Needs analysis
Description:
The production of the needs analysis report by our three-person team was a rich learning experience, but also marked by several challenges specific to collaborative work in an academic setting. The objective was to meet the requirements of the TEN-7001: Systemic Approach and Needs Analysis course, by producing a rigorous document in line with the expectations of our professor, Nadia Naffi.
Teamwork constraints
Although our team was highly motivated, it had to cope with a number of coordination constraints, not least due to the diversity of our professional and academic schedules. As we were all engaged in parallel activities, scheduling virtual and face-to-face meetings required compromises. With each of us bringing a different expertise and perspective to the table, it was sometimes difficult to reconcile these contributions while meeting the deadlines.
Difficulties encountered during the production phase
Data collection was a particularly complex stage. The teacher survey revealed a moderate participation rate, limiting the representative sample. Moreover, analysis of the responses required methodological rigor to avoid any bias in interpretation. Access to institutional resources, while enriching, proved limited by the poor promotion of tools and policies framing the use of generative artificial intelligence (GenAI). The elaboration of current and ideal scenarios also raised questions about the choice of priorities to be put forward, considering the performance objective and the results obtained in data collection.
Differences of opinion on the interpretation of instructions
One of the most striking challenges was the interpretation of the course instructions. While some members favored a descriptive and exhaustive approach, others felt that a synthetic and pragmatic report would better correspond to academic expectations. These differences sometimes led to protracted discussions, but they also enriched the content by enabling a more in-depth analysis. One concrete example concerns the identification of performance gaps. While one member considered it essential to prioritize the technical aspects (use of GenAI tools), another insisted on the importance of ethical and legal issues. This debate necessitated going back to the original instructions to ensure that they were respected, while maintaining a coherent narrative.
Take aways
The realization of this project generated several opportunities for innovation and growth, thanks to structured thinking and an entrepreneurial approach. Over and above the production of an academic report, this process enabled us to develop key skills for carrying out a needs analysis, particularly in the field of generative artificial intelligence (GenAI) in an educational environment.
Nadia Naffi's last course gave us the opportunity to take stock of the course's key concepts. Personally, my main takeaway is that a needs analysis is a fundamental step in any request that may or may not result in a design project of any kind. The very reason is to find a why, and there can be many whys lurking beneath the others. So it's vital to keep in mind that you need to dig to the end. It's also important to remember that a needs analysis is just as beneficial to us as it is to the customer, as it forms a solid contract between the two parties. As an exercise, we had 15 minutes as a team to create an infographic representing the 10 most important points of a needs analysis. Obviously, we turned to AI to speed up the process, and ended up with a result that we consider, at best, a good approximation. The shortcomings of this infographic were discussed in class.
FINAL EXERCISE: INFOGRAPHY (in 15 minutes)
This experience underlined the importance of collaborative innovation. Differences of opinion within the team fostered multidimensional thinking, essential for anticipating the needs of the educational market and tackling the idea of proposing sustainable solutions in the work that followed: Potential Solutions.
In this respect, one of the key learnings was the importance of seeing each challenge as an opportunity to create value. For example, the limitations identified in access to teaching resources and in teacher training on RTI revealed a potential for developing customized tools, such as interactive modules or collaborative platforms. These innovations could greatly improve teacher engagement and thus reduce the performance gap identified in our report.