3.1. Sample Demographics
This study involved a diverse group of 93 participants selected to evaluate the impact of augmented reality (AR) technologies in educational settings. The participant pool consisted of 54 students (58.7%) and 38 university or college graduates (41.3%), reflecting a robust cross-section of the current educational landscape. It is noteworthy that there was one non-response concerning educational level, leading to a total of 92 responses being processed for demographic analysis.
Table 1 provides a detailed breakdown of the demographic characteristics:
Gender Distribution: the sample included 43 males (46.2%) and 50 females (53.8%), showcasing a slight female predominance.
Educational Background: A significant portion of the participants were students (58.7%), with university or college graduates making up the remaining 41.3%. This distribution underscores the study’s relevance to ongoing learners and educational professionals alike.
Age Range: The majority of respondents (55.9%) were between 18–25 years old, capturing a predominantly youthful demographic. Additionally, 21.5% were aged 26–35 years, and 22.6% were older than 36 years. This age diversity highlights the broad appeal and applicability of AR technologies across different age groups.
Table 1.
Sample demographics.
Table 1.
Sample demographics.
Gender |
---|
| Frequency | Percent | Valid Percent | Cumulative Percent |
---|
Male | 43 | 46.2 | 46.2 | 46.2 |
Female | 50 | 53.8 | 53.8 | 100.0 |
Total | 93 | 100.0 | 100.0 | |
Educational level |
| Frequency | Percent | Valid Percent | Cumulative Percent |
Student | 54 | 58.1 | 58.7 | 58.7 |
University/College | 38 | 40.9 | 41.3 | 100.0 |
Total | 92 | 98.9 | 100.0 | |
No answers | 1 | 1.1 | | |
Total | 93 | 100.0 | | |
Age |
| Frequency | Percent | Valid Percent | Cumulative Percent |
18–25 | 52 | 55.9 | 55.9 | 55.9 |
26–35 | 20 | 21.5 | 21.5 | 77.4 |
36+ | 21 | 22.6 | 22.6 | 100.0 |
Total | 93 | 100.0 | 100.0 | |
This demographic overview, detailed in
Table 1, ensures a comprehensive understanding of the study’s participant base, which is critical for contextualizing the findings within real-world educational environments.
The demographic data presented in
Table 1 are crucial for understanding the context in which the AR technology was used. The youthful nature of the sample is indicative of a group likely to be familiar with and receptive to new technologies, which is essential for studies involving innovative tools like AR.
3.3. Parametric Tests
3.3.1. Normality Tests
Prior to conducting the t-tests, we assessed the normality of the age distribution among our study participants to ensure the appropriateness of using parametric tests. The normality was evaluated using graphical methods: Histograms and Q–Q plots were generated for the age data. The histogram displayed a bell-shaped curve, while the Q–Q plot indicated that the data points closely followed the theoretical normal line, suggesting a normal distribution.
These tests confirmed that the assumptions of normality required for the application of t-tests were met, supporting the subsequent statistical analyses presented in this study.
3.3.2. Independent t-Tests (Gender)
Independent
t-tests were used to evaluate whether responses towards augmented reality (AR) differ significantly between genders, as depicted in
Table 3. These tests are crucial for understanding if gender influences perceptions of AR’s utility, which can guide targeted strategies in AR development and application [
19].
The null hypotheses tested were as follows:
H0 (Education): there is no difference in mean scores between males and females regarding their perceptions of the usefulness of AR in education.
H0 (Everyday Life): there is no difference in mean scores between males and females regarding their perceptions of the usefulness of AR in everyday life.
Significant differences were found in perceptions of AR’s usefulness in education (p = 0.001) and everyday life (p = 0.001), with males reporting higher agreement levels than females. These findings suggest that men are more positively disposed towards the benefits of AR. Understanding these gender differences is crucial for stakeholders aiming to develop, market, and utilize AR technology more effectively.
To provide a more comprehensive understanding of these differences, 95% confidence intervals for the mean differences were calculated. These intervals offer insights into the magnitude and precision of the differences observed:
Usefulness in Education: the mean difference was 0.36698 with a 95% confidence interval of [0.15460; 0.57935].
Usefulness in Everyday life: the mean difference was 0.40047 with a 95% confidence interval of [0.16492; 0.63601].
These results support the need for gender-tailored strategies in the development and implementation of AR technologies, as they clearly indicate different levels of acceptance and perceived utility between males and females.
3.3.3. Independent t-Tests (Educational Level)
The influence of educational level on attitudes towards AR was also examined, comparing responses between students and university/college graduates as presented in
Table 4.
Educational level showed significant differences in perceptions of AR’s usefulness, ease of use, and interest. Students reported higher levels of agreement across these aspects than university/college graduates, indicating that younger, currently enrolled students may be more receptive to AR technologies. This could be due to greater exposure to innovative educational tools and technologies during their studies.
3.3.4. One-Way ANOVA (Age)
A one-way ANOVA was conducted to investigate if age groups differ in their responses toward AR. This analysis, presented in
Table 5, is critical for identifying which age group is more inclined towards adopting AR technologies.
The one-way ANOVA results indicate that younger participants (18–25 years) exhibit significantly higher levels of agreement that AR is useful, easy to use, and interesting compared to older age groups. This pattern suggests that younger users are more positively inclined towards AR, likely due to greater technological engagement and openness to new experiences.
3.4. Correlations
Pearson correlation analysis was used to assess the relationships between various perceptions of augmented reality (AR). This statistical method helps quantify the strength and direction of linear relationships between pairs of continuous variables, providing insights into how different aspects of AR perception are interrelated [
20].
A very strong positive correlation (r = 0.845,
p < 0.01) between the perceptions of AR’s usefulness in education and its usefulness in everyday life was identified, as introduced in
Table 6. This suggests that individuals who recognize AR’s benefits for educational purposes are likely to perceive similar benefits in their daily activities. This finding indicates a broad appreciation of AR’s utility, extending beyond educational settings.
There is a moderate positive correlation (r = 0.285,
p < 0.01) between the ease of use of AR and interest in AR (
Table 7). This relationship highlights the importance of intuitive and user-friendly design in fostering interest and engagement with AR technology. Enhancing ease of use may, therefore, be a critical factor in increasing AR’s appeal and adoption.
Significant positive correlations were found between the ease of use of AR and its perceived usefulness both in education (r = 0.423,
p < 0.01) and in everyday life (r = 0.442,
p < 0.01), as presented in
Table 8. These findings suggest that the more user-friendly an AR system is, the more useful it is considered, reinforcing the necessity for developers to prioritize ease of use in AR design.
Strong positive correlations exist between interest in AR and its perceived usefulness in both educational (r = 0.721,
p < 0.01) and everyday contexts (r = 0.704,
p < 0.01), as introduced in
Table 9. This indicates that interest in AR is closely linked with perceptions of its utility. The more individuals find AR interesting, the more likely they are to consider it useful, which could drive higher adoption rates and more innovative applications of AR technology.
3.5. Usability Test
The usability test was meticulously designed to evaluate user experience with the “Google Arts & Culture” app, particularly focusing on the “Art Projector” tool. The tasks were specifically tailored to assess the intuitiveness of the app’s navigation, the effectiveness of its interaction design, and overall user engagement with AR features (
Table 10).
Downloading and Initial Access (
Figure 5): All participants successfully downloaded and launched the app without any issues, indicating a smooth, user-friendly initial setup. This ease of entry is critical for first-time users and sets a positive tone for the app’s usability.
Locating the Art Projector Tool (
Figure 6): Of all the users, 26.3% initially struggled to find the Art Projector tool, suggesting that the app could benefit from more prominent placement or clearer directions for accessing this feature. Improving discoverability could enhance user experience and reduce initial frustration.
Detailed Task Insights:
Task 1 (
Figure 7): Nearly half of the participants (47.3%) had difficulty initially finding the option to switch artworks. This challenge highlights a need for more intuitive navigation cues. Clearer interface guidance could help users understand how to navigate between different artworks more seamlessly.
Task 2: Although all participants successfully used the navigation arrow to change artworks, the feedback indicated that this arrow did not align with users’ expectations of a “shortcut”. This suggests that while functional, the navigation could be enhanced by incorporating more conventional shortcut elements that users might expect.
Task 3 (
Figure 8): The context-sensitive messages displayed by the app were well received, with all participants understanding the instructions without confusion. This success points to effective communication within the app, aiding user interaction by providing clear, actionable information at the right moments.
Task 4: The difficulties experienced by 36.8% of participants in manipulating the paintings, especially with zoom controls, indicate that the gestures or controls might not be intuitive or are insufficiently explained. Enhancing gesture recognition or providing clearer instructions could improve user interaction with the AR features.
Task 5 (
Figure 9): The inability of participants to interact with text descriptions significantly restricts access to detailed information about the artworks. Issues with the display of long titles, which were truncated, further hindered the educational value of the app. This suggests a critical area for improvement in content layout and interactive elements to ensure all text is accessible and fully visible.
Task 6: The absence of a specific undo button highlights a gap in the navigation design. Although participants used the navigation arrow to backtrack, a dedicated undo feature could enhance user control and improve the navigation experience.
Task 7: Initial difficulties in understanding how to manipulate AR artworks indicate a steep learning curve for users unfamiliar with AR interfaces. Offering initial guidance or tutorials could mitigate these challenges and enhance the user’s ability to engage with the technology.
Task 8: The limitation of viewing one painting at a time was noted as a significant drawback for users who wished to engage in comparative analysis or broader exploration of artworks. This feedback suggests a need for interface adjustments to allow simultaneous viewing of multiple artworks or easier toggling between them.
Comments ranged from technical issues like app stability to subjective experiences comparing AR to physical artworks. Positive feedback highlighted the educational value of the app, especially its provision of cultural information at no cost. However, technical problems and navigational challenges detracted from the user experience.
The usability test revealed several key areas where the “Google Arts & Culture” app could improve, particularly in enhancing the intuitiveness of navigation and interaction within the Art Projector tool. Addressing these issues could significantly improve user satisfaction and broaden the app’s appeal, making it not only a tool for viewing art but also an effective educational resource.
By analyzing user interactions and feedback in detail, developers can better understand how to refine the app to meet user needs and preferences, ultimately leading to a more engaging and user-friendly experience.