15,134 research outputs found
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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
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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
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Discussion Analytics: Identifying Conversations and Social Learners in FutureLearn MOOCs
Discussion among learners in MOOCs has been hailed as beneficial for social constructive learning. To understand the pedagogical value of MOOC discussion forums, several researchers have utilized content analysis techniques to associate individual postings with differing levels of cognitive activity. However, this analysis typically ignores the turn taking among discussion postings, such as learners responding to others’ replies to their posts, learners receiving no reply for their posts, or learners just posting without conversing with others. This information is particularly important in understanding patterns of conversations that occur in MOOCs, and learners’ commenting behaviors. Therefore, in this paper we categorize comments in a FutureLearn MOOC based on their nature (post vs. reply to others’ post), classify learners based on their contributions for each type of post-ing, and identify conversations based on the types of comments composing them. This categorization quantifies the dynamics of conversations in the discussion activities, allowing monitoring of on-going discussion activities in FutureLearn and further analysis of identified conversations, social learners, and course steps with an unusually high number of a particular type of comment
Massive Open Online Courses Temporal Profiling for Dropout Prediction
Massive Open Online Courses (MOOCs) are attracting the attention of people
all over the world. Regardless the platform, numbers of registrants for online
courses are impressive but in the same time, completion rates are
disappointing. Understanding the mechanisms of dropping out based on the
learner profile arises as a crucial task in MOOCs, since it will allow
intervening at the right moment in order to assist the learner in completing
the course. In this paper, the dropout behaviour of learners in a MOOC is
thoroughly studied by first extracting features that describe the behavior of
learners within the course and then by comparing three classifiers (Logistic
Regression, Random Forest and AdaBoost) in two tasks: predicting which users
will have dropped out by a certain week and predicting which users will drop
out on a specific week. The former has showed to be considerably easier, with
all three classifiers performing equally well. However, the accuracy for the
second task is lower, and Logistic Regression tends to perform slightly better
than the other two algorithms. We found that features that reflect an active
attitude of the user towards the MOOC, such as submitting their assignment,
posting on the Forum and filling their Profile, are strong indicators of
persistence.Comment: 8 pages, ICTAI1
A MOOC taxonomy based on classification schemes of MOOCs
In recent years there has been a significant growth in the number of online courses known as MOOCs available via online providers such as edX and Coursera. The result has been a marked reduction in the clarity around the different course offerings and this has created a need to reconsider the classification schemes for MOOCs to help inform potential participants. Many classifications have been proposed which cover the needs of academics and providers but may not be suitable for learners choosing a course. In this paper, the various classifications used by MOOC providers and aggregator services to categorise MOOCs in presenting information to prospective learners are gathered and analysed. As a result, 13 different categories are identified, which cover information provided to learners before entering a course. These categories are then compared and combined with classifications from the literature to create a taxonomy centred round eight terms: Massive (e.g. enrolments), Open (e.g. pre-requisites), Online (e.g. Timings), Assessment, Pedagogy (e.g. instructor-led), Quality (e.g. reviews), Delivery (e.g. educators), Subject (e.g. Syllabus). Thus, producing a taxonomy capable of categorising MOOCs from a wider perspective
Digital resilience in higher education
Higher education institutions face a number of opportunities and challenges as the result of the digital revolution. The institutions perform a number of scholarship functions which can be affected by new technologies, and the desire is to retain these functions where appropriate, whilst the form they take may change. Much of the reaction to technological change comes from those with a vested interest in either wholesale change or maintaining the status quo. Taking the resilience metaphor from ecology, the authors propose a framework for analysing an institution’s ability to adapt to digital challenges. This framework is examined at two institutions (the UK Open University and Canada’s Athabasca University) using two current digital challenges, namely Massive Open Online Courses (MOOCs) and Open Access publishing
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