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.
Recently, inspired by a group of colleagues to submitted and entry, I decided to offer a submission to the Re-imagining the Higher Education Ecosystem challenge put out by the US Department of Education Office of Educational Technology. Reading through the call for participation, I was excited by how this challenge set right at the interaction of so much of my work, writing, and research over the past number of years. There was workforce development, learn agency, learner-centered educational ideals, a sympathy for alternative credentials and alternative learning pathways, as well as a vision for increasing access and opportunity. So, browsing some of the other proposals and drawing upon some of my most recent musings, I put together the following draft of a proposal for what I call Challenge-Based Hiring. It isn’t revolutionary. It isn’t even brand new. Yet, the more that I began to pull together disparate ideas into this promising experiment, the more excited I got about the possibilities. As such, I’ve included a rough draft of the proposal below, with a few sections (like the timeline) removed. I welcome your thoughts. By the way, the challenge allows for others to join teams, so if this captures your interest and you would like to join in potentially making this idea a reality, consider becoming a partner.
The Challenge Based Hiring and Learning Platform
Elevator Pitch (it can be the same than the “Summary” section)
What if we could turn job postings into authentic learning challenges that increase access and opportunity, give rich and authentic learning experiences that lead to current or future employment, provide opportunity for anyone to show their skills (or develop them along the way) and readiness, and improve the match between employer and prospective employee? The Challenge Based Hiring Platform is designed to do just that.
Describe the Education Ecosystem of 2030
The education ecosystem of 2030 will be more open, blurring the lines of learning across context, but also blurring the lines between activities that are currently separated from one another. One such line is the process that companies use to search for and hire new talent, and the world of learning and preparation for such jobs. As such, the Challenge Based Hiring Platform is an experiment in blending these two worlds in a way that has promise to benefit both employers and individuals seeking work or just ongoing, authentic learning experiences that can build competence, confidence, and create new opportunities.
Challenge Based Hiring is intended to be an alternative to current job boards, and the standard process of job postings, applications/resumes, interviews, and then companies struggling to find the right match. Rather than focusing upon past credentials, diplomas, or degrees; challenge-based hiring is focused upon whether people can demonstrate, in the present, that they have the knowledge, skill, and dispositions necessary to do a job well. Or, if someone does not yet have the skill, participation in one or more challenges allows the person to develop new skills and document them in a sharable and discover-able online profile.
First, this platform will help employers take a job description for a vacancy and turn it into one or more authentic, tasks-based challenges/competitions. Employers agree to offer some sort of reward or prize for finalists, a small but reasonable cash prize that recognizes the time and effort devotes by one or more people, or perhaps a recognition of accomplishment or endorsement of work. Challenges can be designed where there is one winner, a select number of winners, or a large number “winners”; and awards are created and distributed accordingly. Those who take on and complete challenges also get to create profile that includes past experiences, education, credentials from across contexts, etc. However, challenges are designed so that employers/challenge creators are not able to view participant profiles until after they judge/select/identify winners. After winners are assigned (again, this can be one or more), profile data is released to the challenge organizer / employer, and an introduction is made for the possible next steps of employment.
Each challenge is designed and aligned with a core set of skills that the employer deems essential or non-negotiable to complete a current and specific job/vacancy at the company. As such, those who participate in challenges, regardless of whether they are hired, are engaging in challenge-based learning experiences that deepen their knowledge and skill, and further equip them for skills that are indeed vetted and valued in an actual workplace environment. Participants are encouraged to take on challenges that extend beyond their current abilities.
In the future, higher education partners might contribute challenges that align with common, non-negotiable skills for jobs posted in past challenges, allowing for the addition of a school-based credentials or recognition. However, this is not essential to the model or the early pilot.
As part of a challenge, participants are provided with guidelines, suggested resources to guide their work on the challenge, and sometimes learning resources, courses, and training modules provided by third parties (curated to align with the challenge).
Upon completion of a challenge, feedback is provided to finalists (and others when deemed possible and reasonable). In addition, all participants are provided with further resources, links to online courses, and other learning opportunities that can deepen their expertise in the area of the challenge. Use of these resources can be documented and added to a person’s ever-growing profile.
The participant profile will be designed in such way that participants can include information about their performance and learning from past challenges and associated courses and training. As such, those on the platform are building an increasingly substantive portfolio of lifelong learning. This increases their ability to communicate their knowledge and skills to employers. It also gives richer information that employers can use to connect with them. In future iterations of the platform, there is the possibility of using simple algorithms to recommend certain challenges to people based upon their background, experiences, and interests described in their profile.
A secondary but significant benefit of this platform is that is teaches employers to think about hiring in a new way, paying greater attention to competencies and proven skills, and less to formal but indirect signals of competence like degrees and other credentials. As such, we are focusing our early efforts on jobs with varying level of skills, but those that do not have a legal requirement for specific credentials (like some in healthcare or other professions that require licenses). With that said, future iterations could entail participation in a series of challenges that lead toward some licences and credentials in high demand fields, or at least provide early progress toward that. Successful completion of challenges could also eventually be considered as alternative evidence of learning, even used as evidence for prior learning credit that is offered by higher education institutions as part of a degree program. This allows the platform to serve and function within the current and dominant formal education ecosystem while also preparing for a more open and cross-organization ecosystem likely to develop as we look to education in 2030.
Describe a pilot or “scalable beta” that will move us toward this vision of the future.
For the initial pilot, we will recruit 3-5 companies in a highly populated area. Each will agree to work with us to design at least one challenge that is tied to the job description of a current or future vacancy. We will work with these companies to carefully design high interest, high value challenges and curated resources that could serve as learning tools for these challenges. Then we will release the challenges to the public, targeting people living within a reasonable commute of these places of employment, working with community organizations, education institutions, and government agencies to ensure that we reach a wide array of potential participants.
We anticipate that this approach will reach and motivate certain individuals and not others, and the pilot will provide us with greater insight on how to reach and engage an increasingly diverse population in the community. While future iterations can go national or beyond, we want to start local so that we can refine the process, gain actionable insight, and increase our chance of participants in the pilot obtaining valued job skills and some getting gainful employment. As such, we anticipate pursuing a series of challenges and using each one to deepen our understanding of what is working, what is not, and how to improve.
Who are the “users” or beneficiaries and how will their experience in the future of learning and working be impacted by your pilot?
Users are both employers, particularly employers who have difficulty filling vacancies for jobs, but who offer a solid, living wage, good benefits, and opportunity for employee growth and increased opportunity over time. Users are also people in the community who are already working, in school, or who are seeking new employment in the present and future. While the challenges are designed to connect people with employers, they are equally designed to deepen and promote lifelong learning that increases competence, confidence, agency, access, and new opportunities. As such, users might be job seekers, those seeking ongoing learning and professional development, and/or both.
How will your project be inclusive of a diverse population of students and their needs?
Working with community, government, and education partners will be an important part of this project. We will need to experiment with them so that we can determine the most effective ways to encourage participation from those who want to, but might lack the confidence. As such, given the necessary resources, we plan to include embedded guides who monitor participant involvement in challenges, and explore a myriad of creative, playful, and specific ways to encourage persistence in the challenges. Some computer-generated game design features in future iterations may assist with this as well. How will success of your project impact the learning ecosystem of the future and how will you measure this? Based upon insights from the pilot, the intent is to partner with more employers, working with them to design further challenges on the platform. A great success would be a growing number of employers partnering with us to embrace or at least experiment with challenge based hiring, early wins by successful matches between employers and job seekers, growing numbers of participants engaging in and completing challenges (and expanding their profiles), and either the exponential growth of this platform, or a number of other organizations creating comparable platforms. In the latter case, it would be highly desirable for platforms to build a consortium that allows the interoperability with regard to learner profiles.
What is a diploma mill? Scan the web and you will find a range of answers. Some limit their idea of a diploma mill to illegitimate schools. They are scams that claim to be legitimate organizations, but only to take your money, sending you a diplomas for little to no work. Others think of diploma mills as actual colleges or universities that offer degrees, but these schools might not be accredited, and/or they offer degrees with a watered down academic experience. Still others use the phrase “diploma mill” to describe a college or university that is more interested in getting a student’s money than in the quality of the education. They are essentially selling diplomas. All of these represent working definitions for a diploma mill today, but I’d like to offer an expanded definition.
A diploma mill is any college, university, or organization that has the earning and issuing of diplomas as its core value and/or function as described by employees at the organization and/or the students.
Notice that many definitions of a diploma mill speak to the idea of organizations that issue diplomas for a price, but without the academic rigor or the evidence of actual student learning. That is indeed a diploma mill, but I’d like to suggest that this is only one type. There are also diploma mills that make things quite hard for students. They add lots of rules and academic hoops. They might have challenging tests. They require papers and projects. They might even offer good and valuable learning experiences. Yet, in the end, they or the students that they serve see them as mainly just steps toward getting that piece of paper at the end. That is a diploma mill. A mill is a place where machines grind out grain into flour. A diploma mill is a school that grinds out people into graduates with pieces of paper. The focus is upon how many pieces of paper they issue. The process is not as important as the end goal of getting the piece of paper.
You know that you are in a diploma mill if you were to get half way through a degree program, leave, and believe that it was therefore a complete waste of your time and money. The focus was on getting a diploma, and you didn’t get it, so you basically look at it like going to the store, paying for a product, but leaving the store without the product.
For non- diploma mills, the product, if we want to stay with that metaphor, is not the diploma. It is the mentoring, the learning, and the journey of personal transformation. Do you consider that idealistic? If so, then I’d like to suggest that such a reaction is further evidence about how rampant diploma mills are in higher education today.
Scan online advertisements and you will read grand promises of earning your diploma in increasingly shorter periods of time. There is a drive to condense and crunch things into smaller chunks of time, removing time for reflection and deeper learning. There are ads that place a prominent image of a student crossing the stage with the diploma in hand, since that is the goal in a diploma mill.
The paper is not without value. Companies value it when posting jobs. Certain pieces of paper from colleges are required to even apply to some jobs. The paper adds some level of prestige. It serves as a signal of achievement and progress in a person’s life. Family members and recipients often beam with pride.
None of this is inherently bad, but I’d like to suggest that it is separate from real, deep, important, substantive learning and education. If you go back far enough in the history of Harvard, you will find a time when students who graduated didn’t even get diplomas. If they wanted one, they had to hire someone to create it and they go the President’s office, paying the president to sign it. That was in addition to and/or separate from the real focus of Harvard, which was to provide a rich and valuable education.
In the flurry of higher education innovation today, and the praise of massive enrollments, we are wise to not lose sight of the dangers associated with becoming a diploma mill. They risk devaluing the essence of great higher education. In addition to that, the more that colleges see themselves as mainly selling verification and diplomas, the more that they set themselves up for a future and impending disruption. Things might go well for them, even exceptionally well, in the short to mid-term, but that will eventually change. The learning communities that truly contribute to something important in life and the world are the ones that likely issue diplomas at the end, but that is not the main thing for them. They are true communities of learning, inquiry, and transformation.