In today’s digital education landscape, data is more than just numbers—it is the foundation for understanding how students learn, struggle, and succeed. Digital Learning Platforms (DLPs) generate extensive data that could significantly contribute to education research but often remain underutilized. While data-sharing comes with considerations around privacy, security, and governance, it also presents opportunities for product improvement, innovation, and evidence-based decision-making.
For DLP leaders contemplating data-sharing, here are 12 critical questions to consider as you are thinking about participating in an initiative to share data with researchers.
- Why Share Data?
Data-sharing isn’t just about transparency; it can fuel innovation, improve product and student learning experiences, and strengthen your platform’s impact. Ask yourself:
a. Broadly, what is the value of making it possible to more easily share data for your organization?
b. How can data-sharing help your organization improve its products and outcomes?
c. What broader benefits can it provide for the educational community?
- Defining Research Goal: What’s In, What’s Out?
Not all research aligns with your platform’s vision. Consider:
a. What kinds of research questions or goals do you imagine you would support?
b. What would be uninteresting or out of scope?
- How Does Data Value Connect to Research Questions?
a. How do you think of connecting this broad vision of the value (#1) to more specific research questions (#2)?
b. How would you guide researchers to ask meaningful and important questions?
- What Kinds of Data Do You Have?
Understanding your datasets is the first step toward impactful collaboration. Your data might include:
a. Click-stream data tracking user interactions
b. Student-generated content (assignments, essays, discussions, assessments, and others)
c. Repository of curricular units or lessons and associated meta-data
d. User demographics, profiles, and learning trajectories
- What Kinds of Research Methods Would You Want to Support?
It often makes sense to start simple, and build over time.
a. Descriptive & correlational
b. Longitudinal
c. Building measures, building instruments, building models
d. Creating tools to support others analysis of your data (e.g. engagement detectors, qualitative codebooks, tools for validating measures)
e. Tools for experiments and A/B tests
f. Beyond traditional data sources, co-designing research with stakeholders can offer valuable insights. Advisory groups—including students and educators—can provide perspectives often overlooked in conventional research. For example, student advisory boards can validate research assumptions by offering direct insights into how students interact with learning platforms.
g. School administrative data
h. Observations and qualitative feedback collected outside the system
i. Surveys of students and educators
- What Privacy and Data Security Concerns Must You Consider?
a. What does the mechanism of gaining access look like?
b. What privacy issues are you most concerned about?
c. Could you offer sample datasets, synthetic data, or secure analysis environments?
- Who Are Your Ideal Research Partners?
a. Do you have ideas of who the initial researchers might be?
b. Have you worked with researchers before? What worked well?
c. Would schools, policymakers, or funders also benefit from insights generated by this research? How can their involvement add value?
- What Kind of Data and Infrastructure Would You Like to Make Available?
When you prepare for sharing data with researchers, there is a list of items you would need to prepare.
a. Descriptions of the data (e.g. meta-data) to help researchers understand the structure of your data.
b. Sample data to allow researchers to explore before full access.
c. Synthetic data as an alternative for preliminary analyses (not often used yet).
d. Ability to run code that analyzes a big data set.
e. Restricted access data sets.
- What Support Will Researchers Need?
Data-sharing isn’t just about access—it’s about usability. Think about:
a. Running some kind of RFPs for researchers to attract the right studies
b. Feasibility+Desirability discussions / resolution process
c. Developing MOUs, DSAs, and other forms of agreement necessary
d. Supports for data access
e. Supports for data use (e.g. how to make sense of the data structures)
f. Support for merging with other data sets (later or not)
g. Hosting community among researchers
h. Support for dissemination / what we are learning
- What are the Open Science and Ethical Considerations?
a. What commitments do you want researchers to make regarding open science?
b. How can you facilitate responsible sharing of findings?
c. What ethical considerations should be emphasized?
- Where do you feel most / least prepared?
Every organization will have strengths and gaps in its data-sharing readiness. Ask yourself:
a. What aspects of data-sharing feel straightforward, and what areas need more clarity or investment?
b. Are there internal resources, expertise, or partnerships that could strengthen your readiness?
- What Role Do Funders and Partners Play?
Sustaining a data-sharing initiative requires long-term support. Consider:
a. What are your assumptions about funders and funding for this work?
b. How can funders and partners help sustain this effort?
c. What partnerships could enhance the value of shared data?
By addressing these questions, DLPs can move from uncertainty to informed decision-making. SEERNet and SafeInsights provide resources to help platforms navigate the complexities of data-sharing, ensuring their contributions support meaningful and scalable educational research.
Advancing education research relies on improved data access. The key question is—how will your platform contribute to shaping the future?If your platform is exploring data-sharing, what challenges are you facing? We invite you to join the conversation. Join us on LinkedIn and sign up for our interest list.
