The top 10 strategic technologies for 2020 were identified from a list of 98 technologies. Numbers in parentheses are the 2019 rankings for those technologies that were also in last year's top 10.

1. Uses of APIs (1)
2. Institutional support for accessibility technologies (6)
3. Blended data center (on premises and cloud based) (3)
4. Incorporation of mobile devices in teaching and learning (4)
4. Open educational resources (5)
6. Technologies for improving analysis of student data (7)
7. Security analytics
8. Integrated student success planning and advising systems (10)
9. Mobile apps for enterprise applications
10. Predictive analytics for student success (institutional level) (9)

1. Uses of APIs: An API defines how a system interacts with other systems and how data can be shared and manipulated across programs. A good set of APIs is like building blocks that allow developers to more easily use data and technologies from various programs. APIs are used in many ways in higher education—for example, to pull data from the student information system into the learning management system, to integrate cloud-based with on-premises services, as an approach to security, and to access web-based resources.
2. Institutional support for accessibility technologies: A wide range of accessibility technologies are available for students, faculty, and staff with physical, cognitive, or other kinds of disabilities. Institutional support for such technologies may focus on straightforward educational applications (e.g., language learning) or otherwise improving access.
3. Blended data center (on premises and cloud based): As institutions move services to the cloud, they usually move into a blended environment where they continue to maintain an on-premises data center while also managing a set of services that may run the gamut from software as a service to infrastructure as a service. While cloud-based solutions offer advantages related to agility, performance, and scalability, the blended environment requires a shift in strategy to one that encompasses both environments.
4. Incorporation of mobile devices in teaching and learning: Mobile devices integrated into courses can be used for course assignments, field work, collaboration, and other activities. Such integration includes ensuring that course content functions well on mobile devices, as well as leveraging the unique capabilities of mobile devices for learning.
4. Open educational resources: Open educational resources (OER) are freely accessible, openly licensed documents and media that may be useful for teaching, learning, assessing, and research. OER are used in various learning settings including online, face-to-face, and blended, as well as structured learning environments such as college courses and self-paced, student-driven learning.
6. Technologies for improving analysis of student data: These technologies enable immediate access to and rapid analysis of large, complex data sets, making it possible to discern trends in student engagement, in the types of difficulties students are encountering, and in the likelihood of success in attaining credentials across the student body.
7. Security analytics: Security analytics uses analytics, adaptive learning, and other tools to detect, anticipate, and respond to incidents and compliance issues.
8. Integrated student success planning and advising systems: Student success planning systems aggregate a broad range of academic, learning, financial, and other data, enabling personnel throughout the institution to collaborate in support of retention and completion.
9. Mobile apps for enterprise applications: Mobile apps for enterprise applications refers to web-based applications that run on mobile devices and are designed to integrate with all aspects of an organization's businesses and processes. These apps make it possible to access enterprise-wide resources (such as course catalogs, student information systems, and human resource systems) and conduct enterprise transactions from mobile devices.
10. Predictive analytics for student success (institutional level): Predictive analytics for student success is the statistical analysis of massive amounts of data to create models that establish risk factors relating to student persistence, retention, and completion. These models enable proactive institutional support of student success.

Each technology was assigned an "attention" score that is a weighted combination of intentions to plan for, track, or implement a technology in 2020 (see the Methodology section for more details). The top 10 are the technologies with the highest attention scores. We tested for statistically significant institutional differences in attention scores by three variables:

• Carnegie Classification: Associate's, bachelor's, public master's, private master's, public doctoral, private doctoral, other US, and non-US.
• Institutional size: Fewer than 2,000 FTEs (students), 2,000–3,999 FTEs, 4,000–7,999 FTEs, 8,000–14,999 FTEs, and 15,000+ FTEs.
• Institutional approach to technology adoption: Early (before other institutions), mainstream (about the same time as peer institutions), and late (after peer institutions). Early adopters accounted for 41% of respondents, mainstream 43%, and late adopters 16%.

We found institutional differences for 3 of the 10 technologies in the list (see figure 2). In general, early technology adopters, associate's and public master's institutions, and mid-sized institutions (8,000–14,999 FTEs) are devoting more attention to strategic technologies than are smaller (less than 2,000 FTE) institutions; public and private bachelor's, private doctoral, and non-US institutions; and late adopters. Public master's institutions are investing more effort into technologies for improving analysis of student data than are other institution types, with public and private bachelor's and non-US institutions devoting significantly less attention than the rest of the pack. Associate's institutions are devoting significantly more attention to integrated student success planning and advising systems. Mid-sized (8,000–14,999 FTE) institutions and early technology adopters are focusing on predictive analytics for student success at the institutional level significantly more than other institutions; the smallest institutions (less than 2,000 FTE) and late technology adopters are farthest behind. Figure 3 offers a summary view of the top 10 strategic technologies by Carnegie class (including technologies that are in the top 10 for specific Carnegie groups but that are not part of the overall top 10).