The top 10 strategic technologies for 2019 were identified from a list of 77 technologies. Numbers in parentheses are the 2018 rankings for technologies in last year’s top 10. Because of a tie for the 10th spot, this year’s list includes 11 technologies.

1. Uses of APIs (1)
2. Active learning classrooms (2)
3. Blended data center (on premises and cloud based) (7)
4. Incorporation of mobile devices in teaching and learning (3)
5. Open educational resources
6. Institutional support for accessibility technologies*
7. Technologies for improving analysis of student data (5)
8. Application performance monitoring
9. Predictive analytics for student success (institutional level) (8)
10. Integrated student success planning and advising systems (10; tie)
10. IT asset management tools (e.g., CMDB) (10; tie)

*This technology was new in the 2019 survey.

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. Active learning classrooms: Active learning classrooms (ALCs) are student-centered, technology-rich learning environments designed on the principles of active pedagogical approaches. ALCs typically feature moveable furniture, large displays, projectors, and other tools that support active learning.
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.
5. Open educational resources: Open educational resources (OERs) are freely accessible, openly licensed documents and media that may be useful for teaching, learning, assessing, and research. OERs 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. 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.
7. 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.
8. Application performance monitoring: Application performance monitoring tools track the performance of applications in relation to end users’ experiences and to internal metrics (for example, for load and capacity) that may be leading indicators of future performance issues. The goal of these tools is to automate tracking and improve the reliability of application performance.
9. 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.
10. (tie) 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.
10. (tie) IT asset management tools (e.g., CMDB): IT asset management tools provide an account of the significant components of the IT environment, including dependencies and life cycles. As IT assets expand beyond central IT, both on campus and in the cloud, asset management becomes more complex. IT asset management tools can help institutions better understand, plan for, and make decisions about the resulting technology mix.

Each technology was assigned an “attention” score that was a weighted combination of intentions to plan for, track, or implement a technology in 2019 (see the Methodology section for more details). The top 10 were 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 4 of the 11 technologies in the list (see figure 2). Generally, early technology adopters, public and private doctoral institutions, and mid-sized institutions (8,000-14,999 FTEs) are devoting more attention to strategic technologies than are smaller institutions and those that adopt technology later. 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). Public and private doctoral institutions are investing more effort into active learning classrooms than associate’s, private master’s, and non-US institutions. Early technology adopters are devoting significantly more attention to open educational resources and institutional support for accessibility technologies than other institutions. Larger institutions are focusing on predictive analytics for student success at the institutional level significantly more than are smaller institutions, particularly among institutions with 8,000–14,999 student FTEs.

What do these data tell us about the kind of progress higher education might make with the technologies measured in this study? We used institutions’ 2019 intentions for implementing and planning technologies to estimate deployment of all 77 technologies within roughly two years (2020–21) and within roughly five years (2022–24). We used the following categories to group estimates for when each technology is expected to be:

• Experimental (deployed institution-wide in 20% or fewer institutions)
• Emergent (deployed institution-wide in 21–40% of institutions)
• Growing (deployed institution-wide in 41–60% of institutions)
• Mainstream (deployed institution-wide in 61–80% of institutions)
• Universal (deployed institution-wide in 81–100% of institutions)

The 2018 top 10 strategic technologies list included 8 of the 11 technologies on this year’s list, enabling us to compare our 2018 predicted pace of adoption with actual progress. Higher education, perhaps predictably, is not moving as quickly as our estimates suggested. We predicted that uses of APIs and active learning classrooms would achieve growing adoption by the end of 2020, yet all are still at the experimental level (see figure 4). We also predicted that blended data centers, the incorporation of mobile devices for teaching and learning, technologies for improving the analysis of student data, predictive analytics for student success, and IT asset management tools would be emergent, yet they all remain at the experimental level. Next year’s research will afford us the opportunity to finalize our comparison for these seven technologies, at least.