From “smile sheets" to measurable learning impact

The challenge

Together with Relevance Learning, a leadership and organizational development company, Business Monitor wanted to explore whether data science and AI could help create a more structured and evidence-based way to measure learning impact.

The broader ambition was ambitious but concrete: move from fragmented, highly customized evaluations toward a standardized methodology that could compare learning outcomes across organizations and over time.

The challenge for the students centered around several connected questions:

• Which data is needed to measure the impact of learning programs?
• How can organizations structure that data consistently?
• Which statistical models and analyses are suitable for linking learning activities to organizational outcomes?
• And how can those insights be translated into dashboards and reporting tools that organizations can actually use?

The project combined practical constraints with an open research question. Existing literature on learning impact offered frameworks and theories, but according to Lagendijk, little consensus existed on standardized measurement approaches. Much of the available work relied on custom implementations tailored to individual organizations.

The students therefore worked with a combination of existing datasets, domain knowledge, industry literature, and exploratory modeling. One important limitation quickly became clear: meaningful impact measurement requires longitudinal and well-structured data, something that is rarely readily available.

What the students built

The students approached the challenge from multiple angles. Rather than producing one single solution, each group focused on a different aspect of the broader problem.

Some teams explored which survey questions and data structures would best support standardized benchmarking. Others focused on statistical analysis models, dashboarding, or reporting concepts. One group developed a proof-of-concept dashboard that visualized learning outcomes and organizational impact metrics in a more actionable way for clients.

According to Lagendijk, the diversity of approaches became one of the strengths of the project. Because each group worked from a different perspective, the collaboration produced a broader exploration of the challenge space instead of variations of the same solution.

The setup of the Master Challenge also played an important role. Regular presentations and peer feedback sessions meant students continuously learned from each other’s progress, while still developing their own direction. That created a mix of collaboration and healthy competition, pushing groups to sharpen their ideas and improve their results over time.

For Business Monitor, the collaboration also stood out because of the students’ level of ownership and engagement. The teams stayed closely involved throughout the process, regularly discussing choices, assumptions, and trade-offs with the client.

The result

The project delivered more than a set of academic reports. For Business Monitor and Relevance Learning, the collaboration helped translate an abstract ambition into a tangible proof of concept.

The student teams demonstrated how standardized question libraries, structured data collection, and dashboarding could support a more scalable approach to measuring learning impact. Several of the project outcomes are now being incorporated into the further development of the platform and methodology.

One concrete next step is the development of a standardized question library that can support benchmarking across organizations and training programs. Business Monitor is also continuing discussions with JADS about potential follow-up collaborations as more longitudinal datasets become available.

For Lagendijk, the project also reinforced the importance of combining domain expertise with data science capabilities. The challenge was not simply technical. It required understanding organizational learning, behavioral measurement, reporting, and practical implementation at the same time.

As he describes it, the ambition remains clear: move beyond “smile sheets” toward measurable and eventually even predictable learning impact.