A Data Mining Service to Assist Instructors Involved in Virtual Education

In this chapter we present a BI application delivered as a service on-demand. In particular, it is a data mining service that aims to help instructors involved in distance education to discover their students’ behavior profiles and models about how they navigate and work in their virtual courses offered in Learning Content Management Systems such as Blackboard or Moodle. The main characteristic is that the users do not require data mining knowledge to use the service; they only have to send a data file according to one of the templates provided by the system and request the results. The service carries out the KDD process itself. Furthermore, the service provides an interface based on Web services, which can be called by external software. In short, the chapter talks about the necessity of a service with these characteristics and includes the description of its architecture and its method of operation as well as a discussion about some of the patterns it offers and how these provide instructors valuable knowledge to make decisions.

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

[2]  Àngela Nebot,et al.  A soft computing decision support framework to improve the e-learning experience , 2008, SpringSim '08.

[3]  Rahul Ramachandran,et al.  ADaM: a data mining toolkit for scientists and engineers , 2005, Comput. Geosci..

[4]  Mats Danielson,et al.  Exploring the e-Learning State of Art , 2008 .

[5]  Jari Stenvall,et al.  Data Mining in Public and Private Sectors: Organizational and Government Applications , 2010 .

[6]  Keng Siau,et al.  Web Services, Service-Oriented Computing, and Service-Oriented Architecture: Separating Hype from Reality , 2008, J. Database Manag..

[7]  Eamonn J. Keogh,et al.  Towards parameter-free data mining , 2004, KDD.

[8]  Wagner Meira,et al.  Anteater: A Service-Oriented Architecture for High-Performance Data Mining , 2006, IEEE Internet Computing.

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

[10]  Vidyasagar Potdar,et al.  A state of the art opinion mining and its application domains , 2009, 2009 IEEE International Conference on Industrial Technology.

[11]  Fabrice Guillet,et al.  Quality Measures in Data Mining (Studies in Computational Intelligence) , 2007 .

[12]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

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

[14]  Iljoo Kim,et al.  Personal Financial Aggregation and Social Media Mining: A New Framework for Actionable Financial Business Intelligence (AFBI) , 2014, Int. J. Bus. Intell. Res..

[15]  Abdelghani Bellaachia,et al.  Minel: a framework for mining e-learning logs , 2006 .

[16]  Xindong Wu,et al.  10 Challenging Problems in Data Mining Research , 2006, Int. J. Inf. Technol. Decis. Mak..

[17]  Lixin Wang,et al.  ASCCN: Arbitrary Shaped Clustering Method with Compatible Nucleoids , 2010, Int. J. Data Warehous. Min..

[18]  Jusung Jun,et al.  Understanding E-dropout? , 2005 .

[19]  S. Ventura Soto,et al.  Using sequential pattern mining for links recommendation in adaptive hypermedia educational systems , 2006 .

[20]  José L. Balcázar.  Parameter-free Association Rule Mining with Yacaree , 2011, EGC.

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

[22]  M. Kavakli,et al.  Data mining in the investigation of money laundering and terrorist financing , 2011 .

[23]  Marco R. Spruit,et al.  Business Intelligence as a Service: A Vendor's Approach , 2014, Int. J. Bus. Intell. Res..

[24]  Fabrice Guillet,et al.  Quality Measures in Data Mining , 2009, Studies in Computational Intelligence.

[25]  Jaideep Srivastava,et al.  Web usage mining: discovery and applications of usage patterns from Web data , 2000, SKDD.

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

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

[28]  Vassilis Loumos,et al.  Dropout prediction in e-learning courses through the combination of machine learning techniques , 2009, Comput. Educ..

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

[30]  Vania Dimitrova,et al.  CourseVis: A graphical student monitoring tool for supporting instructors in web-based distance courses , 2007, Int. J. Hum. Comput. Stud..

[31]  Marc Boullé,et al.  A Parameter-Free Classification Method for Large Scale Learning , 2009, J. Mach. Learn. Res..

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

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

[34]  William F. Punch,et al.  Data mining for a web-based educational system , 2005 .

[35]  Sharanjit Kaur,et al.  A Parameterized Framework for Clustering Streams , 2009, Int. J. Data Warehous. Min..

[36]  Gregory Piatetsky-Shapiro,et al.  The KDD process for extracting useful knowledge from volumes of data , 1996, CACM.

[37]  Sebastián Ventura,et al.  Discovering Prediction Rules in AHA! Courses , 2003, User Modeling.

[38]  Malcolm James Beynon,et al.  Data Mining Using Fuzzy Decision Trees: An Exposition from a Study of Public Services Strategy in the USA , 2010 .

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

[40]  Karin Becker,et al.  O3R: Ontology-based mechanism for a human-centered environment targeted at the analysis of navigation patterns , 2010, Knowl. Based Syst..

[41]  Andrew W. Moore,et al.  X-means: Extending K-means with Efficient Estimation of the Number of Clusters , 2000, ICML.

[42]  GuptaVipul,et al.  Personal Financial Aggregation and Social Media Mining , 2014 .

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

[44]  Christian Borgelt,et al.  EFFICIENT IMPLEMENTATIONS OF APRIORI AND ECLAT , 2003 .

[45]  John Wang,et al.  Encyclopedia of Business Analytics and Optimization , 2018 .

[46]  Ryan S. Baker,et al.  The State of Educational Data Mining in 2009: A Review and Future Visions. , 2009, EDM 2009.