Personalizing education for special needs

One of the most compelling arguments for personalized learning is the importance of providing an appropriate education to students with special needs. Such students challenge the system, with unexpected strengths and weaknesses that are out of scale with the norm. Simply slowing down (or speeding up) the pace of instruction won’t serve their needs, particularly as they may be exceptional on more than one dimension and in more than one direction. For them, personalized learning that decouples different skills is imperative, a way to serve their needs and extend their abilities at the same time.

While special-education laws are limited in scope due to their approach of simply setting minimum requirements, they do provide critical safeguards for supporting students at the K-12 level. As they graduate to adulthood, these students are expected to assume more responsibility to advocate for and seek accommodations for their needs if they pursue advanced study at institutions of higher education (IHEs). Even so, these minimum accommodations only grant access, and sometimes may not do enough to constitute effective instruction that enables success. Simply fulfilling minimum requirements may allow IHEs to avoid litigation, but failing to adequately serve their students is a failure to invest their resources wisely.

Challenging though it may be to (re)design instructional materials with different constraints, IHEs may find that special-needs students can provide a valuable test case, instantiating extremes on the spectrum of students they serve. These adjustments will also help them support English language learners, disadvantaged-but-capable students with gaps in their backgrounds, returning students who remember some lessons but forgot others, and career changers in search of very specific skills to flesh out their resume—deserving students whom the traditional system fails, all too often. Not all students fit the same mold, nor should they. Adapting instruction around their needs develops their potential and gives them the opportunity to give back.

An economic argument for personalized learning

As described on The Economist’s “Schumpeter” blog, economic growth depends on innovation and more flexible job preparation:

Entrepreneurs repeatedly complain that they cannot hire the right people because universities are failing to keep pace with a fast-changing job market. Small firms lack the resources to provide training and are consequently making do with fewer people working longer hours.

The claim that “established firms are usually in the business of preserving the old world” bears interesting parallels to universities, which tend to replicate their own status quo. Instead of producing numerous graduates of the programs of yesteryear, universities need to update their training to develop knowledge and skills in current demand. Adapting to these demands through lengthy committee review and accreditation requirements is unlikely to be fast enough for the “agile-development” expectations of today’s startup culture. Educational institutions thus need new processes for tailoring programs of study to modern demands with both integrity and efficiency.

An even stronger motivation for allowing students to tailor their own course of study to their particular needs is that employers seek teams of people with a mix of complementary skills, not multiple copies of people with the same skill set. Instead of trying to differentiate candidates on some imagined basis of unidimensional merit, employers need to differentiate them along multiple dimensions of value to their particular needs. Employers are constantly talking about “fit”; educational institutions should facilitate discovery of a good fit by using personalized assessment, to provide richer information about how a candidate’s unique strengths and experiences may match a particular profile of needs.

Messy personalized learning

Phil Nichols describes his youthful adventures reappropriating the humble graphing calculator to program games:

For me, it began with “Mario” — a TI-BASIC game based loosely on its Nintendo-trademarked namesake. In the program, users guided an “M” around obstacles to collect asterisks (coins, presumably) across three levels. Though engaging, the game could be completed in a matter of minutes. I decided to remedy this by programming an extended version. I studied the game’s code, copying every line into a notebook then writing an explanation beside each command. I sought counsel from online tutorials, message boards, and chat rooms. I sketched new levels on graph paper, strategically placing asterisks in a way that would present a challenge to experienced players. Finally, after a grueling process of trial and error, I transformed my designs into code for three additional stages.

As he summarizes, his non-school-sanctioned explorations of an otherwise school-based tool led to sophisticated discoveries and creations:

[W]ith the aid of my calculator, I’d crafted narratives, drawn storyboards, visualized foreign and familiar environments and coded them into existence. I’d learned two programming languages and developed an online network of support from experienced programmers. I’d honed heuristics for research and discovered workarounds when I ran into obstacles. I’d found outlets to share my creations and used feedback from others to revise and refine my work. The TI-83 Plus had helped me cultivate many of the overt and discrete habits of mind necessary for autonomous, self-directed learning. And even more, it did this without resorting to grades, rewards, or other extrinsic motivators that schools often use to coerce student engagement.

While he positions calculator programming as a balance between the complementary educational goals of “convention” and “subversion,” this also echoes tradeoffs between routine expertise and adaptive expertise, between efficiency and creativity, or between convergent and divergent thinking. It remains an ongoing risk in overly restrictive learning environments. Standards that dictate the time and sequence of each stage of students’ progression fail to allow for the different paths which personalization accommodates. Yet even adaptive learning systems that seek to anticipate every next step a student might take must be careful not to add so many constraints that crowd out productive paths the student might otherwise have pursued. Personalized learning needs to leave room for error and open-ended discovery, because some things just aren’t known yet.

Is adaptivity a qualitative or quantitative problem?

One common criticism of adaptive learning is that by tailoring instruction so closely to students’ needs, it doesn’t challenge them enough. As embodied by James Paul Gee’s critique:

People who never confront challenge and frustration, who never acquire new styles of learning, and who never face failure squarely may in the end become impoverished humans. They may become forever stuck with who they are now, never growing and transforming, because they never face new experiences that have not been customized to their current needs and desires.

While I agree with the dangers of what he describes, I question the causal attribution.

First, adaptive learning systems that indulge in too much customization may instead be guilty of relying on a too-narrow prescription for the student’s “zone of proximal development (ZPD)”. Individualized learning does not require giving only incremental steps; it can (and should) include more ambitious steps to occasionally challenge students, perhaps just beyond their conventional ZPD (or at the limits of their ZPD when defined by “lots of help”). Students need to struggle—manageably—as part of their learning. Adapting to students’ needs can include optimizing the nature and amount of that struggle based on past experiences and future expectations.

Second, this can also be overcome by building a certain amount of variability into the system, for the sake of both the students and the system. Occasionally presenting students with problems that may or may not lie within their ZPD can help them learn “what to do when you don’t know what to do” (in the words of a dear colleague of mine, Joe Wise). Whether framed as desirable difficulties, germane cognitive load, preparation for future learning, or the development of adaptive expertise rather than just routine expertise, unexpected challenges can offer invaluable learning opportunities. Further, adaptive learning systems need to reach beyond what is already known in order to improve themselves. A truly intelligent system should be discovering new knowledge about its particular learners and even about learning in general. The possible paths a student might take are infinite, and the system’s designers don’t know what’s best—only what tends to be better compared to other paths that have already been examined. That is, adaptive learning must itself be an adaptive learner.

Both of these issues point to a quantitative problem due to adapting too narrowly or too often. The deeper question is whether adaptivity is a fundamental, qualitative problem: Does having any adaptivity at all invite complacency among students accustomed to having their learning experiences at least partly tailored to their needs? Given the well-established importance of scaffolding instruction according to students’ needs, I would argue that adaptive learning is a valuable tool not simply for accelerating but also for enriching instruction.

Personalized instruction: The other half of personalized learning

As I have explained in a previous post on personalized learning, an important dimension along which personalized learning goes beyond merely adaptive learning is to personalize the experience on the instructional side, not just the learner side. Amidst all the excitement about adaptive learning, teachers remain an often-forgotten yet crucial part of the equation. Well-designed personalization takes advantage of the human intelligence embedded in expert instructors, including opportunities for them to exercise their professional judgment in deciding which activities will work best for their students given their particular contexts and constraints.

This EdSurge report mentions Rocketship’s upcoming changes, as “New model attempts to bring teachers closer to students’ online learning experience” by returning some classroom control back to the teacher:

Rocketship’s new model will shift focus from running purely adaptive programs, to using programs that give teachers greater control over content that gets assigned.

What this highlights is the need for the design of personalized learning programs to identify when to allocate decisions to teachers (possibly with recommendations among which to choose) and when to adapt the students’ learning experience immediately, without need for waiting for additional human input. While this depends in part on the professional knowledge of the instructors implementing the system, some decisions may be straightforward or simple enough to automate. Decisions best left to expert human intervention are likely to be more complex, to depend on more contingencies, to require interpersonal contact, or to have more uncertainty in their effectiveness. Where that balance lies is subject to continual readjustment, but since there are always unknowns and since social interaction is fundamental to the human experience, there will always remain a need for personalization.

Individualized instruction as a subset of personalized learning

David Warlick muses on the distinction between individualized instruction and personalized learning, noting that the former is decreasing while the latter is increasing in popularity, according to Google Trends. As he summarizes:

Personalized learning, in essence, is a life-long practice, as it is for you and me, as we live and learn independent of teachers, textbooks, and learning standards.  Individualized instruction is more contained.

Part of me is tempted to wonder what a word-cloud analysis would reveal as the key differences between how the two phrases get used. Absent such an analysis, I would focus on the two dimensions highlighted by the words themselves: personalized vs. individualized, and learning vs. instruction. The latter distinction is quite straightforward, with instruction emphasizing what others do to the student and learning emphasizing what the student does to learn.

The former distinction highlights the learner as a person, not merely an individual. As articulated in my earlier post explaining personalized learning, the core of personalization is the role of the learner as an intelligent and social person making choices for herself and interacting with others in order to learn. I would thus add to Warlick’s matrix, under “student’s role,” an explicit expectation for the student to direct her own learning and collaborate with and challenge fellow learners in making sense of the world. Warlick already emphasizes the role of the teacher’s expertise in deciding how to craft the learning environment; here, under “teacher’s role,” I would also add the responsibility to create and guide learning experiences within social settings. This highlights the importance of how students learn from communicating and collaborating with each other in an environment that truly recognizes them as intelligent, interdependent people.

Repositioning personalized and adaptive learning

Amidst all the excitement about personalized and adaptive learning is a lot of confusion about what the terms mean. In this post, I will examine some of the commonly-used definitions around personalized learning and explore the relationships between the ideas to clarify how I intend to use the terms here. My next post will explore the differences between personalized and adaptive assessment.

Education Growth Advisors describes personalization as “moving beyond a one-size-fits-all approach to instruction,” in which students might receive additional work as challenge or remediation, depending on their current performance. In their view, adaptivity is a more specialized form of personalization which “takes a more sophisticated, data-driven, and, in some cases, non-linear approach to remediation.” Some examples might use past performance to adjust the focus, timing, or path of content delivered to a learner.

In contrast, EdSurge places personalized learning at the top of its hierarchy with this definition: “When instruction is truly geared to the student: learning objectives, content, presentation methods and pace may all vary depending on the learner.” By their definitions, adaptive learning varies the content, individualized learning is self-paced, and differentiated learning varies the content and presentation methods, all three being lesser versions of personalization.

While both groups classify adaptive learning as a subcategory of personalized learning, they differ in where they place that subcategory along the personalized-learning spectrum, with Education Growth Advisors deeming it more sophisticated and EdSurge considering it less sophisticated compared to other forms of personalized learning. My definition will likewise situate adaptive learning as being narrower than personalized learning, but without making any claims as to which is superior in either sophistication or effectiveness. In being broader than adaptive learning, personalized learning can be both better and worse. The difference between them depends on what is being tailored to the individual, not how well it is executed.

EdSurge restricts adaptivity to selection of content; my definition will also encompass adaptivity of learning objectives, presentation methods, focus, timing, and path through content. (This is akin to Education Growth Advisors’ definition of adaptivity and closer to EdSurge’s definition of personalization.) The reasoning is that the learning environment may be tailored along all of these dimensions according to individual needs.

In my definition, personalized learning includes other features beyond adaptive learning to make the learning experience more personal—quite simply, involving the person. These may include individual preferences, chosen by the user; instructor actions, informed by a faculty dashboard offering analytics and recommendations but with decisions left up to the teacher; and social interaction, facilitated by collaborative tools and engineered to encourage productive learning, but again leaving room for human input. While adaptive learning relies on data-driven decisions from machine intelligence to tailor the experience to the learner, personalized learning also adds an explicit role for judgments made by human intelligence.

A fully personalized learning ecosystem may allocate some decisions to be determined by machine intelligence and delivered through its automated adaptive learning system, designating others to be handled by human intelligence and provided by various actors in the ecosystem. The adaptive component optimizes the environment, to the extent that it can control it, along parameters which its data and algorithms predict will be beneficial. The personalized components make allowances where human intervention is deemed to be more valuable (such as user choice, yet-to-be-modeled human expertise, and social interaction). Still, the unpredictability of human behavior also means that those components cannot claim to be truly adaptive, only personalized.

Thus, personalized learning has the potential to be much more sophisticated and powerful than adaptive learning, if realized effectively. Early iterations of personalization may not take full advantage of these possibilities, and in some cases, human decisionmaking may end up being maladaptive. Our goal is to help clarify those dimensions and parameters to enable better design and use of personalized learning.

Why personalized learning and assessment?

Much of the recent buzz in educational technology and higher education has focused on issues of access, whether through online classes, open educational resources, or both (e.g., massive open online courses, or MOOCs). Yet access is only the beginning; other questions remain about outcomes (what to assess and how) and process (how to provide instruction that enables effective learning). Some anticipate that innovations in personalized learning and assessment will revolutionize both, while others question their effectiveness given broader constraints. The goal of this blog is to explore both the potential promises and pitfalls of personalized and adaptive learning and assessment, to better understand not just what they can do, but what they should do.