Introduction:
I have long been a proponent of innovative ways to use data in education. I feel that data usage in the education sector still lags behind that of other industries. Because our students deserve it, we should maximize the capabilities of a data-driven education system. Consider this concept: crowdsourcing in education. Crowdsourcing is an idea more prevalent in other industries, but it can give educators, students, families, and community members the opportunity to help schools collect, analyze, and apply data more effectively.
From identifying learning gaps to co-designing curriculum improvements, crowdsourced data can enable a new level of insight and responsiveness. Its use in education is not a completely foreign concept, but it is one I think could be utilized more often. In this post, we’ll explore how crowdsourcing isn’t just a tool for engagement—it’s a game-changer in educational data strategy.
What Is Crowdsourcing in Educational Data?
To begin, I’d like to define two different but related terms:
Crowdsourcing – the practice of obtaining information or input into a task or project by enlisting the services of a large number of people
Citizen Science – the collection and analysis of data relating to the natural world by members of the general public, typically as part of a collaborative project with professional scientists
Crowdsourcing is a term that was coined in the early 2000s to refer to a practice that was picking up steam on the internet. As a portmanteau of “crowd” and “outsourcing”, it is a way for technology companies to engage a body of people to provide knowledge or services, often unpaid. A good example of this is Wikipedia, a website that exists because anyone can add content to increase its knowledge base. Obviously, this method has the advantage of saving a company big money and, at the same time, tapping into a huge knowledge base.
Crowdsourcing in educational data refers to the practice of gathering input, feedback, observations, and even raw data from a wide base of stakeholders to guide decisions about student learning, curriculum, instruction, and school operations. It’s a shift from exclusive, institutionalized data collection to inclusive, participatory models that value insight from every level of the learning ecosystem.
And this is where citizen science comes in.
Originally rooted in environmental and health research, citizen science empowers non-professionals to actively participate in data collection, analysis, and discovery. In education, this concept has potential—students conducting local research projects, families collecting data on school experiences, and teachers collaboratively analyzing trends in classroom behavior or academic performance.
Here are a few examples that could be implemented:
- Student-driven data projects, such as tracking school air quality, biodiversity, or local demographics, build both content knowledge and data literacy
- Teacher crowdsourcing initiatives gather large-scale feedback on instructional materials or assessment effectiveness
- Parent and community surveys function as citizen-led tools for identifying needs, setting priorities, or evaluating school climate
- Multi-stakeholder data dives that bring individuals from various roles together to analyze the data using an organized analysis protocol
This citizen science meets education approach doesn’t just democratize data—it makes it more meaningful. It fosters a culture of inquiry, critical thinking, and collaboration where everyone contributes to the story schools are telling through their data.
By combining crowdsourced insights with structured analysis, schools can access more relevant, actionable intelligence—and ultimately make smarter, more responsive decisions that reflect the realities of the people they serve.
Benefits of Crowdsourcing for Data Collection and Analysis in Schools
There are plenty of benefits to this idea, many of which have already been discovered by other industries:
- Broader, more representative data sets from diverse stakeholders
- Real-time feedback from teachers, students, and parents
- Increased transparency and stakeholder trust in decision-making
- Enables localized, context-aware decision-making
- Fosters a culture of continuous improvement and shared accountability
When it comes to data collection, there is potential for methods that might be considered less intrusive than top-down data collection mechanisms like standardized tests. Building data collection directly into the learning structure (such as making it a student project) kills two birds with one stone by teaching data literacy and utilizing it for school improvement.
When it comes to data analysis, the decisions will be more informed by engaging a larger group of diverse minds. The shared accountability for decision making will also have positive impacts on the school (and on the administrators).
There’s Power in Cognitive Diversity
James Surowiecki’s 2004 book, The Wisdom of Crowds, argues that groups can and usually do make better decisions than any single member of the group would be able to. He proposes many reasons for this but a big one is the power of having a diverse set of brains working on the same topic. Surowiecki writes, “Diversity helps because it actually adds perspectives that would otherwise be absent and because it takes away, or at least weakens, some of the destructive characteristics of group decision making [such as herd mentalities].”
It’s the same reason why, when I play bar trivia, I try to build a team that goes beyond just me and my closest friends, all of whom have the same interests (largely sports and The Office). Including my wife and her knowledge of the Real Housewives franchise gives us a diverse knowledge base and puts us in a better position to win (which I swear does occasionally happen).
It’s not enough to include only certain people in data analysis and decision making, even if those people are really smart. Surowiecki writes, “…on the group level, intelligence alone isn’t enough, because intelligence alone cannot guarantee you different perspectives on a problem.” There is incredible value to opening these opportunities up to as many stakeholders as possible. For this reason, I always advocate for organized “data dives”, where a group of people come together and follow an analysis protocol that keeps everyone involved and ensures equity of voice.
Planning Collaborative Data Dives
One of the easiest ways to implement crowdsourcing concepts in a school setting is to plan a collaborative data dive. Doing this can bring a variety of perspectives into the data analysis process and can improve stakeholder buy-in by making everyone feel included. Here are some basic steps:
- Schedule a meeting. I’d recommend it being in-person, but I have conducted data analysis meetings virtually as well.
- Invite participants. This could be done with teachers, parents, students, or anyone. You’ll just want to make sure you frame the process in a way that is appropriate for the participants.
- Determine logistics. If you’ve got a large number of participants, you’ll want to split into breakout groups of 4-8. This will ensure that everyone has the chance to be heard.
- Prepare the data, You’ll want to have things organized and accessible ahead of time.
- Choose a data analysis protocol. This will be what guides the discussion. One that I like is the ATLAS Looking at Data protocol, but there are plenty of options out there.
- Consider next steps. After completing the discussion, there are lots of directions you could take this. A collaborative root-cause analysis or the development of an action plan might be appropriate next steps.
The Bottom Line:
This is not a completely original idea nor is it particularly revolutionary, but it is an underused practice that can have a major impact. These types of lesser utilized practices can greatly improve educational outcomes. If we’re not trying to do that, we’re doing our students a disservice.
