The Need for Greater Productivity through Online Learning, Part 2
Published by: WCET | 1/23/2014
January 23, 2014
This is the second of two parts of a guest post by noted educational technology thinker, Tony Bates. The first part focused on Main Concepts and Principles. Again, thank you to Tony Bates for his contribution.
Russ Poulin, WCET
Part 2: Identifying Promising Areas of Productivity for Online Learning
In my previous post for WCET’s Frontiers blog, I outlined why productivity is an important issue for online learning, and laid out some of the main concepts and principles that need to be considered in any discussion of productivity and online learning. In particular, it is important to be able to identify and measure appropriate outputs for education, as well as costs or inputs, and their relationships.
In this post, I want to discuss some possible ways in which online learning could be used to increase the productivity of the post-secondary education system.
Increasing access to higher education
There is a strong argument that online learning can help governments boost participation rates more effectively than by building more campuses and funding more campus-based education, because online learning has less overhead costs for buildings and land, etc. A good example of this is the recent creation of UF Online in Florida to accommodate the many qualified applicants who cannot find places on campus in the University of Florida system.
Free or massively scalable content
Nowhere in online learning is there such potential for increases in educational productivity as in content development and delivery. Once learning materials are created, they can be stored, accessed, delivered, and used by an unlimited number of learners, thus potentially achieving large economies of scale and thereby reducing costs per learner. MOOCs are an obvious example of massively scalable content.
Another important factor contributing to economies of scale in online learning is the increasing availability of open educational resources. Particularly in foundational courses and many ‘standard’ undergraduate courses, ‘open’ material is already available and does not have to be re-created. The main cost is selecting and organizing existing open source materials, but this is likely to be less time-consuming for faculty than creating materials from scratch. Open online textbooks can have a direct and immediate impact on reducing student costs.
Nevertheless, there is a number of impediments to achieving productivity gains through free or massively scalable content, such as faculty resistance, concerns about quality, and a focus on only one component of the learning process (content). Despite these impediments, in certain circumstances (i.e. where there is a large market and best practices are applied to content design), online content development and delivery is already resulting in increased productivity in post-secondary education, although it has yet to be well measured.
Course design based on sound pedagogical principles
One important reason for the success of many for-credit online courses and programs has been the introduction of best practices in course design, drawing on cognitive science research, best teaching practices, and prior experience of teaching students at a distance. These practices include situated learning that draws on learners’ own work and life experiences, student time-management support, collaborative learning, student activities resulting in greater time on task, and regular and constructive feedback to students through continuous assessment.
In particular a focus in online courses on ’21st century skills’ development, such as knowledge management and independent learning, could have two productivity benefits. It would improve outputs (turning out graduates with the skills needed). Second, content development and delivery becomes subsidiary to helping students find, analyze, organize, and apply content themselves. Thus less time would be spent by instructors on course development and delivery and more on learner support.
Productivity is improved through application of such quality course design because more students achieve higher levels of learning and more students complete courses and programs. Once good online design templates are in place, these can be easily replicated. Thus, although it is not the technology itself that results in better outcomes, the technology facilitates the change to more effective teaching methods.
Instructional MOOCs (xMOOCs) have basically removed learner support, at least in terms of human (instructor) support, but this has resulted in a very low number of MOOC learners passing end-of-course assessments of learning. Indeed, prior research into credit-based learning has established that instructor online ‘presence’ is a critical factor in retaining students. So far, it has proved difficult to scale up learner support on a massive scale, except through the use of computer technology, such as automated feedback. However, Carey and Trick (2013) and indeed faculty at elite institutions who are offering xMOOCs (see Thrun and ‘the Magic of the Campus‘) have argued that such computer support does not support ‘the learning that matters most’.
Computer-based approaches to learner support to date have been inadequate for formal assessment of higher order learning skills such as original, critical or strategic thinking, evaluation of strategies, or alternative explanations. To assess such forms of learning, deep expertise and qualitative assessment is required, and to date not only human instructors, but instructors with a deep subject understanding and high levels of expertise, are needed to both develop and assess such high level skills. Given the long history of trying to apply artificial intelligence to instruction, immediate and major breakthroughs seem unlikely, at least in the short term.
However, there are other ways in which the productivity of learner support might be improved. In cMOOCs, which are more like communities of practice and thus contain many participants with already high levels of expertise, that expertise and judgment can be provided by the participants themselves.
Also, credit-based online learning has achieved some economies of scale and scope by re-organizing the learner support process, through the hire and training of lower-paid contract adjuncts who still have high level academic qualifications, under the supervision of a senior faculty member. In other words, team teaching approaches (with the senior academic working more as a teaching consultant, setting curriculum, designing assessments and creating rubrics, and supervising the learner support provided by a team of adjuncts) can help to achieve modest economies of scale in learner support, especially when combined with best practices in course design.
Connectivism and the wisdom of the crowd
In any discussion of productivity in online learning we need to consider a newly emerging area, the impact of social media and in particular the impact on learning and knowledge of massive inter-connections and communications across the Internet .
Some, such as George Siemens and Stephen Downes, argue that informal learning, through online communities of practice or ad hoc or informal online connections through social media, and self-learning through Internet searching and networking, have massive potential for reducing the costs of education by content becoming increasingly freely accessible on the Internet and by eliminating or dramatically reducing the need for professional teachers or even more importantly the overheads associated with institutional costs.
Innovation versus standardization
In industry, innovation is often another way of saying ‘investment in technology’. However, there is more to innovation than just replacing a human activity with a computer-based activity. What the technology usually brings about is a change in process at the same time. Thus there is a natural tension between ‘best practice’, based on experience of doing things in an ‘old’ way, and innovation, which means doing something differently. Real, sustainable innovation occurs then when new technology is combined with new processes.
In education, perhaps the main ‘process’ that we need to examine is the instructional model, particularly that based around the lecture system. As public post-secondary education has become massified, the lecture has become the default model, because in a classroom based system, it has proved the only way to ‘scale up.’ In a knowledge-based society with unlimited access to sources of knowledge, though, knowledge management becomes more important than mere transmission of knowledge.
True innovation requires a change of process or method as well as a change of technology. However, if we look at xMOOCs we have taken a new technology – video lecture capture and Internet transmission – and applied it to a model of teaching based on knowledge transmission (i.e. lectures). Online learning offers an opportunity to break out of this redundant and increasingly less productive lecture model of teaching, but it also means changing the predominant teaching model of information transmission.
It will have been noted that I have offered very little empirical evidence to support these arguments. That is because although institutions now have an increasingly large amount of data available, it has rarely been collected or analyzed through the lens of productivity. We need to start thinking about productivity in post-secondary education, and how innovations such as online learning can improve productivity.
1. Government and institutional leaders need to set improved productivity as a key goal for investment in learning technologies. This means setting benchmarks and implementing means of measuring success or otherwise in improving productivity through learning technologies/online learning. Data analytics now make this measurement more feasible than in the past, but it also requires agreed models or a theoretical framework for assessing what constitutes productivity in a post-secondary educational setting.
2. Understanding the basic cost structures of online learning, compared to the costs of classroom teaching, is an essential first step to increasing productivity in post-secondary education.
3. Content or information transmission is only one component of teaching (and an increasingly less important component); other components such as learner support and assessment are even more important. In looking at productivity issues, all these factors need to be examined together.
4. Any attempts at increasing economies of scale or scope in content development and delivery need to be balanced by ensuring quality does not suffer and that output is at least maintained or improved. This means paying as much attention to learner support and assessment as to content delivery. However, online course development has the potential, through good course design, to improve quality rather than reduce it.
5. The ‘learning that matters most’ mainly addresses university teaching, but also increasingly technical, vocational, and corporate training; the aim is to develop the knowledge and skills needed in a knowledge-based society. Online learning can handle the ‘learning that matters most’ as well, in most cases, as on-campus teaching, although there will always be some exceptions.
6. However, there are major difficulties in scaling up the learner support and assessment activities that are needed for the ‘learning that matters most,’ both online or on campus. The danger in scaling up is the loss of quality in terms of learning outcomes, particularly if learner support is sacrificed.
7. Adaptive learning software that helps individualize learning, and learning analytics, may help to a small degree in enabling instructors to handle slightly more students without loss of quality, but cannot as yet replace a skilled instructor, and probably never will. Higher education requires expertise and qualitative assessment for the learning that matters most, and this will require human instructors into the foreseeable future.
8. For the ‘learning that matters most,’ new online course designs built around the use of new technologies have perhaps the greatest potential for increases in productivity – through producing better learning outcomes, maximizing the time of top professors, and drawing on the collective knowledge of learners themselves. Indeed, improving quality is more likely to lead to better productivity gains than by trying to reduce unit costs by scaling and the replacement of instructors by computers.
9. We need more empirical research on the relationship between teaching methods, mode of delivery, costs, and the type of learning outcomes that constitute the ‘learning that matters most’ (not to mention better definitions and theoretical frameworks).
Investments in learning technologies are unlikely on their own to provide the massive productivity gains hoped for by politicians, and promised by MOOCs, if quality outcomes are to be achieved. Nevertheless, although increasing productivity through the use of learning technologies will not be easy, it is possible. Indeed, it is hard to justify the investment in learning technologies if we cannot show significant productivity gains from the investment.
However, to obtain such productivity gains, a major change of attitude at the leadership level is needed, with a greater focus on improved productivity as a strategic goal for learning technologies. Done well, though, this will lead to improvements in quality together with some significant reductions in cost, without destroying the highly skilled labor base on which higher education so clearly depends.
Tony Bates Associates Ltd
The full set of posts on productivity and online learning: