# Innovating Statistics Education: The Design of a Novel App Using Design Thinking
## 5 Results: EstApp
### Limitations and Opportunities for Improvement  
Although we conducted 14 interviews, surveyed 440 students, and performed a user test with 17 students to validate EstApp, the sample was limited to the university context of the Pontificia Universidad Catolica de Valparaiso, Chile. This may limit the generalizability of the results to other university environments or to students with different cultural and educational backgrounds. However, as the first version of EstApp, we consider that we have achieved good results, and its functionalities could be applicable in other contexts. With respect to functionalities, we prioritized the \"Probability Calculator\", \"Data Generator\", \"Data Visualizer\", \"AI Tutor\", and \"Report Generation\" as the most valuable features for students and professors. However, key functionalities such as advanced data manipulation or statistical inference analysis may not have been considered. This could limit the use of EstApp for more advanced topics within statistics courses. On the other hand, while we conducted usability/UX tests, these were limited to a prototype version of EstApp. We have yet to test it in a long-term educational setting, which may reveal other challenges or areas for improvement when used continuously.  
For a future version of EstApp, it would be useful to conduct studies in different academic contexts, including universities in other countries and regions (and in languages other than Spanish), to validate the application's usefulness for a broader audience. The user sample could also be expanded to include individuals with varying levels of statistical expertise, including students from different disciplines.  
Additionally, future versions could incorporate advanced functionalities, such as inferential statistical analysis, linear regression, and tools for working with large datasets. These improvements would extend the application's use to more advanced statistics courses. Another opportunity for improvement would be to integrate EstApp with learning management systems (LMS, such as Moodle [47]), allowing professors to manage student progress centrally.  
Finally, it is crucial to conduct studies in real educational settings, testing the application's effectiveness over several semesters. This would allow for observations of how the app impacts student learning and motivation in the long term and identify potential issues or areas of improvement not discovered during the initial usability tests. Specifically, to evaluate the long-term effects of EstApp on academic performance and motivation, it is essential to collect and analyze empirical data to support its effectiveness. One approach could be to define control groups (who do not use EstApp) and experimental groups (who use the new application) within a course, and track over time how the application influences knowledge retention, statistical reasoning, performance, and motivation. Pre- and post-assessments could be conducted using questionnaires to evaluate technology acceptance (TAM [48]) or scales to measure motivation [49], assessing factors such as intrinsic motivation, extrinsic motivation, and amotivation. Additionally, by comparing these results with those obtained by users of other applications, we could strengthen the argument that EstApp not only improves the learning of statistical concepts but also offers a more personalized and tailored experience to meet students' individual needs.