Universitat Pompeu Fabra (SPAIN)
About this paper:
Appears in: EDULEARN17 Proceedings
Publication year: 2017
Pages: 6277-6286
ISBN: 978-84-697-3777-4
ISSN: 2340-1117
doi: 10.21125/edulearn.2017.2422
Conference name: 9th International Conference on Education and New Learning Technologies
Dates: 3-5 July, 2017
Location: Barcelona, Spain
Collaborative Learning is the pedagogical approach that considers social interactions as key means to trigger rich learning processes. Computer Supported Collaborative Learning (CSCL) is the research field that studies and proposes technological support to orchestrate learners when forming groups, allocating roles or activity resources and changing activity phases. Collaborative Learning Flow Patterns (CLFPs) define best practices to orchestrate the activity flow while implying collaboration mechanisms that can be modelled as learning design constraints (e.g., characteristics of group formation). CLFPs have been experimented and evaluated as effective in small scale CSCL settings for decades. But, research around scalable collaborative learning approaches, models and technologies for large classrooms or large learning communities is scattered across without a comprehensive body of knowledge. Direct application of existing CSCL methods to large leaning scenarios is challenging due to lack of scalability in the proposed methods or due to the dynamic nature of the community as in Massive Open Online Courses (MOOCs) where diverse learner motivations and behaviours are inherent. Some attempts have shown positive results in applying CSCL in large classes. Yet, such studies are either contextualised or lack in structuring orchestration following CLFPs. Therefore within this work, we try to understand potential flow patterns and related concerns when applying collaborative learning in large learner communities.

In this work, we present an analysis of three commonly used patterns (Pyramid, Jigsaw and TAPPS CLFPs) in small-scale settings in order to be adaptable in large learner communities. The methodology followed is analytical; more precisely, each pattern is analysed considering four dimensions, to inspect the level of pedagogical appealing within such contexts, how far it is scalable from both student and practitioner perspectives, how it is MOOC-adaptable considering much diverse and unpredictable nature of a MOOC, and finally how collaborations can be made further meaningful. As scalability we have considered the practicality and feasibility of managing the learning scenario by practitioners and the ability for learners to easily engage in the activity. We synthesise series of potential use cases expressing the applicability of CLFPs to large learner communities as a key result derived from the analytical study. Moreover, the results indicate that most encouraging prospects for scalability are from the cases of Pyramid CLFP. Consequently, Pyramid pattern is instantiated as a scalable model and technologically implemented as “PyramidApp” which has been evaluated in 13 experimentations across 4 higher educational levels varying from undergraduate to Masters’ levels including 2 studies in a MOOC. With accumulating collaborations while being scalable and preserving dynamism, PyramidApp received positive perceptions from participants. PyramidApp particularization is one example of scalable CSCL approach; yet this analytical study reveals more suggestions to design and implement scalable CSCL orchestration technologies inspired by the use cases synthesised.
Collaborative Learning, Computer-Supported Collaborative Learning, Collaborative Learning Flow Patterns, Learning at Scale, MOOC