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Investigating self-regulated learning in massive open online courses : a design science research approach
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Onah, Daniel F. O. (2017) Investigating self-regulated learning in massive open online courses : a design science research approach. PhD thesis, University of Warwick.
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Official URL: http://webcat.warwick.ac.uk/record=b3098615~S15
Abstract
Massive open online courses (MOOCs) have received wide publicity and many institutions have invested considerable effort in developing, promoting and delivering such courses. However, there are still many unresolved questions relating to MOOCs and their effectiveness. One of the major recurring issues raised in both academic literature and the popular press is the consistently high dropout rate of MOOC learners. Despite the impressive levels of enrolment MOOCs attract, many participants do not complete these courses resulting in completion rates of below 15% for most MOOCs. Although there are many reasons for attrition, a lack of understanding of how diverse learners can be supported to study effectively within this format has been identified as an important contributing issue. The current research addresses two factors which relate to how MOOC participants learn and their ability to make effective progress. Firstly, MOOCs require a high degree of self-regulated learning (SRL) skills but most do not appear to offer adequate support for the development of such skills. To determine the implications of this and develop appropriate support strategies it is necessary to understand more about the concept of SRL in the context of MOOCs and MOOC participants. Related to the issue of self-regulation is the inflexibility and passivity of many current MOOC formats, preventing individuals from setting their own learning objectives and directing their own learning.
MOOCs have so far been used mainly to provide stand-alone distance learning opportunities for independent learners. However, there is an increasing focus on their benefits when incorporated into a blended-learning approach. This study investigates the issues of self-regulation and learner autonomy within MOOCs. To better understand the contextual differences between the two very different learning modes, the research considers two separate MOOC applications: one stand-alone, the other blended. Both qualitative and quantitative data collection methods were used to explore learners' SRL skills, autonomous choices and ways of working. An existing conceptualisation of SRL incorporating six separate contributing dimensions was adopted as the theoretical framework for the investigation.
Overall, a design science methodology was adopted. Central to this was the development of a novel MOOC platform (eLDa) which was designed to support learners' individual choices relating to goal-setting and the selection of learning path. Elements of established good-practice for MOOC platforms were incorporated into the design together with additional functionality to support the novel features of optional self-direction. In order to study the two contexts noted above, two separate courses were implemented and delivered using this platform. The first was an open online course for independent learners regardless of location; the second was incorporated as part of a blended-learning approach within a traditional campus university module. Data gathered from these courses provide insights into learners' self-regulation within the two contexts individually and also allow a comparative analysis of the different dimensions of SRL between differing teaching modalities. Qualitative data from students also contribute to an understanding of their experience of MOOC study and of how they regulate their learning in practice.
The first major contribution of this work is an architecture for and the development of a novel MOOC platform which can be used to provide the necessary functionalities to a greater degree of supporting learners' self-direction. Analysis of the data obtained from the two case studies shows different patterns of SRL. The online course results indicate that there is a high demand for more flexible, self-directed learning but that MOOC learners exhibit deficiencies in specific SRL dimensions. Help seeking and deploying task strategies were indicated as being problematic for the fully online learners. Participants in the blended-learning course generally had lower scores on time management and self-evaluation. Although there were considerable differences between individual students, even learners with a strong formal educational background and an existing track-record of successful learning mostly did not obtain high SRL scores. A high level of social interaction and support-seeking from peers was reported, indicating the increasing importance of social online learning even within a campus university. Analysis of the qualitative data reveals study practices which are obviously highly effective for the learners who employ them but which do not necessarily fall within existing conceptualisations of SRL.
This study demonstrates that the novel approach taken to supporting self-direction within MOOCs is one which users evaluate as being both desirable and useful. Further, it points to areas of SRL for which MOOCs should in general develop better support, while at the same time indicating strategies for SRL which are not accommodated within current definitions. This work lends support to the view that SRL is highly context-dependent and suggests that further investigation is needed to capture more appropriate conceptualisations of SRL for online and blended-learning with MOOCs.
Item Type: | Thesis (PhD) | ||||
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Subjects: | L Education > LB Theory and practice of education Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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Library of Congress Subject Headings (LCSH): | MOOCs (Web-based instruction) -- Evaluation, Web-based instruction -- Evaluation, Self-managed learning, Independent study, Blended learning | ||||
Official Date: | January 2017 | ||||
Dates: |
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Institution: | University of Warwick | ||||
Theses Department: | Department of Computer Science | ||||
Thesis Type: | PhD | ||||
Publication Status: | Unpublished | ||||
Supervisor(s)/Advisor: | Sinclair, Jane ; Joy, Mike ; Liakata, M. | ||||
Format of File: | |||||
Extent: | xxxi, 356 leaves : illustrations | ||||
Language: | eng |
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