Data-Driven EdTech Decisions Matter

May 11th, 2026

By Chris Puddy, Learning Technologist

“Are we choosing edtech tools based on evidence — or convenience?”

I recently explored a resource from The Learning Accelerator on using data to drive more equitable edtech investments, and it prompted me to reflect on how we approach technology decisions in higher education. With the rapid growth of digital tools, it’s easy to adopt platforms based on availability or surface-level appeal. However, this resource emphasizes a more intentional, data-informed approach — one that centers both effectiveness and equity.

One idea that stood out to me was the importance of using data not just to select tools, but to clearly identify actual instructional needs. Rather than starting with a tool, we should begin with the problem: What are students struggling with? Where are gaps in access or outcomes? From there, data can guide more strategic decisions. Equally important is evaluating the impact of edtech after implementation. It’s not enough to pilot a tool — we need to assess whether it meaningfully improves learning and supports all students.

This raised an important connection to engineering education. Programs often adopt tools with the goal of enhancing learning, but how frequently do we evaluate whether those tools are actually working? And for whom? Data can help us move beyond assumptions and better understand issues of equity, access, and student success. At the FSE Learning and Teaching Hub, this also highlights an opportunity to support faculty in designing more intentional evaluation practices — helping them ask the right questions and interpret meaningful data.

A particularly interesting concept for me was the idea of evaluating implementation fidelity during the early demo and discovery phases. If we don’t examine how well a tool integrates into existing systems and teaching practices upfront, we risk challenges later during pilots or full adoption. Being more deliberate early in the process could help avoid inefficiencies and ensure smoother, more impactful implementation.

Ultimately, this resource reinforced a simple but powerful idea: thoughtful, data-informed decision-making is essential if we want edtech to truly support teaching and learning. For those of us working with faculty and course design, it’s a reminder to slow down, ask better questions, and prioritize evidence over convenience.

_{Author’s Note: This article was drafted with the assistance of a generative AI tool to support wording and formatting; content generated by AI has been reviewed and approved by the author.}

References

Arizona State University. (n.d.). FSE Learning and Teaching Hub. Ira A. Fulton Schools of Engineering. https://lth.engineering.asu.edu/

The Learning Accelerator. (n.d.). Using data to drive equitable EdTech investments. https://practices.learningaccelerator.org/strategies/using-data-to-drive-equitable-edtech-investments