BURSTColleges and universities are at a crossroads when it comes to student data. They have more information at their fingertips than ever before, yet harnessing it to drive meaningful change remains a challenge. A 2022 UCLA-MIT Press study found that higher education struggles to capture and leverage data for impact. This digital disconnect isn’t just a result of outdated systems; it’s about the complex web of cultural, organizational and infrastructural barriers that leave many institutions data-rich but insight-poor.
Suzanne CarbonaroVice President of Postsecondary Education & Workforce Programs, 1EdTech
To discuss how institutions can turn raw data into real impact, EdSurge spoke with Suzanne Carbonaro, Vice President of Post-Secondary Education and Workforce Programs at 1EdTech Consortium (1EdTech). With 27 years of experience in higher education and assessment, she has served as a faculty member, held leadership roles in assessment and accreditation, and led competency-driven curriculum development at the Philadelphia College of Pharmacy, the nation’s oldest pharmacy school.
EdSurge: What types of data do higher education institutions find most difficult to access, and why?
Carbonaro: Despite the abundance of student data, higher education institutions face significant challenges in accessing and using it effectively. The first issue is [the existence of] data silos. Learning applications, student information systems (SIS), financial aid platforms and testing applications often operate independently, with no communication between them. While students move fluidly between these systems, their data does not. Each system exists on its own “island,” disconnected from others and from holistic student records.
Second, there’s a poor signal-to-noise ratio. Even when learning applications share data, much of it is unstructured or lacks context. For example, random clickstream data often doesn’t shed light on a student’s learning journey. Also, different systems may use inconsistent identifiers for the same student, making it difficult to track progress or connect data across platforms.
Third, the cost of solving these problems is prohibitive for many institutions. Untangling this data jungle often requires external consultants or expensive tools that many colleges and universities simply cannot afford.
What key barriers prevent institutions from obtaining and using that data effectively?
Institutions struggle with more than just technical challenges; they also face cultural and organizational barriers. Faculty often feel judged by analytics or decisions made based on incomplete data. This mistrust can hinder buy-in for adopting new tools or processes.
Privacy concerns also play a role. Institutions must ensure that applications meet rigorous standards for privacy, security and accessibility before adoption. For example, AI tools should use data responsibly, improving learning outcomes without storing sensitive information in proprietary, vendor-controlled data lakes.
Finally, institutions often don’t know what questions to ask about their learners upfront. Without clear goals or frameworks for using data, they risk collecting information that isn’t actionable or waiting too long to intervene when students need support.
How can adopting open standards help institutions access and leverage that data in a more actionable way?
Open standards serve as the foundation for solving these challenges. Think of them like plumbing in a home: Standardized infrastructure allows you to connect any faucet or appliance seamlessly. Similarly, interoperability standards like Caliper Analytics and Learning Tools Interoperability (LTI) ensure that edtech tools can work together without disruption when institutions switch vendors or adopt new technologies.
For instance, open standards allow institutions to track meaningful learner event data — such as clicks, time spent on tasks or questions asked in AI tools — and contextualize it alongside other holistic student information. This structured approach eliminates silos and makes data actionable in real time.
Despite the abundance of student data, higher education institutions face significant challenges in accessing and using it effectively... Untangling this data jungle often requires external consultants or expensive tools that many colleges and universities simply cannot afford.
— Suzanne Carbonaro
At 1EdTech, we create open standards that connect disparate systems into cohesive ecosystems. These standards enable institutions to change vendors when necessary without losing access to critical data or disrupting operations.
Can you share specific examples of how improved data access has positively impacted learner success?
In pharmacy education, the faculty aligned curricular outcomes with exam questions through a testing platform, allowing real-time tracking of student performance. By analyzing this data quickly, we identified students who struggled with specific foundational knowledge areas. Linking this information to learner attributes helped us support students from different high schools or colleges who needed additional help before the exam. This also enabled collaboration with those institutions to reinforce critical concepts for future cohorts.
Another example comes from our work using comprehensive learner records (CLRs). By linking pharmacy competencies to key assignments across courses within modules and allowing students to view their performance in near real time through CLR dashboards, we empowered them to take ownership of their learning journeys. Students and their mentors could see trends across months of coursework — not just grades — and make informed decisions about where to focus their efforts.
Currently, we’re working on a $20 million National Science Foundation grant with Georgia Institute of Technology and other institutions to study the impact of seven different AI assistants deployed in online courses aimed at supporting adult learners. Initially, this project faced challenges due to the disparate AI applications emitting different data streams into separate visualization tools, with no way to combine data for longitudinal discovery. By implementing a tripod of open data standards — Edu-API, LTI and Caliper Analytics — we unified these systems into one cohesive pipeline that provides contextualized insights about learner engagement.
The AI applications range from tutors supporting foundational knowledge gaps to social connection facilitators designed for online learners who might otherwise feel isolated. By consolidating these tools into one reference architecture using open standards, we’ve enabled institutions like Georgia Tech to scale their efforts while maintaining flexibility across platforms.
What can other institutions do now to get access to that data in a way that provides meaningful insights?
Institutions can take immediate steps to improve access to actionable data:
- Demand Open Standards: When issuing [requests for proposal] or procuring new tools, make it clear that vendors must provide data in standardized formats like Caliper Analytics rather than unstructured CSV files.
- Use Pre-Built Pipelines: For K-12 districts and other postsecondary institutions lacking resources to build their own infrastructure, access open standards frameworks, such as Learning Data Reference Architecture (LDRA).
- Focus on Real-Time Data: Collecting event-based data, such as who used what tools and for how long, combined with other key metrics, such as outcomes-based assessment data, enables institutional stakeholders to be proactive in supporting their learners rather than waiting weeks for insights that may already be outdated.
- Ask the Right Questions: Instead of collecting data reactively or employing gut-based decision-making, start by identifying what you want to know about your learners upfront so you can personalize their learning and identify what support services they need for their success.
Recommended Resources:
- 1EdTech Learning Impact Conference 2025
- Getting the Most from Your Data Starts with Governance
- With Data Comes Greater Responsibility
- Questions to Ask When Building Up Your EdTech Ecosystem
- Create a Data-Informed Culture
By taking these steps now, institutions can create a foundation for more effective decision-making and learner support.
What work still needs to be done?
While progress has been made in building open architectures and pipelines like LDRA, there’s still much work ahead:
- Fostering Trust: Faculty need assurance that analytics are meant to support — not judge — their teaching practices.
- Professional Development: Faculty and administrators must understand why interoperability matters and how it benefits learners.
- Privacy Standards: Institutions must continue vetting applications rigorously for privacy and security concerns while ensuring accessibility for all users.
- Scaling Solutions: Models like LDRA must be extended beyond pilot programs into full-scale implementations across diverse educational contexts.
1EdTech is a united community committed to achieving an open, trusted, and innovative education technology ecosystem that serves the lifelong needs of every learner. We unite the education community to build an integrated foundation of open standards that makes educational technology work better for everyone — reducing complexity, accelerating innovation and expanding possibilities for learners worldwide.