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ILEIID 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXIV October 2025
Integrating XR Technology in Education: A Study on Enhancing
Student Satisfaction through Sentiment Analysis
*1
Nurul Nisha Mohd Shah,
2
Tuan Norhafizah Tuan Zakaria,
3
Mohd Radzman Basinon
1
Faculty of Communication and Media Studies
2
Center of Computing Sciences, Faculty of Computer and Mathematical Sciences
3
Sixtysix Action Academy
*Corresponding Author
DOI: https://dx.doi.org/10.47772/IJRISS.2025.924ILEIID0063
Received: 23 September 2025; Accepted: 30 September 2025; Published: 31 October 2025
ABSTRACT
Advancements in educational technology have brought significant transformations, particularly in online
learning. The integration of virtual reality (VR), augmented reality (AR), and mixed reality (MR) into live
classroom teaching represents a promising approach to enhancing student satisfaction. However, many
educators continue to rely on traditional teaching methods, often resulting in monotonous and less interactive
learning environments. This study aims to explore how the incorporation of XR technology in online learning
can improve student satisfaction through sentiment analysis. The key outcome of this research is the
development of an interactive XR-based learning platform that combines VR, AR, and MR technologies within
live teaching practices. This platform is designed to enhance students’ learning experiences and satisfaction in
online environments. Employing a quantitative approach, the study analyzed student feedback using sentiment
analysis software. Findings indicate that XR technology fosters greater engagement, interest, and
comprehension among students, leading to higher levels of satisfaction. Furthermore, the results highlight the
necessity for educators to be equipped with adequate knowledge and skills to effectively implement digital
teaching strategies. The study recommends that educational policymakers and institutional leaders provide
appropriate training and support to enable teachers to integrate XR technologies more effectively into their
pedagogical practices.
Keywords: XR Technology, Online education, Student satisfaction, Sentiment Analysis, Educational
Technology
INTRODUCTION
The rapid evolution of digital technologies has fundamentally transformed the landscape of contemporary
education, reshaping pedagogical practices and redefining learner expectations (Selwyn, 2016; Anderson,
2019). Within this context, Extended Reality (XR), which includes Virtual Reality (VR), Augmented Reality
(AR), and Mixed Reality (MR), has emerged as a disruptive force in higher education and online learning
ecosystems (Radianti et al., 2020; Akçayır & Akçayır, 2017). Scholars have argued that immersive
technologies not only extend the cognitive presence of learners but also foster heightened affective
engagement, thereby enriching both individual and collective learning experiences (Merchant et al., 2014;
Jensen & Konradsen, 2018). The integration of XR technologies into educational settings aligns with the
broader paradigm shift from teacher-centered to learner-centered pedagogies, as emphasized in constructivist
and experiential learning theories (Kolb, 2015; Vygotsky, 1978). By situating students in highly interactive and
contextually relevant environments, XR enables multisensory engagement that bridges the gap between
abstract knowledge and applied practice (Johnson-Glenberg, 2018; Hew & Cheung, 2014). Such affordances
are particularly significant in online and blended learning modalities, where the absence of physical presence
often undermines motivation, interactivity, and sustained attention (Hrastinski, 2019; Martin et al., 2020).
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ILEIID 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXIV October 2025
Despite these advantages, the adoption of XR in education remains uneven, with many instructors continuing
to rely on traditional lecture-based models that may inadvertently constrain learner participation (Laurillard,
2012; Bates, 2019). Empirical studies suggest that student satisfaction, conceptualized as a multidimensional
construct comprising cognitive, affective, and behavioral components, serves as a critical determinant of
learning persistence, academic performance, and institutional reputation (Kuo et al., 2014; Moore & Kearsley,
2011). Consequently, examining the role of XR in enhancing student satisfaction through rigorous analytical
methods is both timely and necessary (Alalwan et al., 2020; Bond et al., 2020). In this regard, sentiment
analysis has emerged as a powerful methodological tool for assessing learner perceptions and emotions at scale
(Feldman, 2013; Cambria et al., 2017). By systematically evaluating student-generated textual feedback,
sentiment analysis provides granular insights into the affective dimensions of learning experiences that are
often overlooked in conventional survey-based evaluations (Bozkurt et al., 2021; Zou et al., 2019). When
applied to XR-based interventions, this approach not only illuminates the emotional valence of students’
responses but also informs evidence-based pedagogical strategies aimed at fostering satisfaction, engagement,
and retention (Shum et al., 2018; Rienties & Toetenel, 2016). Against this backdrop, the present study
investigates the integration of XR technologies into online learning and evaluates their impact on student
satisfaction through sentiment analysis. By situating the inquiry at the intersection of educational technology,
learning analytics, and affective computing, this research contributes to the growing body of knowledge on
immersive pedagogies while offering actionable implications for educators, institutional leaders, and
policymakers in designing digitally enhanced learning environments (Ifenthaler & Yau, 2020; Zawacki-Richter
et al., 2019).
LITERATURE REVIEW
XR Technologies in Education Affordances and Challenges
Extended Reality (XR), which encompasses Virtual Reality (VR), Augmented Reality (AR), and Mixed
Reality (MR), is increasingly recognized as a transformative innovation in education due to its immersive and
interactive features that allow learners to engage with complex concepts through embodied experiences,
thereby enhancing comprehension, retention, and motivation (Merchant et al., 2014; Radianti et al., 2020). For
example, VR enables students to explore simulated environments, AR provides contextual information in real
time, and MR integrates physical and virtual objects to foster experiential learning (Akçayır & Akçayır, 2017;
Jensen & Konradsen, 2018). Despite these advantages, adoption of XR technologies remains constrained by
challenges such as high implementation costs, limited access to technical infrastructure, and insufficient
teacher preparedness (Bates, 2019; Laurillard, 2012), while some scholars argue that the novelty effect of XR
may overshadow pedagogical objectives, producing only short-term engagement without sustained learning
outcomes (Hew & Cheung, 2014; Hrastinski, 2019), thus highlighting the need for evidence-based evaluations
that move beyond technology-centric discussions to focus on the genuine pedagogical impact of XR.
Student Satisfaction as a Multidimensional Construct
Student satisfaction has been consistently identified as a critical determinant of academic persistence,
performance, and institutional credibility (Kuo et al., 2014; Moore & Kearsley, 2011), and is often
conceptualized as a multidimensional construct encompassing cognitive (perceptions of learning
effectiveness), affective (emotional responses to learning), and behavioral (willingness to engage) components
(Alalwan et al., 2020; Bond et al., 2020). Within online learning, satisfaction becomes even more crucial as it
directly influences motivation and reduces dropout rates (Martin et al., 2020), and the integration of XR into
such environments has been shown to positively affect satisfaction by enhancing interactivity, personalization,
and presence (Johnson-Glenberg, 2018; Radianti et al., 2020). Immersive learning experiences foster deeper
engagement by shifting learners from passive content consumption toward active knowledge construction
(Kolb, 2015), yet some studies caution that poorly designed XR applications may generate excessive cognitive
load and diminish satisfaction (Selwyn, 2016; Jensen & Konradsen, 2018), underscoring the importance of
pedagogically sound integration that prioritizes instructional objectives over technological novelty.
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ILEIID 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXIV October 2025
Sentiment Analysis in Evaluating Learning Experiences
Sentiment analysis has emerged as an increasingly valuable methodological tool in education, enabling large-
scale evaluation of student perceptions and emotions through computational text mining (Feldman, 2013;
Cambria et al., 2017). By systematically processing learner-generated feedback, sentiment analysis captures
nuanced affective dimensions such as satisfaction, frustration, or enthusiasm that are often overlooked in
traditional survey-based evaluations (Bozkurt et al., 2021; Zou et al., 2019). Within the context of XR-
enhanced learning, this approach provides critical insights into how immersive technologies shape both
cognitive and emotional experiences, allowing researchers and educators to assess whether novelty-driven
engagement translates into sustained satisfaction and meaningful learning outcomes (Rienties & Toetenel,
2016; Shum et al., 2018). Nevertheless, limitations remain because natural language processing algorithms
may misinterpret contextual subtleties such as sarcasm, cultural idioms, or mixed affective tones, which
underscores the importance of triangulating sentiment analysis with qualitative and quantitative methods to
ensure robust and valid interpretations (Cambria et al., 2017).
METHODOLOGY
This study adopted a quantitative research design that utilized sentiment analysis to evaluate student
satisfaction in XR-integrated online classes, focusing on both polarity and lexical frequency to capture the
affective dimensions of learner feedback. A total of 439 responses were collected from participants who
attended XR-based online learning sessions, with data obtained through structured online forms distributed
immediately after the sessions. Feedback entries were processed using sentiment analysis software that
classified textual inputs into positive, neutral, and negative categories, assigning polarity scores ranging from
0, which indicated neutrality, to 1, which indicated strong positivity. The analysis was conducted in two stages,
beginning with polarity classification, which revealed that 241 responses were neutral, 189 were positive, and
only 9 were negative, followed by lexical frequency analysis that identified the most commonly used words
such as “program,” “baik,” “manfaat,” “ilmu,” and “good,” which were subsequently visualized through word
clouds to illustrate their prominence. The validity of the study was enhanced by triangulating polarity scores
with word frequency analysis, reducing reliance on a single evaluative measure, while reliability was
strengthened by employing automated tools that minimized human bias in classification. Ethical considerations
were observed throughout the process, with all participants informed that their responses would remain
anonymous and would be used solely for research purposes, ensuring compliance with academic integrity and
participant confidentiality standards.
RESULTS AND DISCUSSION
Figure 1: Distribution of Sentiment
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ILEIID 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXIV October 2025
The analysis of 439 student feedback entries revealed that 241 were neutral, 189 were positive, and only 9
were negative, with sentiment polarity scores ranging from 0.35 to 1.0, the highest reflecting strong
enthusiasm such as “Coach Nisha the best!” (score = 1.0), while neutral responses often comprised brief
remarks like “tiada” or “ok” that indicated functional satisfaction without emotional intensity, and the few
negative responses were mainly associated with technical issues such as sound clarity during delivery.
Table 1: Lexical Frequency Analysis
Word
Frequency
program
115
tiada
65
baik
55
manfaat
29
tarik
29
terima
27
kasih
26
serta
21
acara
20
ilmu
19
terus
17
good
14
Based on Table 1, lexical frequency analysis further reinforced these findings, with positive descriptors like
baik (55), manfaat (29), ilmu (19), and good (14) appearing most prominently, alongside program (115), which
reflected the holistic appreciation of the learning experience.
Figure 2: Word Cloud of the Review
The prevalence of affirmative terms, later visualized in word cloud form (as depicted in Figure 2), illustrated
that learners valued not only the technological novelty of XR but also its ability to deliver beneficial and
practical knowledge, a result consistent with prior studies linking immersive technologies to enhanced
engagement and satisfaction (Radianti et al., 2020; Johnson-Glenberg, 2018). Importantly, the predominance of
positive (43%) and neutral (55%) sentiment, with only 2% negative, suggests that XR integration was well
received and did not create alienation or significant usability challenges, thus addressing long-standing issues
of monotony and disengagement in online learning environments (Hrastinski, 2019). Nevertheless, the large
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proportion of neutral responses also signals that while XR was effective in sustaining attention and providing
interactivity, further personalization and adaptive pedagogical scaffolding may be necessary to evoke stronger
emotional resonance, as cautioned by studies on the potential of cognitive overload in poorly designed XR
applications (Selwyn, 2016; Jensen & Konradsen, 2018). Overall, these findings underscore that the success of
XR in enhancing student satisfaction depends not only on its immersive affordances but also on deliberate
pedagogical integration that aligns with constructivist principles and ensures that technology serves as a
medium for meaningful learning rather than as an end in itself (Kolb, 2015; Hew & Cheung, 2014).
CONCLUSION
This study explored the integration of XR technology in online learning and its impact on student satisfaction
through sentiment analysis of 439 feedback entries. The results showed that most responses were positive or
neutral, with only a few negative comments, suggesting that XR enhanced immersion, interactivity, and
authenticity in the learning process. Lexical analysis highlighted affirmative words such as baik, manfaat,
ilmu, and good, reinforcing the overall positive reception and confirming that learners valued both the novelty
and the practical benefits of XR. Theoretically, the study contributes to immersive pedagogy by extending
constructivist and experiential learning frameworks into digital contexts and by demonstrating the usefulness
of sentiment analysis as a tool to capture affective aspects of learning often missed by surveys. Practically, the
findings emphasize the need for educators to integrate XR with clear instructional goals and adaptive activities
rather than relying on novelty alone. At the policy level, the study highlights the importance of investment in
infrastructure, institutional support, and teacher training to ensure effective and equitable XR adoption. Despite
its contributions, the study acknowledges limitations such as the reliance on sentiment analysis tools that may
misinterpret context, the restricted scope of data, and the absence of longitudinal outcomes. Future research
should therefore expand to different disciplines, apply mixed methods, and examine not only satisfaction but
also learning achievement and skill development. Overall, XR shows strong potential to transform digital
education, but its long-term success depends on sound pedagogy, institutional readiness, and sustained policy
support.
ACKNOWLEDGEMENTS
The authors would like to express their sincere appreciation to Universiti Teknologi MARA (UiTM) Cawangan
Negeri Sembilan for the institutional support provided throughout the conduct of this research. Special thanks
are extended to the students and participants who generously shared their feedback, which formed the basis of
the sentiment analysis in this study. The authors also acknowledge the contributions of colleagues and
collaborators whose insights and encouragement enriched the development of this work.
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