Analysis of Massive E-learning Processes: An Approach Based on Big Association Rules Mining

In today’s learning environments (MOOCs), process learning in highly dynamic. This dynamicity generates a large volume of data describing the handling of such processes. Mining that data is a powerful discipline for getting valuable insights. Thus big data mining becomes more and more an integral part of handling a business process management (BPM) project. Furthermore, using data mining outputs to extend process model with additional knowledge allows to provide more details for future process improvement. In this paper we aim to apply data mining techniques (Association rules mining) to recorded data in the context of massive learning process.

[1]  Robin Bergenthum,et al.  Modeling and Mining of Learnflows , 2012, Trans. Petri Nets Other Model. Concurr..

[2]  Qing He,et al.  Parallel Implementation of Apriori Algorithm Based on MapReduce , 2012, SNPD.

[3]  Wil M. P. van der Aalst,et al.  Decision Mining in ProM , 2006, Business Process Management.

[4]  Wil M. P. van der Aalst,et al.  Process Mining: Overview and Opportunities , 2012, ACM Trans. Manag. Inf. Syst..

[5]  Denis Helic.  Technology-supported management of collaborative learning processes , 2006 .

[6]  Jane Sinclair,et al.  Dropout rates of massive open online courses : behavioural patterns , 2014 .

[7]  George Siemens,et al.  Learning analytics and educational data mining: towards communication and collaboration , 2012, LAK.

[8]  Ashutosh Tiwari,et al.  Business Process Analysis and Optimization: Beyond Reengineering , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[9]  Hajo A. Reijers,et al.  Supporting the BPM lifecycle with FileNet , 2006 .

[10]  Maria E. Orlowska,et al.  Using Workflow Technology to Manage Flexible e-Learning Services , 2002, J. Educ. Technol. Soc..

[11]  Weiming Shen,et al.  Incremental FP-Growth mining strategy for dynamic threshold value and database based on MapReduce , 2014, Proceedings of the 2014 IEEE 18th International Conference on Computer Supported Cooperative Work in Design (CSCWD).

[12]  B. JovaniA.Jiménez,et al.  Integrating Business Process Management and Data Mining for Organizational Decision Making , 2015, Res. Comput. Sci..

[13]  I. Ioniță.  Data mining technique for e-learning , 2016 .

[14]  Ricardo Colomo Palacios,et al.  Business Process Analytics Using a Big Data Approach , 2013, IT Professional.

[15]  Carlos J. Costa,et al.  Analysis of e-learning processes , 2011, OSDOC '11.

[16]  P. K. Mishra,et al.  Review of Apriori Based Algorithms on MapReduce Framework , 2017, ArXiv.

[17]  Àngela Nebot,et al.  Applying Data Mining Techniques to e-Learning Problems , 2007 .

[18]  Cristóbal Romero,et al.  Clustering for improving educational process mining , 2014, LAK.

[19]  Ricardo Seguel,et al.  Process Mining Manifesto , 2011, Business Process Management Workshops.

[20]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[21]  Hermann A. Maurer,et al.  An Analysis of Application of Business Process Management Technology in E-Learning Systems , 2005 .

[22]  Mirko Cesarini,et al.  Carrying on the e-learning process with a workflow management engine , 2004, SAC '04.

[23]  Stefan Rüping,et al.  On Integrating Data Mining into Business Processes , 2010, BIS.