Multidimensional interest and achievement outcomes following immersive virtual reality integration in undergraduate chemistry: a quasi-experimental study in a Caribbean context

Abstract

The use of Immersive Virtual Reality (IVR) as a pedagogical tool for chemistry has gained momentum over the past decade. However, less is known about the specific dimensions of student interest that may or may not shift following IVR integration under authentic instructional constraints. This quasi-experimental pre-test post-test study examined the effects of smartphone-based IVR implementation on undergraduate chemistry interest and academic performance in a Caribbean university context. Two equivalent groups (Test, n = 58; Control, n = 58) completed the Chemistry Study Interest Questionnaire (C-SIQ) before and after the intervention, and a 60-item achievement test across six introductory topics. The Test group demonstrated significant gains in overall interest (p = 1.52 × 10⁻²) and in the dimension of intrinsic orientation (p = 4.47 × 10⁻⁴), while feelings-related and value-related valences did not change significantly (p > 0.05). Achievement outcomes favoured the Test group overall (p = 1.27 × 10⁻²¹). These findings are consistent with theoretical perspectives suggesting that IVR affordances may align more strongly with intrinsic motivational processes than affective or value-based components. The study contributes evidence from a resource-constrained Small Island Developing State (SIDS) context and highlights the importance of theoretical alignment and topic sensitivity in evaluating IVR integration in chemistry education.

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Introduction

In chemistry, meaningful learning depends on students’ ability to coordinate multiple levels of representation, including the macroscopic, sub microscopic, and symbolic (Kozma and Russell, 1997; Gilbert, 2008). Many foundational topics in undergraduate chemistry, such as molecular orbital theory, atomic structure, electrostatics, and intermolecular interactions, require learners to visualise entities and processes that are not directly observable (Johnstone, 1991; Wu et al., 2001; Gilbert and Treagust, 2009). The cognitive demands associated with constructing and translating among these representations are well documented, and difficulties in developing accurate mental models often lead to fragmented understanding and reliance on procedural knowledge rather than conceptual reasoning (Taber, 2001). As a result, there has been sustained interest in pedagogical approaches that can support students’ visualisation, interpretation, and integration of abstract chemical concepts (Treagust et al., 2003; Wu and Shah, 2004).

Immersive virtual reality (IVR) has emerged as a promising instructional tool in this regard (Jensen and Konradsen, 2018; Makransky and Lilleholt, 2018; Radianti et al., 2020). By situating learners within interactive three-dimensional environments, IVR allows users to inspect, manipulate, and navigate representations of chemical structures and processes in ways that are not possible with traditional two-dimensional media. Research in STEM education suggests that immersive environments can support learning when appropriately integrated into instruction (Merchant et al., 2014; Radianti et al., 2020; Makransky and Petersen, 2021). These affordances are particularly relevant for chemistry, where spatial reasoning and the ability to conceptualise structure, property relationships are central to disciplinary understanding (Wu and Shah, 2004; Kozma and Russell, 2005; Stieff, 2013). However, despite increasing interest in IVR, questions remain regarding the extent to which it contributes to meaningful learning beyond novelty effects, and whether its impact is consistent across different learning outcomes (Makransky et al., 2019; Shin, 2024; Thiele et al., 2025).

From a constructivist perspective, learning is understood as an active process in which students construct knowledge through interaction with materials, tasks, and peers (Piaget, 1970; Vygotsky, 1978; Bodner, 1986). Within this framework, the value of IVR lies not simply in its technological novelty, but in its capacity to provide interactive environments in which learners can explore representations, test ideas, and refine their understanding (Dalgarno and Lee, 2010; Merchant et al., 2014; Makransky and Petersen, 2021). The ability to manipulate three-dimensional structures and observe immediate feedback may support learners in restructuring prior conceptions and developing more coherent scientific understanding (Dede et al., 2017; Matovu et al., 2023). In addition, immersive environments can support embodied forms of learning, where physical interaction and spatial engagement contribute to cognitive processing (Johnson-Glenberg, 2018). Such affordances are particularly relevant in chemistry, where understanding depends on appreciating orientation, proximity, and dynamic relationships among molecular entities (Wu and Shah, 2004; Kozma and Russell, 2005; Stieff and Uttal, 2015).

Empirical work in chemistry education supports these theoretical claims. Collaborative IVR experiences have been shown to improve students’ conceptual understanding of hydrogen bonding and molecular structures by enabling interaction with representations and peer discussion (Matovu et al., 2023). Similarly, IVR-based learning has been found to support students’ understanding of complex biochemical processes, such as enzyme–substrate interactions, by facilitating the integration of structural, electronic, and spatial concepts (Matovu et al., 2025). At the same time, these studies highlight that the benefits of IVR are not automatic and depend critically on instructional design, task structure, and the nature of assessment.

Despite these advances, the literature remains limited in two important respects. First, research examining students’ interest in chemistry following IVR exposure has frequently treated interest as a unidimensional construct, limiting insight into how different components of interest respond to immersive learning environments. Second, relatively little work has examined IVR implementation under authentic instructional conditions in resource-constrained contexts, such as Small Island Developing States (SIDS), where technological access, cost, and curricular alignment present unique challenges. These gaps limit both the theoretical precision and the contextual relevance of existing findings.

The present study addresses these limitations by examining how IVR integration influences multidimensional chemistry interest and academic performance in an undergraduate Caribbean context. Rather than asking whether IVR “works” in general terms, this study investigates which dimensions of interest change, and whether those changes are associated with measurable differences in achievement across specific chemistry topics.

Theoretical framework

This study is grounded in a multidimensional conceptualisation of interest as a distinct construct in educational psychology. Interest is not treated as synonymous with motivation. Instead, it is understood as a specific psychological state and disposition that reflects engagement, value, and affective orientation toward a domain (Schiefele, 1991; Krapp, 2002; Hidi and Renninger, 2006). Within chemistry education, this distinction is operationalised through the Chemistry Study Interest Questionnaire (C-SIQ), which differentiates among intrinsic orientation, feelings-related valences, and value-related valences (Schiefele et al., 1992). This framework allows for a more precise analysis of how different aspects of students’ engagement with chemistry may be differentially influenced by instructional interventions (Hidi and Renninger, 2006; Krapp and Prenzel, 2011; Lin et al., 2013; Renninger and Hidi, 2016; Akkerman et al., 2020).

Intrinsic orientation refers to internally driven engagement and curiosity toward chemistry content, whereas feelings-related valences capture affective responses, and value-related valences reflect perceived importance or relevance of the subject. Theoretical models of interest development suggest that these components do not necessarily change in parallel, and that instructional environments may selectively influence particular dimensions depending on their design and affordances (Krapp, 2002; Hidi and Renninger, 2006).

While interest is conceptually distinct from motivation, Self-Determination Theory (SDT) provides a useful complementary perspective for understanding how instructional environments may support changes in interest-related processes. SDT posits that learning environments that support autonomy and competence can foster more internally driven forms of engagement (Deci and Ryan, 2000). IVR environments, through their interactive and exploratory affordances, may provide opportunities for learners to engage with chemistry content in ways that support these psychological needs. For example, the ability to manipulate molecular structures, explore representations from multiple perspectives, and control the pace of interaction may support learners’ sense of agency and conceptual understanding (Wu and Shah, 2004; Kozma and Russell, 2005; Merchant et al., 2014; Parong and Mayer, 2018; Makransky and Petersen, 2021).

The theoretical model guiding this study suggests that IVR may be more likely to influence intrinsic orientation than other components of interest, given its emphasis on interactive and exploratory engagement. Changes in feelings-related or value-related dimensions may require additional contextualisation, such as explicit connections to real-world applications or career pathways, which extend beyond immersive visualisation alone (Deci and Ryan, 2000; Eccles and Wigfield, 2002; Krapp, 2002; Hidi and Renninger, 2006; Renninger and Hidi, 2016; Makransky and Petersen, 2021). This perspective frames IVR not as a universally transformative tool, but as one whose effects may be selective and dependent on both instructional design and context.

Research questions

Guided by this theoretical framework, the present study addresses the following research questions:

(1) Does IVR integration produce changes in overall chemistry interest and its subcomponents?

(2) Does IVR integration improve chemistry achievement relative to traditional instruction?

(3) Are IVR effects topic-dependent across introductory chemistry content?

Research objectives

To answer the research questions, the following objectives were defined:

(1) To investigate the effect of 3D IVR technology aided immersion on chemistry interest in introductory undergraduate chemistry students with the use of the Chemistry Study Interest Questionnaire (C-SIQ).

(2) To compare the effect of 3D IVR technology aided immersion on the subcomponents of interest as defined in the C-SIQ.

(3) To investigate the effect of 3D IVR technology aided immersion on chemistry performance in undergraduate chemistry students with a customized chemistry performance quiz based on molecular orbital theory, the periodic table, atomic properties, isomerism, the gas laws and electrostatics.

(4) To compare the effect of 3D IVR across topics quizzed for chemistry performance analysis.

Study hypotheses

The researchers formulated hypotheses to be tested:

H₀: I_TG1 = I_TG2 (H₀1) H₀: I_CG1 = I_CG2 (H₀2)

where I = chemistry interest, TG1 = Test group pre-test, TG2 = Test group post-test, CG1 = Control group pre-test, CG2 = Control group post-test

H₀: F_TG1 = F_TG2 (H₀3) H₀: F_CG1 = F_CG2 (H₀4)

where F = Feeling-related valences

H₀: V_TG1 = V_TG2 (H₀5) H₀: V_CG1 = V_CG2 (H₀6)

where V = value-related valences

H₀: IO_TG1 = IO_TG2 (H₀7) H₀: IO_CG1 = IO_CG2 (H₀8)

where IO = intrinsic orientation

H₀: P_TG = P_CG (H₀9)

where P = chemistry performance, TG = Test group, CG = Control group.

Methodology

This section outlines the research design, participants, intervention, instruments, and analytical procedures employed in the study.

Participants and design

A quasi-experimental design was employed involving undergraduate students enrolled in Introductory Chemistry I (CHEM 1066) at The University of the West Indies, St. Augustine. Participants were divided into two groups. One was a TG (n = 58), which received IVR-supported instruction as well as traditional instruction (scheduled lectures and tutorials). The second was a CG (n = 58), which received traditional instruction alone. Group assignment followed an odd/even allocation approach consistent with institutional scheduling constraints. This design reflects authentic classroom conditions while allowing for comparison between instructional approaches.

Baseline equivalence between groups was assessed prior to the intervention. Independent samples tests indicated no statistically significant differences between the TG and CG on overall Chemistry Study Interest Questionnaire (C-SIQ) scores or any of its subscales (p > 0.05). Additionally, demographic characteristics and prior academic qualifications, including Caribbean Secondary Education Certificate (CSEC) and Caribbean Advanced Proficiency Examination (CAPE) results, were comparable across groups, supporting the validity of subsequent comparisons.

Intervention

The intervention was implemented over the course of the instructional period, over 9 weeks. During this time students were allowed flexible access to the IVR resources and could engage with the content at their own pace. Additionally, the TG received the traditional instruction scheduled for the semester. This approach was intended to reflect realistic implementation conditions within a resource-constrained educational setting. The CG received equivalent instructional content through standard scheduled lectures and tutorials without the use of IVR.

Hardware and software

Students allocated to the TG were equipped with an immersive virtual reality headset system, consisting of a VR Head Mounted Display (HMD) paired with a smartphone (Fig. 1). Each TG participant in the study was given a set of instructions for using the hardware and accessing the software for the study (Fig. 2). These instructions were provided after the phones in the headsets were connected by Bluetooth. Once the set up was completed, the TG participants were then free to navigate the MEL VR Application.

Fig. 1 HMD and smartphone used for this study.

Fig. 2 Instructions for use of the VR headset and MEL VR software to TG.

The IVR learning experience

The IVR intervention utilised the MEL Chemistry VR platform delivered through smartphone-based head-mounted displays. Following an orientation session, students in the TG were given access to the IVR resources for nine weeks and were free to engage with the learning experiences outside scheduled class time. The intervention was primarily exploratory in nature rather than instructor guided. Students independently navigated the virtual environments and interacted with three-dimensional representations of chemistry concepts aligned with topics being covered in Introductory Chemistry 1 (CHEM 1066).

The chemistry lessons began in a virtual lab (Fig. 3) and once selected, the user was taken to the lesson selected. The lessons were between 3 minutes and 7 minutes long and the user was able to zoom in to the molecular level. For example, in the lesson on atomic properties, users were able to “shrink” up to a billion times to observe atoms and the crystal structure of Sulphur (see Fig. 4). The lessons on isomerism included structural isomerism, cis–trans isomerism and optical isomerism. There were also VR labs including those where users were able to build isomers and another where users had to determine if a molecule was an isomer or not (Fig. 5).

Fig. 3 Screenshot of the start of the MEL VR chemistry lessons.

Fig. 4 The start (left) and end (right) of zooming in on the crystal structure of sulphur.

Fig. 5 Screenshots of the lessons and labs on isomerism in the MEL VR application. (A) Structural isomers, (B) cis–trans isomers, (C) optical isomers, (D) isomers or not (lab), (E) optical isomers (lab), (F) build isomers (lab).

These experiences allowed learners to observe chemical entities and processes from multiple perspectives and to interact with representations that are typically inaccessible in traditional classroom settings. The IVR activities were intended to complement rather than replace lectures and tutorials, with all students continuing to receive the standard curriculum.

Student participation was facilitated through flexible access to the hardware and software. Although usage was not formally monitored through learning analytics, students were encouraged to engage with the resources throughout the intervention period at their own pace. This implementation approach was designed to reflect authentic educational conditions within a resource-constrained university setting.

Instruments

Student interest in chemistry was measured using the Chemistry Study Interest Questionnaire (C-SIQ), administered both before and after the intervention. The instrument assesses interest across three dimensions: intrinsic orientation, feelings-related valences, and value-related valences. Internal consistency reliability for the present sample, as measured by Cronbach's alpha, ranged from acceptable to strong across subscales. Confirmatory factor analysis confirmed construct validity of the instrument within this context.

Academic performance was assessed using a 60-item multiple-choice test (SI) developed by course instructors and aligned with the learning objectives of the course. The assessment covered six key topics: molecular orbital theory, periodic table, atomic properties, isomerism, gas laws, and electrostatics. As the knowledge assessment was designed as a curriculum-aligned measure of student performance rather than a standalone psychometric instrument, evidence for its coherence is presented through test design, topic balance, and alignment with learning outcomes. Internal consistency reliability of the assessment, evaluated using the Kuder–Richardson Formula 20 (KR-20), was 0.65. Given that the assessment was developed as a curriculum-aligned measure spanning six distinct introductory chemistry topics, this value was considered acceptable for the exploratory purposes of the present study. Further details are provided in the SI (Appendix S1).

Statistical analysis

Statistical analyses were conducted using Microsoft Excel and IBM SPSS Statistics. Differences in chemistry interest between TG and CG across pre- and post-intervention measurements were examined using analysis of variance (ANOVA), with additional analyses conducted to evaluate changes across the three dimensions of the Chemistry Study Interest Questionnaire (C-SIQ): intrinsic orientation, feelings-related valences, and value-related valences. Hypotheses were tested at a 95% confidence level (p < 0.05. A summary of statistical outputs is provided in the SI (Appendix S2) accompanying this paper.

The psychometric properties of the C-SIQ were examined to support its use in the present study context. Evidence for structural validity was assessed using confirmatory factor analysis (CFA), with model fit evaluated using multiple indices, including the chi-square statistic (χ²), Goodness of Fit Index (GFI), Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR), using commonly accepted interpretive thresholds (Kline, 2016; Hair et al., 2019). Internal consistency reliability of the instrument was evaluated using Cronbach's alpha (Cronbach, 1951).

Post-intervention academic performance differences between the TG and CG were examined using ANOVA, with additional topic-specific analyses conducted across the six chemistry topics assessed: molecular orbital theory, periodic table, atomic properties, gas laws, electrostatics, and isomerism. To complement significance testing and provide an indication of practical educational impact, effect sizes were calculated using Cohen's d (Cohen, 1988). All inferential analyses were conducted at a 95% confidence level.

Reliability and supporting validity evidence for the 60-item chemistry knowledge assessment were also examined. Content validity was supported through test blueprinting and alignment of items with course learning objectives. Internal consistency reliability of the dichotomously scored assessment was evaluated using the Kuder–Richardson Formula 20 (KR-20), appropriate for binary-scored instruments (Kuder and Richardson, 1937). Item-level analyses, including item difficulty and point-biserial discrimination indices, were used to evaluate item quality and the assessment's capacity to differentiate levels of student understanding. A summary of these analyses is provided in the SI (Appendix S1).

Ethical considerations

Ethical approval for this study was obtained from the Campus Research Ethics Committee of The University of the West Indies, St. Augustine, prior to data collection. All procedures involving human participants were conducted in accordance with institutional ethical standards. Participants were recruited from the Introductory Chemistry I course and were provided with detailed information about the purpose and procedures of the study via the course learning management system. Informed consent was obtained electronically from all participants prior to participation. Participation was entirely voluntary, and students were informed of their right to withdraw from the study at any time without penalty. To ensure confidentiality, no personally identifiable information was collected, and all data were anonymised prior to analysis. Data were securely stored and accessible only to the research team. The study adhered to established ethical guidelines for research involving human subjects, including respect for participant autonomy, confidentiality, and data protection.

Findings

Analysis of overall chemistry interest using the C-SIQ indicated a statistically significant increase for the TG following IVR exposure (p = 1.52 × 10⁻²), while no significant change was observed for the CG (p = 0.569). Further analysis of the C-SIQ subscales revealed that this increase was not uniform across all dimensions of interest (Table 1). A statistically significant improvement was observed for intrinsic orientation in the TG (p = 4.47 × 10⁻⁴), whereas no significant differences were found for feelings-related valences (p = 0.159) or value-related valences (p = 0.179). For the CG, no statistically significant changes were observed across any of the three subscales (p > 0.05), indicating stability in interest levels in the absence of IVR exposure.

Table 1 Statistical comparison of overall interest and subscale scores before and after IVR exposure

Interest scale	p-Value	Interpretation
TG overall interest	0.015244	Reject null hypothesis
CG overall interest	0.568897	Fail to reject null hypothesis
TG feelings-related valences	0.159467	Fail to reject null hypothesis
CG feelings-related valences	0.514960	Fail to reject null hypothesis
TG value-related valences	0.178804	Fail to reject null hypothesis
CG value-related valences	0.665446	Fail to reject null hypothesis
TG intrinsic orientation	0.000447	Reject null hypothesis
CG intrinsic orientation	0.640717	Fail to reject null hypothesis

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The psychometric properties of the C-SIQ were assessed to confirm suitability for the study context (Table 2). Confirmatory factor analysis indicated acceptable model fit, with Goodness of Fit Index (GFI = 0.92), Comparative Fit Index (CFI = 0.94), Root Mean Square Error of Approximation (RMSEA = 0.05), and Standardized Root Mean Square Residual (SRMR = 0.04). The chi-square statistic (χ² = 120.35, df = 135) was non-significant, further supporting model adequacy. Internal consistency reliability was high, with Cronbach's alpha values exceeding acceptable thresholds, indicating strong reliability of the instrument within this sample.

Table 2 Model fit indices for C-SIQ in this study

Fit index	Value	Threshold	Interpretation
χ^2	120.35	—	—
df	135	—	—
χ^2/df	0.89	<3.00	Excellent fit
p-Value	>0.05	<0.05	Non-significant
GFI	0.92	≥0.90	Acceptable fit
CFI	0.94	≥0.90	Acceptable fit
RMSEA	0.05	<0.06	Good fit
SRMR	0.04	<0.08	Good fit
Cronbach's α	0.88	>0.70	Acceptable internal consistency

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Post-intervention comparisons of academic performance revealed a statistically significant difference between the TG and CG (p = 1.27 × 10⁻²¹), with the former achieving higher mean scores across all assessed topics (Table 3). Topic-specific analysis indicated that the magnitude of differences varied across content areas (Fig. 6). The TG achieved higher mean scores in molecular orbital theory (82.6% vs. 70.5%), periodic table (87.6% vs. 68.8%), atomic properties (82.8% vs. 69.1%), gas laws (82.4% vs. 72.2%), and electrostatics (92.2% vs. 75.9%). In contrast, the difference observed for isomerism was comparatively smaller (75.0% vs. 71.6%). Cohen's d values varied substantially across topics, ranging from a small-to-moderate effect for isomerism (d = 0.38) to very large effects for periodic table (d = 1.46) and atomic properties (d = 1.47), with large effects observed for molecular orbital theory (d = 0.81), gas laws (d = 1.02), and electrostatics (d = 1.23) (Table 4).

Table 3 TG and CG comparison – overall chemistry performance

Anova: single factor
Summary
Groups	Count	Sum	Average	Variance
TG mean quiz score	58	48.58333	0.837644	0.000931
CG mean quiz score	58	41.38333	0.713506	0.005424

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Anova
Source of variation	SS	df	MS	F	p-Value	Ferit
Between groups	0.446897	1	0.446897	140.6453	1.27 × 10^−21	3.92433
Within groups	0.362232	114	0.003177
Total	0.809128	115

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Fig. 6 Topic specific comparison for chemistry performance.

Table 4 Cohen's D comparison for 6 chemistry topics

Topic	Cohen's D	Interpretation
Molecular orbital theory	0.81	Large effect
Periodic table	1.46	Very large effect
Atomic properties	1.47	Very large effect
Isomorphism	0.38	Small – moderate effect
Gas laws	1.02	Large effect
Electrostatics	1.23	Large effect

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Internal consistency reliability of the assessment, evaluated using the Kuder–Richardson Formula 20 (KR-20), was 0.65. Given that the assessment was developed as a curriculum-aligned measure spanning six distinct introductory chemistry topics, this value was considered acceptable for the exploratory purposes of the present study.

Discussion

The present study examined the effects of immersive virtual reality (IVR) integration on undergraduate chemistry interest and academic performance within a Caribbean higher education context, with particular emphasis on the multidimensional nature of interest. The findings indicate that there was a significant increase in overall chemistry interest, driven specifically by gains in intrinsic orientation, while feelings-related and value-related valences remained unchanged. In parallel, there was a significantly higher academic performance across all assessed topics, with effect sizes ranging from small-to-moderate to very large depending on the content area. Taken together, these findings suggest that IVR does not exert a uniform influence on student outcomes but rather operates in a selective and context-dependent manner across both affective and cognitive domains. It is important to note the present study did not directly examine the relationship between changes in intrinsic orientation and achievement outcomes. Consequently, no causal or correlational interpretation should be inferred regarding the parallel improvements observed in these two domains.

Selective effects of IVR on multidimensional interest

A central contribution of this study lies in demonstrating that IVR does not influence chemistry interest as a unitary construct. The observed increase in overall interest was attributable specifically to gains in intrinsic orientation, with no corresponding changes in feelings-related or value-related valences. This finding provides empirical support for the multidimensional conceptualisation of interest (Krapp, 2002; Hidi and Renninger, 2006) and underscores the importance of disaggregating interest into its constituent components when evaluating instructional interventions.

Theoretically, this pattern aligns with the proposition that instructional environments characterised by interactivity, learner control, and immediate feedback preferentially support internally driven engagement. Within the framework of Self-Determination Theory, intrinsic orientation reflects learners’ experiences of autonomy and competence (Deci and Ryan, 2000). The IVR environment in this study afforded students the ability to manipulate three-dimensional representations, explore chemical structures from multiple perspectives, and regulate their pace of interaction. These affordances are consistent with conditions known to support intrinsic forms of engagement and may explain the observed increase in intrinsic orientation (Deci and Ryan, 2000; Hidi and Renninger, 2006; Renninger and Hidi, 2016; Parong and Mayer, 2018; Makransky and Petersen, 2021).

In contrast, the absence of change in feelings-related and value-related valences suggests that immersive visualisation alone is insufficient to influence affective attachment or perceived relevance of chemistry. These dimensions are more strongly shaped by contextual and experiential factors, including real-world applications, career pathways, and personal meaning. This finding is consistent with theoretical models of interest development, which posit that different components of interest evolve through distinct mechanisms and may require sustained or contextually rich experiences to shift meaningfully (Krapp, 2002; Hidi and Renninger, 2006).

Importantly, this study advances current IVR research by moving beyond general claims of increased engagement to a more precise account of which dimensions of interest are affected and why. This level of specificity remains underdeveloped in the literature, where interest is frequently treated as a single construct.

Topic-dependent effects on academic performance

The results further indicate that IVR exposure was associated with improved academic performance across all assessed topics; however, the magnitude of these effects varied substantially. Very large effects were observed for periodic table and atomic properties, large effects for molecular orbital theory, gas laws, and electrostatics, and a comparatively smaller effect for isomerism. This variation provides strong empirical support for the proposition that the effectiveness of IVR is dependent on the alignment between its affordances and the representational demands of specific chemistry topics (Makransky and Petersen, 2021).

Topics such as molecular orbital theory, atomic properties, and electrostatics involve abstract, spatially complex representations that are difficult to visualise using traditional instructional approaches. The ability to interact with three-dimensional models and observe dynamic relationships in IVR environments directly supports the cognitive processes required for understanding these concepts. In contrast, isomerism, while involving spatial reasoning, also requires the ability to translate between multiple representational forms, including symbolic notation and structural diagrams. Without explicit scaffolding to support this representational translation, the benefits of immersive visualisation may be attenuated (Kozma and Russell, 1997; Treagust et al., 2003; Wu and Shah, 2004; Gilbert and Treagust, 2009; Sevian and Talanquer, 2014). The comparatively smaller effect observed for isomerism may reflect the fact that successful learning in this topic requires not only spatial visualisation but also translation between symbolic conventions, structural formulas, and nomenclature systems. These representational demands may not be fully addressed through immersive visualisation alone.

This finding is significant because it challenges the implicit assumption that IVR is uniformly beneficial across chemistry content. Instead, it highlights the need for careful alignment between technological affordances and the cognitive and representational demands of the subject matter. Such alignment is essential if IVR is to support meaningful learning rather than surface-level engagement.

Implications for theory and practice

Taken together, these findings contribute to a more theoretically grounded understanding of how IVR functions as a pedagogical tool in chemistry education. First, the selective effect on intrinsic orientation supports the argument that IVR primarily operates through mechanisms associated with autonomy and competence, rather than broadly enhancing all dimensions of student interest. Second, the topic-dependent variation in performance outcomes demonstrates that the effectiveness of IVR is contingent on its alignment with disciplinary representations.

From a practical perspective, these results suggest that IVR should not be implemented as a stand-alone solution, but rather as part of an integrated instructional design. To influence broader dimensions of interest, such as value and affect, IVR experiences may need to be combined with contextualised learning activities that emphasise real-world relevance and career connections. Similarly, for topics requiring representational translation, IVR should be supplemented with structured guidance and opportunities for reflection.

A further contribution of this study lies in its context. The implementation of smartphone-based IVR in a Small Island Developing State (SIDS) demonstrates that immersive technologies can be integrated within resource-constrained educational environments. This extends the current literature, which is predominantly based in well-resourced contexts, and provides evidence for the feasibility of scalable, cost-effective approaches to immersive learning.

Limitations and future research

Several limitations should be acknowledged. The quasi-experimental design limits causal inference, and variation in students’ engagement with IVR was not directly controlled. Additionally, the potential influence of novelty effects cannot be excluded, as initial exposure to immersive technologies may enhance engagement independently of instructional value.

A further limitation is that the study did not directly examine the relationship between changes in chemistry interest and academic performance. Although increases in intrinsic orientation and achievement were observed concurrently within the TG, the extent to which these outcomes were related was not investigated. Future studies should employ correlational or structural modelling approaches to explore potential associations between affective and cognitive outcomes in IVR-supported chemistry learning.

The achievement component of the study employed a post-test-only comparison and did not include a pre-intervention measure of chemistry achievement for the assessed topics. Although the groups were comparable with respect to demographic characteristics, academic qualifications, and baseline chemistry interest, differences in prior topic-specific knowledge cannot be completely excluded. Consequently, the achievement findings should be interpreted with appropriate caution. The achievement findings should also be interpreted considering the characteristics of the assessment instrument. The 60-item chemistry knowledge assessment demonstrated a KR-20 coefficient of 0.65, indicating acceptable internal consistency for a curriculum-based instrument spanning multiple introductory chemistry topics. Given the broad content coverage of the assessment, the reliability coefficient was considered sufficient for the group-level comparisons undertaken in this study.

Future research should examine the longitudinal stability of the observed gains in intrinsic orientation and investigate how structured and guided IVR interventions influence both interest and learning outcomes. Further work is also needed to develop topic-specific design principles for IVR in chemistry education, particularly for areas requiring complex representational reasoning. Integrating IVR with explicit scaffolding for representational translation represents a promising direction for enhancing its effectiveness across a broader range of chemistry topics.

Conclusion

This study examined the effects of immersive virtual reality (IVR) on undergraduate chemistry interest and academic performance within a Caribbean higher education context, with particular emphasis on the multidimensional nature of interest. The findings demonstrate that IVR integration does not produce uniform effects across student outcomes but instead operates selectively across both affective and cognitive domains.

In relation to interest, the results provide clear evidence that IVR preferentially supports intrinsic orientation, while exerting limited influence on feelings-related and value-related valences. This finding reinforces the importance of conceptualising interest as a multidimensional construct and highlights the need to move beyond aggregate measures when evaluating instructional interventions. In relation to academic performance, the study shows that IVR can support substantial learning gains; however, these gains are strongly dependent on the alignment between IVR affordances and the representational demands of specific chemistry topics. The observed variation in effect sizes across topics underscores the importance of content sensitivity in the design and evaluation of immersive learning environments.

Taken together, these findings contribute to a more nuanced understanding of IVR as a pedagogical tool. Rather than functioning as a universally effective innovation, IVR appears to be most impactful when its interactive and visual affordances are deliberately aligned with both the cognitive and motivational dimensions of learning. This has important implications for theory, suggesting that the effects of immersive technologies are mediated by their relationship to specific psychological processes, and for practice, indicating that effective implementation requires careful integration with instructional design and curriculum objectives.

Importantly, this study extends the existing literature by providing evidence from a Small Island Developing State (SIDS) context, demonstrating that smartphone-based IVR can be meaningfully implemented within resource-constrained environments. This highlights the potential for scalable and accessible approaches to immersive learning in chemistry education, while also emphasising the need for complementary instructional strategies to support broader dimensions of student engagement.

Overall, the findings underscore the importance of theoretically informed and contextually grounded approaches to the integration of immersive technologies in chemistry education. They point to the need for continued research that examines not only whether such technologies are effective, but how, for whom, and under what conditions they support meaningful learning.

Author contributions

Donna Hitlal: conceptualization; methodology; investigation; formal analysis; data curation; writing – original draft; writing – review and editing; visualization. Denise Beckles: supervision; methodology; writing – review and editing; project administration. Dianne Thurab-Nkhosi: validation; formal analysis; writing – review and editing.

Conflicts of interest

There are no conflicts to declare.

Data availability

The datasets generated and analysed during the current study are not publicly available due to ethical and privacy considerations but are available from the corresponding author on reasonable request.

Supplementary information (SI) is available. See DOI: https://doi.org/10.1039/d6rp00196c.

Acknowledgements

The authors would like to thank all the students who volunteered to participate in this study. Thanks as well to The Department of Chemistry in the Faculty of Science and Technology and The Office of Graduate Studies and Research at The University of The West Indies, St. Augustine Campus in Trinidad and Tobago.

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