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# Immersive Virtual Reality Learning and Cognitive Load: A multiple-Day Field Study

Benjamin de Witte, Vincent Reynaert, Danny Kieken, Joseph Jabbour, Catherine Demarey, Anne Dumoulin, Jalal Possik

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## To cite this version:

Benjamin de Witte, Vincent Reynaert, Danny Kieken, Joseph Jabbour, Catherine Demarey, et al. Immersive Virtual Reality Learning and Cognitive Load: A multiple-Day Field Study. Computers in Human Behavior, 2025, 10.1016/j.chb.2025.108853. hal-05372392

HAL Id: hal-05372392

https://hal.science/hal-05372392v1

Submitted on 19 Nov 2025

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# Immersive virtual reality learning and cognitive load: A multiple-day field study

Benjamin De Witte<sup>a,d,*</sup> <u>D</u>, Vincent Reynaert<sup>a,d</sup> <u>D</u>, Danny Kieken<sup>b</sup> <u>D</u>, Joseph Jabbour<sup>b,e</sup> <u>D</u>,
Catherine Demarey<sup>c</sup>, Anne Dumoulin<sup>f</sup>, Jalal Possik<sup>b</sup>

## ARTICLE INFO

### Keywords:

Immersive virtual reality

Education

Cognitive load

Procedural knowledge

Field study

## ABSTRACT

Immersive virtual reality (IVR) has the capacity to act as a complement to skill training. Nevertheless, discrepancies in outcomes persist, and field studies evaluating the use of IVR during multi-day training remain scarce. Furthermore, cognitive load has the potential to provide valuable insights into the instructional framework. Yet, it is often overlooked in practice. This study examines cognitive load and its interaction with other individual features (i.e., self-efficacy, presence, cybersickness and learning outcomes) when using IVR during a multiple-day training programme in molecular biology skills. The primary objective is twofold: firstly, to enhance comprehension of the role of cognitive load during multiple day training and, secondly, to refine the instructional framework for the utilisation of IVR within authentic classroom settings. A total of 54 undergraduate students were recruited for the study. Participants were split into three groups: one with only practical training, and two using IVR either before or after the hands-on training. The findings demonstrated no discrepancy in self-efficacy, cognitive load, and learning outcomes between the IVR groups after training. However, IVR groups demonstrated higher levels of cognitive load but lower learning outcomes and self-efficacy scores in comparison to the CTRL group. Correlations were also exhibited between cognitive load, self-efficacy and cybersickness. The moment of implementation of IVR did not have an effect on the assessed variables. However, in contrast to the CTRL group, the implementation of IVR increased the associated cognitive load, which may have influenced the learning process.

## 1. Introduction

The potential of immersive virtual reality (IVR) tools as reliable learning tools is being increasingly investigated. In particular, technologies such as head-mounted displays (HMDs) enable full immersion in highly realistic learning environments, allowing learners to directly interact with the content (Hamilton et al., 2021). The psychological construct of feeling “inside” is referred to as “sense of presence,” while the ability to interact with the environment is known as “sense of agency.” Both affordances contribute to enhanced learning outcomes, as postulated by the cognitive and affective model of immersive learning (CAMIL) (Makransky & Petersen, 2021). More specifically, studies have

demonstrated that procedural knowledge is particularly well suited to IVR, given its reliance on feedback provided by HMDs (Jongbloed et al., 2024). Moreover, Yang et al. (2024) emphasize that IVR can serve as a valuable complement to traditional skill training. Despite its potential, discrepancies in learning outcomes have been observed (Yang et al., 2024), underscoring the need for more empirical research on IVR implementation in real classroom settings, particularly through field studies (Huang & Roscoe, 2021). More specifically, as outlined by Won et al. (2023) studies should consider pedagogical features instead of only focusing on technological features. Previously, Makransky and Petersen (2021) emphasized on the need to investigate the instructional framework to better understand how and when to implement IVR in real

<footnote><sup>a</sup>Anthropolab, Ethics EA 7446, Lille Catholic University, 14 Boulevard Vauban, 59000, Lille, France</footnote>

<footnote><sup>b</sup> ICL, Junia, Lille Catholic University, LITL, F-59000, Lille, France</footnote>

<footnote><sup>c</sup> PSYCOS, Ethics EA, 7446, Lille Catholic University, Lille, France</footnote>

<footnote><sup>d</sup> Piktura School, 78 Boulevard Du Général Leclerc, 59100, Roubaix, France</footnote>

<footnote><sup>e</sup> Risk Sciences Laboratory, IMT Mines Ales, Ales, 30100, France</footnote>

<footnote><sup>f</sup> Faculty of Management, Economics and Science, Lille Catholic University, Lille, France</footnote>

<footnote>* Corresponding author. Laboratoire ETHICS (Anthropolab), 14 boulevard Vauban, 59000, Lille, France.</footnote>

<footnote>E-mail address: benjamin.dewitte@univ-catholille.fr (B. De Witte).</footnote>
classroom settings. Notably, few studies have examined how cognitive load interacts with learning outcomes and individual factors during intensive, multi-day training programs. Furthermore, greater consideration is required of factors such as task design (Han et al., 2021).

In consequence, this field study investigates the interaction of cognitive load with individual features (e.g., self-efficacy) and learning outcomes in an intensive, multiple-day training programme. To clarify the study's objectives, the following sections outline key concepts including IVR learning, cognitive load theory, and cybersickness, followed by the presentation of the research questions and experimental hypotheses.

## 1.1. IVR

IVR has become an increasingly viable educational tool, accessible through platforms such as cave automatic virtual environments (CAVE) and HMDs (Buttussi & Chittaro, 2017). Both devices provide a highly realistic learning environment and afford users the opportunity to interact with the content in a meaningful way (Hamilton et al., 2021). However, due to their portability, lower costs and higher availability, HMDs are undoubtedly the most prevalent (Wu et al., 2020). The immersive and interactive technological features provided by HMDs enable cognitive constructs, including the "sense of presence" (Dengel & Mågdefrau, 2019) and the "sense of agency" (Taub et al., 2020). To be more precise, immersion can be defined as "an objective property of the physical technology" (Parong & Mayer, 2021). This suggests that the more technological features a device provides, such as high image resolution and sound quality, head tracking and greater degrees of freedom of head rotation, the more it will tend to elicit a greater "sense of being there", which is defined as a sense of presence (Parong & Mayer, 2021). Likewise, interactivity is defined as the degree of freedom afforded by a system or device to physically interact and participate, for example by grasping and manipulating virtual objects in a virtual environment. This concept has been shown to generate a sense of agency, which refers to the psychological sense of controlling and generating actions (Klingenberg et al., 2024). Presence and agency, both act as general affordances, which in IVR can be defined as the action possibilities for learning that emerge from the interplay between the features of the virtual environment and the learner's capacity to perceive and utilise them (Dalgarno & Lee, 2010). In other words, IVR facilitates the generation of feelings (e.g., presence) that in turn influence other affective and cognitive factors (e.g., motivation; self-efficacy), which in turn impact learning outcomes. According to the CAMIL, the nature of this relationship can be positive or negative. A positive relationship is exemplified by self-efficacy (Makransky & Petersen, 2021). Self-efficacy, defined as "one's perceived capabilities for learning or performing actions" (Schunk & DiBenedetto, 2016), is highly dependent on the quality of feedback provided during learning. The provision of highly realistic feedback by HMDs has been demonstrated to enhance self-efficacy levels during the learning process (Liu et al., 2022; Xie et al., 2023; Yan et al., 2025). In this way, HMDs allow learners to practice tasks while absorbing key information, thereby reinforcing self-efficacy. This positive feedback loop has been shown to increase learning outcomes (Xie et al., 2023; Yan et al., 2025).

Realistic feedback is therefore considered one of the core strengths of HMD-based learning (Jongbloed et al., 2024; Makransky & Petersen, 2021). Accordingly, procedural knowledge is widely regarded as particularly well-suited for acquisition through HMD-based IVR (Makransky & Petersen, 2021). Procedural knowledge refers to the acquisition and retention of the skills needed to execute complex tasks, by analysing and adapting to the contextual factors that influence their performance (Buttussi & Chittaro, 2017). Therefore, procedural knowledge depends on cognitive and perceptual feedback, which represents the primary advantage of HMDs for learning. However, it has been observed that despite the alignment between the requirements for procedural knowledge and the capabilities of HMDs, the learning

outcomes achieved through HMDs do not consistently surpass those non-immersive learning tools. This has been delineated in a systematic literature review by Jongbloed et al. (2024). For instance, Schroeder et al. (2017) found no difference in recall performance between participants trained on a mechanical procedure using IVR and those trained via desktop learning. Similarly, Crockatt et al., (2023) investigated IVR-based training in shoulder arthroplasty. In this study, junior residents were assigned to either a control group training on cadavers or an IVR group training with HMDs. The primary outcome measures included completion time, the quality of gestures, and written knowledge scores. The study revealed no significant differences between the two groups in any of the variables examined. Other studies that have examined the acquisition of surgical skills and compared conventional learning methods with IVR are displayed in Gilliland et al. (2024). In this systematic review, the authors analysed 19 included studies, of which six showed no difference in surgical performance following training, and one even concluded that IVR led to worse surgical performance. It is evident that the absence of a significant difference between IVR and other learning methods for acquiring procedural knowledge is not confined to a particular topic or learning medium (Gilliland et al., 2024; Jongbloed et al., 2024; Makransky et al., 2021; Meyer et al., 2019; Yang et al., 2024). Thus, existing literature demonstrates that training with IVR does not invariably yield superior outcomes in comparison to traditional learning means, notwithstanding the evident advantages (i. e., affordances) particularly with regard to procedural knowledge.

Consequently, it is reasonable to hypothesize that learning challenges associated with HMD use warrant further investigation; particularly, in relation to cognitive load as a potential explanatory factor. The CAMIL framework highlights a negative correlation between heightened cognitive load and learning performance. Furthermore, although learning is reliant on it, it was also suggested that higher sense of presence could increase levels of mental demand (Breves & Stein, 2023; De Witte et al., 2024). Petersen et al. (2022) previously demonstrated that elevated cognitive load (i.e., extraneous load) associated with immersion and interaction influences levels of presence. The author hypothesised that difficulties with the interaction technique may result in a decrease in levels of presence, as these interruptions may cause the user to feel outside the virtual environment (i.e. in the real world). This finding suggests that maintaining presence in the virtual environment may necessitate a significant degree of mental demand. As presence is a subjective psychological construct requiring mental effort and neuronal activity (Baumgartner et al., 2008; Clemente et al., 2014), it can be hypothesised that the subjective perception of the exerted mental demand is related to the feeling of being present in the virtual environment. This would also provide insight into the reasons why users report different levels of presence when experiencing the same virtual environment. While the cognitive theory of multimedia learning provides some insight, further research is necessary to fully understand the complex interplay between cognitive load and the learning process in IVR.

## 1.2. Cognitive load and learning strategies

Cognitive load is posited to be caused by the cognitive demands involved in the learning tasks (Makransky & Petersen, 2021). The theory of cognitive load (Sweller et al., 1998), which has been revised by Sweller et al. (2019), posits that learning is enhanced when the conditions align with the human cognitive architecture. Indeed, the processing of new information requires the utilisation of working memory resources, which are limited in their capacity to simultaneously process a finite amount of new information. It is posited that the overall cognitive load should not exceed the learner's capacity for learning (F. Paas et al., 2003; Sweller et al., 2019). More precisely, within this revised theory, the cognitive demands imposed by learning environments are distinguished, as intrinsic cognitive load and extraneous cognitive load (Sweller et al., 2019). It is important to note that the
revised model (Skulmowski & Xu, 2022; Sweller et al., 2019), has
removed the 'germane load', which is defined as the cognitive resources
that can be allocated to the mental processes involved in the construc-
tion and consolidation of newly acquired knowledge within long-term
memory (i.e., schemas), from the additive equation of total load.
Consequently, in the latter model, intrinsic cognitive load signifies the
aggregate of cognitive demands inherent to the learning task itself (i.e.,
the subject matter to be acquired), whereas extraneous cognitive load
denotes the cognitive demands engendered by the manner in which the
task is designed and presented (Sweller et al., 1998). Accordingly,
extraneous cognitive load should be minimised in order to optimise the
effectiveness of the learning process (Sweller et al., 1998). This assertion
assumes particular significance in the context of learning in IVR, where
the integration of development features such as virtual environments,
background music, excessive perceptual details or controlling elements
are often purported to induce supplementary cognitive load that is not
always relevant to the learning process (Concannon et al., 2019; Han
et al., 2021; Parong & Mayer, 2021). To elaborate, Skulmowski and Xu
(2022), emphasized that development features, which are defined as
extraneous load, should not be systematically regarded as an impediu-
ment to the learning process. Indeed, as long as the design of the envi-
ronment aligns with the learning objectives, studies have shown that
these features are essential for the learning task and can thereby
improve the learning performance. In other words, when congruent with
the learning task, features of the immersive learning task are necessary
information; when unrelated, they may distract the learner and generate
irrelevant load, which may lead to cognitive overload and thereby
learning impairment. The management of the design of virtual envi-
ronments appears to be of paramount importance, whether by the
incorporation of accurate virtual environment features or by other
techniques. In a similar vein, the management of overall cognitive load
can be accomplished through the implementation of instructional design
techniques (Sweller et al., 2019) or generative learning strategies
(Fiorella & Mayer, 2016). Mayer and Moreno (2003) proposed several
strategies for managing cognitive load in multimedia learning, including
segmentation; which consists in dividing learning content into
manageable, sequential segments. Research has demonstrated the effi-
cacy of this approach in managing overall cognitive load in IVR learning
(De Witte et al., 2024; Klingenberg et al., 2023). Other methods that
have been employed include pre-training and enactment. For instance,
Meyer et al. (2019) compared IVR and video lesson modalities for
teaching human cell biology, incorporating a pre-training phase for half
of the participants. In this study, the pre-training phase entailed the
provision of key terms and the layout of the content to be learned prior
to the IVR learning session. The objective of this phase was to facilitate
the learners' familiarity with the names and characteristics of the key
concepts, thereby paving the way for effective learning (Mayer & Pile-
gard, 2005). The results of this study show that participants who un-
derwent pre-training exhibited superior performance in comparison to
those who did not receive pre-training. The authors suggested that the
strategic allocation of cognitive resources prior to training reduced the
associated cognitive load, thereby enhancing the efficiency of the IVR
learning phase. Similarly, Makransky et al. (2021) drew parallels be-
tween an IVR and a video lesson modality, this time in conjunction with
enactment. The latter consisted of performing the learned procedure
with concrete manipulatives, which is particularly relevant for proce-
dural learning as a follow-up to simulated environments (Fiorella &
Mayer, 2016). The authors suggest that enactment is particularly rele-
vant to IVR, given that learning with IVR can be overwhelming for
students due to the associated cognitive load, which constrains their
capacity to reflect and self-regulate during a lesson (Makransky et al.,
2021). The integration of instructional methods may, therefore, be
conducive to learning by reducing the associated cognitive load, as
evidenced in multimedia learning (Curum & Khedo, 2021; Mayer &
Moreno, 2003; Mutlu-Bayraktar et al., 2019). Nevertheless, as regards
the studies of Meyer et al. (2019) and Makransky et al. (2021), this

cannot be determined, as the measurement of cognitive load was absent. Consequently, in instances where studies have measured cognitive load while integrating instructional methods in IVR, ambivalent outcomes are observable. For instance, a recent study demonstrated that the management of cognitive load through segmentation does not impede the learning process (De Witte et al., 2024) whereas, a subsequent study which incorporated another instructional method (i.e., signalling) revealed only partial effects (Albus et al., 2021).

It is also important to acknowledge that the discrepancy in outcomes and their understanding is exacerbated by the existence of different instruments that do not measure the same dimensions of cognitive load. As demonstrated in the extant literature, there are a number of methodologies for the measurement of cognitive load, including neurophysiological tools and survey measurements (Naismith & Cavalcanti, 2015; Skulmowski, 2023; Suzuki et al., 2024). The former encompasses a range of metrics, including heart rate variability, electrodermal activity, electroencephalography, and functional near-infrared spectroscopy (fNIRS). As demonstrated in the study by Peng et al. (2024) fNIRS associated to HMDs has been shown to be promising in terms of its resistance to interference and the presence of instrumental issues. Neurophysiological tools are notable for their strong validation, objectivity and capacity for uninterrupted measurement during participant task performance. Nonetheless, the applicability of these tools to field studies is constrained by their limited mobility. Moreover, the acquisition of data may be impeded by artefacts (e.g., electroencephalography), particularly during dynamic tasks Consequently, these tools are better suited to laboratory settings. Conversely, self-reported measures have been developed. For instance, Likert scales have been designed to assess distinct categories of cognitive load (Klepsch et al., 2017; Skulmowski, 2023). These scales provide insights on the perception of the different types of cognitive load as stated in the theory of cognitive load. Another notable and frequently employed instrument is the NASA Task Load Index (NASA-TLX) (Hart & Staveland, 1988). Despite the fact that the dimensions analysed do not align with the cognitive load theory, it provides a comprehensive multidimensional measurement of overall cognitive load, including such aspects, as frustration and mental demand (Mutlu-Bayraktar et al., 2019; Pasaribu et al., 2022). The latter aspects may be valuable as it is known that emotions are essential in learning (Mancuso et al., 2023) and that, more specifically, too much frustration can lead to boredom and decrease learning (Novak et al., 2023). Furthermore, as mental effort has been demonstrated to play a significant role in the learning process (i.e. problem solving; self-regulation) (van Gog et al., 2020), having an insight on this effort appears crucial. Finally, the NASA-TLX is a valuable tool that extends beyond the scope of IVR, making it a useful resource for conducting a comparative analysis of the associated overall cognitive load between IVR tasks and more traditional learning settings.

Taken together, the aforementioned sections confirm that cognitive
load interacts with other features when learning in IVR. This interaction
thus highlights the necessity for further research to investigate the
relationship between IVR, cognitive load, individual features and
learning outcomes, as highlighted by Han et al. (2021) and Lawson,
Lawson, and Mayer (2024). Specifically, there is a paucity of empirical
studies investigating the optimal timing for the implementation of IVR.
It would be a worthwhile endeavour to ascertain whether the imple-
mentation of IVR, whether prior to or subsequent to practical training,
exerts an influence on the associated cognitive load. Indeed, it may be
hypothesised that, in a manner analogous to simulation-based training
in healthcare procedures, prior training in IVR could reduce the cogni-
tive load experienced by learners. This is because it may allow them to
make mistakes without fearing negative consequences (Bong et al.,
2016; Szulewski et al., 2021). Indeed, the act of making errors in a
risk-free environment can provide learners with valuable insights into
sections of the procedure that are considered to be challenging or risky
in real-world settings. This approach has been shown to facilitate
learners in mitigating feelings of uncertainty regarding their capacity to
successfully complete challenging aspects of the procedure during real execution. It can be posited that a reduction in feelings of uncertainty may result in a corresponding decrease in stress and anxiety, which have been demonstrated to engender additional cognitive load (i.e. extra-neous load) (Szulewski et al., 2021). In a similar manner, the imple-mentation of IVR in direct succession to physical training could yield analogous outcomes to that of actual practice. It is a well-documented fact that the repetition of gestures or skills leads to their automa-tisation and thereby reduces the associated cognitive load (Haith & Krakauer, 2018).

### 1.3. Cybersickness in IVR

Cybersickness is characterised by the occurrence of discomfort symptoms during or after the use of HMDs (LaViola, 2000). Despite extensive research in this area (Biswas et al., 2024; Caserman et al., 2021), no general solution has yet been found, and the issue remains a concern (Tian et al., 2022). Research has for instance demonstrated a correlation between cognitive load and cybersickness within a single IVR session (Sepich et al., 2022). Therefore, assessing cybersickness across multiple-day IVR sessions could provide valuable insight into whether its relationship with cognitive load persists over time.

### 1.4. Research questions and hypotheses

As asserted in the extant literature, there is a necessity to reconsider the relationship between IVR learning and cognitive load, with consid-eration given to factors such as individual perception and task design (Han et al., 2021). In consequence, the first research question (RQ1) examines whether the sequencing of IVR training, administered before or after practical instruction, affects cognitive load levels. This inquiry will be juxtaposed with the control condition to provide a comprehen-sive analysis. Secondly, we seek to explore the manner in which cogni-tive load interacts with participants' perceptions, including presence, self-efficacy, and cybersickness, and the extent to which these may in-fluence learning outcomes (RQ2). Addressing these questions will contribute to a deeper understanding of how IVR can function as an effective complementary tool for acquiring procedural knowledge in real classroom settings (Yang et al., 2024). This objective will be accomplished by measuring and comparing levels of cognitive load. Additionally, it would be advantageous to ascertain the existence of any correlations between cognitive load and learning outcomes, self-efficacy, cybersickness and presence. To this end, an experimental design employing a pre-post-test approach will be utilised during an intensive, multiple-day training course in molecular biology procedures. This design will allow for the comparison of three distinct groups. Two groups will undergo IVR training in complement to practical training, with one group undertaking IVR training prior to practical training, and the other following IVR training subsequent the practical training. The third group will undergo a neutral activity followed by practical training, thereby constituting the control group. The following hy-potheses are posited in this study:

* Hypothesis 1 (H1): The cognitive load will be higher for the IVR groups than the control group.

* Hypothesis 2 (H2): In the IVR groups, levels of cognitive load, presence, self-efficacy, and learning outcomes will interact.

* Hypothesis (H2a): As presence requires mental effort, levels of presence will be positively correlated to mental demand levels.

* Hypothesis (H2b): Self-efficacy will be positively correlated to learning outcomes.

* Hypothesis (H2c): Cognitive load will be negatively correlated to self-efficacy and learning outcomes, especially for participants exhibiting high levels of cognitive load.

* Hypothesis (H2d): Cognitive load and cybersickness will be positively correlated.

## 2. Material and methods

### 2.1. Participants

Fifty-four undergraduate students (34 women, 20 men; age range: 19–24 years; M = 20.79, SD = 1.03) participated in the study. All were in their third year at Lille Catholic University, France. Participation was voluntary. Students who declined were excluded from the experiment. Written informed consent was obtained from all participants in accor-dance with the Declaration of Helsinki. To maintain comfort and engagement, participants were offered complimentary refreshments during the training sessions.

### 2.2. Variables

An evaluation of the cognitive load, learning outcomes, self-efficacy, cybersickness and presence was conducted.

#### 2.2.1. Cognitive load

The NASA TLX was used to assess cognitive load (Hart & Staveland, 1988). It is considered to be a reliable tool for measuring cognitive load (Pasaribu et al., 2022; Skulmowski, 2023). The assessment consists of two parts. The first part assesses the impact of six factors on the cogni-tive load of the learning task: (1) mental demand, (2) physical demand, (3) time pressure, (4) performance, (5) frustration level, and (6) overall effort. These factors help to characterise the nature of the cognitive load during the executed task (i.e., combination of molecular biology task and IVR or neutral activity). The second part requires participants to compare each factor with the others in a series of 15 pairwise compar-isons to determine which factor contributes most to the overall cognitive load. The frequency with which each factor is selected is recorded and tallied, and a single coefficient is assigned to reflect its contribution to the overall cognitive load. The overall cognitive load score is derived by multiplying the rating of each factor by its corresponding weight. This procedure ensures a high degree of internal consistency and reliability.

#### 2.2.2. Learning Outcomes

In order to ascertain the learning outcomes, an objective assessment tool was developed by the experts (i.e., university associate professors). The present assessment tool has been developed with inspiration drawn from tools utilised within the domain of medical education (Doyle et al., 2007; Zoller et al., 2021). The assessment tool has been conceived in such a manner as to promote an objective assessment of the technical abilities and the requisite concomitant knowledge that students must acquire during their training. More specifically, the assessment tool comprised a 7-point Likert scale, with scores ranging from 1 = less than three elements necessary to execute the task are cited, to 7 = all ele-ments necessary for the task are cited and applied in the correct order. The tool measured seven main technical abilities (e.g., naming the main components of the ligation mixture, listing the stages of bacterial transformation by heat shock). The latter were defined by the experts as the most important steps of the entire learned procedure. The scores obtained for each item were then summed up.

#### 2.2.3. Self-efficacy

The measurement of self-efficacy was conducted through the uti-lisation of a 7-point Likert scale questionnaire, with responses ranging from 1 (strongly disagree) to 7 (strongly agree). The questionnaire contained 17 items, which were provided by the experts in the field who were responsible for delivering the molecular biology curriculum. The aforementioned experts identified the 17 items as being the most sig-nificant and pertinent skills, steps and knowledge to be acquired during the curriculum. As done in previous research dealing with IVR and self-efficacy (Buttussi & Chittaro, 2017; Huang, 2022), the questionnaire was built by adapting the general self-efficacy scale to the molecular biology domain. More precisely, the items were designed according to
the guidelines of Bandura (2006) to respect the semantic structure of self-efficacy scales. The initial set of ten items focused on the skills and gestures that students were expected to acquire during the multiple-day training, while the subsequent seven items addressed the concomitant knowledge that students were expected to acquire to complete the curricula. Examples of items include:

* I feel confident in my ability to carry out chain polymerisation

* I feel confident in my ability to interpret the results of an electrophoresis.

As Bandura (2006) suggested, respondents were informed their scores would be kept anonymous. Furthermore, in order to avoid any potential bias and to encourage respondents to provide frank responses, the scale was labelled, "training information questionnaire" instead of "self-efficacy questionnaire". The administration of the questionnaire occurred in two phases: initially as a pre-test prior to the commencement of the training, and subsequently as a post-test following the conclusion of the training. The rationale behind this design was to evaluate the impact of diverse training modalities on the variable in question. The scores obtained for each item were then summed up.

### 2.2.4. Cybersickness

In order to assess cybersickness, the present study elected to utilise a revised version of the simulator sickness questionnaire, namely the virtual reality sickness questionnaire (VRSQ), which was developed by Kim et al. (2018). The decision to utilise this particular test was motivated by its structural similarity to the simulator sickness questionnaire developed by Kennedy et al. (1993). However, in contrast to the latter, the VRSQ is less time-consuming due to its more elementary underlying structure. Furthermore, the simulator sickness questionnaire was initially developed to measure sickness in the context of simulation, while the VRSQ assesses sickness in the context of virtual reality (VR). The latter point is of particular significance, as the VRSQ takes into account IVR features such as variations in postures, interaction possibilities and immersive components which may occur during IVR experience.

### 2.2.5. Presence

In order to assess the sense of presence, a number of studies made use of a variety of tests (Grassini & Laumann, 2020). In this particular field study, in order not to burden the participants, the Slater-Usoh-Steed questionnaire was selected on the basis of its brevity when compared, for instance, with the "Presence Questionnaire" (Witmer & Singer, 1998). The questionnaire comprised seven questions, assessing three dimensions of presence:

* The impression of being present in the virtual environment

* The extent to which the virtual environment becomes the dominant reality.

* The extent to which the virtual environment is remembered as "a place"

The terminology of the questions was adapted to the virtual environment presented in the study, as has been previously done in other studies. Participants were required to rate their feelings on a 7-point Likert scale. The questions posed to participants included inquiries such as, "To what extent were there moments during the virtual experience the laboratory was real to you?" 1. Never ... 7. Always. Total score was calculated by adding scores of each item.

## 2.3. Material

For the virtual learning sessions, participants used PICO 4 Enterprise® headsets. This model offers a high-resolution display of 1200 PPI resolution and a large field of view of 105° (PICO 4 Enterprise, s. d.).

## 2.4. Procedure

Prior to the initiation of the experiment, the students were informed of the option to utilise virtual reality HMDs in conjunction with practical training. Following the provision of written consent, the participants were requested to complete a demographic questionnaire and the pre-test self-efficacy questionnaire on the same day. The experiment was conducted three weeks later at Lille Catholic University from the third to the sixth of March 2025. As the experiment formed part of the curriculum for the participants, they were already enrolled in a class with a university instructor. Due to logistical constraints, experimental groups were thus based on this distribution and could not be modified. In other words, the composition of the groups was predetermined for reasons pertaining to the scheduling. It is important to note that, despite being enrolled in the same Bachelor curricula, students may follow diverse learning subjects. This variation in subjects can influence the composition of timetables, thereby precluding the possibility of adjusting group configurations. However, the randomisation (i.e., random drawing) process was employed for the allocation of experimental conditions. The experiment consisted of three consecutive training sessions during which the participants were required to perform specific gestures and manipulations related to the molecular biology procedure. This training constituted the final part of a curriculum that the participants had been following throughout their Bachelor's degree. Participants had already received theoretical knowledge lessons, yet lacked practical proficiency in the execution of procedures, gestures and manipulations. The training programme was meticulously designed to encompass a comprehensive procedure, which was then divided into three sessions, scheduled over three days (i.e., one session per day). To ensure the uniformity of the training across all groups, the sessions were overseen by university instructors (one per group) and were script-based. This script delineated the sequence of steps to be followed in a chronological manner and provided instructions. Each training session was scheduled to last 4 h, with the practical training component occupying 3 h and the IVR training component spanning 1 h. The CTRL group participated in a training session during the morning hours, while the IVR groups engaged in their training sessions in the afternoon. It should be noted, however, that the control group engaged in a neutral activity as an alternative. Throughout the 4 h training period, in addition to minor interruptions necessitated by the practical nature of the training, a 10-min rest period was allocated to all the groups (see Fig. 1) between the practical training and the experimental task (i.e., virtual training or neutral activity). This resting period permitted the participants to transition from the molecular biology laboratory to the VR room (i.e., for the IVR groups) or a conventional learning room (i.e., for the control group). Participants were instructed to refrain from discussing the experiment or the lesson in order to avoid any potential bias. The sole variation between the groups pertained to the virtual-reality training session. In one group, the virtual-reality training session was administered prior to the practical training, while in the other group, the virtual-reality training followed the practical training session.

Finally, throughout the training programme, participants were also required to complete a series of assessments. The presence assessment test and the VRSQ had to be completed after the IVR training and were restricted to the IVR groups. The NASA TLX had to be completed after each 4-h training session. The assessment of learning outcomes was conducted at the commencement of each practical training session by the same rater, with the objective of mitigating the impact of inter-rater reliability biases. Consequently, the first evaluation of the learning outcomes was conducted on the second day to assess the competencies acquired on the first day, and this sequence was repeated for subsequent days. Specifically, the university instructor interrupted and isolated each participant to ask him to demonstrate he mastered the abilities learned the day before. Participants were required to simulate and provide a rationale for the actions in question. On the second day, two abilities were assessed; on the third day, three; and on the fourth day, two.
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Timeline of the experience for each group. Time is displayed in minutes (e.g., 60'). (PT=Practical training; PA Test = Presence Assessment test; CS Test = Cybersickness questionnaire; LO = Learning Outcomes assessment.)</figcaption></figure>

Finally, on the fourth day, in addition to the learning outcomes, the participants were also requested to complete a second time the self-efficacy test (i.e., post-test). The complete training schedule can be found in Fig. 1.

### 2.4.1. Virtual reality training

The VR training was executed using HMDs, which were utilised in an autonomous manner (i.e., not connected to a computer), as the training content was uploaded and stored in the HMDs. To facilitate stand-alone

learning, all the instructions required to complete the learning session were provided in the virtual environment (i.e., orally and in written form) (see Fig. 2a–d). Nevertheless, experimenters were available to address any technical issues, such as technical difficulties. The VR training consisted of an accurate reproduction of the practical training (see Fig. 2b-c-d) incorporating when possible cognitive design recommendations (Mayer & Moreno, 2003) and the following technological features:

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Screenshots of the virtual environment. (a) Tutorial on how to use the pipette in the virtual environment. The instructions were conveyed in both written and oral formats. (b) Example of setup, encompassing all the requisite tools for the execution of a specific step in the procedure. (c) Recall of a tutorial on how to use the controllers in function of the selected virtual tool. (d) Visual feedback provided upon the completion of procedural steps.</figcaption></figure>
* The capacity for movement within the virtual environment was confined to the designated play area, delineated at the initiation of the application. It was not possible for any translational motion or teleportation to occur.

* Objects could be grabbed and manipulated in the virtual environment through the direct controllers. In scenarios where such functionality is deemed necessary, specific gestures or actions can be performed by pressing designated buttons. (e.g., remove the cap from a test tube).

* In order to transition (e.g., from the tutorial scene to the learning scene), participants were required to utilise the controllers, which displayed a virtual ray or laser, in order to select the appropriate learning session.

* No background music or ambient sound was included.

* It is noteworthy that the majority of the participants were novices in the use of HMDs, and thus a tutorial was conducted prior to the commencement of the VR training. The session, with a duration of approximately 10 min, was designed to instruct the participants on the utilisation of both the HMD and the controllers (see Fig. 2a–c-d). The objective was to guarantee that all the necessary technological functions to correctly interact with the objects or the virtual environment were fully understood and acquired. This approach was adopted to facilitate learning, enabling the learner to focus on the procedures and actions to be acquired, once the learning sessions started (Mayer et al., 2022). This tutorial provided a comprehensive overview of the principles and practices involved in interacting with the virtual environment. In the tutorial instructions were conveyed through both oral and written mediums (see Vid. 1). The “dual channel” was implemented due to the unavailability of the technical resources to rehearse the audio. However, the instructions remained accessible to the participant in a written form. The successful completion of the manipulations required by the user was a prerequisite for the conclusion of the tutorial. Subsequently, oral feedback was provided.

* It is important to note that a virtual interface (constantly located on the left side of the participant's position in the virtual environment) with instructions regarding the technological functions, acting as a reminder, remained visible even during the learning session.

* The entire procedure was meticulously divided into three distinct sessions (i.e., user interfaces), each corresponding precisely to the specific manipulations that had been learned during the practical training session. The fundamental principle of this system was that each user interface corresponded to a single session, thereby ensuring that no participant would attempt to attend a learning session other than the one that had been designated. During each learning session, the instructions were imparted verbally, in addition to animations illustrating steps not performed by the participants in the virtual session were also provided (e.g. the close and departure of an oven in order to heat a solution). The sequence of actions to be undertaken by each participant was clearly delineated on a sheet of paper located on the virtual desk. The validation of each step was indicated by the provision of visual feedback in the form of a green check mark. The objective of this initiative was to assist the participants in ascertaining their current position in the session and identifying the steps that remained to be completed.

* In the event of an erroneous execution of a manipulation or component of the procedure by a participant during a learning session, written feedback appeared to inform the participant regarding their error.

* The interaction with the environment was facilitated by the design of virtual non-realistic hands, as it may enhance the sense of body ownership (Zhang et al., 2023).

* The virtual environment was configured to replicate all the necessary materials and tools required for the execution of the procedure. In order to avoid any unnecessary visual distractions, no additional visual information was included.

To illustrate the aforementioned elements, we provide a video that includes all tutorials as well as an overview of each daily session setup (see Vid. 1). It is important to note that the duration of the VR training was shorter than that of the practical training for two specific reasons:

* Firstly, as the training consisted of molecular biology manipulations and procedures, the time which is needed to heat or centrifugate the solutions, liquids or preparations in the practical training was not necessary in the “virtual” training.

* Secondly, during the practical training, the university teachers had to check and validate each step of the procedure before proceeding, which was not the case in the VR training. Once the participants had completed one step or part of the procedure, feedback was provided immediately by the HMD application.

### 2.4.2. Neutral activity

The neutral activity was conducted prior to the practical training session for the control group (see Fig. 1). It comprised unstructured discourse from the participants. Participants were allocated 1 h for this activity. During this time, participants were permitted to consume refreshments and engage in conversation with each other, provided that the subject matter did not pertain to the procedure to be learned. To ensure the latter, three experimenters remained with the participants during this time.

### 2.4.3. Practical training

Practical training comprised the execution of various gestures and manipulations, which formed part of specific procedures. As previously mentioned, the procedures undertaken during the multiple-day training programme were complementary in nature, as they were all part of a single, cohesive process. Consequently, the procedures included for instance DNA extraction, chain polymerisation and DNA fragment separation.

## 2.5. Data analysis

Participant data were initially recorded in multiple Excel files and later consolidated into a single.csv dataset for analysis. Statistical analysis was performed using JASP software (version 0.19.3). Given that the three-day training programme comprised a single procedure, which was divided into sessions, thereby requiring related skills and knowledge across these sessions, it was decided to average the scores of the following variables (overall cognitive load, mental demand, frustration, presence and cybersickness). For instance, the mean cognitive load over the multiple day training was calculated. Most of the statistical analysis was thus conducted with these mean values. The mental demand and frustration dimensions, which are part of the overall cognitive load, were also analysed, as the literature outlines their relevance and impact in the context of IVR (De Witte et al., 2024; Galy et al., 2018). Nevertheless, given the objective is to provide a comprehensive overview of the potential changes that may occur over the course of several training days, it was also deemed necessary to investigate day-to-day dynamics. This investigation was conducted separately for total cognitive load and for part of its subscales (i.e. Mental Demand and Frustration) as for the sense of presence. Regarding the remaining variables (i.e., learning outcomes and self-efficacy) aggregation was performed in accordance with the literature (Bandura, 2006; Doyle et al., 2007; Zoller et al., 2021). Prior to conducting the statistical analysis, normality and equality of variances were tested. One-way analysis of variance (ANOVA) was employed to compare the group effect, repeated-measures ANOVAs with Day (1–3) as a within-subject factor and Group (CTRL, VRPT, PTVR) as a between-subject factor were employed for day to day dynamics while independent t-tests were used to compare the CTRL condition versus the IVR condition. Correlation tests were conducted to examine interactions. Statistical significance was set at $p < .05$.
## 2.6. Groups description and assumptions tests

The control group (CTRL) counted 22 participants (7 men and 15 women, mean age = 20.77, SD = 0.813) while the group following VR training prior practical training (VRPT) counted 19 participants (8 men and 11 women, mean age = 20.90, SD = 1.311). Finally, the group who followed practical training before VR (PTVR) counted 13 participants (5 men and 8 women, mean age = 20.60, SD = 0.961). Assumption tests revealed homogeneity of groups. The Shapiro-wilk test and Q-Q plots showed that all variables were normally distributed and Levene's test showed no difference in variances between the groups, validating the null hypothesis.

# 3. Results

## 3.1. Cognitive load

For the cognitive load, we first analysed the overall results and then we focused on mental demand and frustration to finish with the remaining dimensions.

### 3.1.1. Overall cognitive load

The ANOVA on overall cognitive load scores did not reach statistical significance, *F* (2, 51) = 2.109, *p* = .132, *ω*² = 0.039. Descriptive statistics indicated that the CTRL group (M = 52.39, SD = 12.38, CV = 0.236) reported lower cognitive load compared to the PTVR (M = 60.28, SD = 11.02, CV = 0.183) and VRPT groups (M = 57.07, SD = 10.34, CV = 0.181) (Fig. 3).

Furthermore, a t-test was conducted in order to ascertain the existence of any potential differences between the group who underwent only practical training and the aggregation of the groups who underwent additional VR training (hereafter referred to as the IVR groups). The t-test did not yield significant difference (*p* = .062, Hedges' *g* = 0.520) between the two groups. Descriptive statistics indicated respectively (M = 52.39, SD = 12.38) for the CTRL group vs. (M = 58.37, SD = 10.56)

for the IVR groups (Fig. 4).

Finally, day-by-day cognitive load was analysed with a repeated-measures ANOVA (Within: Day 1–3; Between: Group). There was a main effect of Day, *F* (2, 96) = 11.742, *p* < .001, *ω*² = 0.084, indicating that overall workload declined over time. Holm-adjusted post-hoc tests showed Day 1 > Day 2 (mean difference = 8.557, *p* = .004, Cohen's *d* = 0.559) and Day 1 > Day 3 (mean difference = 11.53, *p* < .001, Cohen's *d* = 0.754), whereas Day 2 vs Day 3 was not significant (*p* = .180). The Day × Group interaction was also significant, *F* (4, 96) = 3.13, *p* = .018, *ω*² = 0.025. The only Holm-significant contrast was CTRL: Day 1 > Day 3 (mean difference = 16.841, *p* = .003, Cohen's *d* = 1.101). The between-subjects main effect of Group on the day-level analysis was not significant, *F* (2, 48) = 2.073, *p* = .137, *ω*² = 0.014. Interestingly, descriptive analysis showed that for CTRL and PTVR groups the cognitive load decreased constantly over time whereas for VRPT a rebound was found for the last day (see Fig. 5a).

### 3.1.2. Mental demand

For the mental demand dimension, the ANOVA did not reach statistical significance, *F* (2, 51) = 3.052, *p* = .056, *ω*² = 0.071 suggesting a trend toward group differences. The descriptive data revealed that the PTVR group (M = 65.58, SD = 13.27, CV = 0.202) reported higher mental demand than both the CTRL (M = 52.80, SD = 15.32, CV = 0.290) and VRPT groups (M = 58.73, SD = 15.35, CV = 0.261). Tukey's post hoc comparisons revealed that the PTVR group rated mental demand significantly higher than the CTRL group (mean difference = 12.77, SE = 5.203, *t* (51) = -2.455, *p* = .045), whereas the differences between CTRL and VRPT (mean difference = 5.93, *p* = .417) and between PTVR and VRPT (mean difference = 6.85, *p* = .413) were not significant (Fig. 3).

The t-test yielded a significant difference (*p* = .041, Hedges' *g* = -0.573) between the two groups, with the CTRL group demonstrating lower mental demand (M = 52.80, SD = 15.32) than the IVR groups (M = 61.51, SD = 14.71) (Fig. 4).

Finally, the repeated-measures ANOVA (Within: Day 1–3; Between:

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Mean scores of overall cognitive load, mental demand, frustration, learning outcomes, and self-efficacy (post-test) across groups (CTRL, VR, PTVR, and VRPT). Error bars represent standard deviations.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Fig. 4. Comparison of mean scores for each cognitive load dimension between the IVR groups and the CTRL group. Error bars represent standard deviations. As- terisks indicate significant differences based on independent t-tests: *p* < .05 (*), *p* < .001 (**), and non-significant results are labelled as "NS".</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Fig. 5. Day-by-day evolution with mean scores (i.e., points) and standard deviations (i.e., vertical bars) of. (a) Overall Cognitive Load, (b) Mental Demand, and (c) Frustration for the 3 groups (i.e., CTRL; PTVR and VRPT). Abbreviations: CTRL = Control; PT = practical training; VR = virtual reality.</figcaption></figure>

Group) on mental demand raised a significant main effect of Day, *F* (2, 96) = 16.025, *p* < .001, *ω*² = 0.098. However, the Day × Group inter-action was not significant, *F* (4, 96) = 0.85, *p* = .497, *ω*² = 0. Holm-adjusted post-hoc tests showed Day 1 > Day 3 (mean difference = 15.489, *p* < .001, Cohen's *d* = 0.818) and Day 2 > Day 3 (mean difference = 10.847, *p* < .001, Cohen's *d* = 0.573), whereas Day 1 vs Day 2 was not significant (*p* = .093). The between-subjects main effect of Group on the day-level analysis was not significant, *F* (2, 48) = 2.813, *p* = .07, *ω*² = 0.024. Interestingly, descriptive analysis showed that for each group the mental demand decreased constantly over time even if this decrease is slower for VRPT (see Fig. 5b).

3.1.3. Frustration

The ANOVA for the frustration dimension revealed a significant main effect of group, *F* (2, 51) = 3.614, *p* = .034, *ω*² = 0.088. Descriptive statistics showed that the CTRL group (M = 45.08, SD = 18.99, CV = 0.421) reported lower frustration than both the PTVR (M = 59.94, SD = 19.99, CV = 0.334) and VRPT groups (M = 58.73, SD = 18.43, CV = 0.314). However, Tukey post hoc comparisons were only marginal: the CTRL group differed from PTVR (mean difference = -14.86, SE = 6.659, t (51) = -2.232, *p* = .075) and from VRPT (mean difference = -13.65, SE = 5.962, t (51) = -2.290, *p* = .066), while no significant difference emerged between the PTVR and VRPT groups (mean difference = 1.21, *p* > .98) (Fig. 3).

The *t*-test yielded a significant difference (*p* = .009, Hedges' *g* =
0.73) between the two groups, with the CTRL group demonstrating lower frustration levels (M = 45.08, SD = 18.99) than the IVR groups (M = 59.21, SD = 18.76) (Fig. 4).

Finally, the repeated-measures ANOVA (Within: Day 1–3; Between: Group) on frustration did not raise a significant main effect of Day, $F(2, 96) = 0.454, p = .637, \omega^2 = 0$. However, the Day × Group interaction was significant, $F(4, 96) = 5.170, p < .001, \omega^2 = 0.052$. The only Holm-significant contrast was Day 3: VRPT > CTRL (mean difference = 30.119, $p = .024$, Cohen's $d = 1.168$). The between-subjects main effect of Group on the day-level analysis was not significant, $F(2, 48) = 2.073, p = .105, \omega^2 = 0.014$. Interestingly, descriptive analysis showed that for CTRL and PTVR groups the frustration decreased constantly over time whereas for VRPT it increased constantly (see Fig. 5c).

### 3.1.4. Remaining dimensions

Subsequent to the completion of the t-test analysis of the remaining dimensions, the physical dimension was found not significant. The IVR groups demonstrated higher levels of physical demand than the CTRL group, respectively (M = 46.16, SD = 14.32 vs. M = 38.10, SD = 14.34; $p = .052$, Hedges' g = 0.544). The analysis did not reveal any significant differences between the CTRL group and the IVR groups. With regard to performance, the mean values for the CTRL group (M = 49.47, SD = 17.31) and the IVR groups (M = 48.12, SD = 12.39) did not differ significantly ($p = .741$, Hedges' g = 0.091). With regard to effort, the mean values were (M = 51.32, SD = 14.16) and (M = 56.95, SD = 13.69) for the CTRL and IVR groups, respectively, and the difference was not statistically significant ($p = .149$, Hedges' g = -0.399). With regard to temporal demand, the mean values were (M = 48.25, SD = 16.87) and (M = 48.64, SD = 15.39) respectively, for the control and IVR groups, and the difference was not statistically significant ($p = .931$, Hedges' g = -0.024) (Fig. 4).

### 3.2. Learning outcomes

An ANOVA was conducted to examine the effect of group (CTRL, PTVR, and VRPT) learning outcomes. The analysis revealed a statistically significant main effect of group, $F(2, 50) = 3.24, p = .048, \omega^2 = 0.078$. Descriptive statistics indicated that the CTRL group (M = 31.57, SD = 6.70) exhibited higher learning outcomes relative to both the PTVR (M = 28.62, SD = 6.06) and VRPT groups (M = 26.79, SD = 5.04). Tukey's post hoc comparisons revealed that the difference between the CTRL and VRPT groups was statistically significant (mean difference = 4.78, SE = 1.90, t (5) = 2.52, $p = .039$). In contrast, the differences between CTRL and PTVR (mean difference = 2.96, SE = 2.12, t (50) = 1.40, $p = .350$) and between PTVR and VRPT (mean difference = 1.40, SE = 0.35, t (50) = 2.16, $p = .847$) were not significant (Fig. 3).

The t-test yielded a significant difference ($p = .020$, Hedges' g = 0.666) in favour of the CTRL group, who demonstrated superior scores in comparison to the IVR groups, respectively (M = 31.57, SD = 6.69 vs. M = 27.5, SD = 5.45) (Fig. 3).

### 3.3. Self-efficacy

Participants' self-efficacy was measured before (pre-test) and after (post-test) the experimental session. Internal consistency tests of the questionnaire revealed a high Cronbach alpha coefficient on pre-test (0.92) as on post-test (0.89).

At the pre-test, the one-way ANOVA revealed no significant group differences in self-efficacy, $F(2, 51) = 1.631, p = .206, \omega^2 = 0.023$. Descriptive statistics indicated that the CTRL group (M = 78.55, SD = 15.78), PTVR group (M = 69.62, SD = 16.79), and VRPT group (M = 78.26, SD = 13.65) did not differ significantly at baseline.

In contrast, at the post-test, the ANOVA showed a significant main effect of group on self-efficacy, $F(2, 51) = 8.157, p < .001, \omega^2 = 0.021$. The CTRL group reported the highest self-efficacy scores (M = 93.90, SD = 9.88), followed by the VRPT group (M = 85.32, SD = 8.88) and the

PTVR group (M = 80.23, SD = 12.19). Tukey's post hoc comparisons revealed that the CTRL group's self-efficacy was significantly higher than that of the PTVR group (mean difference = 13.68, SE = 3.55, t = 3.852, $p < .001$) and also higher than that of the VRPT group (mean difference = 8.59, SE = 3.18, t = 2.703, $p = .025$), whereas the difference between the PTVR and VRPT groups was not significant (mean difference = -5.09, SE = 3.65, t = -1.392, $p = .353$) (Fig. 3).

The t-test revealed no significant difference between the groups on the pre-test ($p = .382$, Hedges' g = 0.241) respectively (M = 78.55, SD = 15.78) for the CTRL group and (M = 74.75, SD = 15.35) for the IVR groups. Conversely, on the post-test, the t-test yielded a significant difference between the groups ($p < .001$, Hedges' g = 1.026) with the CTRL group demonstrating higher scores than the IVR groups, respectively (M = 93.90, SD = 9.88 vs M = 83.25, SD = 10.47).

### 3.4. Cybersickness

T-test analysis showed no significant difference on cybersickness scores between the PTVR and VRPT groups respectively (M = 69.15, SD = 20.41 vs M = 59.31, SD = 15.73; $p = .134$, Hedges' g = 0.540). When averaged IVR groups exhibit (M = 63.31, SD = 18.14).

### 3.5. Presence

T-test analysis showed a significant difference on presence scores between the PTVR and VRPT groups respectively (M = 30.25, SD = 6.4 vs M = 25.28, SD = 5.7; $p = .03$, Hedges' g = 0.801). When averaged IVR groups exhibit (M = 27.30, SD = 6.45).

Finally, the repeated-measures ANOVA (Within: Day 1–3; Between: Group) on presence did not raise a significant main effect of Day, $F(2, 58) = 0.47, p = .627, \omega^2 = 0$. Similarly, the Day × Group interaction was not significant, $F(2, 58) = 0.35, p = .706, \omega^2 = 0$. The between-subjects main effect of Group on the day-level analysis was not significant, $F(1, 29) = 3.779, p = .062, \omega^2 = 0.044$. Interestingly, descriptive analysis showed that for VRPT the presence decreased about 3 points over time whereas for PTVR the decrease was less than 1 point between Day 1 and 3 (see Fig. 6).

### 3.6. Pearson's correlations

Correlations were conducted on the CTRL group and the IVR groups and are exhibited in the following tables (Tables 1 and 2).

One can observe that within the CTRL group, a negative correlation is observed between overall cognitive load and self-efficacy ($r = -0.420$,

<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Day-by-day evolution of presence scores for the VRPT and PTVR groups. Points show mean scores with standard deviation (i.e., vertical bars) Abbreviations: PT = practical training; VR = virtual reality.</figcaption></figure>
<table><caption>**Table 1**
Pearson's correlations exhibited for the CTRL group.</caption><thead><tr><th>Variable</th><th></th><th>Overall cognitive load</th><th>Mental demand</th><th>Frustration</th><th>Learning outcomes</th><th>Self-efficacy (post-test)</th></tr></thead><tbody><tr><td rowspan="2">1. Overall cognitive load</td><td>Pearson's r</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>p-value</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="2">2. Mental demand</td><td>Pearson's r</td><td>0.917</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>p-value</td><td>&lt;0.001**</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="2">3. Frustration</td><td>Pearson's r</td><td>0.636</td><td>0.477</td><td>-</td><td>-</td><td>-</td></tr><tr><td>p-value</td><td>0.001**</td><td>0.025*</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="2">4. Learning outcomes</td><td>Pearson's r</td><td>-0.325</td><td>-0.296</td><td>-0.367</td><td>-</td><td>-</td></tr><tr><td>p-value</td><td>0.150</td><td>0.193</td><td>0.102</td><td>-</td><td>-</td></tr><tr><td rowspan="2">5. Self-efficacy (post-test)</td><td>Pearson's r</td><td>-0.420</td><td>-0.576</td><td>-0.027</td><td>0.366</td><td>-</td></tr><tr><td>p-value</td><td>0.052</td><td>0.005*</td><td>0.904</td><td>0.102</td><td>-</td></tr></tbody></table>

<table><caption>**Table 2**
Pearson's correlations exhibited for the IVR groups.</caption><thead><tr><th>Variable</th><th></th><th>Overall cognitive load</th><th>Mental demand</th><th>Frustration</th><th>Learning outcomes</th><th>Self-efficacy (post-test)</th><th>Cybersickness</th><th>Presence</th></tr></thead><tbody><tr><td rowspan="2">1. Overall cognitive load</td><td>Pearson's r</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>p-value</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="2">2. Mental demand</td><td>Pearson's r</td><td>0.748</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>p-value</td><td>&lt;0.001**</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="2">3. Frustration</td><td>Pearson's r</td><td>0.767</td><td>0.354</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>p-value</td><td>&lt;0.001**</td><td>0.047*</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="2">4. Learning outcomes</td><td>Pearson's r</td><td>0.150</td><td>-0.058</td><td>0.080</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>p-value</td><td>0.413</td><td>0.753</td><td>0.662</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="2">5. Self-efficacy (post-test)</td><td>Pearson's r</td><td>-0.400</td><td>-0.347</td><td>-0.272</td><td>-0.066</td><td>-</td><td>-</td><td>-</td></tr><tr><td>p-value</td><td>0.023*</td><td>0.052</td><td>0.132</td><td>0.719</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="2">6. Cybersickness</td><td>Pearson's r</td><td>0.529</td><td>0.502</td><td>0.443</td><td>0.112</td><td>-0.135</td><td>-</td><td>-</td></tr><tr><td>p-value</td><td>0.002*</td><td>0.003*</td><td>0.011*</td><td>0.543</td><td>0.463</td><td>-</td><td>-</td></tr><tr><td rowspan="2">7. Presence</td><td>Pearson's r</td><td>-0.156</td><td>-0.122</td><td>-0.179</td><td>0.028</td><td>0.203</td><td>0.012</td><td>-</td></tr><tr><td>p-value</td><td>0.394</td><td>0.507</td><td>0.327</td><td>0.881</td><td>0.265</td><td>0.946</td><td>-</td></tr></tbody></table>

$p = .052$) and between mental demand and self-efficacy ($r = -0.576, p = .005$) (Table 1).

In the case of the IVR groups, analogous outcomes are observed, with negative correlations evident between overall cognitive load and self-efficacy ($r = -0.400, p = .023$) and between mental demand and self-efficacy ($r = -0.347, p = .052$). In addition, positive correlations are exhibited between overall cognitive load and cybersickness ($r = 0.529, p = .002$), between mental demand and cybersickness ($r = 0.502, p = .003$), and finally between frustration and cybersickness ($r = 0.443, p = .011$) (Table 2).

# 4. Discussion

## 4.1. Study objectives

The objective of this field study is twofold. Firstly, it was intended to facilitate a more profound comprehension of the role of cognitive load (e.g., on other affordances) during multiple-day training sessions. This endeavour is of particular pertinence in view of the limited number of intensive, multiple-day field studies that have to date evaluated cognitive load (Han et al., 2021). Secondly, insights were to be provided on instructional features, more specifically by investigating the value of IVR in complement to practical training regarding the assessed variables, but also to see when the implementation in real classroom settings would be the most relevant. The overarching aim of this study is to contribute to a more nuanced understanding of the implementation instructions that should accompany IVR use in real classroom settings.

## 4.2. Research hypotheses

**Hypothesis 1.** The results demonstrate no significant difference in terms of overall cognitive load between the CTRL group and the PTVR & VRPT groups (Fig. 3). However, as previously mentioned, it is important to consider the mental demand and frustration dimension, as previous

research demonstrated that it facilitates a more profound level of insight (De Witte et al., 2024; Galy et al., 2018). An examination of the mental demand dimension reveals that the CTRL group reported lower mental demand levels in comparison to the PTVR and VRPT groups. However, statistical significance was not attained for the VRPT group. When comparing the CTRL group with the IVR condition the difference is found to be statistically significant, with the IVR groups reporting higher mental demand. A similar outcome is observed in the frustration dimension, with the CTRL group demonstrating lower levels of frustration compared to both the PTVR and VRPT groups. A further comparison of the CTRL group with the IVR groups reveals a heightened discrepancy for the frustration dimension (Fig. 4). These findings partially support H1, as differences were observed in the dimensions, although the overall cognitive load was not significant.

**Hypothesis 2a.** The second hypothesis suggests that, given the inherent requirement of mental effort associated with concept of presence, a correlation may exist between presence levels and mental demand dimension. However, correlation analysis refuted this assumption, indicating that mental demand levels are not exclusively attributable to the mental effort necessary to project into the virtual environment. This finding aligns with the conclusions of previous studies (Breves & Stein, 2023) and suggests that the capacity to experience the virtual environment may not require an equivalent degree of mental exertion among HMD users. The latter can be explained as the sense of presence being a psychological construct depending on an individual's ability to project in a virtual scene (Witmer & Singer, 1998). With regard to other cognitive abilities (e.g., mental imagery; spatial abilities), the reliance on individual differences is evident (Hegarty & Waller, 2005; Isaac & Marks, 1994), with minimal mental effort being required in some people and greater cognitive effort in others.

**Hypothesis 2b.** It was hypothesised that learning outcomes would demonstrate a positive correlation with self-efficacy on the post-test. However, this assumption was refuted by the results of the Pearson
correlation analysis. This outcome is counterintuitive, given the body of literature that has previously demonstrated a positive correlation between self-efficacy and learning outcomes in IVR (Makransky & Petersen, 2021).

**Hypothesis 2c.** Hypothesis 2c proposed a negative interaction between overall cognitive load levels and self-efficacy, as well as learning outcomes. The statistical analysis provided partial support for this hypothesis, as no correlation was identified between overall cognitive load and learning outcomes. However, negative medium correlations were observed between overall cognitive load, mental demand and self-efficacy scores. One can observe that a similar correlation was exhibited for the CTRL group. This finding is consistent with the extant literature (Feldon et al., 2018). Indeed, as stated, levels of self-efficacy are known to increase when learners estimate that to complete a task requires less mental demand (Yeo & Neal, 2008). Consequently, in the present study, the reduced mental demand exhibited by the CTRL group augmented the perceived self-efficacy. Conversely, for the IVR groups, the elevated mental demand levels gave rise to diminished self-efficacy scores. Furthermore, although no correlation was demonstrated, it appears that self-efficacy has influenced the learning outcomes in our study (i.e., as the CTRL group had higher levels of self-efficacy and higher learning scores). Indeed, the absence of correlation may be attributable to the subjective nature of self-efficacy beliefs (Bandura, 2006). Consequently, it is plausible that some participants may have over- or underestimated their abilities.

**Hypothesis 2d.** This hypothesis proposed an association between overall cognitive load and the occurrence of cybersickness. Indeed, extant literature has demonstrated a correlation between these variables, even in cases where the duration of VR exposure is less. The outcomes demonstrate a robust correlation between the overall cognitive load, mental demand, frustration, and the intensity of cybersickness symptoms. This analysis corroborates earlier findings that elevated levels of concentration and negative emotions exert an influence on the biological state (Sepich et al., 2022). Specifically, it appears that overall cognitive load, mental demand and frustration acted as a mediator explaining this relationship. In summary, the individual perception of virtual features and task demands has been shown to increase levels of overall cognitive load, mental demand and frustration, which in turn affects levels of cybersickness. Consequently, the hypothesis is proposed that a reduction in overall cognitive load, mental demand and frustration could result in a corresponding decrease in cybersickness. The extant literature has identified a number of technological (e.g., latency, display resolution), individual (e.g., age, gender, physical condition) or instructional (e.g., motion, visual complexity) factors that affect cybersickness (Biswas et al., 2024; Caserman et al., 2021; Chang et al., 2020). Despite the implementation of these recommendations, cybersickness continues to occur, suggesting that cybersickness may not be attributable to a singular feature, but rather, to an interplay of virtual features (e.g., cognitive, instructional, technological) that, once they reach the individual capacity of the working memory, result in cybersickness symptoms.

## 5. Conclusion

The primary research question pertained to the investigation of whether cognitive load was influenced by the utilisation of IVR prior to or following practical training. The utilisation of IVR during intensive, multiple-day training regimens has been shown to elicit higher levels of overall cognitive load, mental demand and frustration in comparison to the control condition. Firstly, the observed differences can be attributed to the experimental design, and more specifically, to the contrast between the neutral activity and the IVR training. It appears that the participants of the IVR training may have been required to learn twice on the same day as much as other groups. Specifically, the learning occurred during two distinct phases: the first phase was the practical

training, and the second phase was IVR training. Despite the identical nature of the procedure to be acquired, the transition period between the two sessions was inadequate to facilitate the assimilation of the novel skills. Indeed, according to Cotton and Ricker (2022), when two learning phases occur in close succession, the limited time between them may hinder the consolidation of the first memory. Insufficient intervals can prevent working memory representations from stabilizing, which may reduce the effectiveness of long-term memory formation. Consequently, this lack in segmentation between the practical and IVR training may have led to heightened mental load (Klingenberg et al., 2023). In a similar vein, the incorporation of the neutral activity for the CTRL group may have led to a reduction in overall cognitive load. (Fig. 3). Indeed, as reported in the literature, the introduction of rest periods mitigates reductions in working memory capacity, which in turn reduces the associated cognitive load (Chen et al., 2018). Similarly, the inherently relaxing nature of the neutral activity (i.e. refreshments; informal discussion) may have contributed to decrease participants' stress and anxiety in the CTRL group, which has also been shown to alleviate cognitive load (Szulewski et al., 2021). Examination of the temporal dynamics across the study days (Fig. 5a) reveals that, on the first day, values were comparable between groups; however, from the second day onwards, the CTRL group exhibited a reduction in overall cognitive load, which was further pronounced on the third day. While a similar trend is apparent for the PTVR group, the reported scores remained higher than those of the CTRL group, a difference that can plausibly be ascribed to the additional cognitive demands associated with the IVR activity. Variations in mental demand (Fig. 5b), although not statistically significant, which limits the strength of the conclusion, may nonetheless provide further insight. Specifically, combining practical training with IVR appears to be more mentally demanding, as reflected in the higher mental demand scores for the IVR groups, compared with the combination of practical training and the neutral activity (i.e., CTRL group). The observed dynamics, however, suggest that participants of the IVR groups gradually acclimate to the mental demands of integrating IVR with practical training, although both mental demand and overall cognitive load scores remain higher in the IVR groups relative to the CTRL group.

Furthermore, in this field study, the moment of implementation did not affect the levels of cognitive load, as both IVR groups demonstrated similar levels of overall cognitive load, mental demand and frustration (Fig. 3). Although not statistically significant, which limits the strength of any conclusions, the evolution of descriptive day-by-day data shows different dynamics that may allow a more nuanced interpretation. In (Fig. 5), it can be observed that overall cognitive load, mental demand, and frustration decrease day by day for the CTRL group. However, these dynamics differ for the IVR groups. Specifically, in the PTVR group, although their initial levels (on day 1) are higher than those of the other groups, a decrease can be observed across all three variables, albeit less pronounced. In contrast, in the VRPT group, only a slight decrease in mental demand is evident, while frustration levels increase day by day, consequently affecting the total cognitive load. It may be hypothesised that the execution of the virtual training programme without prior practical experience resulted in a greater level of challenge in completing the task, which consequently led to elevated levels of frustration. This suggests that, in this study, the virtual environment was more suitable as a complementary tool to be used after practical training than before (a notion seemingly confirmed by the dynamics observed in the PTVR group). Indeed, although speculative, performing a task that is already challenging in real-life settings, combined with the complexity of using HMD technology, may have induced frustration, as participants experienced failure. This aligns with previous findings demonstrating that failure can lead to frustration (Hove & Corcoran, 2008). This effect is illustrated by the learning outcome scores, which were the lowest among the three groups (Fig. 3). This was less pronounced in the PTVR group, who had previously completed the practical training and therefore only had to manage the additional complexity of executing the task
in VR. Although the differences were not statistically significant in our study, these observations are consistent with literature emphasising the importance of emotions in learning dynamics (Pekrun, 2014).

Secondly, in addition to the explanations provided above regarding the experimental design, the observed disparities in overall cognitive load, mental demand and frustration between the CTRL and IVR group may also be attributable to the task design as posited in the literature (F. G. Paas, 1992). A common explanation states that the variety of stimuli in a novel virtual environment can increase the associated cognitive load (Concannon et al., 2019). However, in the present study, this appears improbable, as the environment was designed to minimise irrelevant stimuli and incorporate step-by-step instructions (Fig. 2a-b-c-d; Vid. 1). Furthermore, the virtual training programme was an accurate reproduction of the practical training, and the entire sample followed theoretical training prior to the multiple day training. This suggests that the tools in the virtual environment and the learning aims were not novel to the learners. Moreover, while the IVR training was an accurate reproduction, the sequence was shorter and the gestures to be executed were fewer. The utilisation of controllers in the execution of these movements is noteworthy, as it results in distinct motor behaviours and thus feedback from those observed during manual execution. This discrepancy in haptic feedback between the two different modalities of the same procedure may have elicited supplementary mental demand, as the learning of the virtual procedure occurred through the controllers and not directly through the hands. In line with Cowan's (2001) account of chunking in short-term memory, whereby coherent information is more readily integrated and recalled, the observed discrepancy may have resulted in the virtual learning content and the practical learning being processed as distinct chunks rather than as interconnected material. Given the limited capacity of short-term memory, this separation may have increased the perceived mental demand of IVR participants and hindered consolidation. This discrepancy may also have led to heightened frustration. The visual similarity between the virtual environment and the practical training did not translate into equivalent execution and feedback pathways, suggesting the potential for enhanced frustration. This was also induced by direct observation, with some students, for instance, failing to comprehend the rationale behind their successful execution of the manipulation during practical training, and subsequently unable to reproduce it during virtual training and vice versa. It has been asserted that congruent feedback represents a significant strength of IVR in facilitating enhanced learning in procedural knowledge (Jongbloed et al., 2024). However, this assertion can be qualified by the finding that for optimal efficacy, feedback should not only be visually congruent (i.e., the virtual visual feedback creates the illusion that the actions in VR align with the real actions) but should also be congruent from a haptic perspective (i.e., the use of controllers does not enable an acute reproduction of the real motor actions, which influences the haptic feedback). Indeed, Sigrist et al. (2013) showed that mismatched feedback (i.e., where the sensory modality doesn't align with motor action) can disrupt the formation of accurate internal models, leading to poorer motor learning outcomes. This finding suggests that IVR may be useful for procedural training, provided that it is complementary to practical training and not contradictory regarding the associated feedback despite the execution of the same procedure in both environments.

Regarding learning outcomes and self-efficacy, comparison between the CTRL and IVR groups indicated that the CTRL group demonstrated higher levels of both self-efficacy and learning outcomes. The findings suggest that the reported high levels of mental demand interacted with the IVR participants' belief in one's skills, in contrast to the CTRL group. This is illustrated by the greater progress in self-efficacy scores between the pre-test and post-test for the CTRL group, respectively (M = 78.55 to M = 93.90), in contrast to the IVR group (M = 74.75 to M = 83.25). The findings demonstrate that mental demand has the capacity to exert a detrimental effect on self-efficacy scores, which aligns with previous research (Feldon et al., 2018). As stated, self-efficacy increases when

learners' beliefs about the mental resources required to complete a task are reduced. This is evidenced in the present study, where the CTRL group reported diminished mental demand levels, thereby augmenting their self-efficacy and consequently their learning outcomes. This finding offers a partial response to the second research question, which sought to enhance comprehension of the interactions of cognitive load, with other variables in the context of IVR utilisation. Furthermore, it is also noteworthy that this study refutes the notion that mental demand is contingent upon presence levels, thereby corroborating the findings of preceding studies (Breves & Stein, 2023). As presence is a cognitive construct that shares common features with other cognitive abilities (Iachini et al., 2019), it can be hypothesised that its levels are heterogeneous among the population, as is the case for other cognitive abilities (e.g., spatial abilities; mental imagery) (Hegarty & Waller, 2005; Isaac & Marks, 1994). This suggests that levels of mental effort required to elicit similar levels of presence vary among the population. Finally, the findings indicated a correlation between overall cognitive load, mental demand, frustration, and cybersickness. It is therefore hypothesised that reducing overall cognitive load, mental demand, and frustration may mitigate cybersickness. While technological (e.g., latency, display resolution), individual (e.g., age, gender, physical condition), and instructional (e.g., motion, visual complexity) factors contribute to its occurrence (Biswas et al., 2024; Caserman et al., 2021; Chang et al., 2020), cybersickness appears to result from the interaction of these factors, which, when exceeding an individual's working memory capacity, trigger symptoms. It is evident that further research is required in analogous settings to substantiate this hypothesis.

To conclude the learning of specific motor actions (i.e., fine motricity) in dense learning settings through IVR needs thus to be deeper investigated, as for the moment literature is scarce (Ratcliffe & Tokarchuk, 2021). This, especially with the increase of technological solutions enabling better haptic feedback (Radhakrishnan et al., 2021). One could suggest that instead of replicating a whole procedure, training should prioritize specific components that offer congruent visual, cognitive, and haptic feedback, especially when it comes to highly precise motor actions (e.g., medical training (Babu et al., 2024)). In summary, IVR training should be directed towards procedures that are either highly hazardous or costly, and only when the motor actions trained during IVR do not conflict with real motor actions, as it might impair skills transfers (Sigrist et al., 2013).

## 5.1. Practical implications

From a practical point of view the following implications can be outlined:

* IVR training, when combined with practical training, elicits higher levels of mental demand and frustration than practical training alone. More specifically, integrating IVR training with practical training increased the associated mental demand compared with practical training only, while implementing IVR prior to practical training elicits also greater frustration over time.

* IVR should be complementary to practical training in that way it does not provoke two different motor learning pathways, as the latter can be contradictory instead of complementary.

* Developers should be especially careful when integrating fine motricity in IVR especially when controllers are used. Indeed, using controllers does not induce the same haptic feedback in both situations, IVR and practical although the visual feedback is congruent.

* Cognitive load seems to be indicative of cybersickness, in the way that higher overall cognitive load, mental demand and frustration are related to cybersickness. Aiming to reduce overall cognitive load and its major dimensions may thus be a way to decrease cybersickness.
## 5.2. Limitations and future directions

* This field study presents several limitations that warrant consideration.

* *Sample size and group composition.* Although the overall sample size was adequate, the PTVR group comprised only 13 participants, which may have reduced the statistical power of the analysis. Furthermore, discrepancies in group sizes limit the generalisability of the findings. Future field studies with larger and more balanced samples are therefore required to strengthen data robustness.

* *Assessment of cognitive load.* As highlighted in the introduction, several instruments exist to evaluate cognitive load. While the NASA-TLX is widely used in the literature (Skulmowski, 2023) and facilitates comparability across studies, it is not sufficiently sensitive to capture more specific processes (e.g., intrinsic or extraneous load). In ecological conditions, the use of multiple measures is less feasible than in laboratory settings. Nonetheless, future research should seek to combine complementary assessments (e.g., interviews, Likert scales) enabling triangulation and thereby enhancing the validity of interpretations (Suzuki et al., 2024). Multimethod approaches not only strengthen data interpretation but also offer insights into related cognitive processes (e.g., spatial abilities). For example, eye-tracking could have provided information on blinks, pupil diameter, fixation frequency, gaze points, and saccades, which have been shown to reflect cognitive load levels (Souchet et al., 2022). Eye-tracking also yields valuable information on visual fatigue and attentional patterns, in our study it could have enabled a distinction between mental demands induced by virtual training and those arising from practical training. However, despite their promise, such methods present challenges in terms of data acquisition and interpretation. This raises the broader question of how best to integrate reliable physiological and behavioural measures into long-term, ecologically valid learning contexts.

* *Individual differences and prior knowledge.* Field studies provide valuable insights into real-world classroom settings, yet they are subject to contextual constraints. Participant motivation, for example, cannot be fully controlled; some students may have agreed to participate primarily out of a desire to please their instructors. In addition, prior knowledge was not systematically assessed. Although all participants were enrolled in the same curriculum, it is possible that some were more familiar with the required manipulations, thereby influencing outcomes. Similarly, while a tutorial was provided, validated before training, and prior HMD use was recorded, differences in participants' comfort with the technology cannot be excluded. Variations in motivation, interest, or comprehension of the technological features may have been further exacerbated by the complexity of the task. Future studies could for instance consider integrating an assessment score to capture participants' mastery of both the manipulations and the HMD's technological features, allowing this to be controlled for in the analysis.

* *Design of the virtual training.* Although the design of the training environment was informed by recommendations from Mayer and Moreno (2003), more recent methodological guidelines specific to IVR learning environments were proposed by Petersen and Makransky (2024). As the designing of our virtual environment was completed before this publication, its recommendations could not be implemented. Future research should seek to apply these guidelines systematically and evaluate their effectiveness.

* *Procedural and contextual factors.* Organisational constraints also introduced potential confounds. Sessions for the control group were conducted in the morning, whereas IVR sessions took place in the afternoon. Circadian rhythms may therefore have influenced participants' cognitive or emotional states, and subsequently their performance. In addition, as several participants used IVR in the same room, vocal expressions of emotion may have influenced others' experiences.

* *Implications and future directions.* Taken together, these limitations highlight several priorities for future research. Studies should examine cognitive load across different instructional designs (e.g., distributing IVR and practical training across separate days, integrating specific motor feedback, or focusing only on key procedural actions) within extended training programmes. The adoption of more sensitive multimethod measures would facilitate deeper analysis of cognitive processes, prior knowledge, and mastery of IVR technology. Moreover, a critical overarching question remains: how can reliable measurement techniques be effectively integrated into ecological learning contexts, given the technological limitations of some devices and the unpredictability of technical issues? Such challenges are particularly problematic in field studies, where conditions rarely allow for troubleshooting without influencing student perceptions.

Despite these limitations, the present study underscores the value of conducting field-based research. While imperfect, such studies provide crucial insights into both the challenges and opportunities associated with implementing IVR in real classroom environments.

### CRediT authorship contribution statement

Benjamin De Witte: Writing – review & editing, Writing – original draft, Validation, Supervision, Project administration, Methodology, Investigation, Formal analysis, Conceptualization. Vincent Reynaert: Writing – review & editing, Supervision, Software, Formal analysis, Conceptualization. Danny Kieken: Software, Investigation. Joseph Jabbour: Formal analysis. Catherine Demarey: Funding acquisition. Anne Dumoulin: Resources. Jalal Possik: Writing – review & editing, Resources.

### Declaration of competing interest

The authors declare no financial or personal interests that could affect the objectivity of this study.

### Acknowledgements

The authors would like to thank the French National Agency for Research for funding the “DemoES” project. This research is part of the “DemoES” project (ANR-DMES-0014), which examines the relevance of immersive learning tools in education. For the Lille catholic university, this project is led by Catherine Demarey, Vice-Rector in charge of academic affairs.

The authors would also like to thank Sadia Benamrouz-Vanneste, Deville Pascal, Machelart Sophie and Place Christelle for their assistance during the experimental process.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.chb.2025.108853.

### Data availability

Data will be made available on request.

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