“Students who go to college and drop out with debt and no degree are worse off than if they would have never attended.” That is a statement shared in a recent panel of presidents at the NEASC/NECHE Annual Meeting. Sitting in the back row of a large room, I saw countless nods and nonverbal affirmations to this statement. Most people quickly recognize the wisdom of such a statement. I’d like to offer an alterantive perspective.
There is more than one potential solution.
When we start to talk about the problem of students dropping out of college, we rarely take time to explore the possibility that there may indeed be more than one solution. To often, we assume that the solution is for people not to drop out. Only, doesn’t this depend upon why people are dropping out of college in the first place? Could it be that the answer draws from a combination of factors? Reductionist approaches to understanding the “problem” often lead to reductionist solutions. Finding ways to help people persist and graduate certainly seems like a reasonable and valuable effort, and there is plenty of reserach to indicate that going to college and graduating creates more opportunities than going to college and not graduating. Yet, people are complex, and so are their backgrounds, goals, aspirations, dispositions, and life situations.
Is the only value of a college education the degree conferred at the end?
Don’t people gain new knowledge and experiences prior to getting the piece of paper at the end? If so, then there is clearly something more to college than getting the diploma. So, perhaps part of a portfolio of solutions relates to reconsidering the relationship between higher education learning communities and the credentials issued at the end. Maybe there are better ways to recognize learning and valued experiences than a legacy transcript and piece of paper that you mount on the wall. Maybe there are ways to celebrate and document learning along the way such that a person’s resume (or public and online identity) is continually updated. Perhaps this will make traits and knowledge discoverable on the basis of a person’s ongoing development of new experiences, knowledge, and skills. This could be a solution for someone who, for any number of situations in life, stops out or drops out of college. Even for people who graduate, this invites us to think about a means of making lifelong learning an integrated and discoverable part of a person’s public persona.
What if we thought of college education like fitness. Maybe you start with the goal of being able to run a marathon. If you run five days a week but never achieve a marathon fitness level, the running still benefits you in ways that we can prove with empirical evidence. Or, maybe you reach marathon fitness level but some life situation gets in the way of you showing up on race day. You still have the capacity to run the race. You could even do it on your own over the weekend. The people who run 26.2 miles in their neighborhood for fun and the people who do it in a formal race may well have the same fitness level. Why not think of higher education in a similar way? Why does it have to be an all-or-nothing situation? Fortunately, we are in an era where any number of potential technologies and innovations can help us move away from such a limiting mindset about education.
What does it take to recreate college so that even dropping out with debt moves you forward and is worth the money?
Given what I’ve already written, I’d like to offer those of us in higher education a challenge. Continue on with efforts to increase completion rates. That makes sense. Yet, how about embracing some creative solution for re-imaginging college so that it is clear and evident to the learner, society, and potential employers that a rich and robust higher education experience has transformative value with or without the piece of paper at the end?
Diploma-ism is not good for the reputation of higher education.
I continue to be convinced that making it all about the piece of paper also diminishes the perceived value of modern highe education. The diploma has always been limited in what it says bout a person, or about the breadth of learning and transformation that takes place amid the college education journey. By finding ways to uplift, recognize, and celebrate learning along the way (and making it a discoverable part of a person’s public and online identity), we are likely to find that we are also enhancing the greater public’s appreciation for the value of college.
This is achievable.
What I’m proposing here is achievable. There are people already piloting efforts. Right now most of those efforts are looking at the issue on an institution level. To make the most progress, we need to pursue system-level approaches. Some of those are underway as well. Yet, this can’t just be about small credentials. For the most traction, I contend that the focus needs to be on discoverability, public and online identity management, and new methods of matchmaking on the basis on people’s learning and experience profiles.
What would you say if I suggested that the letter grade system has the potential to be a dangerous form of structural bullying?
For those of us who experienced bullying as students, we know how much it can work on a person, drawing someone into completely different thoughts and behaviors. In 5th grade, I was riding the bus to school when a boy grabbed my neck from behind. Next I felt a cold, hard piece of metal below my chin as he whispered clinched-tooth threats into my ear. I remember the sour smell of his breath, the stench of dirty laundry, and how the cold of the knife filled my entire body. He got off the bus, and a few friends helped me up, trembling, cold, but not feeling much of anything else. The bathroom where I spent the morning throwing up felt like the walk in freezer behind the meat counter at my uncle’s family grocery store. So did the nurse’s office…and each classroom. All lessons that day repeated. Whether it was science or spelling, I heard incoherent background noise as I relived the morning and dreaded the end of the day when I would sit on the same bus, in an assigned seat right in front of the angry boy with a knife, the boy who I’ve come to believe experienced his own share of bullying somewhere along the way.
People define bullying differently, but most definitions involve a person using some form of superioriority, power, or position to influence and intimidate another person. To this day, I don’t know what the boy wanted, except that he wanted me to be scared, and possibly dead; and that he wanted some control in his life.
I share this personal story because I want to acknowledge the depth of meaning that I attach to a word like bullying. Perhaps that is why it was so jarring in a recent conversation when I spoke with someone who explained what he didn’t like about teaching at a particular well-known and well-respected University. From this person’s perspective, the school’s restrictions got in the way of the learning. Amid the conversation, he listed several limitations, everthing from the length of classes to the required grading system. As he talked through this list, one statement grabbed my attention. He said, “And the students were bullied by grades.” They focused more on what they had to do to earn the grade than to develop the skill or hone their craft. They heard less what the teacher was trying to teach them and more what the teacher required of them.
Bullying can be subtle, or it can be like what I experienced as a middle schooler. I’m sensitive to the danger of using the word to describe something like letter grades. Yet, the more that I thought about this concept of grades as a kind of bullying, the more it helped me to recongize that bullying in education, at least in the broadest sense, can be due to the structures and systems as much as from the people. These systems, like letter grades, can hijack our minds and senses so much that they completely transform the environment, so much so that the good stuff sounds like mumbles and the the kindled flame of learning turns cold.
Is it extreme to call grading a source of bullying in our school systems? Perhaps it is, but for me, in the moment of that conversation, the comparison offered me a renewed sense of why I believe that the pursuit of a more hopeful and humane education system calls for a measure of radicalism, looking beyond the surface to the fundamental elements of our learning communities. How we measure learning matters. How we give feedback makes a difference. If a bullying environment is one that uses power to control, influence and intimidate another; then then its opposite is a community where people have personal control, voice, choice, and experience self-empowerment. Which do we want for our schools and learning communities today, and how does our approach to even the basics like assessment and feedback reflect our answer?
For well over a decade, I’ve visited, studied, interviewed, and learned from countless innovative and experimental K-12 schools throughout the United States. These range from Montessori to project-based, experimental to STEM academies, game-based learning schools to those that specialize in personalized learning plans for each student. Then there are the many other schools that blend these and other ideas to create inspiring and engaging learning communities. They might be local public schools, independent schools, public charter schools, magnet schools, or even organizations designed to support students who are officially registered as homeschoolers. These schools are quite different in their methods and their underpinning philosophies, but when I speak to leaders in these schools, there is a consistent and common problem, finding teachers/guides/mentors/coaches who resonate with the school mission, embrace the philosophy, an are well-equipped to plan a needed and active role in these distinct schools.
Opinions range on this matter, but some founders and leaders of these schoools are so frustrated and consistently disappointed when they try to hire people who came out of traditional teacher education prorgrams that they consider a BA/BS in Education to actually be a mark against a person in the applicant process. As one school founder explained to me, “By the time they get through one of those education programs, they know all about standards and teacher-driven communities, but they struggle to even imagine how a learner-driven community could possibly work in the real world.” Even those who want to be in a different type of school sometimes get hired only to find themselves completely lost and frustrated.
I’ve had this conversation with colleagues before. Some think that getting a teaching degree equips you to be agile and teach in any type of school. That is not the message that I hear from leaders in these innovative school models. They often find people who have set recipes and methods for “doing education.” Or, more than that, they come with a fixed idea of what school should be, how it should work, the role of the teacher, and the role of the student. When they do experiment with something new, like project-based learning, some persist long enough to develop the competence and confidence to be a strong guide for students in such an environment. Others give up after one or two attempts, blaming the method for the disappointing results and experience.
This has led to what I call the great experimental education teacher shortage. Or, in some cases, it is the great experimental education teacher turnover problem. What can we do about it?
I’m only a week into my new role as President of Goddard College, a college launched in the 1930s with a bold and compelling vision for learner-driven community. Goddard has gone through different experimental models over the years, but today, it is a leading voice and model for low residency degree programs. Students come to our rural Vermont campus (or one of our locations in Washington) twice a year for intensive community and learning. Students work with guides who help them each design their own personal learning plan for the semester. Then they head back to their home and communities, learning while living the rest of their lives. They touch base with their guides often and get rich narrative feedback on their packet of self-designed work (we don’t do letter grades or traditional tests and assessments at Goddard). What I’ve come to learn is that Goddard offers both undergraduate and graduate degrees in education, and the faculty members are well-versed in learner-driven pedagogy. Not only do the students at Goddard experience it firsthand. It is a place where they can grow in their competence and confidence to launch a new school or to lead in any number of distinct school models.
Of course, I’m biased, but as I explored the possibility of coming to Goddard as the next president, I was excited to contribute to a college that is exceedingly well-equipped to help nurture the next generation of teachers in experimental and alternative schools. Similarly, it delighted me to know that our programs help current or future teachers bring learner-driven practices into more traditional schools. This is an important and needed niche in the education space, and I’m excited to spread the word about it.
I didn’t start this article with the intent of promoting Goddard’s programs, but I’m certainly proud to do so. The experimental and alternative school teacher shortage or challenge is real, and Goddard’s learner-driven programs offer a promising solution.
This is the third installment of a seven-part exploration of data, analytics, and AI in higher education. In the first article, I set the groundwork for the series. I introduced the framework from which I intended to write, identifying what I consider to be six distinct aspects of data and AI’s influence in higher education. It simply follows the course of a learner’s relationship with a learning community. As such, the prior article looked at the pre-enrollment relationship, considering how data and AI are changing and will continue to change the way in which learners find, select, and build a relationship with a learning community (whether it be a formal college/University, informal learning, open learning resources or communities, people and experiences that foster learner, or a combination of these). Now, in this third article, I focus my attention upon how data, analytics, and AI will continue to influence the actual learning experience.
I have not intention of providing an exhaustive exploration of the topic. Of course, that would require countless volumes. Instead, what I offer at this point are a couple main observation about the challenges and opportunities that come when we venture into questions about how data, analytics, and/or AI might influence the learning experience.
The Adaptive Learning Revolution Will Really Be a Revolution
For those who still largely conceptualize the bones of the college learning experience as a collection of classes that culminate in the issuing of a credential, the world of data and analytics gives us even more opportunity to reconsider that construct.
“Learning analytics” is a phrase that we often use in reference to all the data that we can collect and analyze to determine the extent to which students are learning, or the extent to which they exhibit behaviors that someone deems important for student success. Related to this, “adaptive learning” is a system that uses these data points to adjust the learning or learner experience with the hope of some improved outcome. On the K-12 level, we see rudimentary examples with software like Aleks Math or Dreambox. The software includes a hierarchy of levels of mastery. It introduces learners to content, challenges, and lessons; and the learner interacts with this content. All along, the learner’s performance is being assessed, and the software is adjusting accordingly. Each person is taken on a slightly (or sometimes significantly) different learning journey based upon prior knowledge and performance while using the software.
Most of the examples that are prominent today are early experiments with adaptive learning, but the truly interesting stuff is still on its way. While some critique such software as too mechanistic or representative of a simple and behaviorist approach to learning and mastery, that will change. It is already beginning to change. I commend the ingenuity and creativity of early developers of adaptive learning software for math and language acquisition, for example, but most of the work at this point lacks depth. It has yet to dip into the incredible pool of research and insight that now exists about the nature of learning. The companies and learning communities that demonstrate the forethought and wisdom to soak their adaptive learning software into this larger body of knowledge may well end up being some of the most powerful influencers of learning in the 21st and 22nd century.
You might recall the buzz when Thomas Frey made the prediction that the largest Internet company of 2030 will be an online school. I’ll build on his idea to say that it will be a organization that taps into the combined power of AI and the best of learning science research. It will boast of performance increases and learning outcomes that make some dominant teaching practices look like the tools of prehistoric cavemen.
In this new and emerging learning context, we are not talking about a collection of classes that lead to a credential. We are looking at constantly monitored and documented ebbs and flows in learning. We are considering measurements of mastery with regard to discrete skills and knowledge, the nuanced changes in learner motivation, mindset, traits, and disposition as well. As the research develops, we will also see far more complex calculations that look at patterns of thinking and behavior that are predictive of success or failure in various life, work, and real world contexts. Not only that, these new contexts will make massive strides in providing greater insight about knowledge transfer challenges, the extent to which someone’s learning in a classroom or on a computer adequately transfers to various real-world or novel contexts.
Similarly, measurement of learning will not just be about a moment in time. These emerging technologies will make it possible to monitor what is retained, lost, refined, or re-purposed over years or decades. As such, the monitoring of learning for someone like a surgeon will not stop when the MD is earned (if the construct of an MD persists into the 22nd century). Monitoring of learning and performance will continue and be tracked throughout one’s career.
There is much caution when it comes to such measurements. They are often crude and disregard important nuances and factors. Measurements become values-laden clubs that beat people and communities into submission. Over time, they become so accepted and commonplace that many think you are a social deviant to question their propriety.
Education and learning is rarely just about outcomes. As I so often write, education is always values-laden, which is the source of most great debates in the past and present when it comes to the what, why, and how of education in different contexts. Education isn’t just about the future as well. This AI future that I am describing is most likely inevitable, but we are still wise to not forget the significance of the hidden curriculum. We are wise to consider how these new technology-driven learning contexts shape how we think and our sense of humanity. We are wise to discuss and consider the values-laden nature of each new iteration. The most progressive transhumanists among us might dismiss these warnings as nostalgic hogwash. I do not. I hope that many who read this do not consider it hogwash, especially those who will help bring about the AI in education revolution.
In such a future, holding on to sentimentality will not be a successful resistance. We must think deeply about what it means to use, live, and teach in a world of big data and artificial intelligence. What do teachers do best? Or, what do we want to be provided by other humans instead of from technology, even if empirical data might suggest that the technology achieves the same or a better result?
Clayton Christiansen’s theory of disruptive innovation suggests that innovations often take root by serving a population largely overlooked, under-served, or even disregarded by others; and they do so with what might initially be an inferior product. Over time, the product improves and gradually captures a larger market until it has the potential to disrupt or displace what was happening before. This is already occurrring when it comes to data-enhanced instruction and learning analytics software. These are supplements to traditional teachers. They are also being used as replacements. As the outcomes of these substitutes or replacements increase, a growing number of people will choose to skip the traditional teacher and classroom. We can lament the loss. We can grieve for what this means about the deeply human and personal aspect of education. It will still happen.
Research from the psychology of attention is being purposed to track facial expressions, heart rate, and more. These too will eventually be part of the data set used in adaptive learning software. Smart watches and health trackers are getting more sophisticated every year. All of these lead to data points that will allow for increasingly nuanced and sophisticated learning analytic data sets. Are there problems and concerns with all of this? Absolutely. Yet this is also an indication of what is possible with AI and learning analytics. These systems will eventually capture psychological nuances and social cues that a single teacher cannot possibly notice and take into account when teaching a group of 50 in a traditional classroom.
This doesn’t mean that people will become obsolete in learning of the future. However, it does challenge us to reconsider roles. In a recent interview with Stephen Downes, he noted that many of these musings about AI are much further in the future than some suspect, and that the most promising present possibilities reside with open networks of learning, new technology-enhanced connections and communities among people. It is amid these connections and among these communities that we can see some of the greatest strides in the more immediate future. We can see support for his position in the rapid adoption of social media, open learning communities, and other such networking over the past couple decades. Perhaps it is within these connections and communities that we can make greater sense of the roles best played by people and the extent to which AI and adaptive learning might be integrated. A blend of these two is what we already see emerging, and I see no evidence of that slowing. What happens in the long-term is yet to be seen.
The Future of Learning
I have no doubt that AI and adaptive learning will bring about some of the more significant changes to how people learn over the upcoming decades. Tbe algorithms will develop and evolve to draw upon increasingly complex data sets. Incredibly consequential errors will be made along the way. Tension will grow and persist regarding the role of AI alongside the role of human interaction in learning. This is the future of learning in the rest of the 21st century.