Massive open online course completion rates revisited: Assessment, length and attrition

This analysis is based upon enrolment and completion data collected for a total of 221 Massive Open Online Courses (MOOCs). It extends previously reported work (Jordan, 2014) with an expanded dataset; the original work is extended to include a multiple regression analysis of factors that affect completion rates and analysis of attrition rates during courses. Completion rates (defined as the percentage of enrolled students who completed the course) vary from 0.7% to 52.1%, with a median value of 12.6%. Since their inception, enrolments on MOOCs have fallen while completion rates have increased. Completion rates vary significantly according to course length (longer courses having lower completion rates), start date (more recent courses having higher percentage completion) and assessment type (courses using auto grading only having higher completion rates). For a sub-sample of courses where rates of active use and assessment submission across the course are available, the first and second weeks appear to be critical in achieving student engagement, after which the proportion of active students and those submitting assessments levels out, with less than 3% difference between them

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

Quality in MOOCs: Surveying the Terrain

The purpose of this review is to identify quality measures and to highlight some of the tensions surrounding notions of quality, as well as the need for new ways of thinking about and approaching quality in MOOCs. It draws on the literature on both MOOCs and quality in education more generally in order to provide a framework for thinking about quality and the different variables and questions that must be considered when conceptualising quality in MOOCs. The review adopts a relativist approach, positioning quality as a measure for a specific purpose. The review draws upon Biggs’s (1993) 3P model to explore notions and dimensions of quality in relation to MOOCs — presage, process and product variables — which correspond to an input–environment–output model. The review brings together literature examining how quality should be interpreted and assessed in MOOCs at a more general and theoretical level, as well as empirical research studies that explore how these ideas about quality can be operationalised, including the measures and instruments that can be employed. What emerges from the literature are the complexities involved in interpreting and measuring quality in MOOCs and the importance of both context and perspective to discussions of quality

The OpenupEd quality label: benchmarks for MOOCs

In this paper we report on the development of the OpenupEd Quality Label, a self-assessment and review quality assurance process for the new European OpenupEd portal (www.openuped.eu) for MOOCs (massive open online courses). This process is focused on benchmark statements that seek to capture good practice, both at the level of the institution and at the level of individual courses. The benchmark statements for MOOCs are derived from benchmarks produced by the E xcellence e learning quality projects (E-xcellencelabel.eadtu.eu/). A process of self-assessment and review is intended to encourage quality enhancement, captured in an action plan. We suggest that a quality label for MOOCs will benefit all MOOC stakeholders

Pedagogical strategies and technologies for peer assessment in Massively Open Online Courses (MOOCs)

Peer assessment has been mooted as an effective strategy for scaling­-up higher education and its core values to the proportions envisaged in the idea of Massively Open Online Courses (MOOCs). If this is to become reality, what role will academic technologies play? What technologies will we need to provide? What learning design strategies and patterns will those technologies need to enable? This paper aims to explore the potential role of peer assessment in MOOCs, so as to get an informed sense of technology requirements. However, as will be seen, three of the four elements in the title “pedagogical strategies and technologies for peer assessment in MOOCs” vary radically for both practical and philosophical reasons, with significant implications for technology requirements. Worse still, the picture is evolving in non­linear relation to new technologies and MOOC initiatives. An overview of the various trends and differences is useful, but not conclusive. At points in the text learning design strategies, patterns and technologies are mentioned as possible ways in which peer assessment in MOOCs of various kinds might be implemented. These cases are highlighted in bold so as to stand out. They are also, in some cases, developed into simple design patterns, described in Appendix A. It should be noted, however, that they should be read within the wider pedagogical contexts within which they appear in the main body of the text

Quality Assessment for E-learning: a Benchmarking Approach (Third edition)

The primary purpose of this manual is to provide a set of benchmarks, quality criteria and notes for guidance against which e-learning programmes and their support systems may be judged. The manual should therefore be seen primarily as a reference tool for the assessment or review of e-learning programmes and the systems which support them. However, the manual should also prove to be useful to staff in institutions concerned with the design, development, teaching, assessment and support of e-learning programmes. It is hoped that course developers, teachers and other stakeholders will see the manual as a useful development and/or improvement tool for incorporation in their own institutional systems of monitoring, evaluation and enhancement

A Learner-Centred Approach for Lifelong Learning Powered by the Blockchain

The emergence of Blockchain technology promises to revolutionise not only the financial world, but also lifelong learning in many different ways. Blockchain technology offers opportunities to thoroughly rethink how we find educational content and training services online, how we register and pay for them, as well as how we get accredited for what we have learned and how this accreditation affects our career trajectory. This paper explores the different aspects of lifelong learning that are affected by this new paradigm and describes an ecosystem that places the learner at the centre of the learning process and its associated data. More specifically, we outline the ways that ePortfolios, accreditation and tutoring can evolve within this learner-centred ecosystem and we discuss the various benefits that this evolution bears for lifelong learners

Developing sustainable business models for institutions’ provision of open educational resources: Learning from OpenLearn users’ motivations and experiences

Universities across the globe have, for some time, been exploring the possibilities for achieving public benefit and generating business and visibility through releasing and sharing open educational resources (OER). Many have written about the need to develop sustainable and profitable business models around the production and release of OER. Downes (2006), for example, has questioned the financial sustainability of OER production at scale. Many of the proposed business models focus on OER’s value in generating revenue and detractors of OER have questioned whether they are in competition with formal education. This paper reports on a study intended to broaden the conversation about OER business models to consider the motivations and experiences of OER users as the basis for making a better informed decision about whether OER and formal learning are competitive or complementary with each other. The study focused on OpenLearn - the Open University’s (OU) web-based platform for OER, which hosts hundreds of online courses and videos and is accessed by over 3,000,000 users a year. A large scale survey and follow-up interviews with OpenLearn users worldwide revealed that university provided OER can offer learners a bridge to formal education, allowing them to try out a subject before registering on a formal course and to build confidence in their abilities as learners. In addition, it was found that using OER during formal paid-for study can improve learners’ performance and self-reliance, leading to increased retention and satisfaction with the learning experience