A decision support system to improve e-learning environments

Nowadays, due to the lack of face-to-face contact, distance course instructors have real difficulties knowing who their students are, how their students behave in the virtual course, what difficulties they find, what probability they have of passing the subject, in short, they need to have feedback which helps them to improve the learning-teaching process. Although most Learning Content Management Systems (LCMS) offer a reporting tool, in general, these do not show a clear vision of each student's academic progression. In this work, we propose a decision making system which helps instructors to answer these and other questions using data mining techniques applied to data from LCMSs databases. The goal of this system is that instructors do not require data mining knowledge, they only need to request a pattern or model, interpret the result and take the educational actions which they consider necessary.

[1]  Marta E. Zorrilla.  Data Warehouse Technology for E-Learning , 2009 .

[2]  Andrew R. Brown,et al.  Elements of Effective e-Learning Design , 2005 .

[3]  Yiyu Yao,et al.  Explanation Oriented Association Mining Using Rough Set Theory , 2003, RSFDGrC.

[4]  María José del Jesús,et al.  KEEL: a software tool to assess evolutionary algorithms for data mining problems , 2008, Soft Comput..

[5]  Wa Usa,et al.  Effective eLearning Design , 2008 .

[6]  Sebastián Ventura,et al.  Educational data mining: A survey from 1995 to 2005 , 2007, Expert Syst. Appl..

[7]  Chieh-Yuan Tsai,et al.  A dynamic Web service based data mining process system , 2005, The Fifth International Conference on Computer and Information Technology (CIT'05).

[8]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[9]  Heidrun Schumann,et al.  A Flexible Approach for Visual Data Mining , 2002, IEEE Trans. Vis. Comput. Graph..

[10]  Elena Álvarez,et al.  MATEP: Monitoring and Analysis Tool for E-Learning Platforms , 2008, 2008 Eighth IEEE International Conference on Advanced Learning Technologies.

[11]  Dianne Conrad,et al.  Engagement, Excitement, Anxiety, and Fear: Learners' Experiences of Starting an Online Course , 2002 .

[12]  Elena Gaudioso,et al.  Mining Student Data To Characterize Similar Behavior Groups In Unstructured Collaboration Spaces , 2004 .

[13]  Judy Kay,et al.  Clustering and Sequential Pattern Mining of Online Collaborative Learning Data , 2009, IEEE Transactions on Knowledge and Data Engineering.

[14]  VoBay,et al.  Interestingness measures for association rules , 2011 .

[15]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[16]  H. Gordon.  Identifying Learning Styles. , 1998 .

[17]  Udo Winand,et al.  The sharable content object reference model (SCORM) - a critical review , 2002, International Conference on Computers in Education, 2002. Proceedings..

[18]  Ian H. Witten,et al.  Data mining - practical machine learning tools and techniques, Second Edition , 2005, The Morgan Kaufmann series in data management systems.

[19]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[20]  Ian Witten,et al.  Data Mining , 2000 .

[21]  Jack P. Krichen.  Investigating learning styles in the online educational environment , 2007, SIGITE '07.

[22]  César Hervás-Martínez,et al.  Data Mining Algorithms to Classify Students , 2008, EDM.

[23]  Sergey Brin,et al.  Dynamic Data Mining: Exploring Large Rule Spaces by Sampling. , 1999 .

[24]  Osmar R. Zaïane,et al.  Building a Recommender Agent for e-Learning Systems , 2002, ICCE.

[25]  Andrea Valente,et al.  Exploring theoretical computer science using paper toys (for kids) , 2004, IEEE International Conference on Advanced Learning Technologies, 2004. Proceedings..

[26]  Bruno Crémilleux,et al.  Visualizing Transactional Data with Multiple Clusterings for Knowledge Discovery , 2006, ISMIS.

[27]  Marta Elena Zorrilla Pantaleón,et al.  Orientaciones en el diseño y evaluación de un curso virtual para la enseñanza de aplicaciones informáticas , 2008 .

[28]  Maomi Ueno,et al.  Bayesian Agent in e-Learning , 2007, Seventh IEEE International Conference on Advanced Learning Technologies (ICALT 2007).

[29]  Xiaohua Hu,et al.  A data warehouse/online analytic processing framework for web usage mining and business intelligence reporting , 2004, Int. J. Intell. Syst..

[30]  Ian Douglas.  Measuring Participation in Internet Supported Courses , 2008, 2008 International Conference on Computer Science and Software Engineering.

[31]  R Barthel,et al.  Standardization in e-Learning. The "Sharable Content Object Reference Model (SCORM)” , 2004 .

[32]  Pak Chung Wong,et al.  Visualizing association rules for text mining , 1999, Proceedings 1999 IEEE Symposium on Information Visualization (InfoVis'99).

[33]  Ke Zhang,et al.  Revealing Online Learning Behaviors and Activity Patterns and Making Predictions with Data Mining Techniques in Online Teaching , 2008 .

[34]  Ingo Mierswa,et al.  YALE: rapid prototyping for complex data mining tasks , 2006, KDD '06.

[35]  Parag C. Pendharkar.  Managing Data Mining Technologies in Organizations: Techniques and Applications , 2003 .

[36]  Tzu-Chien Liu,et al.  Identifying Learning Styles in Learning Management Systems by Using Indications from Students' Behaviour , 2008, 2008 Eighth IEEE International Conference on Advanced Learning Technologies.

[37]  Ron Kohavi,et al.  Supervised and Unsupervised Discretization of Continuous Features , 1995, ICML.

[38]  Marina Teresa Pires Vieira,et al.  Using Data Warehouse and Data Mining Resources for Ongoing Assessment of Distance Learning , 2002 .

[39]  Yiyu Yao,et al.  User-centered Interactive Data Mining , 2006, IEEE ICCI.

[40]  Kalina Yacef,et al.  Interestingness Measures for Associations Rules in Educational Data , 2008, EDM.