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

Description

The Augmented Reality, Learning Satisfaction, and Student Engagement—Measurement Model (tom Dieck et al., 2024) was developed as part of a study grounded in the uses and gratifications theory (Shao, 2009), exploring how Augmented Reality (AR) impacts learning satisfaction and student engagement, with a focus on differences in learning styles. Drawing from Kolb’s learning cycle (Kolb, 2014), the model adapts previous research (Li et al., 2021; Rauschnabel et al., 2017; Chae et al., 2015) to assess these factors in the context of AR. Data was collected from higher education students in the UK, providing insights into factor structure, reliability, and validity for this 36-item measure.

Authors

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

Author Identifiers

• Bamford, David: ORCID: 0000-0002-1050-1357

Affiliations

• tom Dieck, M. Claudia: Manchester Metropolitan University, Department of Operations, Technology, Events, and Hospitality Management
• Cranmer, Eleanor: Manchester Metropolitan University, Department of Operations, Technology, Events, and Hospitality Management
• Prim, Alexandre: Faculdade SENAC – National Commercial Training Service, Blumenau Campus, Department of Management
• Bamford, David: Manchester Metropolitan University, Department of Operations, Technology, Events, and Hospitality Management

Emails

• tom Dieck, M. Claudia: [email protected]

Test Year

2024

Items of Augmented Reality, Learning Satisfaction, and Student Engagement

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

Instrument Type

Inventory/Questionnaire

Constructs

• Augmented Reality
• Learning Satisfaction
• Student Engagement

Purpose

The purpose of this measurement model is to evaluate different learning styles rooted in Kolb’s learning cycle, while exploring the use of AR in learning satisfaction and student engagement, based on the uses and gratifications theory.

Reliability

• Internal Consistency: Cronbach’s alpha (Alpha) and Composite Reliability (CR) values were both above 0.7, indicating good internal consistency (Hair et al., 2019).

Validity

• Convergent Validity: Average Variance Extracted (AVE) indicators were above 0.5, suggesting convergent validity (Hair et al., 2019).
• Discriminant Validity: Discriminant validity was confirmed, as correlation values between constructs were lower than the individual factor loadings, indicating a distinguished measurement for each construct (Henseler et al., 2015).

Factor Analysis

• Confirmatory Factor Analysis (CFA): CFA was conducted, with items having factor loadings above 0.7 being retained. One item (Novelty 4 – “It was an unusual experience”) was removed due to low factor loading.

Test Methodology

• Test Validity
• Convergent Validity
• Discriminant Validity
• Test Reliability
• Internal Consistency
• Factor Analysis
• Confirmatory Factor Analysis
• Measurement Model

Reference

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