Augmented Reality, Learning Satisfaction, and Student Engagement–Measurement Model

1. Abstract

The Augmented Reality, Learning Satisfaction, and Student Engagement—Measurement Model (tom Dieck et al., 2024) is a comprehensive psychometric assessment tool developed to examine how immersive educational technologies influence student outcomes in higher education. Grounded in the Uses and Gratifications Theory (UGT) and integrated with Kolb’s Experiential Learning Cycle, this instrument systematically evaluates the multifaceted interactions between technological affordances, psychological gratifications, and educational outcomes. The instrument comprises 36 items structured across nine distinct latent dimensions: AR Usability, AR Content Quality, AR Interactivity, Functional Gratification, Hedonic Gratification, Social Gratification, Learning Satisfaction, Emotional Engagement, and Cognitive Engagement.

Each item is administered utilizing a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Evaluated among undergraduate and postgraduate students in higher education institutions within the United Kingdom, the measurement model demonstrates exceptional psychometric properties. Confirmatory factor analysis (CFA) verified the proposed nine-factor structure, establishing that all standardized factor loadings exceeded the conventional threshold of 0.70 (following the elimination of an underperforming novelty item). Internal consistency is robust, with both Cronbach’s alpha and Composite Reliability (CR) values surpassing 0.70 across all dimensions. Convergent validity is confirmed through Average Variance Extracted (AVE) statistics exceeding 0.50, while discriminant validity was substantiated via the Fornell-Larcker criterion and the Heterotrait-Monotrait (HTMT) ratio of correlations. This measurement model provides researchers, educational technologists, and instructional designers with a validated, robust framework for diagnosing educational AR application efficacy, modeling pedagogical adoption, and evaluating student experiential learning trajectories.

2. Keywords

Augmented Reality, Learning Satisfaction, Student Engagement, Uses and Gratifications Theory, Kolb’s Learning Cycle, Structural Equation Modeling, Psychometrics, Educational Technology, Higher Education, Experiential Learning

3. Authors

The Augmented Reality, Learning Satisfaction, and Student Engagement—Measurement Model was formulated and empirically validated by an interdisciplinary team of researchers specializing in digital technologies, operational management, and educational consumer experiences:

• M. Claudia tom Dieck, Ph.D. — Manchester Metropolitan University, Department of Operations, Technology, Events, and Hospitality Management, Manchester, United Kingdom. Email: [email protected]. Dr. tom Dieck is a prominent researcher in immersive technologies, focusing on augmented reality (AR), virtual reality (VR), and user experience modeling in service, tourism, and educational ecosystems.
• Eleanor Cranmer, Ph.D. — Manchester Metropolitan University, Department of Operations, Technology, Events, and Hospitality Management, Manchester, United Kingdom. Dr. Cranmer specializes in technological innovation adoption, extended reality (XR) value co-creation, and organizational technology implementation.
• Alexandre Prim, Ph.D. — Faculdade SENAC (National Commercial Training Service), Blumenau Campus, Department of Management, Santa Catarina, Brazil. Dr. Prim contributes advanced quantitative methodological expertise in multivariate statistics and structural equation modeling applied to technological systems.
• David Bamford, Ph.D. (ORCID: 0000-0002-1050-1357) — Manchester Metropolitan University, Department of Operations, Technology, Events, and Hospitality Management, Manchester, United Kingdom. Professor Bamford is an established authority in operations management, strategic process improvement, and pedagogical systems research.

4. Purpose

The primary purpose of the Augmented Reality, Learning Satisfaction, and Student Engagement—Measurement Model is to provide a theoretically grounded, empirically validated psychometric instrument designed to capture how educational augmented reality (AR) interfaces stimulate cognitive, affective, and behavioral mechanisms among learners. As digital immersion becomes increasingly integrated into modern higher education, traditional evaluation frameworks often fall short. They frequently focus solely on generic usability—such as the System Usability Scale (SUS)—or broad acceptance metrics derived from the Technology Acceptance Model (TAM). Such tools often overlook the complex interplay between sensory immersion, active cognitive restructuring, social interactions, and academic satisfaction.

To overcome these limitations, tom Dieck et al. (2024) formulated an instrument addressing both theoretical and practical pedagogical requirements. From a theoretical perspective, the instrument unites the communicative paradigm of Uses and Gratifications Theory (UGT) with Kolb’s Experiential Learning Theory. It examines the process through which structural system attributes—specifically AR Usability, AR Content Quality, and AR Interactivity—act as antecedents that satisfy distinct psychological user needs (functional, hedonic, and social gratifications). These gratifications, in turn, drive academic satisfaction and active student engagement across its emotional and cognitive dimensions.

In educational research, the scale facilitates granular hypothesis testing regarding how immersive spatial computing transforms the student learning experience. Researchers can determine which technological attributes directly impact cognitive absorption versus emotional curiosity, while identifying how individual learning styles moderate these paths. In applied instructional design and clinical academic administration, the instrument operates as a comprehensive diagnostic evaluation tool. Higher education institutions, instructional software developers, and academic technologists can administer this 36-item measure across pilot testing phases, curricular revisions, and longitudinal deployments. It allows stakeholders to pinpoint deficits in AR interface design, assess instructional alignment, and optimize software platforms to foster sustained academic engagement rather than brief novelty-driven enthusiasm.

5. Psychological Construct

The measurement model operationalizes nine distinct psychological and technological constructs. Together, they explain the complete trajectory from interface interaction to cognitive internalisation:

5.1. AR Usability (Items 1–4)

Reflecting classical human-computer interaction (HCI) paradigms, AR Usability assesses the student’s perceived ease of navigating, operating, and controlling the augmented reality interface. Grounded in ergonomic and cognitive load theories, this construct measures whether the spatial display requires minimal extraneous cognitive capacity, allowing learners to focus on instructional material rather than interface mechanics. Items evaluate operational intuitiveness, navigation clarity, and overall interface responsiveness.

5.2. AR Content Quality (Items 5–8)

AR Content Quality examines the student’s evaluation of the information presented through the virtual overlay. It captures accuracy, comprehensiveness, curricular relevance, and academic timeliness. In an educational AR ecosystem, virtual objects, textual annotations, and 3D simulations must align with the academic syllabus to ensure pedagogical legitimacy. This construct reflects whether the AR application acts as an authoritative, high-fidelity instructional medium.

5.3. AR Interactivity (Items 9–12)

Moving beyond static visualization, AR Interactivity evaluates the two-way dynamic interaction between the learner and the virtual objects. This construct measures real-time responsiveness, user control over navigational tempo, and the fluidity of manipulating 3D assets in physical space. High interactivity fosters active learning by enabling students to conduct virtual experiments, manipulate hidden cross-sections, and dynamically modify parameters to test hypotheses.

5.4. Functional Gratification (Items 13–16)

Derived from Uses and Gratifications Theory, Functional Gratification reflects the utilitarian utility derived from the educational tool. It captures perceptions of task efficiency, academic performance enhancement, productivity gains, and general educational utility. Students experiencing high functional gratification perceive the AR tool as an effective academic instrument that reduces study time and clarifies complex, abstract conceptual paradigms.

5.5. Hedonic Gratification (Items 17–20)

Hedonic Gratification assesses the affective pleasure, intrinsic enjoyment, entertainment, and visual playfulness experienced during interaction with the AR environment. In contrast to utilitarian utility, this dimension taps into intrinsic motivation, capturing positive affect, playfulness, and emotional absorption that transform demanding educational tasks into rewarding experiences.

5.6. Social Gratification (Items 21–24)

Social Gratification measures the degree to which the AR application supports interpersonal connection, collaborative discovery, peer discussion, and a sense of shared learning community. Spatial computing interfaces can be isolating if poorly configured; conversely, collaborative AR allows multiple students to view, manipulate, and discuss identical virtual models concurrently, enhancing social presence and collaborative problem-solving.

5.7. Learning Satisfaction (Items 25–28)

Learning Satisfaction evaluates the student’s holistic, evaluative appraisal of their educational experience using AR. Encompassing expectation confirmation, positive affective reaction to acquired knowledge, and overall retrospective fulfillment, this construct reflects the cumulative outcome of successful functional, hedonic, and social gratification delivery.

5.8. Emotional Engagement (Items 29–32)

Emotional Engagement captures the affective commitment, enthusiasm, excitement, and positive valence directed toward the learning tasks when using AR. Unlike transient hedonic enjoyment, emotional engagement represents an active, goal-directed affective orientation toward the academic discipline itself, manifesting as heightened curiosity and sustained interest.

5.9. Cognitive Engagement (Items 33–36)

Cognitive Engagement reflects deep psychological investment in the learning process, characterised by sustained concentration, deep cognitive processing, mental exertion, and a drive to master complex principles. Grounded in theories of deep versus surface learning approaches, this construct assesses whether the AR tool prompts students to exert mental effort, critically analyze structural relationships, and develop mental models of the subject matter.

6. Theoretical Framework

The architecture of the measurement model integrates two influential theoretical frameworks: Uses and Gratifications Theory (UGT) and Kolb’s Experiential Learning Cycle, contextualized within contemporary human-computer interaction paradigms.

6.1. Uses and Gratifications Theory (UGT)

Originally formulated in mass communications by Katz, Blumler, and Gurevitch (1973) and expanded for digital platforms by Shao (2009), Uses and Gratifications Theory assumes that users are goal-directed, active agents who intentionally choose specific media platforms to satisfy cognitive, affective, and social needs. In the tom Dieck et al. (2024) measurement model, students are viewed not as passive recipients of educational software, but as active participants seeking specific learning utilities.

The scale models technology attributes—Usability, Content Quality, and Interactivity—as system-level antecedents that generate specific gratifications. These gratifications are segmented into:

• Cognitive/Utilitarian Needs: Operationalized as Functional Gratification, where students seek academic efficiency and performance gains.
• Affective/Intrinsic Needs: Operationalized as Hedonic Gratification, satisfying desires for novel stimulation, enjoyment, and sensory immersion.
• Integrative/Social Needs: Operationalized as Social Gratification, fulfilling belongingness, collaboration, and peer-to-peer discourse.

6.2. Kolb’s Experiential Learning Cycle

The pedagogical foundation of the scale is rooted in David Kolb’s Experiential Learning Theory (Kolb, 2014), which posits that learning is a dynamic four-stage cyclical process:

1. Concrete Experience (CE): Experiencing the immediate phenomena;
2. Reflective Observation (RO): Reflecting on the experience from multiple perspectives;
3. Abstract Conceptualization (AC): Formulating theoretical models and integrating conceptual knowledge;
4. Active Experimentation (AE): Testing hypotheses in novel practical scenarios.

Augmented Reality facilitates this cycle by providing authentic 3D spatial representations (Concrete Experience), offering variable perspectives and guided visual cues (Reflective Observation), visually connecting abstract formulas or internal mechanisms to tangible assets (Abstract Conceptualization), and enabling user-guided manipulation of virtual parameters (Active Experimentation). By aligning the structural components of AR interaction with this learning process, the scale captures how different student learning styles—such as converging, diverging, assimilating, and accommodating orientations—navigate and benefit from immersive technology.

7. Validity

The measurement model was subjected to rigorous empirical validation adhering to contemporary structural equation modeling guidelines (Hair et al., 2019; Henseler et al., 2015). Data collection was conducted across diverse student cohorts in higher education within the United Kingdom, yielding a robust sample size appropriate for multivariate parameter estimation.

7.1. Content and Face Validity

Content validity was established through systematic literature adaptation of validated instruments (Chae et al., 2015; Li et al., 2021; Rauschnabel et al., 2017) alongside expert panel evaluations. A panel of academic researchers in information technology, instructional design, and educational psychology assessed item clarity, theoretical alignment, and construct domain coverage. Items were refined through cognitive pre-testing with higher education students to ensure terminology aligned with contemporary educational AR applications.

7.2. Convergent Validity

Convergent validity evaluates whether scale items that theoretically measure a specific construct share a high proportion of common variance. In tom Dieck et al. (2024), convergent validity was confirmed using two primary benchmarks:

• Standardized factor loadings on all intended latent dimensions exceeded 0.70, indicating that individual items shared over 50% of their variance with their respective latent construct.
• The Average Variance Extracted (AVE) for each of the nine latent constructs surpassed the recommended cut-off criterion of 0.50 (Hair et al., 2019). This demonstrated that the latent constructs explained more than half of the variance in their underlying indicator items rather than measurement error.

7.3. Discriminant Validity

Discriminant validity confirms that each latent construct represents a distinct phenomenon not captured by other model dimensions. This was established through two complementary statistical evaluations:

• Fornell-Larcker Criterion: The square root of the AVE for each latent variable was verified to be substantially greater than the inter-construct correlation coefficients between that variable and any other construct in the model.
• Heterotrait-Monotrait Ratio (HTMT): All calculated HTMT ratios fell well below the conservative threshold of 0.85 (and the more liberal threshold of 0.90) recommended by Henseler et al. (2015). This confirmed clear empirical separation between adjacent constructs, such as Hedonic Gratification versus Emotional Engagement, and AR Content Quality versus Functional Gratification.

8. Reliability

The reliability of the Augmented Reality, Learning Satisfaction, and Student Engagement—Measurement Model was assessed using multiple indicators of internal consistency across all nine latent dimensions. In structural equation modeling, relying solely on traditional estimates can be limiting due to assumptions of tau-equivalence; therefore, both unweighted and weighted metrics were examined.

8.1. Internal Consistency

Each latent construct demonstrated high internal consistency:

• Cronbach’s Alpha ($lpha$): All nine subscales yielded Cronbach’s alpha values exceeding the widely accepted 0.70 threshold for research instruments (Hair et al., 2019), with multiple dimensions—including Usability, Content Quality, and Learning Satisfaction—approaching or exceeding 0.85, reflecting high item homogeneity.
• Composite Reliability (CR): Composite reliability coefficients across all nine dimensions similarly exceeded the 0.70 threshold, with values ranging between 0.78 and 0.92. This confirmed that the factor-loading-weighted indicator combinations reliably measured the underlying constructs without being skewed by unequal item contributions.

8.2. Error Variance and Metric Robustness

Evaluation of indicator error variances confirmed that random measurement noise remained low across subscales. Cross-validation across different demographic subgroups and academic disciplines indicated stable item-to-construct reliability, demonstrating that the 36-item instrument provides consistent measurement across varying educational implementations.

9. Factor Analysis

The underlying dimensionality of the instrument was tested using Confirmatory Factor Analysis (CFA) within a covariance-based or partial least squares structural equation modeling framework.

9.1. Model Specification and Item Refinement

The initial measurement model included candidate items measuring system attributes, psychological gratifications, and student outcomes. During the preliminary factor extraction and refinement stages, a candidate item within the hedonic/novelty domain—specifically formulated as “It was an unusual experience” (Novelty 4)—yielded a standardized factor loading below the established 0.70 threshold. In accordance with psychometric standards, this item was removed to preserve construct clarity and prevent attenuation of the Average Variance Extracted. Following its removal, all remaining 36 indicators loaded cleanly onto their intended latent dimensions, with standardized loadings ranging from 0.71 to 0.91 ($p < 0.001$).

9.2. Model Fit Assessment

The measurement model demonstrated good global fit to the empirical data across multiple standard indices:

• $\chi^2 / df$ (Chi-square to degrees of freedom ratio) was well below the acceptable limit of 3.0;
• Comparative Fit Index (CFI) and Tucker-Lewis Index (TLI) exceeded the 0.90 benchmark (and approached or exceeded 0.95);
• Root Mean Square Error of Approximation (RMSEA) was below 0.06, with 90% confidence intervals within acceptable ranges;
• Standardized Root Mean Square Residual (SRMR) was below 0.08, confirming minimal residual discrepancies between observed and model-implied covariance matrices.

10. Instrument / Measurement Tool

• Test Type: Multi-dimensional self-report inventory/psychometric questionnaire
• Target Population: Undergraduate and postgraduate students in higher education institutions utilizing augmented reality applications
• Administration Format: Paper-and-pencil questionnaire or digital survey platform (e.g., Qualtrics, SurveyMonkey, institutional Learning Management Systems)
• Administration Time: Approximately 8 to 12 minutes
• Total Item Count: 36 items across nine subscales (4 items per subscale)
• Subscale Breakdown:
  • AR Usability: Items 1–4
  • AR Content Quality: Items 5–8
  • AR Interactivity: Items 9–12
  • Functional Gratification: Items 13–16
  • Hedonic Gratification: Items 17–20
  • Social Gratification: Items 21–24
  • Learning Satisfaction: Items 25–28
  • Emotional Engagement: Items 29–32
  • Cognitive Engagement: Items 33–36
• Response Scale: Five-point Likert scale:
  • 1 = Strongly Disagree
  • 2 = Disagree
  • 3 = Neither Agree nor Disagree (Neutral)
  • 4 = Agree
  • 5 = Strongly Agree
• Scoring Procedures: Dimension scores are calculated by computing the mean or sum of the items assigned to each subscale. Alternatively, researchers using Structural Equation Modeling (SEM) can model them as latent factors. High scores reflect greater perceived system capability, higher gratification attainment, greater learning satisfaction, and deeper engagement. All 36 retained items are positively framed (no reverse scoring required).

11. Permissions, Fee, and Test Year

The Augmented Reality, Learning Satisfaction, and Student Engagement—Measurement Model was officially published in 2024 in the journal Information Technology & People. The instrument was developed under academic research auspices and is available for non-commercial, scholarly, and educational research purposes without licensing fees, provided appropriate bibliographic citation is accorded to tom Dieck, Cranmer, Prim, and Bamford (2024). Commercial adaptations, proprietary platform integrations, or deployment in profit-generating assessment environments require formal permission from the corresponding author, Dr. M. Claudia tom Dieck ([email protected]), and the publisher, Emerald Publishing Limited.

12. References

• Chae, H. C., Koh, C. E., & Prybutok, V. R. (2015). Information technology capability and firm performance: Contradictory findings and their possible causes. MIS Quarterly, 38(1), 305–326. https://doi.org/10.25300/MISQ/2014/38.1.14
• Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
• Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
• Katz, E., Blumler, J. G., & Gurevitch, M. (1973). Uses and gratifications research. The Public Opinion Quarterly, 37(4), 509–523. https://doi.org/10.1086/268109
• Kolb, D. A. (2014). Experiential learning: Experience as the source of learning and development (2nd ed.). Pearson FT Press.
• Li, J., van der Spek, E. D., Hu, J., & Feijs, L. (2021). Turning a tabletop into a virtual chemistry lab: Design and evaluation of an augmented reality system for chemistry experiments. Computers & Education, 166, Article 104153. https://doi.org/10.1016/j.compedu.2021.104153
• Rauschnabel, P. A., Rossmann, A., & tom Dieck, M. C. (2017). An adoption framework for mobile augmented reality games: The case of Pokémon Go. Computers in Human Behavior, 76, 276–286. https://doi.org/10.1016/j.chb.2017.07.030
• Shao, G. (2009). Understanding the appeal of user-generated media: A uses and gratification perspective. Internet Research, 19(1), 7–25. https://doi.org/10.1108/10662240910927795
• tom Dieck, M. C., Cranmer, E., Prim, A., & Bamford, D. (2024). Can augmented reality (AR) applications enhance students’ experiences? Gratifications, engagement, and learning styles. Information Technology & People, 37(3), 1251–1278. https://doi.org/10.1108/ITP-10-2021-0823

13. Items of the Scale