of the time and a clear increase of the scores.

Table 3.

Time and Score for Each VR Session (Time Unit in Second).

	Lab Training 1	Lab Training 2	VR Test
Time period	657.23	479.85	404.08
Score	1419	1992	2108

Figure 4.

Time and Score for Each VR Session (Unit in Time Is Second).

For the embodiment experience, the users provided all positive evaluations as shown in Table 4. The following Table 5 shows the results for the cognitive load questions. Generally, the participants did not self-estimate high consumption of their cognitive load.

Table 4.

Embodied Experience.

	*M(n = 13)	SD(n = 13)
E1. I felt like my virtual hands/arms were an extension of myself.	3.9	1.07
E2. I felt like I was actually in the lab as my virtual hands/arms were in the lab.	4.0	.56
E3. I felt like I was experiencing what my virtual hands/arms were experiencing.	4.0	.71
E4. I felt like my hand gestures and body movements manipulating the virtual objects were congruent with what were required by the lab (i.e., such gestures and movements were actually part of the operations).	3.9	.80
E5. I felt like such gestures and movements were part of my training/learning process rather than simply driving the lab training to proceed.	4.2	.80
E6. I felt like such gestures and movements blurred the boundary between the virtual and the real, and made me feel more immersed into the training process itself.	4.1	.76

Note. *Strongly disagree (1point) – strongly agree (5 points).

Table 5.

Cognitive Load.

	*M(n = 13)	SD(n = 13)
Cog1. I felt the task very much mentally demanding.	2.6	.96
Cog2. I found the task very much physically demanding.	2.4	.96
Cog3. I found I am very much rushed in doing the task.	2.2	.90
Cog4. I felt irritated, stressed, and annoyed while doing the task.	1.9	.90
Cog5. I worked very hard to accomplish my level of performance.	2.9	1.2
Cog6. I felt insecure and discouraged while doing the task.	2.2	1.2

Note. *Strongly disagree (1point) – strongly agree (5 points).

Running the correlation tests (Pearson and Spearman), we examined the relationships among the constructs of the students’ perceived embodied experience and their cognitive load. For the limited number of participants that made it hard to judge the score distributions, we opted for the results of the non-parametric Spearman correlation test (with very similar results to the Pearson’s). Table 6 shows the results of the correlations among the constructs of interest.

Table 6.

Correlations Among Elements in Embodied Experience and Cognitive Load.

		Embody1	Embody2	Embody3	Embody4	Embody5	Embody6
Spearman's rho
Cog1	Correlation Coefficient	−.445	−.548	−.410	−.794**	−.556*	−.607*
	Sig. (2-tailed)	.128	.053	.164	.001	.049	.028
	N	13	13	13	13	13	13
Cog2	Correlation Coefficient	−.351	−.592*	−.499	−.733**	−.275	−.478
	Sig. (2-tailed)	.239	.033	.082	.004	.364	.099
	N	13	13	13	13	13	13
Cog3	Correlation Coefficient	−.353	−.319	−.520	−.639*	−.863**	−.389
	Sig. (2-tailed)	.237	.289	.068	.019	.000	.189
	N	13	13	13	13	13	13
Cog4	Correlation Coefficient	−.675*	−.637*	−.114	−.611*	−.463	−.476
	Sig. (2-tailed)	.011	.019	.711	.027	.111	.100
	N	13	13	13	13	13	13
Cog5
	Correlation Coefficient	−.028	−.175	.286	−.071	−.012	−.285
	Sig. (2-tailed)	.926	.567	.343	.818	.968	.345
	N	13	13	13	13	13	13
Cog6	Correlation Coefficient	−.806**	−.681*	−.159	−.582*	−.258	−.502
	Sig. (2-tailed)	.001	.010	.604	.037	.394	.080
	N	13	13	13	13	13	13

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

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

Discussions and Conclusions

In this paper, we present an immersive VR lab (iVRLab) training session featuring embodied interactive learning. The iVRLab highlighted a large amount of physical movements, high congruency between embodied interactions and lab training content, and abundant immersive experience. The design of iVRLab followed the nine principles for embodied VR learning environment proposed by Johnson-Glenberg (2018, 2019) and upheld the four-plus-one criteria in Potkonjak et al. (2016) for virtual lab simulations. The study results showed that the current iVRLab intervention brought significant improvement in the content knowledge acquisition, which is the answer to research question 1. Although it was not the intension of this current study to compare the learning effects of embodied interventions with any non-embodied ones, the result showed consistent outcomes like those from existing studies (Hung et al., 2015; Lindgren et al., 2016) that embodied features brought positive instructional and learning effects. After the lab, the students were able to better understand what the specific lab devices were and how to apply such devices to certain lab operations. Lab work at college level involves not only mechanical repetition of the certain steps, but also the know-how of what and why particular devices and chemicals are used, and how they are used. With the actual practices through embodied interactions, the students could have established a cognitive connection between their body movements and the lab work.

The embodied interactions in iVRLab contributed to the cognitive connection in the following ways. First, as existing studies have suggested, body movements facilitated information coding and concretization, and served another modality in cognitive processing (Arzarello et al., 2009; Cook et al., 2010; Weisberg & Newcombe, 2017). Second, since the embodied interactions in iVRLab were carefully designed to reflect the actual lab operations, such unified congruency helped the students to concentrate their cognitive resources to the learning itself. The students could have established a direct link between the body movements and the lab content rather than having to interpret some representational actions or metaphoric ones. Third, the embodied experience in iVRLab featured both active and passive embodiment – the students actively executed their physical movements during the training session to control their virtual bodies and interact with the objects, received feedback, and accomplished tasks; meanwhile, watching a pair of virtual arms and experiencing the instant change of views within the virtual lab, the students also get passively or visually embodied through such animated body movements. In Post et al. (2013), the children participants gestured while watching animated letters move when learning language grammars. The researchers suggested that observing and making gestures together could have imposed extraneous cognitive load for the kids to handle which might hinder their cognitive processing for the useful information, especially for children with lower ability. However, it should be noted that in Post et al. (2013) and other similar studies, the active and passive (observed) gestures were not controlled and synchronized by the users themselves. The users simply followed and mimicked the animations rather than initiated and controlled the animations to happen. There should be a cognitive delay in this kind of combination of gesture-observing and gesture-making which may cause extra effort to process the visual information – the users could spare extra cognitive load to inspect what the observed gestures were and to explore how they should move to follow the gestures. In our current study, the observed arm movements, the change of the first-person views, and the students’ body movements were seamlessly synchronized together. The students initiated every movements in both the physical and the virtual worlds. There was no cognitive lag between the observed and the actual muscular movements. From the cognitive load test results (see Table 5), it is reasonable to infer that little extraneous cognitive load was introduced in the iVRLab session. In addition, since the students actively initiated the physical movement in the real worlds, they would actually expect to see what was happening in the virtual world as a consequence of such embodied interactions. We designated this a ‘what you see is what you do’ approach in our iVRLab context.

It is also worth mentioning that the students’ content test result was in consistency with how their levels of self-efficacy (Bandura, 1977, 1986, 1990) changed before and after the iVRLab intervention. In the broad sense, self-efficacy indicates people’s judgement or belief of the capability to produce given levels of attainment (Bandura, 1997), fulfill certain tasks and to achieve certain goals – to successfully accomplish designated lab operations in our case. Bandura (1997) clearly made the distinction between self-efficacy and confidence stating that the former “includes both an affirmation of a capability level and the strength of that belief”, and the latter is merely “a catchword rather than a construct embedded in a theoretical system” (p. 382). It is not our intension to further distinguish these two words, but we regard the perceived confidence level that we asked from our participants for their belief in accomplishing the lab operations as a synonym of self-efficacy in our context. Establishing the confidence of achievement requires a complex course to cognitively process a variety of resources which include performance accomplishments and other experiences. As “performance accomplishments are supposed to provide the most dependable confidence information because they are based on one's own mastery experiences” (Druckman & Bjork, 1994, p. 178), the statistically significant increase in the students’ confidence level in successful lab operations endorsed their performances enabled by the embodied interactions during the iVRLab session.

For the second research question, looking at how students perceived their embodied experience in iVRLab (see Table 4), we found all positive experience in recognizing their body movements as part of the learning process and in connecting the real and the virtual worlds in a seamless way. Such a result also echoed the ‘what you see is what you do’ approach mentioned above and indicated the students’ sense of agency that described the feeling and judgement that they had the control over the actions and the corresponding consequences (Braun et al., 2018; Moore, 2016; Moore & Fletcher, 2012). Meanwhile, the haptic feedbacks that iVRLab offered while a student grabbed, touched, and pointed at some of the objects also increased the embodied experience in the lab. As some researchers argued that it would be extremely hard to control the actions without any haptic feedback (Baud-Bovy & Balzarotti, 2017; Johansson & Flanagan, 2009), we regarded the haptic responses in iVRLab as part of the students’ embodied interactions which contributed to their embodied experience.

And for the relationship between the students’ embodied experience and the cognitive load which research question 3 addressed, the current study result showed some interesting findings (see Table 5) for the participating students. First, Embody4 was negatively related to all cognitive load indicators except for Cog5. It made much sense that, since the students regarded their body movements as congruent with the lab operations (Embody4), i.e., the “what you see is what you do”, they might not feel it necessary to have much extra mental and physical endeavors (Cog1–3) in order to interpret the connections between their actions and the lab activities; and, consequently, they would turn to have little emotional tensions and insecurity (Cog5–6). Experiencing and regarding embodiment as part of the learning process to actively interact with the lab, the students would no longer feel being just passively driven by the lab tasks, but would feel engaging actively in performing the lab operations and solving the problems. Second, Embody1 and Embody2 were both negatively related to Cog4 and Cog6. As the students regarded the virtual arms as part of themselves and felt they were actually inside the lab, they had full control of such an extended part of the body so that, with this kind of sense of agency (Braun et al., 2018; Moore, 2016; Moore & Fletcher, 2012), they would perceive it safe and confident in doing the lab work and accomplishing the tasks. Meanwhile, Embody2 was also negatively related to Cog2 because with the perception of embodiedly being in the lab, the students might not need any extra physical endeavors in engaging themselves and doing the lab work. Third, Embody5 was negatively related to Cog1 and Cog3. During the intervention, the students regarded their embodied interactions as part of the learning process so that they might not feel any extra mental demands other than the learning process itself. And for the negative relationship between Embody6 and Cog1, since the embodied interactions were designed to be so genuine that afforded the students an authentic real-world lab operating experience, the students were inclined to feel the activities no more mentally demanding than that in a real physical lab. Summarizing the relationships between how the students perceived the embodied experience and their cognitive load, we may argue that while the students regarded the body movements and the lab work naturally connected and seamlessly bridged between the virtual and the real, they managed to perform the tasks and accomplish the lab goals without much extraneous cognitive load.

Apart from the conventional text-based test on the lab content, the iVRLab also examined the students’ lab performance within the VR itself applying embodied interactions, which responded to the call to embed embodied features in the assessment to reflect the instructional and learning process that involves embodied interactions (Johnson-Glenberg & Megowan-Romanowicz, 2017). Johnson-Glenberg and Megowan-Romanowicz (2017) designed their study intervention that prompted the students to use their body movements and gestures to learn; and after the intervention, hand gestures (using the fingers) were used to interact with a smart-screen in order to answer the test items. In our study, an embodied interactive test was done after the two lab training sessions. In the test, a student would do lab work as what was learned and practiced in the training sessions. They lab environment remained the same as that in the training session, but with the instant feedback, voice hints, and scoring reminders removed. Recording the average scores they received and the time they spent in completing the test (see Table 3 and Figure 4), we found that the time periods for completing the lab sessions showed a consistent decrement, while the scores indicated a consistent increment. Although not direct indicators about the content knowledge in this in-VR test, the trend in the result did reflect the growth of the lab-related know-how.

As a summary, iVRLab could bring positive learning effects in terms of knowledge acquisition and confidence increase to students for this particular lab training without compromising on the extraneous cognitive load. The embodied interactions in iVRLab served the virtual lab operations well and were regarded as a natural part of the learning process. While the best technology will be something that a user barely feels its existence, a natural embodied interactive experience within a virtual lab may enable a seamless transition of the lab operations. Design-wise, this study may serve as a reference for researchers and practitioners to implement the nine principles (Johnson-Glenberg, 2018, 2019) and the four-plus-one criteria (Potkonjak et al., 2016) in their VR-based embodied learning environment designs. VR-based embodied lab training with such design elements may afford the students to “feel like they are working with real authentic devices in a real authentic space” (Potkonjak et al., 2016, p. 311) while employing their body movements like they are moving and working in the real authentic lab space. Meanwhile, iVRLab also owns the potential to be used individually by any student in need of the lab practice during the pandemic without having to go to the campus.

Limitations and Future Studies

Limitations existed in this iVRLab study. First, for the COVID-19 pandemic, we failed to recruit more student participants for the test, and for a control group for any comparison study. The statistical results should be regarded as exploratory at this stage and would reflect a trend for future study outcomes. The readers are suggested to treat the study results as an anchor for further research and discussions. Second, this particular training concentrated on a single engineering lab session and the training took place for only once. There was no information about the students’ lab knowledge retention after a period of time.

We will treat this current study as part of a design-based research approach (Wang & Hannafin, 2005) to include more lab training sessions and to implement a more comprehensive mixed-method research. Future study will feature more quantitative and qualitative data such as students’ in-lab behaviors and their interviewed experiences, and the corresponding data analyses to gain a better picture of how embodiment is playing the role in the iVRLab. Peer researchers are also suggested to conduct more studies on embodied learning in immersive virtual, mixed, and extended realities.

Appendix

Content test questions

1. What equipment is this?

A. Mask aligner

B. Spin coater

C. Hot plate

D. None of the above

2. What equipment is this?

A. Mask aligner

B. Spin coater

C. Hot plate

D. None of the above

3. Arrange the following photolithography steps in the proper order from first (1) to last (6).

Order	Process Step
	bake on the hot plate
	clean the wafer by flushing the sample with acetone and Isopropanol 2
	apply photoresist on the wafer
	spin coat the photoresist in the spin coater
	place the wafer in the spin coater
	dry with the nitrogen gun

4. The photoresist film is applied in which of the following photolithography steps.

A. Coat

B. Mask

C. Expose

D. Develop

5. The purpose of baking the Photoresist on hotplate

A. The photoresist will still have some solvent left in it after spinning. It is heated slightly (the “soft bake”) to drive off any remaining solvent.

B. Photoresist needs to be heated up to be functional

C. To expose the wafer to the light in the lab, so it undergoes a chemical change.

D. None of the above

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 study was made possible by the University of Missouri Research Council grant support (#URC‐19‐117).

ORCID iD

Xinhao Xu

Author Biographies

Xinhao Xu is an assistant professor of Information Science and Learning Technologies at the University of Missouri. He has been actively conducting research on learning and cognition in technology-enhanced learning and training environments utilizing both quantitative and qualitative methods. His current research interests mainly focus on embodied interactions and learning, immersive virtual learning environments, and game-based learning for STEM subjects. He is a member of multiple professional organizations such as IEEE, AERA, and AECT.

Fang Wang is an assistant teaching professor of Electrical Engineering and Computer Science at the University of Missouri. Her research interests include simulation software development, virtual/augmented reality, and mobile application development and research. She is actively involved in utilizing innovative technology as an intervention means to improve the understanding of technology and its implication in the learning, health and well beings of a person. Before joining MU, she worked in Motorola, Freescale, and Ansys as a research and development engineer.

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