Teaching and Learning

Arguably the most important mission of higher education, the teaching and learning domain has the largest combination of trends (16) and technologies (25) of any of the domains covered in this report. This complexity of the teaching and learning domain is well known to educational technologists and often stands as one of the greatest obstacles to securing the buy-in of the end users with whom the responsibility of teaching and learning resides—faculty. Faculty are often depicted as resistant to the use of educational technologies in service to teaching and learning. ECAR has found, however, that faculty on average have fairly positive views about technology and are very much open to the possibility that technologies may make them more effective instructors, provided that there is a clear indication or evidence that students would benefit from incorporating a given technology, and provided that faculty have release time to design and/or redesign their courses. Additionally, faculty (along with students) are buying into the student success movement, viewing student success technologies that support retention, learning, and completion as important tools.

This section covers the 16 trends and 25 technologies included in the teaching and learning domain. The most influential trend on teaching and learning is the focus and imperative of student success. Of the 25 technologies occupying the domain of teaching and learning, 7 of them appear in the Top 10 Strategic Technologies for 2018: uses of APIs (#1); active learning classrooms (#2); incorporation of mobile devices in teaching and learning (#3); technologies for improving analysis of student data (#5); technologies for planning and mapping student educational plans (#6); predictive analytics for student success (institutional level) (#8); and student success planning systems (#10). Given the mounting evidence of the impact of digital and analog teaching and learning environments, students' preferences for blended learning environments, and faculty and student perceptions of the utility of student success technologies, teaching and learning with technology is once again poised to shape higher education.

Technologies

Included in this domain:

  1. Active learning classrooms
  2. Adaptive learning
  3. Augmented and virtual reality for teaching and learning
  4. Blockchain
  5. Courseware
  6. Digital microcredentials (including badging)
  7. Games and gamification
  8. Incorporation of mobile devices in teaching and learning
  9. Institutional support for speech recognition
  10. Integration/uses of voice-user interfaces
  11. IT accessibility assessment tools
  12. Mobile app development
  13. Next-generation LMS/digital learning environment
  14. Open educational resources
  15. Predictive analytics for student success (institutional level)
  16. Predictive learning analytics (course level)
  17. Remote proctoring services
  18. Student success planning systems
  19. Support for use of personal cloud services
  20. Technologies for improving analysis of student data
  21. Technologies for offering self-service resources that reduce advisor workloads
  22. Technologies for planning and mapping student educational plans
  23. Text/content analytics
  24. Uses of APIs
  25. Uses of the Internet of Things for teaching and learning

Complete initial deployment and maintain these technologies.

Our research shows that most institutions are not ready to deploy these teaching and learning strategic technologies. Consider initial deployment only if one of these technologies is essential to your teaching and learning strategy.

Pilot and start deploying these technologies.

At this time, about one-half to two-thirds of institutions are planning to pilot and deploy these nine teaching and learning strategic technologies (listed below from highest to lowest attention):

  • Uses of APIs
  • Active learning classrooms
  • Incorporation of mobile devices in teaching and learning
  • Technologies for improving analysis of student data
  • Technologies for planning and mapping student educational plans
  • Predictive analytics for student success (institutional level)
  • Student success planning systems
  • Technologies for offering self-service resources that reduce advisor workloads
  • Mobile app development

Decide when these technologies fit your strategy, and start planning.

About half of institutions are watching these seven teaching and learning strategic technologies carefully, deciding and planning for potential future deployment (listed below from highest to lowest attention):

  • IT accessibility assessment tools
  • Open educational resources
  • Next-generation LMS/digital learning environment
  • Courseware
  • Adaptive learning
  • Augmented and virtual reality for teaching and learning
  • Games and gamification

Learn about and track these technologies.

A majority of institutions are tracking and learning about the following nine teaching and learning strategic technologies (listed below from highest to lowest attention):

  • Predictive learning analytics (course level)
  • Remote proctoring services
  • Digital microcredentials (including badging)
  • Uses of the Internet of Things for teaching and learning
  • Support for use of personal cloud services
  • Text/content analytics
  • Institutional support for speech recognition
  • Integration/uses of voice-user interfaces
  • Blockchain

Peer Institution Approach to Strategic Technologies

Understanding what peer institutions (both current and aspirational) are doing can help you gauge whether your institution's current approach is on track or might warrant reconsideration. Some technologies are more relevant for some types of institutions than others. We looked at broad demographic categories, including Carnegie class, institutional size, and approach to technology adoption and found differences in attention score based on those factors. (See the methodology section for explanation of our attention score calculation.) In figure 15, the US mean is the average attention score for an item from all US respondents. The minimums and maximums are the lowest and highest average attention scores among all groups within the categories of Carnegie class, institution size, and timing of technology adoption, with labels indicating which group or groups returned that score. In the event of a tie, all tied groups are represented.

Graph showing the attention score averages and differences. Y-axis represents the items. X-axis represents the attention score. All data provided is approximate. Uses of APIs: U.S. Mean = 3.3; Minimum = 2.8 (MA pub.); Maximum = 3.6 (DR priv.). Active learning classrooms: U.S. Mean = 3.0; Minimum = 2.4 (Less than 2,000 FTE); Maximum = 3.7 (DR pub.). Incorporation of mobile devices in teaching and learning: U.S. Mean = 2.9; Minimum = 2.6 (MA priv./DR priv./Late adopters); Maximum = 3.1 (AA/DR pub./8,000-14,999 FTE/Early adopters). Technologies for improving analysis of student data: U.S. Mean = 2.7; Minimum = 2.2 (BA/Non-US/2,000-3,999 FTE); Maximum = 3.2 (MA pub.). Technologies for planning and mapping student educational plans: U.S. Mean = 2.6; Minimum = 1.8 (Non-US); Maximum = 2.9 (MA pub.). Predictive analytics for student success (institutional level):  U.S. Mean = 2.6; Minimum = 2.0 (Non-US/Less than 2,000 FTE/Late adopters); Maximum = 3.0 (8,000-14,999 FTE). Student success planning systems:  U.S. Mean = 2.5; Minimum = 1.7 (Non-US); Maximum = 3.0 (DR pub.). Technologies for offering self-service resources that reduce advisor workloads:  U.S. Mean = 2.5; Minimum = 1.8 (Non-US); Maximum = 3.1 (DR pub.). IT accessibility assessment tools:  U.S. Mean = 2.4; Minimum = 1.4 (Non-US); Maximum = 2.7 (AA/15,000+ FTE). Open educational resources:  U.S. Mean = 2.4; Minimum = 1.9 (DR priv.); Maximum = 3.2 (AA). Mobile app development: U.S. Mean = 2.3; Minimum = 1.4 (Less than 2,000 FTE); Maximum = 3.0 (DR pub.). Next-generation LMS/digital learning environment: U.S. Mean = 2.2; Minimum = 1.7 (AA/MA pub.); Maximum = 2.5 (MA priv.). Courseware: U.S. Mean = 2.0; Minimum = 1.2 (Late adopters); Maximum = 2.3 (BA/Early adopters). Adaptive learning: U.S. Mean = 1.9; Minimum = 1.5 (DR priv.); Maximum = 2.4 (MA priv./Early adopters). Augmented and virtual reality for teaching and learning: U.S. Mean = 1.9; Minimum = 1.4 (Less than 2,000 FTE); Maximum = 2.3 (DR pub.). Games and gamification: U.S. Mean = 1.9; Minimum = 1.4 (Late adopters); Maximum = 2.2 (DR pub./15,000+ FTE/Early adopters). Predictive learning analytics (course level): U.S. Mean = 1.9; Minimum = 1.1 (Less than 2,000 FTE); Maximum = 2.3 (4,000-7,999 FTE). Remote proctoring services: U.S. Mean = 1.6; Minimum = 0.6 (BA/Less than 2,000 FTE); Maximum = 2.1 (MA pub.). Digital microcredentials (including badging): U.S. Mean = 1.5; Minimum = 1.1 (Non-US/Less than 2,000 FTE); Maximum = 1.7 (DR pub./15,000+ FTE/Early adopters). Uses of the Internet of Things for teaching and learning: U.S. Mean = 1.4; Minimum = 1.1 (Late adopters); Maximum = 1.7 (BA/MA priv./Early adopters). Support for use of personal cloud services: U.S. Mean = 1.4; Minimum = 1.0 (DR pub.); Maximum = 1.8 (Less than 2,000 FTE). Text/content analytics: U.S. Mean = 1.2; Minimum = 0.6 (Less than 2,000 FTE); Maximum = 1.4 (MA pub./4,000-7,999 FTE). Institutional support for speech recognition: U.S. Mean = 1.1; Minimum = 0.6 (Non-US); Maximum = 1.4 (DR priv.). Integration/uses of voice-user interfaces: U.S. Mean = 1.1; Minimum = 0.7 (Non-US); Maximum = 1.2 (DR pub.). Blockchain: U.S. Mean = 0.7; Minimum = 0.4 (MA pub./Late adopters); Maximum = 1.0 (DR pub.).
Figure 15. Attention score averages and differences for teaching and learning technologies

Preparing for the Future

Understanding the technologies that are most relevant for your institution and how fast a certain strategic technology may be growing is critical to institutional IT strategy. We estimated the pace of growth based on the percentage of institutions we predict will implement each technology over the next five years (by 2023). Figure 16 positions each technology in one of 12 cells based on institutional intentions (the "recommendation for today") and the expected pace of growth of that technology. Reflecting what was noted above, the figure shows that many of the technologies we tracked are reaching the deployment stage at most institutions.

4 boxes with recommendation for today  Each Box has 3 possible Pace of Growth categories: SLOW, MODERATE, FAST Box 1: Deploy and maintain  N/A Box2: Pilot and deploy  Moderate: • Mobile app development Fast: •	Uses of APIs • Active learning classrooms • Incorporation of mobile devices in teaching and learning • Technologies for improving analysis of student data • Technologies for planning and mapping student educational plans • Predictive analytics for student success (institutional level) • Student success planning systems • Technologies for offering self-service resources that reduce advisor workloads Box 3: Decide and plan  Moderate: • IT accessibility assessment tools • Open educational resources • Next-generation LMS/digital learning environment • Courseware • Adaptive learning • Augmented and virtual reality for teaching and learning • Games and gamification  Box 4: Track and learn  Slow: •	Uses of the Internet of Things for teaching and learning • Support for use of personal cloud services • Text/content analytics • Institutional support for speech recognition • Integration/uses of voice-user interfaces • Blockchain Moderate: • Predictive learning analytics (course level) • Remote proctoring services • Digital microcredentials (including badging)
Figure 16. Plans for 2018 and pace of growth for teaching and learning technologies