Author: Russ Poulin

January 28, 2014

Thank you to Tony Bates for providing his perspective on productivity and online education, including part 1 on “Main Concepts and Principles” and Part 2 on “Identifying Promising Areas of Productivity for Online Learning.” 

We asked five experts to give us their reaction to what Tony wrote.  They also give their own thoughts on the research on this issue and alternative views on key components of productivity in higher education:

• Katrina Meyer, Professor of Higher and Adult Education, University of Memphis:  “Focus on Students and Faculty; More Research is Needed”.
• Carol Twigg, President and CEO of the National Center for Academic Transformation: “NCAT Has the Research to Show Quality and Productivity Gains”.
• Thomas Cavanagh, Associate Vice President, Distributed Learning, University of Central Florida: “A Plurality for Productivity”.
• Dennis Jones, President, National Center for Higher Education Management Systems: ” Focus on the Program (Instead of the Course) for Productivity Gains”.
• Phil Hill, Co-founder of MindWires Consulting and blogger at e-Literate:  “Watch California for the Next Steps in Productivity”.

Thank you to each of them for sharing their thoughts and furthering the discussion.

Russ Poulin, WCET

Focus on Students and Faculty; More Research is Needed

Katrina Meyer

Professor of Higher and Adult Education
University of Memphis
kmeyer@memphis.edu

I agree with Tony’s thoughts on achieving greater productivity in online learning, but would like to add three related thoughts for all of us in online education to ponder.

Focus on individual student needs and how to help them learn.
So many of the current developments in online learning (such as MOOCs and the stress on content) seem to be based on an assumption that we are dealing with an ideal student: the self-disciplined, relatively able student who is motivated to learn. Give them content or a MOOC and they will likely teach themselves. Certainly this applies to a certain portion of students, but by no means all. Many students – be they high school graduates or adults needing further education — arrive at higher education institutions with inadequate preparations that may include poor skills or work habits. They are often new to online learning, expect the same passive learning approaches as they experienced in earlier educational settings, and lack confidence in themselves. Despite the prevalence of the perception that these students are intuitive technology users, many of them are not. We really need a lot more focus on these students’ needs, on both how to help them learn and do so while using institutional resources productively. I would argue that we need more focus on these kinds of students as we continue discussing what is quality education, how we can be more productive, and how to measure it.

Identify when faculty skills are necessary.
My second thought relates to the role of faculty. To be productive as online learning programs, we need to use faculty time and skills more productively. We already know that technology can ably replace faculty when it comes to delivering content or testing knowledge. But we also need to identify those times in the learning process that faculty skills are necessary, such as in identifying students’ thinking errors and solutions to those errors, which pedagogical approaches work best for students with different needs, and what content might be most motivating to a particular student, helpful to sharpen their thinking, or needed to broaden their thinking. Faculty are often able to identify when a student needs reassurance that he or she can do the work of the class, a listener who can perhaps offer alternative solutions, as well as a compliment when earned. Our search for productivity needs to identify these necessary faculty roles and ensure they still occur in the more productive online setting of the future. I suspect that we will begin to think of faculty as critical “learner support,” too.

In my research on faculty who teach online, I have been impressed by the number and variety of faculty who care deeply about their students and work hard at finding ways to help them learn and to do so in a way that is productive for them and their institution. I call this “student learning productivity” which places student learning at the center of productivity efforts of faculty. I do not mean to diminish other types of productivity efforts, but wish to point out that faculty may be especially useful in tackling the challenges of helping students learn more and to do so more efficiently.

We need more research.
Lastly, we do need more research, as Tony mentions. We need research that compares pedagogies rather than focuses on a test of one pedagogical approach or one program. We need research that incorporates an assessment of cost or productivity as well as an assessment of pedagogies or programs. And we need research that identifies which kinds of learner support matters most to learning and whether the cost of all kinds of support structures is worthwhile.

Thank you for allowing me to add these thoughts to Tony’s piece and I hope that a focus on productivity becomes a regular focus of the efforts of WCET members!

NCAT Has the Research to Show Quality and Productivity Gains

Carol Twigg
President and CEO
The National Center for Academic Transformation
ctwigg@theNCAT.org

I’ve been making the argument that technology is key to increasing productivity in higher education since my first published article on this subject, “Improving Productivity in Higher Education: The Need for a Paradigm Shift” in 1992.

Since 1999, in partnership with more than 200 colleges and universities, the National Center for Academic Transformation (NCAT) has proven that it is possible to improve quality and reduce cost (increase productivity) in higher education through course redesign. Course redesign is not just about putting courses online. It is about rethinking the way we deliver instruction in light of the possibilities that new technology offers.

• NCAT and its partner colleges and universities have initiated 195 redesign projects, 80% of which were completed.
• Of the 156 completed projects, 72% improved student learning outcomes; 28% showed learning equivalent to traditional formats.
• Of the 156 completed projects, 153 reduced their costs by 34% on average (ranging from 4% to 81%).
• Institutions participating in Changing the Equation, an NCAT program focused on developmental math at community colleges, reduced their costs by 20% on average; all other redesigns reduced their costs by 37% on average.
• Collectively, the 253 courses that have been redesigned enroll about 250,000 students annually.

Quality Comparisons
NCAT requires each participating institution to conduct a rigorous evaluation of the impact of the redesign on learning outcomes as measured by student performance and achievement. All NCAT redesign projects compare student learning outcomes in the traditional format with those achieved in the redesigned format by 1) running parallel sections of the course in the two formats or 2) comparing baseline data from an offering of the traditional course to a later offering of the redesigned course, looking at differences in outcomes in the “before and after.”  The four measurement methods used to assess student learning include 1) comparisons of common final exams, 2) comparisons of common content items selected from exams, 3) comparisons of pre- and post-tests, and 4) comparisons of student work using common rubrics.

Cost Comparisons
NCAT requires each participating institution to establish a team of faculty and staff who will conduct the redesign. Each team analyzes and documents “before and after” course costs using activity-based costing. NCAT developed a spreadsheet-based cost planning tool (CPT) that supports institutions in this process. By completing the CPT, participants are able to 1) determine all personnel costs; 2) identify the tasks associated with preparing and offering the course in the traditional format and determine how much time each type of personnel spends on each of the tasks; and, 3) identify the tasks associated with preparing and offering the course in the redesigned format and determine how much time each type of personnel spends on each of the tasks. The CPT then automatically calculates the cost of both formats and converts the data to a comparable cost-per-student measure. At the beginning of each project, baseline cost data for the traditional course and projected redesigned course costs are collected; actual redesigned course costs are collected at the end. Completing the CPT allows faculty members to consider changes in specific instructional tasks, make decisions about how to use technology (or not) for specific tasks, visualize duplicative or unnecessary effort and complete a cost/benefit analysis regarding the right type of personnel for each instructional task.

National Research Council Report
In 2012, the National Research Council published a 192-page report, Improving Measurement of Productivity in Higher Education suggesting a new set of metrics that would allow for a sector-wide look at productivity. Sponsored by Lumina Foundation for Education, the report was produced by a National Academies panel chaired by Teresa Sullivan, president of the University of Virginia. NCAT’s work is featured in the report as a contemporary example of how instructional productivity can be increased.

The report presents an analytically well-defined concept of productivity in higher education and recommends empirically valid and operationally practical guidelines for measuring it. Productivity, as the report’s authors define it, would factor in both credit hours completed and degrees awarded compared to labor costs and other expenses. By accounting for both degrees and credit hours, the model would reward institutions for graduating students without penalizing them for having large numbers of part-time students.

A Plurality for Productivity

Thomas Cavanagh
Associate Vice President, Distributed Learning
University of Central Florida
cavanagh@ucf.edu

The issue of productivity is becoming increasingly important. As policymakers place growing pressure on postsecondary institutions to improve efficiency, colleges and universities are being forced to explore new technology-driven models. Simply “putting a course (or a program) online” is no longer the bold innovation it once was. The higher education community must continue to innovate, leveraging technology in the service of the institutional mission.

But what does “productivity” mean? How do we define it? More students completing degrees more quickly? A number of new models have been hyped recently as the solutions to improved productivity. Certainly we have all heard plenty about MOOCs. But thus far they have not reduced anyone’s time to degree. Competency-based education is gaining traction as a model for productivity—let students move as quickly as they want/can through a program. But such an approach isn’t right for everyone. Adaptive learning holds significant promise. However, while there may be significant pedagogical advantages to adaptive learning, whether such an approach can truly improve productivity remains to be seen.

Different types of students must be served in different ways. Many in the postsecondary world, including Clayton Christensen and Paul LeBlanc, speak about the disaggregation—or unbundling—of higher education. As we look at the kinds of students a single institution might be educating in the future, it is an extremely heterogeneous group. For the traditional 18 year old first-time-in-college student, we may offer a comprehensive residential campus experience, complete with dorms, fitness center, and Saturday afternoon football games. For community-college transfer students, a mixture of state college, regional campus, main campus, and online courses may be what is necessary and preferred. For these students, the on-campus residential experience has been “unbundled” from the delivery of instruction. Such students neither need nor want it. For other non-traditional students, including working professionals and active-duty military, a fully online option may be the only choice for educational access. For others, perhaps it is a low residency program, or a competency-based program, or a collection of credits from a variety of institutions (or even MOOCs) brought to your school to complete a degree.

What we need is a plurality of choices—a continuum of options from which students may select. We need technology-enabled structures that support and encourage students in the most effective way to achieve their own individual success. If we can create this educational ecosystem of choice we will empower students to choose the program or combination of programs that best fits their own unique needs and moves them along the most productive path to academic success.

Focus on the Program (Instead of the Course) for Productivity Gains

Dennis Jones
President
National Center for Higher Education Management Systems
dennis@nchems.org

Tony correctly identifies both the forces pressing for increased productivity in higher education and the role that online learning can play in achieving that productivity.  He also points out several different ways in which that contribution could be made.  I would like to build on this foundation and add an additional perspective.

Almost all the work on productivity enhancement through use of online learning has focused on the course as the unit of analysis.  Additional opportunities for productivity gains—accomplished while maintaining or even improving quality—can be found if the program, rather than the course, becomes the focus.  Adopting this perspective allows:

1. Defining the degree as the output of the process.  This both simplifies the measurement and aligns outputs with statements of goals which now almost uniformly include education attainment as a priority.  This doesn’t eliminate a concern for quality, but it does make it an issue of parallel concern rather than one that has to be addressed as a central element.
2. Broadening the arena of investigation by opening the question of how to use online learning to produce a larger number of degrees (the bang) for the buck.
3. Most important, it allows a broader array of choices regarding assignment of human assets to learning and learning support activities.  More specifically, it allows teaching foundational courses with heavy reliance on technology and less costly personnel while using senior faculty in those more advanced classes designed to encourage much deeper learning.  This makes the personnel utilization decision a both/and decision rather than an either/or choice.

At the center, what I’m arguing is that it would be helpful to raise the discussion about use of online learning to a strategic level by asking how it can cost-effectively contribute to goal attainment.  Incidentally, this focus raises the question about how technology and less costly personnel can be deployed to provide support services that aren’t necessarily in service to completion of a single course—the role of student coaches that help students over all the hurdles they face, not just those associated with a single course.

Watch California for the Next Steps in Productivity

Phil Hill
Co-founder of MindWires Consulting and blogger at e-Literate
philonedtech@gmail.com
Twitter: @philonedtech

Tony’s magnum opus on productivity issues from online learning is invaluable, especially if seen as marking the transition from talking about the nebulous concept of ‘whether we can save money by using online education’ to talking about ‘how we can use online education as one tool to help us become more productive in our educational mission’.

Consider California as an example. 2014 is shaping up to be the year where the three public systems and the faculty take ownership in figuring out how to apply online education to ensure students have access to the courses they need for graduation or transfer. While there has been a tendency to view online education as a magical revenue enhancement, the key initiatives (Online Education Initiative, CalState Online & CourseMatch, and UC Online) are increasingly seen as investments in productivity, including $37 million this year in additional state funding, with a real focus on combining the resources of institutions across each system. These changes align well with the concept of ‘increasing access to higher education’. Based on the discussions at the recent 20 Million Minds Foundation Evolve conference, the key initiatives are also focused on ‘course design based on sound pedagogical principles’ and ‘learner support’ as key principles for the upcoming projects.

The California initiatives are based on good intentions and have a more mature understanding of productivity, but all of them are in their infancy. The concepts described in Tony’s posts will likely be seen in practice – for both positive and negative effect – over the coming 2 – 3 years in California. This is a very important topic both in state policy and in systemwide implementation.

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.

Learner support
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.

Conclusions
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.

Therefore:

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
Tony Bates Associates Ltd

The full set of posts on productivity and online learning:

• Technology, teaching and productivity: the need for theory
• Is there a link between flexible access and ‘productivity’ in higher education? 
• Book review by Sir John Daniel: Higher Education in the Digital Age
• A review of the HEQCO report on productivity and quality in online learning in higher education
• Tom Carey’s reflections on the HEQCO report on online learning and productivity: 1-Catching a teachable moment
• Tom Carey’s reflections on the HEQCO report on online learning and productivity: 2 – What we left out – and why.
• Towards a theory or model of productivity for online learning: outcomes, scale and design
• How online content development and delivery could improve the productivity of post-secondary education
• Alternative ways to improve productivity through online learner-content interaction
• Improving productivity in online learning: can we scale the ‘learning that matters most’?
• Can online learning lead to productivity gains through savings on campus facilities?
• Productivity and online learning: a summary of the main concepts
• Productivity and online learning redux

January 21, 2014

In some circles, online learning has become the panacea for increasing access and productivity in higher education.  As distance education professionals, we’ve been comfortable with the access issue, but politicians have pulled us into the productivity mix without fully acknowledging the higher education quagmire that they have created or the fundamental changes required to address this issue in a systematic way.  All too often, politicians are seeking easy answers where there are none.

Tony Bates is a scholar of distance, online, and open learning.  He’s worked and researched these modes of instruction at the campus, provincial, national, and international levels.  We invited him to summarize a very well-written series of blog posts that he recently published on the issue of online learning’s role as a possible solution in the productivity debate.  We are pleased he agreed to do so.  If we don’t study the issues and define the debate, others will do it for us.

Later this week, we will post the second in this two-part series.  Thank you Tony!
Russ Poulin, WCET

PART 1:  Main Concepts and Principles

Introduction
Over the last few months I have been doing a series of blog posts on my own site on this topic (for a full list, see the end of this post). In these two specially commissioned posts for the WCET Frontiers blog, I will summarize the main points raised in this series. In this post I focus on main concepts and principles; in the second post I discuss promising areas where online learning could improve the productivity of higher education.

Why is productivity an important issue for online learning?
There is growing pressure from politicians in particular to increase the productivity of the higher education system (see, for instance, the Ontario government’s ‘discussion paper on innovation to make our university and college system stronger’).

Now I don’t dispute the need for more productivity in the system. We’ve moved to a mass system of higher education, and the more people who access post-secondary education, the higher the cost for governments (and indirectly tax payers) where higher education is publicly funded. Productivity is all about getting the biggest bang possible for each buck spent, whether it comes from tuition fees or taxes. This is what students, parents, and the general taxpayer expects from an effective public higher education system.

To date, the system has generally coped with expansion by increasing class size, hiring more lower paid adjunct faculty, increasing the use of lowly paid graduate teaching assistants, and building more buildings. This has been not so much a deliberate strategy to increase productivity, but more like a creeping, unplanned and short-termed response to changing conditions.

Educational technology and productivity
Universities in the USA and Canada have invested heavily in educational technologies: not just learning management systems, lecture capture systems, and whiteboards and projectors, but in educational technology support staff, and the biggest but most hidden cost, the time of faculty learning how to use learning technologies then applying them. However, almost no-one within the system makes the argument that the investment in learning technologies is to increase productivity – to do so would mean the kiss of death for most learning technology initiatives.

I think it’s time we became a little more grown-up about this whole topic. The growth in post-secondary education is such that we will continue to need professors and instructors for as far as most of us can see into the future. What we should be looking at is how we can get more output from those that are in the system, without necessarily increasing the number of hours that they have to work. To do this, we need to stop fixating on costs alone, but start focusing more on processes (such as teaching methods) and output (such as learning outcomes), and how technology could be used to improve output.

The second point is that models from industry are unlikely to transfer easily to higher education. We need to build models or theories of productivity that fit well with the goals and purposes of education. But that shouldn’t mean that we abandon the idea of productivity – we just need to make sure it fits.

Some key concepts and principles
There is not space here for a full discussion of this topic (see: Productivity and online learning: a summary of the main concepts). Here is a summary of some of the basic concepts or ideas that need to be considered when trying to apply ‘productivity’ to online education or learning technologies.

Inputs and costs
Inputs in education can be measured in various ways, but money invested (or costs) is one convenient way of counting input. Although not without some problems, the measurement of input with regard to online learning is not conceptually difficult.

Outputs
It’s when we come to outputs that there are more difficulties. How do we measure output in education, and in particular how do we relate outputs to inputs? The Organization for Economic Co-operation and Development (OECD) and other agencies use measures such as participation rates, graduation rates, standardized tests, etc., but at a post-secondary level these measures are more difficult. For instance, how do you measure what students have learned and more particularly relate that to the mode of delivery such as classroom versus online? In particular, how do you measure the learning that matters most in higher education?

It’s important that any attempt to measure productivity in education takes account of the nature of learning, and the different views of what constitutes learning. For me, academic learning is a process by which learners develop increasingly deeper and more complex understanding, and above all a capacity for learning how to learn, within a particular subject domain or discipline. This means pushing beyond the superficial presentation and reproduction of information to students understanding what a subject discipline is really about, and behaving and thinking as a professional within that subject domain. My experience of teaching online leads me to believe strongly that this kind of teaching is not only possible but often achieved in online learning. The challenge though is to scale that kind of learning or rather to find ways of teaching online that enable such learning to be successful across large numbers at less cost.

There are likely to be strong disagreements about the definition of valid measures of output, and how to obtain and measure such outputs, which makes the discussion of productivity in education all that more difficult.  Nevertheless, we cannot go on pretending there is no ‘output’ from higher education. We need to agree on some definitions of what constitutes output, and to find ways to validly and reliably measure such outputs.

Scale and scope
One way to increase productivity in industry is through economies of scale: a standardized product that can be replicated very cheaply. MOOCs are a good example of this in education. Once developed they can be made available to an infinite number of learners with no or little increase in marginal costs.

It is worth noting that ‘economies of scale’ are associated with an industrial economic model, but online learning is based on digital not manufacturing technologies. Economies of scope are more associated with post-industrial economies. Economies of scope enable many variations on a standard product to meet individual needs at a low marginal cost for each variation. An example from online learning would be a core curriculum with many optional routes through the material, using adaptive technologies that respond to the inputs from individual students in different ways, depending on the needs of the learner.

Replacement of labor by technology
A key way to improve productivity in the business world is to replace high cost labor with lower cost technology. Post-secondary education certainly has high cost labor, which constitutes a major part of total costs. There is great interest then among policy makers and politicians in the possibility of computer-based learning replacing high labor costs, which partly explains the excitement about MOOCs.

However, there will be no productivity gains by replacing labor (instructors) with technology (computer-based learning), unless outputs are maintained or improved. It is important then to examine carefully the current state of computer-based learning, to see in which parts of teaching and learning computers can effectively replace human instructors. There is also a deeper philosophical issue, which is whether certain aspects of teacher-learner relationships should be replaced by computers. Are there some parts of the educational process that need to remain ‘human’?

Process design and management
Another productivity issue is the efficient design and management of the processes by which ‘inputs’ are turned into ‘outputs.’ The more one can simplify and/or reduce the cost of ‘processing’, while maintaining or enhancing quality, the greater the productivity.

A key process in education is the method of teaching. If we consider for example the development of 21st century skills such as independent learning, this will be heavily influenced not just by the content being taught, but how the teacher designs and/or how learners conduct activities that enable learners to develop such skills. In online learning this is usually called course design. Course design in fact is a major ‘process’ by which the productivity of online learning can be increased.

Conclusion
These are all different ways to look at productivity within a system. It can be seen that it is not a trivial issue, but I still think it is a worthwhile effort to examine at least the potential for online learning to increase productivity, to clarify the issues in doing this, and to have a stab at defining those areas that look most promising, and perhaps even more importantly, identifying possible dangers in certain approaches to productivity.

In my next post I have a stab at identifying promising areas of productivity for online learning.

Tony Bates
Tony Bates Associates Ltd

The full set of posts on productivity and online learning:

• Technology, teaching and productivity: the need for theory
• Is there a link between flexible access and ‘productivity’ in higher education? 
• Book review by Sir John Daniel: Higher Education in the Digital Age
• A review of the HEQCO report on productivity and quality in online learning in higher education
• Tom Carey’s reflections on the HEQCO report on online learning and productivity: 1-Catching a teachable moment
• Tom Carey’s reflections on the HEQCO report on online learning and productivity: 2 – What we left out – and why.
• Towards a theory or model of productivity for online learning: outcomes, scale and design
• How online content development and delivery could improve the productivity of post-secondary education
• Alternative ways to improve productivity through online learner-content interaction
• Improving productivity in online learning: can we scale the ‘learning that matters most’?
• Can online learning lead to productivity gains through savings on campus facilities?
• Productivity and online learning: a summary of the main concepts
• Productivity and online learning redux

Graphic and photos provided by Tony Bates.