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

Abstract

The Augmented Reality, Learning Satisfaction, and Student Engagement—Measurement Model (tom Dieck et al., 2024) is a comprehensive, 36-item psychometric instrument developed to investigate the complex interplay between advanced technology use and academic outcomes. The scale is theoretically grounded in the uses and gratifications theory, specifically exploring how the deployment of Augmented Reality (AR) technology influences both student engagement and overall learning satisfaction.

Furthermore, the model integrates concepts from Kolb’s learning cycle to account for differences in individual learning styles within the AR context. Validation data for this measure were collected from higher education students in the United Kingdom, confirming strong factor structure, reliability, and validity, adapting previous research models to fit the unique requirements of AR-mediated learning environments.

Keywords

Augmented Reality, AR, Learning Satisfaction, Student Engagement, Uses and Gratifications Theory, Kolb’s Learning Cycle, Higher Education, Psychometrics, Measurement Model.

Authors

tom Dieck, M. Claudia, Cranmer, Eleanor, Prim, Alexandre, Bamford, David.

Purpose

The core purpose of this measurement model is to provide a robust framework for assessing the effectiveness of immersive technologies, specifically Augmented Reality, in educational settings. It aims to quantify the psychological impact of AR use on students’ perceived value and involvement in learning.

By drawing upon the uses and gratifications theory, the instrument seeks to understand the motivations behind students’ adoption of AR and how these gratifications translate into enhanced learning satisfaction and higher levels of engagement. The model also allows researchers to evaluate how different learning styles, as classified by Kolb’s learning cycle, mediate the relationship between AR use and educational outcomes.

Construct

The scale is designed to operationalize and measure three distinct yet interrelated psychological constructs relevant to technology-enhanced learning:

• Augmented Reality (AR): Measures the various dimensions of the student experience related to the technological aspects and perceived novelty of using AR applications in education.
• Learning Satisfaction: Captures the emotional and cognitive appraisal of the student concerning their contentment, fulfillment, and positive attitude toward the AR-mediated learning process.
• Student Engagement: Assesses the depth of the student’s involvement, encompassing behavioral investment (effort), emotional connection (interest), and cognitive focus (attention) within the learning activities.

Validity

The validity of the measurement model was established through rigorous structural equation modeling techniques, ensuring the constructs were accurately and distinctly measured.

Convergent Validity: This form of validity was confirmed by examining the Average Variance Extracted (AVE) indicators. All AVE values were found to be above the recommended threshold of 0.5 (Hair et al., 2019), demonstrating that the items intended to measure a specific construct shared a high proportion of variance.

Discriminant Validity: Discriminant validity was successfully verified, indicating that the constructs were truly distinct from one another. This was evidenced by the finding that the correlation values observed between the latent constructs were lower than the square root of the individual factor loadings (Henseler et al., 2015), ensuring that each scale measures a unique psychological concept.

Reliability

The internal consistency of the instrument was tested to ensure the dependability and coherence of the scale items.

Internal Consistency: Reliability was assessed using two primary statistics: Cronbach’s alpha (Alpha) and Composite Reliability (CR). Both measures of internal consistency exceeded the acceptable benchmark of 0.7 (Hair et al., 2019). These results confirm that the 36 items within the measurement model consistently measure the constructs they were designed to assess.

Factor Analysis

A sophisticated statistical approach was employed to refine and confirm the underlying structure of the 36-item measure.

Confirmatory Factor Analysis (CFA): Confirmatory Factor Analysis was the primary method used to test the hypothesized relationships between the latent constructs and their observed indicator variables. The analysis focused on retaining items that exhibited strong factor loadings, specifically those exceeding 0.7.

During the refinement process, one specific item, related to novelty (“It was an unusual experience”), was systematically removed from the final scale due to its low factor loading. This removal optimized the model fit and ensured the remaining items provided a clear and robust measurement of the intended constructs.

Instrument

Test Type: Inventory/Questionnaire

Format: The measurement model consists of 36 items. Responses are solicited using a Five-point Likert scale, anchored by “strongly disagree” (1) and “strongly agree” (5).

Language Available: English

Population Group: Higher Education Students

Age Group: Typically Young Adult to Adult (University Students)

Population Details: The validation sample comprised students enrolled in various higher education institutions across the United Kingdom (UK).

Test Methodology: Test Validity, Convergent Validity, Discriminant Validity, Test Reliability, Internal Consistency, Factor Analysis, Confirmatory Factor Analysis, Measurement Model.

Keywords

Psychometric Scale, Likert Scale, Internal Consistency, Cronbach’s Alpha, Average Variance Extracted, Convergent Validity, Discriminant Validity, Factor Loadings, Higher Education Technology.

Authors

Author ORCID Identifier: Bamford, David: ORCID: 0000-0002-1050-1357

Affiliation Email addresses: tom Dieck, M. Claudia: [email protected]

Correspondence Address: Manchester Metropolitan University, Department of Operations, Technology, Events, and Hospitality Management, Manchester, UK.

Permissions & Fee and Test Year

Test Year: 2024

Permissions and Fees: Researchers interested in utilizing this measurement model should contact the corresponding author, M. Claudia tom Dieck, at the provided Manchester Metropolitan University email address to inquire about usage permissions and any associated licensing fees.

Reference’s

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

• Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning. (Cited for reliability and validity standards).
• 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. (Cited for discriminant validity standards).
• Kolb, D. A. (2014). Experiential learning: Experience as the source of learning and development (2nd ed.). Pearson Education. (Theoretical underpinning).
• Shao, G. (2009). Understanding the appeal of user-generated media: A uses and gratifications perspective. Internet Research, 19(1), 7–25. (Theoretical underpinning).

Items of the Augmented Reality, Learning Satisfaction, and Student Engagement–Measurement Model

IMPORTANT: The following scale items must be preserved in their original language and must not be changed in any way.

• Number of Items: 36
• Rating Scale: Five-point Likert scale (from strongly disagree to strongly agree)