Data Mining and Social Network Analysis in the Educational Field: An Application for Non-Expert Users

With the increasing popularity of social networking services like Facebook, social network analysis (SNA) has emerged again. Undoubtedly, there is an inherent social network in any learning context, where teachers, learners, and learning resources behave as main actors, among which different relationships can be defined, e.g., “participate in” among blogs, students, and learners. From their analysis, information about group cohesion, participation in activities, and connections among subjects can be obtained. At the same time, it is well-known the need of tools that help instructors, in particular those involved in distance education, to discover their students’ behavior profile, models about how they participate in collaborative activities or likely the most important, to know the performance and dropout pattern with the aim of improving the teaching–learning process. Therefore, the goal of this chapter is to describe our e-learning Web Mining tool and the new services that it provides, supported by the use of SNA and classification techniques.

[1]  John Seely Brown,et al.  Intelligent Tutoring Systems , 2016, Lecture Notes in Computer Science.

[2]  Sebastián Ventura,et al.  Data mining in course management systems: Moodle case study and tutorial , 2008, Comput. Educ..

[3]  Marta E. Zorrilla,et al.  Comparing classification methods for predicting distance students' performance , 2011, WAPA.

[4]  Sebastián Ventura,et al.  Educational Data Mining: A Review of the State of the Art , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[5]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994 .

[6]  Chia-Ching Lin,et al.  Participatory learning through behavioral and cognitive engagements in an online collective information searching activity , 2012, Int. J. Comput. Support. Collab. Learn..

[7]  Irene Garrigós,et al.  Business Intelligence Applications and the Web: Models, Systems and Technologies , 2011 .

[8]  Sotiris B. Kotsiantis,et al.  Preventing Student Dropout in Distance Learning Using Machine Learning Techniques , 2003, KES.

[9]  Mohand-Said Hacid,et al.  Extraction et gestion des connaissances , 2003 .

[10]  José L. Balcázar,et al.  Filtering Association Rules with Negations on the Basis of Their Confidence Boost , 2010, KDIR.

[11]  Lubos Popelínský,et al.  Predicting drop-out from social behaviour of students , 2012, EDM.

[12]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[13]  Hae-Sang Park,et al.  A simple and fast algorithm for K-medoids clustering , 2009, Expert Syst. Appl..

[14]  S. Wasserman,et al.  Models and Methods in Social Network Analysis: Structural Analysis in the Social Sciences , 2005 .

[15]  Manuel P. Cuéllar,et al.  Improving learning management through semantic web and social networks in e-learning environments , 2011, Expert Syst. Appl..

[16]  Marta E. Zorrilla,et al.  Towards the development of a classification service for predicting students' performance , 2013, EDM.

[17]  Sebastián Ventura,et al.  Applying Web usage mining for personalizing hyperlinks in Web-based adaptive educational systems , 2009, Comput. Educ..

[18]  Eric Brewe,et al.  Investigating Student Communities with Network Analysis of Interactions in a Physics Learning Center. , 2012 .

[19]  Wilhelmiina Hämäläinen,et al.  Comparison of Machine Learning Methods for Intelligent Tutoring Systems , 2006, Intelligent Tutoring Systems.

[20]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[21]  Sebastián Ventura,et al.  A collaborative educational association rule mining tool , 2011, Internet High. Educ..

[22]  Sebastián Ventura,et al.  Predicting Academic Achievement Using Multiple Instance Genetic Programming , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

[23]  Marta E. Zorrilla,et al.  A service oriented architecture to provide data mining services for non-expert data miners , 2013, Decis. Support Syst..

[24]  Shane Dawson,et al.  Mining LMS data to develop an "early warning system" for educators: A proof of concept , 2010, Comput. Educ..

[25]  Rice,et al.  Moodle : E-learning course development : a complete guide to successful learning using Moodle , 2006 .

[26]  William Rice,et al.  Moodle 1.9 E-Learning Course Development , 2008 .

[27]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[28]  Marta E. Zorrilla,et al.  A Data Mining Service to Assist Instructors Involved in Virtual Education , 2012 .

[29]  P. Schönemann On artificial intelligence , 1985, Behavioral and Brain Sciences.

[30]  Alan Mark Berg,et al.  Sakai Courseware Management: The Official Guide , 2009 .

[31]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[32]  Osmar R. Zaïane,et al.  Social network analysis and mining to support the assessment of on-line student participation , 2012, SKDD.

[33]  Jocelyn Wishart,et al.  Participatory practices: Lessons learnt from two initiatives using online digital technologies to build knowledge , 2012, Comput. Educ..

[34]  Shane Dawson,et al.  'Seeing' the learning community: An exploration of the development of a resource for monitoring online student networking , 2010, Br. J. Educ. Technol..

[35]  Marta E. Zorrilla,et al.  FRINGE: A New Approach to the Detection of Overlapping Communities in Graphs , 2011, ICCSA.

[36]  John Scott Social Network Analysis , 1988 .

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

[38]  Cláudia Antunes,et al.  Social Networks Analysis for Quantifying Students' Performance in Teamwork , 2012, EDM.

[39]  Marta E. Zorrilla,et al.  Social Network Analysis and Data Mining: An Application to the E-Learning Context , 2013, ICCCI.

[40]  Sebastián Ventura,et al.  Data mining in education , 2013, WIREs Data Mining Knowl. Discov..

[41]  Marta E. Zorrilla,et al.  E-learning Web Miner: A Data Mining Application to Help Instructors Involved in Virtual Courses , 2011, EDM.

[42]  J. Potterat,et al.  Social networks and infectious disease: the Colorado Springs Study. , 1994, Social science & medicine.

[43]  Marie Bienkowski,et al.  Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief , 2012 .

[44]  Pat Langley,et al.  Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.

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

[46]  S. A. Becker,et al.  NMC Horizon Report: 2016 Higher Education Edition , 2015 .

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

[48]  Sotiris B. Kotsiantis Use of machine learning techniques for educational proposes: a decision support system for forecasting students’ grades , 2011, Artificial Intelligence Review.

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

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

[51]  Shane Dawson,et al.  Measuring creative potential: Using social network analysis to monitor a learners' creative capacity , 2011 .

[52]  Geoff Holmes,et al.  Generating Rule Sets from Model Trees , 1999, Australian Joint Conference on Artificial Intelligence.

[53]  Gary Marchionini,et al.  The ResultsSpace collaborative search environment , 2012, JCDL '12.

[54]  Sebastián Ventura,et al.  G3P-MI: A genetic programming algorithm for multiple instance learning , 2010, Inf. Sci..

[55]  Mykola Pechenizkiy,et al.  Predicting Students Drop Out: A Case Study , 2009, EDM.

[56]  Susan Zvacek,et al.  Blackboard For Dummies (For Dummies (Computer/Tech)) , 2006 .

[57]  Valdis E. Krebs,et al.  Mapping Networks of Terrorist Cells , 2001 .

[58]  José L. Balcázar,et al.  Towards Parameter-free Data Mining: Mining Educational Data with Yacaree , 2011, EDM.

[59]  Jon Kleinberg,et al.  Authoritative sources in a hyperlinked environment , 1999, SODA '98.

[60]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[61]  Erika Johnson,et al.  Learning analytics for collaborative writing: a prototype and case study , 2012, LAK '12.

[62]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..