Program Innovation

Butchers, Bakers, Instructional Designers, and Learning Engineers

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Should instructional design services and teams prepare for a reboot? Is it time to recast the ubiquitous instructional designer as a "learning engineer," armed with new training in the cognitive and learning sciences and equipped with data-driven development and delivery tools?
For some, this process is already in motion. For others, next steps are less clear. In an April 2016 report, “Online Education: A Catalyst for Higher Education Reforms,” MIT recommended broader adoption of “learning engineering”, and investment in professionals steeped in design thinking, data analytics, and cognitive science. MIT takes the long view on how neuroscience, cognition, and learning science can improve higher education. While the rhetoric of “learning engineering” is compelling, we’re curious whether this approach is simply old wine in new a bottle, or if it really can close persistent gaps between scale and quality in higher education, online or otherwise.

Learning Engineering: Mirage or Reality?

We recently had an opportunity to participate in a research symposium exploring the prospects for learning engineering, co-sponsored by Harvard University’s Graduate School of Education (HGSE) and the Division of Continuing Education (DCE). The symposium showcased new analyses of the impact of learning engineering on improving online learning at scale. Participants came from institutions and companies including Stanford, Duke, Georgia Tech, Kaplan University, 2U and Google. Several takeaways stood out for us:
  • Beyond filming the play. Several participants, including HGSE’s Chris Dede, likened “traditional” online courses to Thomas Edison and other pioneers of cinema filming Shakespeare. A new technology captured a classic artwork and enabled its distribution to a wider audience, but failed to substantively alter or improve the actual experience. For Dede and others, learning engineering implies an evidence-based, iterative use of learning data to continuously improve how students interact with online content. It would not simply “film” a class, but improve faculty-student interaction and mastery of content. An “engineered” course experience would leverage learning analytic data to iteratively improve how students explore new content and are assessed on their mastery of that content. A laudable goal, but can this scale?
  • Learning as a dynamic system. For Stanford faculty Candace Thille, a longtime proponent of learning engineering, learning remains a fundamentally complex process, whether it occurs online or not. Instructional platforms attempt to blur this complexity, in the name of efficiency and consistency. A deep understanding of learning requires collaboration and dialogue between research and practice. Rather than treat online learning as an expedient content-delivery system, learning engineering suggests a continual redesign of the student experience based on data. Thille’s work at the Open Learning Initiative, first at Carnegie Mellon and more recently at Stanford have suggested how research and practice can be cyclical, rather than sequential. Is this iterative approach practical for most schools with online learning, or better suited for a research-oriented subset?
  • Can learning be “Engineered?” The engineering metaphor has dominated recent efforts to systematize, or “program,” online learning, but it comes with baggage. Can learning be reduced to “engineer-able” elements? Can the variables of learning, and in turn of teaching, be manipulated by principles derived from engineering? Other images also surfaced. Some higher education online program management (OPM) companies opt for a “learning architect” metaphor, implying a process that utilizes a “blueprint” to bring a vision of instruction to life. Others reference cooking, carpentry, or interior design as metaphors for creating online content. Can “design” co-exist and compliment “engineering,” or will this further muddy the waters of online course production?
  • Can artisans scale into engineers? The soundtrack at this symposium was that learning engineering is indeed scalable. Conventional online courses were seen to resemble artisan handiwork, in which individual schools, faculty, and instructional designers “craft” unique versions of their courses. In contrast, engineering may yield greater efficiencies and quality. Bror Saxberg, formerly of Kaplan University but soon to join the Chan-Zuckerberg Initiative, argued that Kaplan’s centralized structure and large-scale assessments accelerated the adoption of learning engineering. Kaplan implemented AB testing to inform course enhancements. Can learning engineering take root at less centrally managed institutions?

Prospects for the Future of Learning Engineering

There is little doubt that learning engineering posits a new chapter for online learning. As Eduventures’ recent CHLOE report illustrates, although instructional design is widely available throughout higher ed, it is often used at the discretion of individual faculty. Efforts to improve the quality of online experiences are overshadowed by enrollment and revenue growth. Decisive Factors in Resource Allocation by Institution Size Source: The Changing Landscape of Online Education (CHLOE) report 2017- Eduventures and Quality Matters Instructional design is rarely seen by institutions as a means to re-think cost structures and lower overall cost. A recent WCET survey found that the majority of schools think online learning costs more to develop and deliver compared to conventional courses. Further, instructional design was cited as one of the largest additional costs. Technologist Kai-Fu Lee describes a virtuous circle of product development: “The more data you have, the better your product; the better your product, the more data you can collect; the more data you can collect, the more talent you can attract; the more talent you can attract, the better your product.” In higher education, however, the challenge is that decisions regarding product, data, talent, and quality are often decentralized and highly contested. To achieve scale, learning engineering will require significant cultural changes. Will these changes crowd out smaller, more artisan-like efforts? Will we see the emergence of a new professional tier of learning engineers alongside faculty and instructional designers? Finally, and most importantly, will these efforts result in enhanced online learning experiences for students? These tensions will inform how we track the progress of learning engineering. Our second annual CHLOE report will examine more deeply how online programs make use of instructional design services, and what might be missing from this repertoire. For those interested in participating in the new CHLOE survey, contact our Research Analyst Mughees Khan.  

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