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Virtual learning process environment (VLPE): a BPM-based learning process management architecture

Adesina, Ayodeji (2013) Virtual learning process environment (VLPE): a BPM-based learning process management architecture. PhD thesis, Dublin City University.

Abstract
E-learning systems have significantly impacted the way that learning takes place within universities, particularly in providing self-learning support and flexibility of course delivery. Virtual Learning Environments help facilitate the management of educational courses for students, in particular by assisting course designers and thriving in the management of the learning itself. Current literature has shown that pedagogical modelling and learning process management facilitation are inadequate. In particular, quantitative information on the process of learning that is needed to perform real time or reflective monitoring and statistical analysis of students’ learning processes performance is deficient. Therefore, for a course designer, pedagogical evaluation and reform decisions can be difficult. This thesis presents an alternative e-learning systems architecture - Virtual Learning Process Environment (VLPE) - that uses the Business Process Management (BPM) conceptual framework to design an architecture that addresses the critical quantitative learning process information gaps associated with the conventional VLE frameworks. Within VLPE, course designers can model desired education pedagogies in the form of learning process workflows using an intuitive graphical flow diagram user-interface. Automated agents associated with BPM frameworks are employed to capture quantitative learning information from the learning process workflow. Consequently, course designers are able to monitor, analyse and re-evaluate in real time the effectiveness of their chosen pedagogy using live interactive learning process dashboards. Once a course delivery is complete the collated quantitative information can also be used to make major revisions to pedagogy design for the next iteration of the course. An additional contribution of this work is that this new architecture facilitates individual students in monitoring and analysing their own learning performances in comparison to their peers in a real time anonymous manner through a personal analytics learning process dashboard. A case scenario of the quantitative statistical analysis of a cohort of learners (10 participants in size) is presented. The analytical results of their learning processes, performances and progressions on a short Mathematics course over a five-week period are also presented in order to demonstrate that the proposed framework can significantly help to advance learning analytics and the visualisation of real time learning data.
Metadata
Item Type:Thesis (PhD)
Date of Award:March 2013
Refereed:No
Supervisor(s):Molloy, Derek
Subjects:Social Sciences > Education
Computer Science > Information technology
Social Sciences > Distance education
Social Sciences > Educational technology
Computer Science > Software engineering
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 3.0 License. View License
Funders:IRCSET
ID Code:17711
Deposited On:03 Apr 2013 12:55 by Derek Molloy . Last Modified 19 Jul 2018 14:58
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