Towards a generalised e-learning business process model

Modelling learning scenarios is central for e-learning domain. This has been manifested in the proliferation of the different Educational Modelling Languages, as well as in developed e-learning models. However, the existing modelled scenarios are deficient as they lack flexibility and the agility to respond to the dynamic nature of an e-learning process that is suitable to answer learners’ needs. This paper proposes a novel approach to develop a generalised business process model from a set of related business processes sharing the same goals and associated objectives. The proposed approach has been applied in the e-learning domain, which demonstrated its ability to develop a generalised e-learning business process model that is derived from the existing pedagogical models and technology-enhanced learning artefacts. Moreover, the proposed approach has been evaluated to test its effectiveness in generalising a set of business processes, which paves the ground to apply it in different contexts. The generalised e-learning business process model has been modelled using the industrial standard Business Process Modelling Notations (BPMN 2.0) so that processes can be dynamically enacted in service-oriented environments and, at the same time, adapting to answering e-learners’ learning requirements.

[1]  Michael Derntl,et al.  The Conceptual Structure of IMS Learning Design Does Not Impede Its Use for Authoring , 2012, IEEE Transactions on Learning Technologies.

[2]  Juan Manuel Dodero,et al.  Designing the Execution of Learning Activities in Complex Learning Processes Using LPCEL , 2006, Sixth IEEE International Conference on Advanced Learning Technologies (ICALT'06).

[3]  N. Friesen Three objections to learning objects , 2004 .

[4]  Peter Van Rosmalen,et al.  Survey of Educational Modelling Languages (EMLs) , 2002 .

[5]  J. Watson Psychology As The Behaviorist Views It , 2011 .

[6]  A. Collins,et al.  Cognition and learning. , 1996 .

[7]  M Kalantzis,et al.  The conditions of learning , 2005 .

[8]  Daniel Burgos What is wrong with the IMS Learning Design specification? Constraints And Recommendations , 2010, LWA.

[9]  Olusola O. Adesope,et al.  Intelligent tutoring systems and learning outcomes: A meta-analysis , 2014 .

[10]  Jianming Yong Workflow-based e-learning platform , 2005, Proceedings of the Ninth International Conference on Computer Supported Cooperative Work in Design, 2005..

[11]  Christoph Rensing,et al.  Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey , 2015, IEEE Transactions on Learning Technologies.

[12]  Christian M. Stracke Interoperability and Quality Development in e-Learning: Overview and Reference Model for e-Learning Standards , 2013 .

[13]  R. Boyd,et al.  Redefining the discipline of adult education , 1982 .

[14]  Stephen Downes,et al.  Places to Go: Connectivism & Connective Knowledge , 2008 .

[15]  Sara de Freitas,et al.  Review of e-learning theories, frameworks and models. JISC e-learning models study report , 2004 .

[16]  David Wiley,et al.  Connecting learning objects to instructional design theory: A definition, a metaphor, and a taxonomy , 2000 .

[17]  Wei Wang,et al.  Recommender system application developments: A survey , 2015, Decis. Support Syst..

[18]  John M. Wilson,et al.  Business Processes: Modelling and Analysis for Re-engineering and Improvement , 1995 .

[19]  S. Narciss Feedback Strategies for Interactive Learning Tasks , 2007 .

[20]  Davinia Hernández Leo,et al.  Report of the Results of an IMS Learning Design Expert Workshop , 2010, Int. J. Emerg. Technol. Learn..

[21]  M. Knowles Self-directed learning , 1975 .

[22]  Liz Falconer,et al.  Learning spaces in virtual worlds: bringing our distance students home , 2014 .

[23]  Zaheer Abbas Khan,et al.  Towards A Generic Requirements Model for Hybrid and Cloud-based e-Learning Systems , 2013, 2013 IEEE 5th International Conference on Cloud Computing Technology and Science.

[24]  D. Schunk Learning Theories: An Educational Perspective , 1991 .

[25]  Erik Duval,et al.  Dataset-driven research for improving recommender systems for learning , 2011, LAK.