Two Tough Questions for Online Education

Originally posted on the Higher Education Scholars @ BC blog

Online higher education has been on a rapid rise in the United States. According to NCES data, the proportion of undergraduate students who took at least one online course during the 2011-2012 academic year more than doubled from just eight years prior (from 15.6% in 2003-2004 to 32% in 2011-2012). Proponents who view online courses as democratizing force in higher education applaud this trend, as do the many institutions that have ramped up production capacity and created new institutional structures through which to deliver online education (e.g., Penn State World Campus or Southern New Hampshire University’s College for America). While the increasing prevalence of online education leads to all sorts of important questions, this article focuses on two specific questions related to the teaching and learning process: Are students succeeding in online courses? How does online teaching change faculty work? While some research exists on these questions – a subset of which is explored below – they are both in need of much more thorough investigation by the higher education research community.

Are students succeeding in online courses? Much of the initial research on student outcomes in online courses compared them directly with face-to-face courses, finding “no significant difference” on average. There is significant nuance hiding behind these averages, however. Certain populations actually perform worse in online courses than in face-to-face courses, particularly male students and students from racially minoritized groups, and the reasons for these outcomes are not well understood. Making this question even more difficult is the fact that online courses are no more monolithic than face-to-face courses, meaning that researchers have found markedly different results in student outcomes depending on the instructional techniques utilized in online courses or the instructional techniques utilized in the face-to-face courses to which they are being compared. What seems possible to generalize from these results is that students can learn as well in online courses as in face-to-face courses, but the ascertaining the reasons for success will require additional research.

Online education significantly changes faculty work. Instructors in face-to-face courses typically incorporate some mix of direct instruction (e.g., lecture) and class activities (e.g., class discussion) on a flexible basis. They receive a steady stream of oral and non-verbal feedback from students and can clear up misconceptions on the spot. In the online context, instructors may still provide direct instruction, but it is more likely to be in pre-recorded video form without the same built-in feedback mechanisms. Class discussions often shift from oral to textual and from immediate to time-dispersed, significantly changing the nature of interaction that instructors have with students. Synchronous video sessions can overcome some of these challenges, but instructors face a learning curve in this environment as well. Producing online courses is a significant time investment for faculty members and must be done months in advance of the term in which the course will be offered. While these changes are not necessarily negative, they still significant change the nature of faculty work.

Perhaps more significantly, many institutions have fundamentally restructured the nature of faculty work in the production of online courses. In typical face-to-face courses, faculty members have substantial autonomy in decisions about methods and content, exercising their professional judgment about how best to instruct students and communicate subject matter. To develop online courses, however, it is common for institutions to hire one instructor to develop the course – usually called a Subject Matter Expert – and then hire a separate instructor to teach the course. Both instructors are typically hired on an adjunct basis and paid accordingly, with all rights to the course held by the institution. While this disaggregation of the instructional role may reduce costs to the institution and ensure that the resulting course can be reused nearly ad infinitum, it also depersonalizes the course and deprofessionalizes the instructor teaching it. Rather than making judgments about the best way to teach a given subject, the instructor uses the pre-built template as a script and takes on a role not dissimilar from a teaching assistant or grader. Understood alongside the increasing prevalence of online education, this approach may exacerbate the continuing decline in the proportion of tenure-line faculty positions in higher education, further weakening the professoriate.

Considering the continued growth of online education, it is even more important to understand its impact on students and faculty members, not to mention American higher education more generally. Researchers need to reckon with the nuances of online education and attempt to understand the structures that promote student and faculty well-being. Perhaps through applying this research online education can become what its proponents hope for: a true democratizing force in American higher education.

Ruminations on Dewey’s Education and Experience

I just finished John Dewey’s Education and Experience. Dewey’s main argument, as I understand it, is that education should be based in the life experiences of students. Educators should understand students’ current life experiences and facilitate opportunities for building on these experiences. These opportunities should be cooperatively created and, indeed, facilitated by the educator, not imposed by him or her. Rather than expecting students to adhere to a rigid set of externally-imposed guidelines, these cooperatively-created opportunities should enable exploratory freedom within thoughtful frameworks of progressively-increasing rigor. Dewey is clear that he thinks this is a much, much more difficult way to educate than traditional education, but believes that it more closely adheres to the ideals of a democratic society than more authoritarian methods.

Several things occurred to me as I was reading this book. First, Dewey gave the address on which the book was based in 1938 – over 75 years ago. Progressive educators at the time were reacting against rigidly structured education systems that seemed to stifle the natural desires of students to learn. This can lead one to either hope or despair; one either sees hope in the enduring power of progressive thought applied to a similar contemporary confrontation, or despairs that even with the best efforts of progressive educators over three-quarters of a century very little seems to have changed. No matter which view one takes, there is no denying that the questions and conversations about how best to educate have been around for a long time and are likely not amenable to quick fixes. A good deal of hard work, presumably of the sort that Dewey prescribes, will be necessary to effect change in a very old system.

Second, the purpose of education – the why of education – is deeply embedded in this argument. What is education about for Dewey? Nothing less than the ability and desire of students to continue learning throughout their lives: “The most important attitude that can be formed is that of desire to go on learning. If impetus in this direction is weakened instead of being intensified…. the pupil is actually robbed of native capacities which otherwise would enable him to cope with the circumstances that he meets in the course of his life” (Dewey, 1938/1963, p. 48). Education should be about helping students mature to self-actualization, though I don’t think Dewey actually used this phrase. Education is the dynamic interplay between exploration and educator guidance, the connections formed and nurtured in cooperative learning spaces, and the development of self-regulated and self-motivated learners.

Third, themes of connected learning run throughout this book. Most obvious is the connection between educator and student that is fundamental to Dewey’s conception of cooperative creation of meaningful experience-building activities. Educators need to know enough about their students, to understand their students well enough, and presumably to care about their students enough to facilitate these experiences. Connection among students in a class is also part and parcel of Dewey’s philosophy, with the cooperative aspect taking center stage in activity formation. Perhaps most importantly from Dewey’s perspective is the connection of new educational experiences to prior student experiences and the opportunities created for students by these connections. Thus, even in an age that predated most forms of electronic communication, Dewey believed connected experiences to be at the core of good educational practice.

Which leads us back here, to 2014, to ask ourselves what lessons we might take from Dewey’s eloquent statement of education’s purpose. One might argue that the ability to connect has expanded significantly since Dewey’s day, especially with the advent of the internet. Very good – but what does this mean? Are we getting better at working cooperatively with our students to foster learning experiences that build on their prior experiences? We might be getting better at quantifying the prior experiences of our students – if proponents of “big data” are to be believed – but are we putting this toward the creation of meaningful experiences? Are we providing students the freedom to co-create their own learning experiences on the way to self-actualization? Or are we still rigidly defending curricula to be mastered in an order and logic that we (or, perhaps worse, textbook publishers) decide is correct, without input from our students or reflections on their past experiences? In short, are we any closer to achieving the goals for which Dewey advocates because of our greater ability to connect today?


Reference: Dewey, John. (1963) Education and experience. New York: Collier Books. (Original work published 1938)

J. C. R. Licklider – “Man-Computer Symbiosis”

One of the most difficult things about studying history is recreating the social, intellectual, technological, and physical environment of your subject. Mostly this entails putting your own assumptions aside, which means understanding the assumptions that you make about life. This is not always easy. Some assumptions we make about life are so ingrained that we cease to notice them anymore, let alone recognize their absence in the past. Two of my favorite examples of this are access to medical care and access to communications infrastructure. People in 1800, even if they lived in a relatively affluent area, had atrocious medical care by our standards, which translated into much lower life expectancies. A letter – which was the dominant mode of long-distance communication – took anywhere from two weeks to several months to cross any significant distance (like the Atlantic Ocean). We take access to decent medical care for granted, as we do communications infrastructures like telephones (and, of course, the internet).

The reason I bring all of this up in a post about an article from the mid-20th century U.S. is relatively simple: I found it hard to understand exactly what computers of Licklider’s time actually looked like and how they functioned. One has the “room-size” computer in mind, the types that were programmed by punch card and were actually susceptible to physical insects (bugs) clogging up vacuum tubes in their inner workings. But beyond this stereotyped understanding of computing, it is very, very difficult to break out of our current understanding of what computers are and how we use them. I’m typing this on a machine that fits in my lap, and yet has many, many, many times the processing power that the room-size computers of Licklider’s day had. I have instant access to a large portion of the world’s information and can use this machine in an almost infinite variety of ways, all without needing to know a single line of computer code. With some coding skills, this number does rise very close to infinity. I can create amazing representations of complex data structures through free programs like R and Gapminder, and do all of this with data sets freely available from governmental and non-governmental agencies across the globe. Not all of these assumptions about what a computer can do immediately recede when I try to put myself in the mind-space of Licklider, like the idea that I would need to articulate exactly what I wanted to compute before I did it, sit down with a programmer to figure out how to compute it, and then wait for the processing time on the machine itself to become available. When we consider how frankly archaic this sounds to us in 2014, I think we start to get a better sense of how far we’ve come. Consider this quote from him about computer storage:

The first thing to face is that we shall not store all the technical and scientific papers in computer memory. We may store the parts that can be summarized most succinctly-the quantitative parts and the reference citations-but not the whole. Books are among the most beautifully engineered, and human-engineered, components in existence, and they will continue to be functionally important within the context of man-computer symbiosis.

We have the capacity to store all of the world’s information in computer memory. What I’m trying to say is that at least in some senses I think we have achieved Licklider’s vision, or at least are approaching it in meaningful ways. Consider his statement about the work that he is actually doing:

The main suggestion conveyed by the findings just described is that the operations that fill most of the time allegedly devoted to technical thinking are operations that can be performed more effectively by machines than by men. Severe problems are posed by the fact that these operations have to be performed upon diverse variables and in unforeseen and continually changing sequences. If those problems can be solved in such a way as to create a symbiotic relation between a man and a fast information-retrieval and data-processing machine, however, it seems evident that the cooperative interaction would greatly improve the thinking process.

We process weather models, simulations of space shuttle reentry, social network analysis, and any number of things with rapidly changing variables today. We use our computers to help us think through problems, to rapid-prototype, if you will, and get to solutions far faster and with much less effort than in Licklider’s day. Perhaps even more to his point, advances in natural language processing, semantic linking, and machine learning are beginning to let computers “think” more like human beings and reckon with new information by themselves, rather than through the mediation of a human being. Considered from this angle, we live in exciting times indeed.

NMC Keynote: Jason Ohler on “Trends that Bend”

Dr. Jason Ohler gave the Wednesday morning keynote, in which he identified five technology trends of the near future. He believes that these technologies will help all of us cope with the information flood that only increases as we get further and further into living lives that blend the virtual and the real. To give an idea of how much information we now receive and can’t process, Dr. Ohler estimated that it would take between thirty and forty days for him to process all of the information that he receives in one twenty-four hour span. He went through each of the five trends and discussed how each of them might apply to education. I’ll discuss the trends first and then discuss all of the education-related impacts at the end. Here are the trends, in order:

Trend 1: Big Data

Google collects 24 petabytes of data every day. We really don’t have any way to keep ourselves away from the big data juggernaut, but this has both good and bad elements. Text analyzers and predictive data analytics are rapidly improving and can help us make sense of this data. But we need to think about the kinds of data that we’re collecting, especially in educational contexts, and make sure that we are clearly articulating the goals of big data and shaping its future course.

Trend 2: Immersion

Augmented reality is becoming mainstream. Virtual worlds and the real world are becoming increasingly blurry and interconnected. Immersion is the antidote to spam: when two pieces of data are meaningfully connected – like location and reviews, let’s say – then relevance is increasingly assured. This contextualization has demonstrable impacts on our ability to sort through information.

Trend 3: The Semantic Web

It used to be the case that links were page by page, that they linked one large container for or chunk of data to another container or chunk. This is changing, such that very specific pieces of data are being linked to one another to create relationships that augment intelligence. This is increasing alongside another web technology, the internet of things, that will see an even further leap in the connections between machines, data, and people. It is important to realize that now that camera that keeps an eye on the subway station is not just a camera – it is also an application platform that can run apps. These innovations will continue to make the over-abundance of information connected and intelligible, but obviously comes with other risks as well.

Trend 4: Extreme BYOD

Bring-Your-Own-Device has been around for a while, but in the new version expressed here by Dr. Ohler, the customization and personalization of these technologies will continue to test the flexibility of our IT infrastructures. This increased personalization will continue to be a boon to workers, who will increasingly be able to customize their devices to work exactly how they would like to work, allowing some additional filtering of the information flood.

Trend 5: Transmedia

Transmedia storytelling is huge everywhere except education. Rather than telling a linear story through text or bullet points, transmedia enables multiple media types and different transmission methods to coexist to tell a single story. We need to be able to communicate in new ways, especially with visual media, and bridge the gap between creative thinking and critical thinking – leading to Dr. Ohler’s neologism “creatical thinking.”

Educational Impacts

Taking all of this together, it seems clear that these trends are helping to make the amazing amount of information that we encounter more manageable. But what do these technologies mean for education? The big takeaway is that we need to be teaching students how to become active and responsible digital citizens. This can’t be confined to the closed environments of the “school web” either – they must be robust experiences with open technologies that actually model the kinds of meaning-making that students will continue to engage in throughout their lives. Creatical thinking, transmedia, augmented reality, customized devices, and the semantic web all point to the types of skills that students should be building. Moreover, students should be brought into the discussion about responsible use of these technologies and the directions that each technology should point.

There are also very good opportunities for customized learning, akin to digital tutors, that work with students on a more individual level. As algorithms for speech processing and text analysis continue to improve, the Clayton Christensen-style disruption that many in the higher-ed and K-12 space have been talking about for so long may finally be here.


cross-posted at

Educause, Day 2

I thought I had published this post awhile back. Here’s the second installment from Educause.

Day two of my Educause experience was just as interesting as the first. Games and gamification was the theme of the day. The very short version of the story: designing games is hard, and taking design elements and throwing them at non-game contexts until they stick is not a good way to approach gamification.

My day kicked off bright and early with a good session on using badges to recognize granular student achievement. Perhaps the most interesting thing to come from it was the connections I made with others who were undertaking similar projects. It was the most discussion-based of the sessions that I attended, with many different people in the audience weighing in on various aspects of developing a badge system. Two notable insights emerged for me from this session: first, their experience showed that badges could actually drive interest in a course. Second, utilizing badges forces a self-conscious alignment of course activities with learning outcomes – for what is a badge but a demonstration of competency around a learning outcome – and thus there is some question about whether badges themselves are responsible for student learning gains or the thoughtful redesign that generally accompanies the addition of badges. Food for thought, for sure.

Then came the second keynote presenter of the conference: Jane McGonigal. For those not familiar with Jane, she is a gifted game designer committed to doing real societal good through the games that she creates. She gave an inspiring talk about the future of games in education. Two things stood out quite strongly to me. First, as part of a discussion about the ways that games make people feel, I was struck by the most common feeling experienced by gamers: creative agency. I’m willing to bet that this is not the first thing one would think about or talk about it the context of higher education, but it is obviously a powerful motivator for gamers (as evidenced by the now one billion gamers on this planet).

Second, gamers fail an average of 80% of the time. Think about that for a second. When you’re playing a game and trying to accomplish a goal you feel engaged in that game. If you fail the first time that you try the goal, it actually seems to increase your motivation to try again. To make this more concrete, my wife has been playing Candy Crush for a few months. There are times, now that she’s in the really hard levels, that she will try a level twenty or thirty times before mastering it. While this can get frustrating at times, it seems that the act of failure also teaches: trying out a strategy and trying to understand why it fails is a great way to learn (and is not dissimilar, incidentally, from the hypothesis-experiment model in most science disciplines). Once again, this is not the model of higher education. We are all about high-stakes exams, where failure is not an option at any point in the process. What would it look like if we embraced a model more similar to gaming that encouraged students to try something until they mastered it? Our assessments would need to get more sophisticated, for sure, but perhaps rethinking the multiple-choice model would be good for us anyhow.

Educause 2013, Day 1

I’m having a great time at my first Educause. It turns out that being surrounded by thousands of IT professionals dedicated to making their organizations better is a heady experience. Sir Ken Robinson’s keynote this morning was enchanting. He is the kind of speaker who can mix demographics with science fiction and educational paradigms to create a captivating experience for his listeners. Though his message was similar to other talks of his I’ve seen, I was struck by the shared vision of creativity and innovation that he inspired in his listeners. My main takeaway from his talk was that we’re still at the beginning of the digital revolution and, given the kinds of challenges that we are facing with population and climate, we need to become even more creative in our endeavors. Educational systems need to reflect this priority rather than holding on to an outdated, industrial-revolution-era model.

This theme continued with a talk by Mimi Ito of UC Irvine. Mimi is a cultural anthropologist who studies the uses and cultural meanings of technology among young people. She made a great distinction between the ways that learning has changed, especially its new networked and socially-mediated dimensions, and the way that education has not changed. Like Sir Ken, she sees the educational system as based on an outdated paradigm, though she defines the old paradigm slightly differently: information scarcity. Thus she thinks that our current transition to information abundance should cause us to reconsider our educational models. She made an analogy that I found particularly insightful: when human beings lived in an age of caloric scarcity, our biological systems for storing and miserly use of those resources made great sense. In an age of caloric abundance those strategies no longer make sense (and, indeed, can be maladaptive), and our job as eaters becomes one of choosing our food wisely. Likewise, information scarcity leads to the kinds of educational models we still have today, like lecture-based classes and expensive textbooks. But this can (and perhaps should) change with the recognition that scarcity is no longer the reigning paradigm.

Two other things stood out to me in her talk. The first was her call for educators to use insights about how students learn outside of school to change the way that students learn in school, hopefully mediating the culture clash in learning modalities between the two areas. It turns out that students are learning quite a bit through outside-of-school online interactions, including an increasingly important technical literacy. And this idea is not new: Dewey talked about the potential seamlessness of education many years ago.

The second point that stood out to me was the distinction she drew between learning motivated by interest and learning motivated by friendship. The latter is learning that comes from one’s peer group, from keeping up with one’s friends of Facebook and Twitter and other social media outlets. The former is learning that students seek out because they are interested in something, be it web comics or programming or how to build a pumpkin-launching device (no, seriously). This learning does not necessarily have anything to do with their in-person social group, and in fact often creates a sort of distributed peer community organizes around a certain topic. Anyone with a particular hobby can tell you how deep this rabbit hole can go. Her point here, though, was that it may be important for educators to target the interest-based engagement rather than the friendship-based engagement in order to avoid the “creepiness factor” and engage students where there interests lie.


Though I went to three other sessions, the two referenced above were the highlights for me. I’m excited to see what Day 2 has in store – I think it will likely be great.