Professional Learning in Massive Open Online Courses

This study explores the role of Massive Open Online Courses (MOOCs) in supporting and enabling professional learning, or learning for work. The research examines how professionals self-regulate their learning in MOOCs. The study is informed by contemporary theories of professional learning, that argue that conventional forms of learning are no longer effective in knowledge intensive domains. As work roles evolve and learning for work becomes continual and personalised, self-regulation is becoming a critical element of professional learning. Yet, established forms of professional learning generally have not taken advantage of the affordances of social, semantic technologies to support self-regulated learning. MOOCs present a potentially useful approach to professional learning that may be designed to encourage self-regulated learning. The study is contextualised within ‘Fundamentals of clinical trials', a MOOC for health professionals designed and run by the Harvard Medical School, Harvard School of Public Health, and Harvard Catalyst, the Harvard Clinical and Translational Science Center, and offered by edX. The research design builds on the authors' previous studies in the areas of Technology Enhanced Learning and Professional Learning and in particular, research which explored the learning behaviours of education professionals in the Change 11 MOOC. The previous studies demonstrated a link between individual learners SRL profile and their goal setting behaviour in the Change 11 MOOC as well as uncovering other factors which influenced their engagement with the MOOC environment. The present study extends the original study by further focusing on specific aspects of self-regulation identified by the Change11 studies and our parallel studies of self-regulated learning in knowledge workers. The analysis of learner behaviour in the Fundamentals of Clinical Trials is complemented by additional exploration of the design considerations of the MOOC, to determine the extent to which course design can support or inhibit self-regulation of learning. The study poses three research questions: How are Massive Open Online Courses currently designed to support self-regulated learning? What self-regulated learning strategies and behaviours do professionals adopt? and How can MOOCs be designed to encourage professionals to self-regulate their learning? Validated methods and instruments from the original study will be adapted and employed. The research is unique in providing evidence around two critical aspects of MOOCs that are not well understood: the skills and dispositions necessary for self-regulated learning in MOOC environments, and how MOOCs can be designed to encourage the development and emergence of SRL behaviours

Connectivism: a knowledge learning theory for the digital age?

<b>Background</b> The emergence of the internet, particularly Web 2.0 has provided access to the views and opinions of a wide range of individuals opening up opportunities for new forms of communication and knowledge formation. Previous ways of navigating and filtering available information are likely to prove ineffective in these new contexts. Connectivism is one of the most prominent of the network learning theories which have been developed for e-learning environments. It is beginning to be recognised by medical educators. This paper aims to examine connectivism and its potential application.<p></p> <b>Content</b> The conceptual framework and application of connectivism are presented along with an outline of the main criticisms. Its’ potential application in medical education is then considered.<p></p> <b>Conclusions</b> While connectivism provides a useful lens through which teaching and learning using digital technologies can be better understood and managed, further development and testing is required. There is unlikely to be a single theory that will explain learning in technological enabled networks. Educators have an important role to play in online network learning

Community tracking in a cMOOC and nomadic learner behavior identification on a connectivist rhizomatic learning network

This article contributes to the literature on connectivism, connectivist MOOCs (cMOOCs) and rhizomatic learning by examining participant interactions, community formation and nomadic learner behavior in a particular cMOOC, #rhizo15, facilitated for 6 weeks by Dave Cormier. It further focuses on what we can learn by observing Twitter interactions particularly. As an explanatory mixed research design, Social Network Analysis and content analysis were employed for the purposes of the research. SNA is used at the macro, meso and micro levels, and content analysis of one week of the MOOC was conducted using the Community of Inquiry framework. The macro level analysis demonstrates that communities in a rhizomatic connectivist networks have chaotic relationships with other communities in different dimensions (clarified by use of hashtags of concurrent, past and future events). A key finding at the meso level was that as #rhizo15 progressed and number of active participants decreased, interaction increased in overall network. The micro level analysis further reveals that, though completely online, the nature of open online ecosystems are very convenient to facilitate the formation of community. The content analysis of week 3 tweets demonstrated that cognitive presence was the most frequently observed, while teaching presence (teaching behaviors of both facilitator and participants) was the lowest. This research recognizes the limitations of looking only at Twitter when #rhizo15 conversations occurred over multiple platforms frequented by overlapping but not identical groups of people. However, it provides a valuable partial perspective at the macro meso and micro levels that contribute to our understanding of community-building in cMOOCs

Together we stand, Together we fall, Together we win: Dynamic Team Formation in Massive Open Online Courses

Massive Open Online Courses (MOOCs) offer a new scalable paradigm for e-learning by providing students with global exposure and opportunities for connecting and interacting with millions of people all around the world. Very often, students work as teams to effectively accomplish course related tasks. However, due to lack of face to face interaction, it becomes difficult for MOOC students to collaborate. Additionally, the instructor also faces challenges in manually organizing students into teams because students flock to these MOOCs in huge numbers. Thus, the proposed research is aimed at developing a robust methodology for dynamic team formation in MOOCs, the theoretical framework for which is grounded at the confluence of organizational team theory, social network analysis and machine learning. A prerequisite for such an undertaking is that we understand the fact that, each and every informal tie established among students offers the opportunities to influence and be influenced. Therefore, we aim to extract value from the inherent connectedness of students in the MOOC. These connections carry with them radical implications for the way students understand each other in the networked learning community. Our approach will enable course instructors to automatically group students in teams that have fairly balanced social connections with their peers, well defined in terms of appropriately selected qualitative and quantitative network metrics.Comment: In Proceedings of 5th IEEE International Conference on Application of Digital Information & Web Technologies (ICADIWT), India, February 2014 (6 pages, 3 figures

Understanding Communication Patterns in MOOCs: Combining Data Mining and qualitative methods

Massive Open Online Courses (MOOCs) offer unprecedented opportunities to learn at scale. Within a few years, the phenomenon of crowd-based learning has gained enormous popularity with millions of learners across the globe participating in courses ranging from Popular Music to Astrophysics. They have captured the imaginations of many, attracting significant media attention - with The New York Times naming 2012 "The Year of the MOOC." For those engaged in learning analytics and educational data mining, MOOCs have provided an exciting opportunity to develop innovative methodologies that harness big data in education.Comment: Preprint of a chapter to appear in "Data Mining and Learning Analytics: Applications in Educational Research

Mapping the open education landscape: citation network analysis of historical open and distance education research

The term open education has recently been used to refer to topics such as Open Educational Resources (OERs) and Massive Open Online Courses (MOOCs). Historically its roots lie in civil approaches to education and open universities, but this research is rarely referenced or acknowledged in current interpretations. In this article the antecedents of the modern open educational movement are examined, as the basis for connecting the various strands of research. Using a citation analysis method the key references are extracted and their relationships mapped. This work reveals eight distinct sub-topics within the broad open education area, with relatively little overlap. The implications for this are discussed and methods of improving inter-topic research are proposed