The Data Transformation: Powering Precision Education

There is a tsunami of data accumulating every day across the educational landscape. The sheer magnitude presents enormous opportunities for higher education IT organizations and the entire educational sector. Data is pouring in from classrooms, administration and local levels, community programs and organizations, software vendors, federal legislators, and researchers, just to name a few. Higher education IT and leadership departments can affect data-driven change at both micro and macro levels. There are quick and relatively easy ways to incorporate data at the institutional level and exciting long-term shifts happening that will shape the future of precision education.

Examples of Innovation

Many institutions are doing amazing work leveraging data and analytics to drive operational decisions. In particular, the education sector made strides in embracing the use of AI and data to accelerate learning during the pandemic.

Jacksonville University is using current data and analytics through Power BI to proactively inform student registrations, retention details, demographics, course evaluations, student engagement, and financials. Duquesne University is using data to enhance the enrollment process. More specifically, the university is using technology (Power BI and the Microsoft platform) to make data-driven decisions, democratizing data across every level of management, from frontline counselors to the president. In the UK, TEDI-London implemented a new end-to-end student record system built on the Microsoft Higher Education Accelerator. The new system captures all of the institution's marketing, recruitment, and application processes.

These institutions, and many more, are making great strides in shifting to a data-driven culture. There is still a huge opportunity for the industry as a whole to make similar shifts.

Data-Driven Education through Standardization and Open Analytics

The evolution of broad educational data standards is gaining momentum and visibility. To understand where we are and where we can go, let's turn to the health care industry to see how data standardization propelled patient care to a new level.

Lessons from Health Care

Early on, health care information was captured primarily for billing cycles and compliance with government insurance programs. As electronic health records (EHRs) evolved, more clinical workflows were incorporated, and data collection began to improve patient experiences and outcomes. Today, EHRs have become standardized in health information management. Certain clinical departments, like radiology and oncology, flourished and excelled in data collection because the standards for their practices (image management) were easily defined. Legislative actions, such as the Health Information Technology for Economic and Clinical Health Act (HITECH) in 2009 and the Affordable Care Act (ACA) in 2010, greatly accelerated the use of EHR technology and innovations in data collection.

Over the past decade, the acceleration and adoption of Fast Healthcare Interoperability Resources (FHIR) have made the frictionless exchange of data and EHRs commonplace. This interoperability lets clinicians, insurance companies, researchers, and others aggregate data from a complicated and fragmented system to gain new insights, provide better patient outcomes with real-time health information, and improve efficiencies at scale.

Microsoft's Commitment to Health Care and Open Source

Microsoft has been committed to helping advance interoperability and cloud-based health care solutions. In 2018, it began with an open-source project (FHIR Server for Azure) that evolved into the FHIR API that empowers developers with software that fully supports the exchange and management of data in the cloud via the FHIR specification.

Open Education Analytics Move Forward

In every industry, data standards greatly enhance the ability for data services to automate the movement and processing of data in service of any number of use cases. However, defining and evangelizing these data standards and encouraging adoption are fraught with challenges. In the edtech sector, many education data standards are developed and maintained through the diligent work and contributions of members of several different standards bodies, including IMS Global, Ed-Fi, CEDS, A4L (SIF), and PESC, to name a few. These standards cover a large swath of the education data landscape and overlap in many ways, but there is much work to be done to arrive at a more comprehensive and cohesive set of well-adopted global education data standards.

Open Education Analytics (OEA) is a Microsoft-led effort focused on empowering the edtech ecosystem by facilitating the open sharing of common components needed to work effectively with the many different data schemas that exist across edtech data sources and standards. OEA encourages the use of data standards where possible. At the same time, it recognizes that the road to more complete standards is effective crowdsourcing of commonly needed components that reduce time to value for common data analytics and data interoperability use cases. Through the repeated solving of common needs in the market, we, as a collaborative community, can accelerate the process of deriving the comprehensive data standards needed. By emphasizing collaboration, effective use of modern data collection and processing technology, iterative development (of schemas, pipelines, transformation scripts, reports, dashboards, machine learning models, etc.), and documenting and sharing best practices and reusable components, we can all be better positioned to contribute to the process of codifying standards from solutions that have emerged.

The global education industry has an opportunity to greatly accelerate the progress in data analytics and data interoperability through more effective sharing of common low-level components. This is the collaboration that leads to commoditizing the low-level plumbing and allowing the edtech market to differentiate at a higher level and better serve students, teachers, and administrators. This is the collaboration that leads to effective standards through rapid iteration and refinement via real-world use.

The opportunities for the education landscape are exciting, and we are excited to work in tandem with our partners, technology counterparts, and education leaders to accelerate the digital transformation of their systems and make a meaningful impact in education. To get involved with Microsoft OEA, visit https://openeducationanalytics.org/.

Bill Campman is Director of Data and AI, US Education, at Microsoft.

Gene Garcia is Principal Software Engineering Manager at Microsoft.