Data Analysis

The data collected from the student and teacher questionnaire were subjected to a comprehensive analysis, aimed at elucidating the perceptions and experiences of students and teachers involved in the AgileXR project's pilot implementation. The analysis was twofold: descriptive and inferential, each serving a specific purpose in our understanding of the data.

Initially, descriptive statistical methods were employed to provide a foundational understanding of the data. This analysis focused on summarizing and organizing the data in a manner that facilitated a clear and concise presentation of the respondents' characteristics and responses. Key descriptive statistics, such as means, standard deviations, and percentages, were calculated. These statistics offered a preliminary insight into the central tendencies and variability within our dataset, particularly regarding students' engagement, motivation, sense of community, self-regulation, relationships with teachers, perception of learning, acquired skills, and their understanding of the agile mindset.

To further probe the data, Student t-tests were conducted. These tests are particularly useful for comparing the means of two groups and are instrumental in our study for exploring differences between various variables. For instance, the t-tests allowed us to examine whether there were statistically significant differences in students' perceptions and experiences based on different demographic variables (such as gender) or other relevant factors like the type of pilot or the teaching tools.

A significant component of our analysis involved comparing students' responses to the dual-aspect questions in the questionnaire. Each question required students to rate their agreement with a statement and to compare their experience in the pilot with their usual educational settings. This comparative approach enabled us to gauge the relative effectiveness and impact of the AgileXR project's methodologies and tools against traditional educational practices.

The data were analyzed using Jamovi 2.3.28, which provided robust tools for both descriptive and inferential statistical analysis. This software facilitated accurate and efficient processing of the data, ensuring the reliability of our findings.