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.

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.

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.

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.