Educational technology is making a bold mark on the development of higher education. If we define success by user interest and adoption, there have been at least a few major successes in higher education in the past five years.
A prominent example is Massive Open Online Courses (MOOCs), which registered 35 million students, 500+ universities and 4,200 courses. Qualitatively, MOOCs redefined how students learn inside as well as outside the classroom; provided global access to the content of higher education worldwide; and changed how students interact with faculty members at universities.
Other educational innovations contributed to the development of student advising, research preparation, alumni networking and career development. Overall, the amount of VC investment in edtech increased from $0.64 billion in 2011 to $3.1 billion in 2015.
Although edtech overall has seen quite a few successes in the past years, the global uptake of truly innovative educational technology at universities is still limited. The higher-ed market is traditionally characterized by long sales cycles, many stakeholders with various interests and lack of synergy of technology initiatives. While many startup companies use universities as their launching pads for their innovations (a famous recent example being Snapchat), comparatively few of them develop technologies to enhance actual university functions — learning and research — but instead use universities as socially cohesive environments where launching an app is easy.
One of the reasons there are still comparatively few upcoming startups trying to be the metaphorical Davids, and beat the Goliaths of the educational market, is problematic access to data at universities. Data that is critically necessary for educational innovation is restricted, poorly structured or not digitized at all; indeed, data access is a highly sensitive issue.
Improving data access, usefulness and interoperability could go a long way in reforming higher education for the better. Fortunately, the problem of data sharing has already been successfully advanced in other industries, most notably healthcare. Leaders of educational technology could learn from their healthcare peers in establishing better data standards.
Let’s look at the issue of data access at universities and outline a few ways in which innovation could move forward.
Data structure and access issues limit the technology
Navigating university data is quite a challenge. Historically and administratively, many universities are decentralized, thereby leaving the authority for maintaining data to individual divisions, departments, centers and libraries. While such systems allow individual departments to customize data collection to the needs of individual disciplines, data sharing between departments, schools and universities became hindered. For example, the quality of data about the faculty research provided on the departmental websites is often inconsistent even within an individual university, as the function of updating information often rests with individual administrative staff members.
Even some of the most elite and prestigious American universities are still in the habit of keeping information on the servers established decades ago.
Data storage and architecture is another challenge. Even some of the most elite and prestigious American universities are still in the habit of keeping information on the servers established decades ago, limiting the possibilities for modern application of their data. Some of the data that is crucial to learning and research is not even digitized at all. For example, while almost all universities maintain thesis archives, few, if any, made the effort to digitize student works and make them available to the next generation of student learners.
Finally, few players within a university are directly incentivized to keep their data in a common format. Individual faculty members and laboratories often leave their record keeping to the discretion of individual administrators and graduate students. Schools within a university maintain their own research budgets, therefore are not incentivized to promote active sharing of research information. Across the universities, collaboration is often limited; while there are programs of student and faculty exchange, there are few standards for data interoperability and sharing across institutions.
Learning from the health sector: Interoperability standards change the game
In the healthcare industry, change toward interoperability and data standards came from the federal government. In 2004, the government established the Office of the National Coordinator of Health Information Technology, tasked with optimizing the processes around health information. According to the ONC, health IT has seen the dramatic expansion in the subsequent 10 years. Passed in 2009, the HITECH Act stimulated the adoption of electronic health information with the ultimate goal of granting every American access to their health information.
The future progress of educational technology necessitates creating comprehensive standards of data in higher education.
In 2009, just 16 percent of hospitals and 21 percent of health providers maintained electronic records. Crucially, the issue of sharing data was tied to financial incentives for the hospitals and providers. By 2013, a staggering 50 percent of hospitals could query other hospitals for data; by 2014, the number rose to 80 percent. The current roadmap pushes for even more integration and consistency in health records; by 2021, the goal is to have the “learning health system,” such that the health system could continuously improve care, public health and science through real-time data access.
The health sector defined interoperability as “the ability of different information technology systems and software applications to communicate, exchange data, and use the information that has been exchanged.” Importantly, data must be shared across various players of the medical ecosystem, including the clinicians, labs hospitals pharmacies and patients, regardless of the application vendor. By implementing interoperability and involving major stakeholders from all aspects of the health system, healthcare created favorable conditions for an unprecedented rise in health innovation.
Since 2010, every passing year became groundbreaking for health technology. The startups were booming, helped by the establishment of seed funds such as Rock Health and accelerators like StartUp Health, and dozens of others. Every subsequent year, the record of venture capital investment in healthcare got shattered, achieving $16.10 billion invested in 2015. Interoperability of health standards helped spur an unprecedented level of innovation in the sector.
More data for edtech in higher ed
Implementation of modern standards of data sharing and interoperability in higher education must also combine significant commitments by the public and private sectors. A few years ago, the American federal government had already started digitizing and releasing large troves of educational data. Prominently, the National Science Foundation and the National Institutes of Health maintain meticulously detailed databases about federal grants awarded to faculty members at American institutions. The data are available to the public, and is already attracting the interest of startups and innovators that funnel data into actionable insights.
Another example of the movement toward data interoperability is the ORCID ID. Originally developed from the prototype of Thompson Reuters as ResearcherID, ORCID was incorporated as an independent nonprofit organization in 2012. The idea is that each scientific author receives a unique nonproprietary code that identifies the publications of the author and allows for contributor identification across the world. ORCID is freely usable and interoperable with other systems of data; in August 2016, there were more than 2.5 million ORCID accounts. In January 2016, eight major publishers, including PLOS and Science, committed to requiring their authors to use the ORCID ID.
Overall, the future progress of educational technology necessitates creating comprehensive standards of data in higher education. Similar to healthcare, educational data bears an enormous potential for innovation in the sector, control of the education costs and improvement of educational standard for students, scholars, faculty members, publishers, labs and universities alike.