One of the key factors that determines the success and effectiveness of professional learning is self-regulation: the learner’s ability to plan and execute actions that lead to the desired outcomes, and their ability to monitor and respond to their emotional and cognitive reactions to the learning experiences. Most MOOCs are not designed to encourage self-regulated learning, either because MOOC designers are not aware of its importance or because they do not know how to translate the abstract concept into concrete features. Providers require better support in designing MOOCs that will promote and facilitate self-regulated learning.
The Open Education Studio launches a conversation on MOOCs design that is nowadays increasingly relevant for all corporations.
Our conversation starts with an article(1) by Allison Littlejohn and Colin Milligan that presents two sets of tools to guide MOOC designers, promote self-regulated learning and thus help learners meet their individual needs.
Employers are becoming aware of the potential of Massive Open Online Courses (MOOCs) as an emerging form of professional learning, or learning for work(2) . MOOCs have the potential to provide unlimited learning opportunities through online, open access. As such, they offer learning that complements other forms of professional development, such as training or on-the-job learning(3).
In an era when knowledge and job roles are changing continually, companies are constantly seeking new ways to enable their workforce to up-skill quickly(4) . Yet conventional forms of professional learning, such as classroom based training, are becoming less effective as a means of learning in knowledge-intensive domains(5) . Conventional training was developed as a means of skilling large numbers of people for specific jobs. However, as work roles evolve, learning for work becomes continuous and personalized and people have to be able to determine their own learning pathway through self-regulation(6) . Yet, established forms of professional learning generally have not taken advantage of the affordances of social, semantic technologies to support personalized and self-regulated learning.
MOOCs have the potential to transform professional learning by utilizing social, networked technologies to support personalized and self-regulated learning. However, successful innovation requires good design choices. A Caledonian Academy study of the design of 76 different MOOCs concluded that the instructional quality of almost all of the MOOCs examined was low(7) . Most of the 76 MOOCs scored highly on the organisation and presentation of the course material, but few designs supported interaction and feedback, a key principle of effective instructional design (ibid). Many teachers are experts in a specific discipline or skills area, rather than in pedagogy(8) . This mismatch in expertise makes it difficult for teachers to design effective MOOCs. This design problem is intensified in professional contexts, where MOOC designs should encourage professionals to actively self-regulate their learning, so that they may tailor their learning to specific work problems(9) as explored in the 'Professional Learning in MOOCs' study funded by the Bill and Melinda Gates Foundation MOOC Research Initiative (http://www.gcu.ac.uk/academy/pl-mooc/).
MOOC design could be improved using design support tools, such as the pedagogical patterns(10) . These patterns guide teachers and instructional designers in course design, which is particularly important while developing (relatively) new course formats, such as MOOCs. Initiatives such as the MOOC Design Patterns Project (http://www.moocdesign.cde.london.ac.uk/) have sought to articulate emerging MOOC design principles through a pattern approach. This paper outlines the development of patterns to support the design of MOOCs for professional learners. The research addresses the research question: How can Massive Open Online Courses be designed to support self-regulated learning?
Toolsets to support MOOC design are outlined. The first is a set of design patterns to guide teachers in designing MOOCs environments that encourage self-regulated learning and meet the needs of professional learners. The second toolset, the Design Team Questionnaire (MOOC-DTQ), can be used post-design to audit MOOC designs against principles of self-regulated learning to identify effectiveness and potential scope for improvement.
The article outlines the development of these toolsets. The paper begins by problematizing MOOC design. The tool development methods are then presented. Finally the tools are described and discussed.
Problems with MOOC design
MOOC learning is suited to the networked society, founded upon the near ubiquity of digital, networked connections(11) . Rather than viewing learning as the transmission of expert knowledge from an instructor to learners, MOOCs were originally conceptualized around connectivist principles, based on the idea that learning occurs through network connections, as learners connect with their peers and with knowledge resources(12) . As such, MOOCs seem to offer a powerful means of professional learning, where considerable knowledge and expertise resides with the learner as well as with the instructor.
In professional learning, each learner brings a unique knowledge set to the learning environment, along with his or her professional and personal networks(13) . The networked environment acts as a catalyst for the formation of heterogeneous, dynamic learning communities that facilitate knowledge exchange. From this perspective, MOOCs appear to offer a useful environment to support and encourage professional learning.
However, although digital networks provide environments that connect work and learning, established forms of professional learning largely have not taken advantage of the multiple ways in which people and resources can be brought together to enhance learning(14) . There are untapped opportunities around how people collaborate and how feedback can be generated and exploited for learning. In conventional face-to-face teaching, the teacher has a better view of the learner’s progress and pathway than in a MOOC.
Metrics of success range from registration, participation, retention and progression to completion or assessment data and pass rates, with the assumption that these indicators indirectly signify learning. These conventional metrics are being applied to Massive Open Online Courses as a measure of ‘success’. However learners have fewer opportunities to be seen by and to interact directly with instructors, so the responsibility is on the learner to remain active throughout the course. An underlying assumption of MOOC design is that learners have the necessary ability to learn autonomously. However, MOOCs attract a broad range of learners and not all of them self-regulate their learning(15) .
Self-regulation is a critical aspect of professional learning, as learning for work becomes more continual and individualized(16) . In many organizations, people’s work roles are fluid and constantly changing, and people have to draw continuously from knowledge across disciplinary or sectorial frontiers, working within the complex networks found in knowledge intensive workplaces(17) .
Self-regulation is critical under these circumstances. Self-regulated learning enables people to ‘future-proof’ their skills, making them more flexible as workers(18) , allowing them to plan, share and co-develop their learning goals to learn within and from their professional networks(19) .
Self-regulation includes ‘self-generated thoughts, feelings and actions that are planned and cyclically adapted to the attainment of personal goals’(20) . Zimmermann’s theory describes learning in three phases (planning, performance and self-reflection) interconnected through affective, behavioural and cognitive sub-processes. Sub-processes range from cognitive factors such as motivation, interest, self-reflection and self-evaluation; to behavioural factors such as goal-setting and learning strategies; to cognitive factors including self-efficacy and self-satisfaction(21) .
Previous research examining how professionals learn in MOOCs provides empirical evidence that learners with high self-regulation have different cognitive, affective and behavioural responses to learning in a MOOC than those displaying low self-regulation(22) .
Self-regulated learners tend to follow the parts of a MOOC that help them solve a problem. They link their participation in the MOOC to work performance or personal interest. This motivation impacts the learners’ goal setting, self-evaluation, and self-satisfaction.
There is evidence that highly self-regulated learners self-evaluate their performance against their own benchmarks, measuring their progress in relation to their intended goals and ambition. This strategy has a positive impact on self-satisfaction. By contrast low self-regulators tend to follow the instructional pathway of the course. Self-evaluation is more challenging, because these learners self-evaluate their progress against externally prescribed benchmarks set by the course designers. This situation impacted on their self-satisfaction.
Self-regulated learning is not a ‘learning style’ rather; it is a response to a learning situation. A learner’s ability to self-regulate is context dependent - influenced not just by their personal dispositions, but also by factors associated with the environment in which they are learning. There is evidence that learning strategies in MOOCs are influenced not only by learners’ motivation and confidence, but also by the structure of course, the delivery environment and the perceived value of learning(23) .
In formal learning contexts, researchers(24) have explored the role of self-regulation in learner behaviour online. In these studies, a clear link between self-regulation and learning success in online environments is established, focusing on self-efficacy, interactions with others, and strategies for regulation.
Some cognitive, affective and behavioural factors associated with self-regulation can be encouraged through the design of the learning environment(25) . Factors that are relatively easy to influence include help-seeking or learning strategies while other factors, such as self-efficacy, are more difficult to impact. Nevertheless there is opportunity here to design MOOCs that promote self-regulated learning behaviour. The following section describes pre- and post-design tools developed to support MOOC design. These design tools are outputs from a larger study on professional learning in Massive Open Online Courses http://www.gcal.ac.uk/academy/pl-mooc/
MOOC design tools
This section describes pre- and post-design tools developed to support teachers with MOOC design. First the MOOC-SRL patterns were designed to guide teachers and instructional designers on MOOC design features that encourage self-regulated learning. Second, the MOOC Design Team Questionnaire (MOOC-DTQ) tool is a post-design audit instrument to examine the design decisions underlying MOOC environment and learning design. The MOOC-DTQ is available at: http://dx.doi.org/10.6084/m9.figshare.907150, and the full text of the MOOC-SRL (and other) patterns can be found at: http://www.moocdesign.cde.london.ac.uk/outputs/patterns.
1. ADAPTABLE COURSE GOALS/OBJECTIVES
You want to make sure professional learners are engaged in the course, but you find they do not need to learn all the course objectives, so you enable them to set their own objectives.
2. REFLECT ON BOTH THEORY AND PRACTICE
You want the learning to be valuable to the learners’ on-going professional practice, but you find a misalignment between the course content and their work. You encourage the learners to align the course theory with their professional practice.
3. CAPITALISE ON DIVERSITY
You want to make your MOOC valuable to all your learners, but their backgrounds and aspirations are diverse. You encourage the learners to learn through sharing and building knowledge, capitalising on their diversity.
4. BREAK DOWN THE BARRIERS
You want to take advantage of the wealth of learning opportunities that your learners have access to within their professional networks, but they tend to stick to the course pathway (i.e. pre-determined activities and knowledge within the course). You encourage them to discuss their learning with a wide range of people across their professional networks, as well as within the course.
5. PRODUCTIVE MOOCS
You want to leave your learners with more than a certificate at the end of the course, but you find that they focus on achieving the course certificate. You encourage them to engage in authentic tasks to help them gain lasting knowledge.
MOOC Design Team Questionnaire
The MOOC Design Team Questionnaire collects information on MOOC design. The instrument is structured as a set of 54 questions, each focused on the phases and sub-processes of self-regulated learning(26) . Each question probes whether the course design would encourage particular self-regulated learning behaviours.
The audit tool is designed to be used by an independent researcher or self-administered by the course designers. Questions are directed at different members of the course team, depending on their focus: questions about the overall course philosophy are directed to the strategic lead; technical questions are directed to the platform developer, questions about the specific learning design of the course are directed to the course design team, and finally, questions about how the course works in practice are directed to course teaching assistants.
A copy of the instrument is available from figshare (see the outputs page of the PL-MOOC project: http://www.gcu.ac.uk/academy/pl-mooc/outputs/) and was trialled with a team of instructional designers who designed Fundamentals of Clinical Trials MOOC. This audit highlighted the inward focus of the course as well as the inflexibility of the platform. These observations were confirmed in our study interviews as participants articulated how they engaged with the course and their professional networks. Data collected by the instrument provides a record of the learning design of the MOOC, focusing on the mechanisms by which it supports, or fails to support learners in self-regulating their learning. The instrument highlights how design can be influenced by strategic or technical factors, in addition to pedagogical decisions. It also collects information from teaching assistants, regarding how the course is perceived by learners and how those learners engage with the course. The data collected can be used to identify gaps and opportunities for subsequent design revision.
MOOCs should be designed to encourage and facilitate self-regulated learning. This paper has outlined two toolsets that MOOC designers can use to achieve this goal. Design patterns are a mechanism for sharing design knowledge of value to both researchers and practitioners. The MOOC SRL patterns described here emphasises the importance of accommodating the particular needs of professional learners and capitalising on the networks and expertise they bring with them as they learn. For researchers, the patterns provide a common language for describing MOOC designs to support further study. For practitioners, these design patterns demonstrate ways in which courses can take advantage of the knowledge and expertise that professional learners bring to their formal learning experience, and highlight the importance of course design that engages professional learners and meets their individual needs.
The MOOC-DTQ tool guides instructional designers in pedagogic design decisions made at platform (macro) level as well as at course (micro) level. This tool enables instructional designers to audit their design decisions and provides examples of possible interventions that may improve their design. Many of these activities are applicable within Massive Open Online Courses, though the context, discipline and level of study must be taken into consideration.
(1) This article is an abridged version of an article published by the authors in the eLearning papers and available here.
(2) Radford, A. W., Robles, J., Cataylo, S., Horn, L., Thornton, J., & Whitfield, K. E. (2014). The employer potential of MOOCs: A mixed-methods study of human resource professionals’ thinking on MOOCs. The International Review of Research in Open and Distributed Learning, 15(5) 1-25.
(3) Milligan, C. & Littlejohn, A. (2014) Supporting professional learning in a massive open online course. International Review of Research in Open and Distributed Learning 15 (5) 197-213.
(4) Littlejohn, A., & Margaryan, A. (2014). Technology-enhanced professional learning: Mapping out a new domain. In Littlejohn, A., & Margaryan, A. (Eds.). Technology-enhanced professional learning: Processes, practices and tools (pp. 1-13). London: Routledge.
(5) Fiedler, S.H.D. (2014). Commentary on Section I: Work Practices. In Littlejohn, A., & Margaryan, A. (Eds.). Technology-enhanced professional learning: Processes, practices and tools (pp. 50-56). London: Routledge.
(6) Littlejohn, A., & Margaryan, A. (2014). Technology-enhanced professional learning: Mapping out a new domain. In Littlejohn, A., & Margaryan, A. (Eds.). Technology-enhanced professional learning: Processes, practices and tools (pp. 1-13). London: Routledge.
(7) Margaryan, A., Bianco, M., & Littlejohn, A. (2015). Instructional quality of Massive Open Online Courses (MOOCs). Computers and Education, 80, 77- 83.
(8) Goodyear, P. (2005) Educational design and networked learning: Patterns, pattern languages and design practice, Australian Journal of Educational Technology, 21 (1), 82-101 Available from: http://www.ascilite.org.au/ajet/ ajet21/goodyear.html
(9) Milligan, C. & Littlejohn, A. (2014) Supporting professional learning in a massive open online course. International Review of Research in Open and Distributed Learning 15 (5) 197-213.
(10) Eckstein, J., Bergin, J., & Sharp, H. (2002). Patterns for active learning. In Proceedings of PloP (Vol. 2002); Goodyear, P. (2005) Educational design and networked learning: Patterns, pattern languages and design practice, Australian Journal of Educational Technology, 21 (1), 82-101 Available from: http://www.ascilite.org.au/ajet/ ajet21/goodyear.html.
(11) Castells, M. (1996). The information age: economy, society and culture: The rise of the networked society. Oxford, UK: Blackwell.
(12) Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1), 3-10. Retrieved from http://www.itdl.org/Journal/Jan_05/article01.htm; Downes, S. (2009). Connectivist dynamics in communities. Retrieved from http://halfanhour.blogspot.co.uk/2009/02/connectivist-dynamics-in- communities.html
(13) Littlejohn, A., Milligan, C., & Margaryan, A. (2012). Charting Collective Knowledge: Supporting self-regulated learning in the workplace. Journal of Workplace Learning, 24(3) 226-238.
(14) Littlejohn, A., & Margaryan, A. (2014). Technology-enhanced professional learning: Mapping out a new domain. In Littlejohn, A., & Margaryan, A. (Eds.). Technology-enhanced professional learning: Processes, practices and tools (pp. 1-13). London: Routledge.
(15) Milligan, C., Littlejohn, A., & Margaryan, A. (2013) Patterns of engagement in connectivist MOOCs. Journal of Online Learning and Teaching 9 (2) 149- 159; Milligan, C. & Littlejohn, A. (2014) Supporting professional learning in a massive open online course. International Review of Research in Open and Distributed Learning 15 (5) 197-213.
(16) Eraut, M. (2000). Non-formal learning and tacit knowledge in professional work. British Journal of Educational Psychology, 70, 113-136; Tynjälä, P. (2008). Perspectives into learning at the workplace. Educational Research Review, 3 (2), 130-154.
(17) Veen, W., van Staalduinen, J-P. , & Hennis, T. (2011). Informal self-regulated learning in corporate organizations. In G. Dettori & D. Persico (Eds.), Fostering Self-regulated learning through ICT, (pp. 364-379). Hershey, PA: IGI Global.
(18) Lefrere, P. (2007). “Business success - the special contribution of self- regulated learning.” In J. Beishuizen, R. Carniero & K. Steffens (Eds.) Self-regulated Learning in Technology Enhanced Learning Environments: Individual Learning and Communities of Learners. pp. 49-53. Shaker Verlag: Aachen.
(19) Siadaty, M., Jovanović, J., & Gašević, D. (2013). The social semantic web and workplace learning. Chapter 12 in Littlejohn, A., & Margaryan, A. (Eds.). Technology-enhanced professional learning: Processes, practices and tools. Pp132-143. London, Routledge
(20) Zimmerman, B. J. (2000). Attaining Self-regulation: A Social Cognitive Perspective. In M. Boekaerts, P.R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation, San Diego, Academic Press. p. 14.
(21) Fontana, R.P., Milligan, C., Littlejohn, A., and Margaryan, A. (2015) Measuring self-regulated learning in the workplace. International Journal of Training and Development. 19 (1) 32-52.
(22) Milligan, C. & Littlejohn, A. (under review) Self-regulated learning and MOOC Participation; Hood, N., Littlejohn, A., & Milligan, C. (under review). Context counts: Learning in a MOOC.
(23) Kop, R. (2011). The challenges to connectivist learning on open online networks: Learning experiences during a Massive Open Online Course. The International Review of Research in Open and Distributed Learning, 12 (3), 19-38.
(24) Cho, M-H., & Kim, B.J. (2013). Students’ self-regulation for interaction with others in online learning environments. The Internet and higher Education, 17, 69-75.
(25) See Bernacki, M. L., Aguilar, A., & Byrnes, J. (2011). Self-regulated learning and technology-enhanced learning environments: An opportunity propensity analysis. In G. Dettori and D. Persico (Eds.), Fostering self-regulated learning through ICT (pp. 1-. Hershey, PA: IGI Global Publishers for a comprehensive overview of how online course environments promote self-regulated learning
(26) Zimmerman, B. J. (2000). Attaining Self-regulation: A Social Cognitive Perspective. In M. Boekaerts, P.R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation, San Diego, Academic Press.