Dr. Charles M. Reigeluth is a distinguished educational researcher and consultant focused on paradigm change in education. He has a B.A. in economics from Harvard University, and a Ph.D. in instructional psychology from Brigham Young University. He taught high-school science, was a professor at Syracuse University for 10 years and was a professor at Indiana University for 25 years. His research focuses on paradigm change in education, including the design of high-quality personalized, competency-based, learner-centered learning experiences and the design of technology systems to support such learning experiences. His latest books are Instructional-Design Theories and Models, Volume IV: The Learner-Centered Paradigm of Education; Vision and Action: Reinventing Schools through Personalized Competency-Based Education; and Merging the Instructional Design Process with Learner-Centered Theory: The Holistic 4D Model (www.reigeluth.net/holistic-4d). They chronicle and offer guidance for a national transformation in education to the learner-centered, competency-based paradigm. He offers presentations and consulting on this topic.
In a recent interview with Higher Education Digest, Dr. Charles M. Reigeluth discussed his experience with technology and higher education. He shared his views on how technology evolves in higher education, instructional design, and student engagement in higher education, between academic learning and real-world workforce demands, and many more.
Over the years, how have you seen instructional systems technology evolve in higher education?
I would like to begin with some background before I can answer this question. It’s helpful to think in terms of two aspects of instructional systems technology: hard technology, which is basically equipment and objects, and soft technology, which is methods. Then, within each of these aspects there are two kinds of knowledge based on the means-ends distinction or process-product distinction.
For soft technology, we have knowledge about what the instructional methods (the ends) should be like for a given situation, in contrast to knowledge about what the instructional-design (ID) process (the means) should be like for creating such instruction. We call these instructional theory and ID process, respectively.
Instructional theory has two parts: a method and when to use the method (the situation). An example of instructional theory is the recommendation to teach a skill (the kind of learning, which is an aspect of the situation) by telling the learner how to do it (a generality), showing the learner how to do it (a demonstration), having the learner do it (practice), and providing information (feedback) about the quality of the performance and/or how to improve it (the method).
ID process (the other kind of soft technology) has the same two parts: a method and when to use it. The methods generally fall into the categories of analysis, design, development, implementation, and evaluation (ADDIE). Different instructional methods require different ID processes to create the instruction.
For hard technology, which you seem to be most interested in, we have the same means-ends distinction. The ends, media theory, include which media to use (selection) and how to use them (utilization). The means (another aspect of the ID process) address the processes for a professor or instructional designer to select and utilize those media.
So, to address your question, there have been huge changes in both hard and soft technologies and huge advances in knowledge about both means and ends for each. I suspect your readers are well familiar with the changes in hard technologies over the ages, from printed materials, to radio, to TV, to overhead projectors and slide projectors, to film and video, to digital media or multimedia, to virtual reality and augmented reality, and to artificial intelligence. However, knowledge about how best to utilize each of these media in higher education or any kind of purposeful learning environment lags development of the hard technologies themselves.
This lag is hugely important, because of the distinction between medium and message. It is the message – not the medium – that teaches. Different media have different affordances – such as audio, motion, and interactive capabilities – that can allow communication of different messages (e.g., music appreciation requires the audio affordance). But it is the design of the message that counts most for promoting learning. New hard technologies allow the use of new soft technologies.
Fortunately, despite the lag, soft technologies have also advanced impressively over the past few decades. Advances in learning theory (cognitivist and constructivist) have helped spur advances in instructional theory. Please keep in mind that learning theory is descriptive theory, whereas instructional theory is design theory (Simon, 1996). The latter offers guidance for which methods to achieve given goals and is much more useful than learning theory to professors and other teachers.
What are some of the biggest shifts you’ve witnessed?
The biggest shifts in instructional systems technology that I have witnessed are competency-based education, project-based learning, collaborative learning, and self-directed learning. Let me explain why.
First, professors engage in intellectual “self-gratification” (to use a less vulgar term) if they teach without students learning what they have taught. It is learning that counts. This is why competency-based education (CBE) is the most important shift, albeit still in the early stages of adoption in universities. Professors must ensure that learning has occurred in every student. This means that some students must continue to work on mastering the content after others are ready to move on. This soft technology includes competency-based learning targets, competency-based student progress, competency-based student assessment, and competency-based student records. Students learn at different rates, so we should expect some students to need 20 hours per week on a course for which other students need only 5 hours per week. Slower learners should take on fewer courses (or projects) in a semester.
Second, motivation is key to accelerating learning. So, what motivates students to learn? There is much evidence that engaging in authentic projects of interest to the student is among the strongest motivators. Learning by doing includes problem-based learning, project-based learning, inquiry-based learning, task-based learning, and maker-based learning. I use the term project-based learning (PBL) to refer to all these kinds of learning by doing. PBL is usually even more motivating when the project is one that improves the students’ world in addition to enhancing their own learning (Prensky, 2016). The satisfaction of accomplishing something important is highly motivating. In fact, McClelland (1987) has identified three great motivators or human needs: need for achievement, need for affiliation, and need for power. With CBE every student feels a sense of achievement when they master each part of the content and an even more powerful sense of achievement when they successfully complete a meaningful project.
A related advance in soft technology is collaborative learning (CL) in projects, often called team-based learning. This addresses McClelland’s need for affiliation. It also provides a way for students to help each other master the content required for a project, thereby lightening the load for the professor.
Another advance is self-directed learning (SDL) in projects, which addresses McClelland’s need for power. A professor can provide students with choice of projects, since the same content can be learned through many different projects. The professor could even encourage students to design their own projects, subject to the professor’s approval. Second, the professor could allow students to self-organize into teams and manage their own projects. Third, the professor, in some circumstances, can allow students to manage their own just-in-time scaffolding (i.e., instructional support) for learning what they need for success in the project. Fourth, the professor can give some student choice about how to demonstrate mastery of all that is to be learned. Finally, the professor can give student choice about reflecting on what was learned, how it was learned, and how the project was managed.
So, how do advances in hard technology figure in? They are important only insofar as they offer affordances that improve the use of these soft technologies. How can they help ensure that the intended learning has indeed occurred, that every student has mastered all the important content, not just one person on each team (for CBE)? How can they make projects more authentic, relevant to student interests, and motivational (for PBL)? How can they enhance just-in-time support for mastering skills and understandings needed to perform the projects (to accelerate and transfer learning)? How can they enhance teamwork and other forms of collaboration (for CL)? How can they enhance self-direction (for SDL)?
AI can be used to provide just-in-time personalized coaching and tutorials during a project and, through those tutorials, to enhance assessing mastery of content (CBE). Virtual reality and augmented reality are powerful tools for enhancing PBL. AI can coach teamwork and provide help for overcoming interpersonal problems (for CL). And AI can coach self-direction (for SDL). But please note that it is not the hard technology that makes the difference. It is the way the hard technology is used that’s important – the soft technology. The hard technology allows better soft technology to be used more effectively, efficiently, or engagingly (3E).
AI is certainly the most consequential development ever in hard technology. It promises to revolutionize education and all other aspects of our lives. It can help people to learn on their own instead of going to higher education. It could certify competencies better than universities currently do, which means that universities need to provide some additional benefit. AI can help them to provide that additional benefit. To do so, professors need to become masters at prompt engineering and be highly familiar with intelligent agents that can best meet students’ needs. But they need help to use hard and soft technologies effectively.
With the rise of AI, VR, and adaptive learning systems, how are these technologies reshaping instructional design and student engagement in higher education?
These technologies make good instructional design (ID) more challenging. Designers need a deep understanding of all these technologies, when each is most useful, and how best to design each for any given instructional situation they may undertake. This means that it is more important than ever for professors to collaborate with instructional designers who have such expertise. It is no longer reasonable for higher-education administrators to expect professors to design high-quality courses on their own time with only their own expertise.
Universities increasingly need to create a budget line for developing courses (or projects) that is distinct from the budget line for teaching the courses. That new budget line needs to support both a professor and a designer to work together on each course, and it needs to provide them with appropriate hard technologies. Without these resources, universities will gradually cease to attract students, because students have access to increasingly powerful AI learning tools at little to no cost – and those tools will increasingly provide micro credentials and other means to certify competencies, which employers increasingly want to see.
Student engagement is key to attracting more students. Colleges must utilize these advanced hard and soft technologies to dramatically increase student engagement, or students will cease to enroll.
What are some of the biggest challenges universities face when integrating new instructional technologies, and how can they overcome these hurdles?
Universities need a fundamental change in their business model. As I have already indicated, they must recognize the need to fund two separate operations – one for designing and developing courses and another for delivering the courses. Instructional designers work full-time in the former, but professors may work in both operations. However, a well-designed course that takes advantage of the affordances of advanced technologies can usually be taught by a considerably less expensive teacher. This has the potential to lower the cost per course through economies of scale. So, one might envision some top professors who only work with designers to create and periodically update courses, while other less expensive staff conduct those courses.
How do you see data analytics and AI-driven personalization influencing instructional systems in the coming years?
AI-driven personalization is a very powerful tool for increasing the effectiveness, efficiency, and appeal of the instruction. It can support the design or selection of projects, the tailoring of just-in-time tutorials, the use of collaborative learning, and the use of self-directed learning. It considers what the student already knows, what the student’s interests are, and any learning preferences the student may have.
I see small-scale (personalized) data being essential for AI-driven personalization. But I don’t see large-scale (aggregated) data analytics being useful for instruction and learning. Let me give an example. Complex skills and understandings vary considerably in how they are used from one situation to another. Performing a task successfully in one situation does not ensure ability to perform it successfully in different situations. So, mastery cannot not be assessed by a single trial (project). It can only be assessed in a variety of situations that are as different as possible from each other. The solution is to assess mastery through practice until perfect in the just-in-time tutorials. (“Perfect” is defined by the criteria for mastery, which might be something like “10 practice items in a row correct without assistance.”) AI can keep data on each student’s performance and also keep data on each individual competency the student has mastered. Such personalized data is much more useful than aggregated data.
With the growing demand for hybrid and fully online education models, how can institutions balance technology with maintaining strong student engagement and outcomes?
People have often talked about high-tech versus high-touch. But tech does not have to be used as a replacement for humanization. It can be used as an enhancement for it. Think of a lecture hall with minimal technology. How much individual, personal interaction does each student get? Not much. Now think of a small team of students in different locations working together on a project in an online simulation using multi-player game techniques, and each student has a personal AI tutor in the form of an avatar to provide coaching, just-in-time tutorials, and advice for self-directed learning customized for that student and provided in a friendly, supportive, personalized way. Then add in the team having periodic video chats with the professor. Which of these two scenarios provides for stronger student engagement? Technology is not a problem for student engagement, it is a solution. A university can increase student engagement while lowering costs through the two-part business model I mentioned earlier.
How can universities ensure that faculty members are equipped with the necessary skills to leverage instructional technologies effectively?
Universities cannot expect professors to have the hard- and soft-technology skills that are necessary. Hence, universities must adopt the two-part business model. They must move to team-based course development. As professors work with instructional designers, they will acquire the skills they need to carry out the courses they develop.
How can instructional systems technology help bridge the gap between academic learning and real-world workforce demands?
Project-based learning is key. The projects, to be authentic, must reflect real-world demands. Virtual reality can be helpful here. Furthermore, universities can partner with employers to offer internships or practicums in which the students spend some time at the employer’s location to conduct the project using augmented reality that incorporates the best in soft technology.
What trends or innovations do you believe will define the future of instructional technology in higher education over the next five to ten years?
A key innovation is the development of a technology platform to support this kind of education. The platform must serve four major functions to support student learning (Reigeluth et al., 2015). One is planning for student learning. Every student has a personal learning plan that includes career goals, long-term and short-term learning goals, next projects to undertake (where projects have replaced courses as the unit of instruction), deciding on teams, and creating a learning contract for the next learning period (e.g., semester).
A second function is instruction for student learning, which includes projects and scaffolding (coaching and tutoring). “It has a project database, a coaching database, and an instructional module [tutorial] database whose instructional modules are linked to specific points in projects when the instruction is needed just-in-time (JIT).” (Reigeluth et al., 2015, p. 473).
Third is assessment for and of student learning, which includes (a) assessing team performance outcomes in the project and (b) assessing individual student learning outcomes in the instructional modules (tutorials). The assessment function is integrated into the instructional function through practice until perfect.
The fourth function is recordkeeping for student learning, which provides detailed information about student learning. It has a standards inventory, which includes all the competencies that students must or could achieve. It has a personal competencies inventory, which includes all those competencies that each student has already achieved, along with useful learning analytics for each competency. And it has a personal characteristics inventory, which contains each student’s personal characteristics that are relevant to student learning.
These four major functions are all seamlessly integrated so that assessment information for a student is transferred automatically to the recordkeeping function, records information is transferred automatically to the planning function, planning information is passed to the instruction function, and assessment is fully integrated with the instruction function. There are also secondary functions, such as communications and collaboration tools (text, email, videoconferencing, etc.), system administration, and system improvement (Reigeluth et al., 2015). Development of a platform that fully integrates these functions would greatly facilitate university adoption of this powerful approach to higher education.
Perhaps the next most powerful innovation will be the development of specialized AI agents, some for student use and others for professor use.
What is one piece of advice you would give to aspiring instructional designers or educators looking to make an impact in this field?
Focus on the soft technologies – instructional theory in particular. Learn how to design high-quality CBE, including CB targets, CB learner progress, CB assessment, and CB records. Learn how to design high-quality PBL with just-in-time coaching and tutorials. Learn how to design high-quality CL and SDL. And consider which hard technologies provide the best affordances for implementing those soft technologies.
I would also like to offer one piece of advice for universities: create and fully fund a new budget line for designing and developing courses.
References
McClelland, D. C. (1987). Human motivation. Cambridge, England: Cambridge University Press.
Prensky, M. (2016). Education to better their world: Unleashing the power of 21st-century kids. New York, NY: Teachers College Press.
Reigeluth, C. M., Aslan, S., Chen, Z., Dutta, P., Huh, Y., Jung, E., . . . Watson, W. R. (2015). PIES: Technology functions for the learner-centered paradigm of education. Journal of Educational Computing Research, 53(3), 459-496.
Simon, H. A. (1996). The sciences of the artificial (3rd ed.). Cambridge, MA: MIT Press.