Social-learning engineering

The social component of learning has long been overlooked from both a regulatory and a design perspective, with community formation often assumed to happen through the traditions of brick-and-mortar institutions. But as students spend less time at physical campuses, whether due to part-time status, family and work commitments, or online classes, deliberately planning how students will connect meaningfully with each other becomes necessary.

Coursera’s partnership to create “learning hubs” offers one example of how the education, business, and government worlds are exploring solutions to strengthen the tenuous social fabric that keeps students in class. Along with the basics of internet and technology access, these hubs also offer a more fundamental reason to return: social ties. Fellow classmates can offer instrumental support by sharing knowledge and experiences, but they also offer emotional support and validation when uncertainty strikes. While the time and effort required to build social ties may initially seem costly, the investment can pay off through higher enrollment and retention, as well as improved learning and satisfaction.

As these initiatives reveal, personalizing learning effectively goes beyond mere individualization to include genuine integration of the participants as people connected in a community.

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Personalized learning “on demand”

Most personalized learning systems have focused on the business-to-business (B2B) rather than business-to-consumer (B2C) model. The B2B model holds appeal both since it secures large contracts all at once and since it can tailor solutions to unique institutional needs, particularly applicable in the world of education, which is subject to extensive rules and regulations for funding and accreditation at multiple levels.

Yet with giant consumer sites such as Amazon jumping in to the online education space, the possibility of offering personalized learning direct to the consumer and at scale challenges old norms about regulating access. How much will consumers trust content and instruction offered outside of accredited institutions? Along what dimensions will consumers evaluate the educational experiences they choose? How will this affect the nature, content, and quality of instruction provided? How will the added mobility affect the formation of social networks which support learning, student persistence, and consumer “stickiness”? How will learners’ data be shared (or not) across institutional barriers and over time, as the students mature?

Watch this space; there are many changes afoot.

MOOCs plus big data

In The Coming Big Data Education Revolution, Doug Guthrie argues that “big data”, rather than MOOCs, represent the true revolution in education:

MOOCs are not a transformative innovation that will forever remake academia. That honor belongs to a more disruptive and far-reaching innovation – “big data.” A catchall phrase that refers to the vast numbers of data sets that are collected daily, big data promises to revolutionize online learning and, in doing so, higher education.

I agree that there are exciting new discoveries and innovations still yet to be made through the advent of big data in education, and I also agree that MOOCs’ current reliance on scaling up delivery of existing content isn’t particularly revolutionary. Yet I see the two movements as overlapping and complementary, rather than as competing forces.

While MOOCs may not (yet) have revolutionized instruction, they have revolutionized access for many learners. Part of their appeal for those interested in their growth is their potential for enabling large-scale analysis due to the high enrollments as well as the availability of online data. The opportunity to study such large numbers of students across such disparate contexts is rare in traditional academic settings, and it permits discoveries of learning trajectories and error patterns that might otherwise get missed as noise amidst smaller samples.

Another potential innovation which traditional MOOCs (xMOOCs) have not yet explored is new models of building cohorts and communities from amidst a large pool of learners, a goal at the heart of “connectivist MOOCs” (cMOOCs) that highlights peer-learning pedagogy. Combine xMOOCs and cMOOCs, and you can improve educational access even further by enabling courses to spring up whenever and wherever enough people, interest, and resources converge. Add in the analytical power of big data, and then you have the capacity to truly personalize learning, by providing both the experiences that best support students’ learning and the human interactions that will enrich those experiences.

MOOCsperiments: How should we assign credit for their success and failure?

That San Jose State University’s Udacity project is on “pause” due to comparatively low completion rates is understandably big news for a big venture.

We ourselves should take pause to ponder what this means, not just regarding MOOCs in particular, but regarding how to enable effective learning more broadly. The key questions we need to consider are whether the low completion rates come from the massive scale, the online-only modality, the open enrollment, some combination thereof, or extraneous factors in how the courses were implemented. That is, are MOOCs fundamentally problematic? How can we apply these lessons to future educational innovation?

Both SJSU and Udacity have pointed to the difficulties of hasty deployment and starting with at-risk students. In an interview with MIT Review, Thrun credits certificates and student services with helping to boost completion rates in recent pilots, while the inflexible course length can impede some students’ completion. None of these are inherent to the MOOC model, however; face-to-face and hybrid settings experience the same challenges.

As Thrun also points out, online courses offer some access advantages for students who face geographic hurdles in attending traditional institutions. Yet in their present form, they only partly take advantage of the temporal freedom they can potentially provide. While deadlines and time limits may help to forestall indefinite procrastination and to maintain a sense of shared experience, they also interfere with realizing the “anytime, anywhere” vision of education that is so often promoted.

But the second half of “easy come, easy go” online access makes persistence harder. Especially in combination with massive-scale participation that exacerbates student anonymity, no one notices if you’re absent or falling behind. While improved student services may help, there remain undeveloped opportunities for changing the model of student interaction to ramp up the role of the person, requiring more meaningful contributions and individual feedback. In drawing from a larger pool of students who can interact across space and time, massive online education has great untapped potential for pioneering novel models for cohorting and socially-situated learning.

Online learning also can harness the benefits of AI in rapidly aggregating and analyzing student data, where such data are digitally available, and adapting instruction accordingly. This comes at the cost of either providing learning experiences in digital format, or converting the data to digital format. This is a fundamental tension which all computer-delivered education must continually revisit, as technologies and analytical methods change, as access to equipment and network infrastructure changes, and as interaction patterns change.

The challenges of open enrollment, particularly at massive scale, replay the recurring debates about homogeneous tracking and ability-grouping. This is another area ripe for development, since students’ different prior knowledge, backgrounds, preferences, abilities, and goals all influence their learning, yet they benefit from some heterogeneity. Here, the great variability in what can happen exaggerates its importance: compare the consequences of throwing together random collections of people without much support, vs. constraining group formation by certain limits on homogeneity and heterogeneity and instituting productive interaction norms.

As we all continue to explore better methods for facilitating learning, we should be alert to the distinction between integral and incidental factors that hinder progress.

Alternate models for structuring learning interactions

Timothy Chester ponders the power of many-to-many peer networks in facilitating learning:

If there is to be a peer-based, many-to-many collaborative structure ensuring rigor and the mastery of learning outcomes, it must also be deemed authoritative and persuasive by participants. Some ways to ensure authority and persuasiveness might include the following:

  1. The teacher must drive the collaboration. While teachers engaged in many-to-many relationships with students are not the authoritative center of the collaboration, they are responsible for structuring the student experience and stewarding the learning processes that occur.
  2. The collaboration has to be bounded by a mutually agreed upon scope and charter. Compared to traditional one-to-many collaborations, many-to-many forms can appear chaotic or disorganized. In order to drive effective learning, many-to-many collaborations must operate within a set of boundaries – those things we might define as learning objectives, outcomes, standards, or rubrics. As steward of the learning process, the teacher must take responsibility for structuring the learning collaboration within a set of consistent and firm boundaries that include these structures.
  3. There must be incentives for full student participation. Critics of peer grading systems in MOOCs note that such interactions by students many times lack significant investment of time and focus – resulting in peer feedback that is spurious. Both the quality and the quantity of peer feedback within a many-to-many system have to be statistically significant in order to avoid such spuriousness.

There are many models of such networks in both formal and informal learning settings: peer review systems (e.g., Calibrated Peer Review, SWoRD peer review, Expertiza), tutoring and peer learning communities (e.g., Grockit, P2PU, Khan Academy, OpenStudy), Q&A / discussion boards (e.g., StackOverflow), online communities (e.g., DIY, Ravelry), and wikis. The challenge for formal learning environments is to foster and nurture the kind of authentic, meaningful social interactions that emerge from sustained interaction within informal communities, in the context of the top-down and often short-lived peer experiences typically associated with school classes. Yet for personalized learning to succeed on a large scale, it needs to solve this problem effectively, so that learners are not isolated but can benefit from each other’s presence, support, errors, and wisdom.

Distinguishing MOOCs from OER

Stanford mathematics professor Keith Devlin suggests that we should drop MOOCs and focus on MOORs (massively open online resources) or OERs (open educational resources):

no single MOOC should see itself as the primary educational resource for a particular learning topic. Rather, those of us currently engaged in developing and offering MOOCs are, surely, creating resources that will be part of a vast smorgasbord from which people will pick and choose what they want or need at any particular time.

Yet even if current MOOCs follow a mediocre model for structuring learning experiences, they do still attempt to meet a need for learners who seek guidance, structure, and social cohorting for the way they access educational resources. I would be interested in decoupling OERs from MOOCs and similar pathways, in order to broaden the scope of available OERs from which anyone can choose. That opens up possibilities for more innovative approaches to enabling diverse learning paths and cohorting models.

Unpacking degrees

Chris Dillow questions the purpose and value of a university degree (linked from Observational Epidemiology):

What is university for? I ask this old question because the utilitarian answer which was especially popular in the New Labour years – that the economy needs more graduates – might be becoming less plausible. A new paper by Paul Beaudry and colleagues says (pdf) there has been a “great reversal” in the demand for high cognitive skills in the US since around 2000, and the BLS forecasts that the fastest-growing occupations between now and 2020 will be mostly traditionally non-graduate ones, such as care assistants, fast food workers and truck drivers; Allister Heath thinks a similar thing might be true for the UK.

Nevertheless,we should ask: what function would universities serve in an economy where demand for higher cognitive skills is declining? There are many possibilities:

– A signaling device. A degree tells prospective employers that its holder is intelligent, hard-working and moderately conventional – all attractive qualities.

– Network effects. University teaches you to associate with the sort of people who might have good jobs in future, and might give you the contacts to get such jobs later.

– A lottery ticket.A degree doesn’t guarantee getting a good job. But without one, you have no chance.

– Flexibility. A graduate can stack shelves, and might be more attractive as a shelf-stacker than a non-graduate. Beaudry and colleagues decribe how the falling demand for graduates has caused graduates to displace non-graduates in less skilled jobs.

– Maturation & hidden unemployment. 21-year-olds are more employable than 18-year-olds, simply because they are three years less foolish. In this sense, university lets people pass time without showing up in the unemployment data.

– Consumption benefits. University is a less unpleasant way of spending three years than work. And it can provide a stock of consumption capital which improves the quality of our future leisure. By far the most important thing I learnt at Oxford was a love of Hank Williams and Leonard Cohen.

As the signaling function of the degree falls, we should consider how the signaling power of certificates, competencies, and other innovations may rise to overtake it. With specific knowledge and skills unbundled from each other, these markers may be more responsive to actual demand. More specific assessment metrics can help stakeholders better evaluate different programs of study, while more flexible learning paths can help students more efficiently pursue the knowledge and skills that will be most valuable to them.

From the credit hour to competency-based education and personalized learning

On competency-based education:

Proponents tout this model—which allows students to progress at their own pace by mastering measured “competencies” rather than spending a fixed amount of time in class—as a balm for the ills of academe. It will improve quality and expand access for working adults, they argue, while lowering costs for both colleges and students.

According to a report by Amy Laitinen of the New America Foundation, the credit hour is “the root of many problems plaguing America’s higher-education system” and “doesn’t actually represent learning in any kind of consistently meaningful or discernible way.”

Despite a 2006 clause that allows institutions to award federal financial aid based on “direct assessment” of student learning instead of the credit hour, only now will we have anyone taking advantage of it:

In April, [the Education Department] approved Southern New Hampshire University as the first institution to be eligible for financial aid under this provision. The university is rolling out a self-paced online program that has no traditional courses or professors. Instead, students advance by demonstrating mastery of 120 competencies, such as “can use logic, reasoning, and analysis to address a business problem.”

This paves the way for more educational providers to capitalize on the promise of personalized learning in meeting students’ needs.

Freedom and guidance in competency-based education

According to Paul Fain:

competency-based education… looks nothing like traditional college classes. Perhaps the method’s most revolutionary, and controversial, contribution is a changed role for faculty. Instructors don’t teach, because there are no lectures or any other guided path through course material.

Aside from the narrow view of what constitutes “teaching”, this paints only one version of what competency-based education might look like. Competencies refer to the milestones by which stakeholders assess progress, thus constraining the entry and endpoints but not the paths by which those milestones might be reached. Students could all traverse the same path but at their own pace, or they might follow any of a finite set of well-defined trajectories prescribed by instructional designers. They could also be free to chart their own course through open terrain, whether advised by a personal guide or a generic tour book, perhaps even with prerecorded audio or video highlighting landmarks. Recommended or mandated paths can then be tailored to students’ needs, experiences, and preferences. The extra degrees of freedom mean that competency-based education actually has the potential to enable much more personalized guidance than traditional time-based formats.

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.