EXPLORING THE FACTORS INFLUENCING TEACHERS' INTENTION TO ADOPT GAMIFIED LEARNING TOOLS: A UTAUT2-BASED STUDY

Abstract

The rapid advancements in digital technology have influenced various sectors, including education. This has resulted in innovative tools and methodologies such as gamified learning, which has gained considerable attention for its potential to enhance student engagement and learning outcomes. However, teachers' adoption of these tools, especially in secondary schools in Saudi Arabia, remains understudied. This thesis investigates the factors that influence teachers' intention to adopt gamified learning tools in secondary schools in Saudi Arabia. The study was grounded in an expanded version of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model, incorporating two additional factors: Student Motivation and Engagement and Teacher Self-efficacy. An online questionnaire was administered to a purposive sample of 218 secondary school teachers in Saudi Arabia, and the data were analyzed using the Structural Equation Modeling technique. The findings revealed six of the eight hypothesized factors significantly influenced teachers' intentions to adopt gamified learning tools. These factors were Performance Expectancy, Effort Expectancy, Facilitating Conditions, Hedonic Motivation, Student Motivation and Engagement, and Teacher Self-efficacy. Interestingly, Social Influence and Habit did not significantly impact teachers' intentions. This research contributes to the existing knowledge on technology adoption in the educational context, particularly in Saudi Arabia. The findings have practical implications for schools, policymakers, and software companies, offering insights to enhance the adoption of these tools. Despite providing significant insights, the study was confined to the cultural and educational context of Saudi Arabia, focused only on teachers' intentions, and primarily relied on the UTAUT2 framework. Hence, future research can extend this work by exploring different cultural contexts, actual use scenarios, and the influence of other potential factors not covered in the current model.