Data mining in course management systems: Moodle case study and tutorial

Educational data mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. This work is a survey of the specific application of data mining in learning management systems and a case study tutorial with the Moodle system. Our objective is to introduce it both theoretically and practically to all users interested in this new research area, and in particular to online instructors and e-learning administrators. We describe the full process for mining e-learning data step by step as well as how to apply the main data mining techniques used, such as statistics, visualization, classification, clustering and association rule mining of Moodle data. We have used free data mining tools so that any user can immediately begin to apply data mining without having to purchase a commercial tool or program a specific personalized tool.

[1]  Beverly Park Woolf,et al.  Inferring Unobservable Learning Variables from Students' Help Seeking Behavior , 2004, Intelligent Tutoring Systems.

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

[3]  V. Komis,et al.  Logging of fingertip actions is not enough for analysis of learning activities , 2005 .

[4]  Myra Spiliopoulou,et al.  Web usage mining for Web site evaluation , 2000, CACM.

[5]  Feng-Hsu Wang,et al.  Effective personalized recommendation based on time-framed navigation clustering and association mining , 2004, Expert Syst. Appl..

[6]  Fan Yang,et al.  Data Analysis Center Based on E-Learning Platform , 2002 .

[7]  William F. Punch,et al.  Mining interesting contrast rules for a web-based educational system , 2004, 2004 International Conference on Machine Learning and Applications, 2004. Proceedings..

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

[9]  Gerd Stumme,et al.  Semantic resource management for the web: an e-learning application , 2004, WWW Alt. '04.

[10]  A. Tsakalidis,et al.  USING SEMANTIC WEB MINING TECHNOLOGIES FOR PERSONALIZED E-LEARNING EXPERIENCES , 2004 .

[11]  Thomas Hill Statistics: Methods and Applications , 2005 .

[12]  David G. Stork,et al.  Pattern Classification , 1973 .

[13]  Ernestina Menasalvas Ruiz,et al.  Web Usage Mining Project for Improving Web-Based Learning Sites , 2005, EUROCAST.

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

[15]  Jian Pei,et al.  Sequential Pattern Mining by Pattern-Growth: Principles and Extensions , 2005 .

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

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

[18]  Ryan Shaun Joazeiro de Baker,et al.  Detecting Student Misuse of Intelligent Tutoring Systems , 2004, Intelligent Tutoring Systems.

[19]  Jaime Spacco,et al.  Inferring Use Cases from Unit Testing , 2006 .

[20]  Rynson W. H. Lau,et al.  Personalized courseware construction based on Web data mining , 2000, Proceedings of the First International Conference on Web Information Systems Engineering.

[21]  E. Hovy,et al.  Mining and Assessing Discussions on the Web through Speech Act Analysis , 2006 .

[22]  Qiming Chen,et al.  PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth , 2001, Proceedings 17th International Conference on Data Engineering.

[23]  Jie Lu,et al.  A Personalized e-Learning material Recommender System , 2004 .

[24]  Jie Lu A Personalized e-Learning Material Recommender System , 2004 .

[25]  Victoria J. Hodge,et al.  A Survey of Outlier Detection Methodologies , 2004, Artificial Intelligence Review.

[26]  Riccardo Mazza,et al.  Exploring Usage Analysis in Learning Systems: Gaining Insights From Visualisations , 2005 .

[27]  J. Beck,et al.  An Educational Data Mining Tool to Browse Tutor-Student Interactions : Time Will Tell ! , 2005 .

[28]  Judy Kay,et al.  Mining patterns of events in students’ teamwork data , 2006 .

[29]  Vania Dimitrova,et al.  Visualising student tracking data to support instructors in web-based distance education , 2004, WWW Alt. '04.

[30]  David E. Pritchard,et al.  Data from a Web-based Homework Tutor can predict Student’s Final Exam Score , 2005 .

[31]  Neil T. Heffernan,et al.  Looking for Sources of Error in Predicting Student's Knowledge , 2005 .

[32]  S. Graf,et al.  Adaptive and Intelligent Web-Based Educational Systems , 2009 .

[33]  Jiawei Han,et al.  Discovering Web access patterns and trends by applying OLAP and data mining technology on Web logs , 1998, Proceedings IEEE International Forum on Research and Technology Advances in Digital Libraries -ADL'98-.

[34]  Osmar R. Za ¨ õane Building a Recommender Agent for e-Learning Systems , 2002 .

[35]  Gwo-Dong Chen,et al.  Discovering Decision Knowledge from Web Log Portfolio for Managing Classroom Processes by Applying Decision Tree and Data Cube Technology , 2000 .

[36]  Shigeo Abe DrEng Pattern Classification , 2001, Springer London.

[37]  Michel Sala,et al.  Evaluating and Revising Courses from Web Resources Educational , 2002, Intelligent Tutoring Systems.

[38]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[39]  Claus Pahl,et al.  Data Mining Technology for the Evaluation of Web-based Teaching and Learning Systems , 2002 .

[40]  Joseph E. Beck,et al.  High-Level Student Modeling with Machine Learning , 2000, Intelligent Tutoring Systems.

[41]  Pierre Tchounikine,et al.  Mining learning groups' activities in forum-type tools , 2005, CSCL.

[42]  Sang Chan Park,et al.  Web mining for distance education , 2000, Proceedings of the 2000 IEEE International Conference on Management of Innovation and Technology. ICMIT 2000. 'Management in the 21st Century' (Cat. No.00EX457).

[43]  George D. Magoulas,et al.  Adaptable and Adaptive Hypermedia Systems , 2005 .

[44]  Lakhmi C. Jain,et al.  Evolution of Teaching and Learning Paradigms in Intelligent Environment , 2007 .

[45]  Neil T. Heffernan,et al.  Informing Teachers Live about Student Learning: Reporting in Assistment System , 2005 .

[46]  Jonathan E Freyberger,et al.  Using Association Rules to Guide a Search for Best Fitting Transfer Models of Student Learning , 2004 .

[47]  Rosa M. Carro,et al.  The Continuous Empirical Evaluation Approach: Evaluating Adaptive Web-Based Courses , 2003, User Modeling.

[48]  Christoph Peylo,et al.  W2 - Adaptive and Intelligent Web-Based Education Systems , 2003, Intelligent Tutoring Systems.

[49]  Ganesh S. Oak Information Visualization Introduction , 2022 .

[50]  雅文 大喜,et al.  WebCTを使用した講義評価に関連する要因 看護学生に対する「社会福祉コース」履修者のデータ分析から , 2003 .

[51]  Karin Becker,et al.  Distance education: a Web usage mining case study for the evaluation of learning sites , 2003, Proceedings 3rd IEEE International Conference on Advanced Technologies.

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

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

[54]  Enric Mor,et al.  E-learning personalization based on itineraries and long-term navigational behavior , 2004, WWW Alt. '04.

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

[56]  Nikolaos Avouris,et al.  A survey on web usage mining techniques for web-based adaptive hypermedia systems 1 , 2004 .

[57]  Martin Muehlenbrock Automatic Action Analysis in an Interactive Learning Environment , 2005 .

[58]  Julià Minguillón,et al.  Detecting atypical student behaviour on a e-learning system , 2005 .

[59]  D. Mladenic,et al.  EXPLOITING TEXT MINING IN PUBLISHING AND EDUCATION , 2002 .

[60]  Carl Gutwin,et al.  KEA: practical automatic keyphrase extraction , 1999, DL '99.

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

[62]  H.L. Grob,et al.  Controlling open source intermediaries - a Web log mining approach , 2004, 26th International Conference on Information Technology Interfaces, 2004..

[63]  Sebastián Ventura,et al.  Personalized Links Recommendation Based on Data Mining in Adaptive Educational Hypermedia Systems , 2007, EC-TEL.

[64]  Elena Gaudioso,et al.  Data mining to support tutoring in virtual learning communities: experiences and challenges , 2005 .

[65]  Sotiris B. Kotsiantis,et al.  PREDICTING STUDENTS' PERFORMANCE IN DISTANCE LEARNING USING MACHINE LEARNING TECHNIQUES , 2004, Appl. Artif. Intell..

[66]  Khaled M. Hammouda,et al.  Data Mining in E-Learning , 2007 .

[67]  Jing Luan,et al.  Data Mining and Knowledge Management in Higher Education -Potential Applications. , 2002 .

[68]  John Castellani,et al.  Enhancing Learning Environments through Solution-Based Knowledge Discovery Tools: Forecasting for Self-perpetuating Systemic Reform , 2001 .

[69]  Maomi Ueno Data mining and text mining technologies for collaborative learning in an ILMS "Ssamurai" , 2004, IEEE International Conference on Advanced Learning Technologies, 2004. Proceedings..

[70]  E. Sutinen,et al.  Data Mining In Personalizing DistanceEducation Courses , 2006 .

[71]  Laurie P. Dringus,et al.  Using data mining as a strategy for assessing asynchronous discussion forums , 2005, Comput. Educ..

[72]  Gwo-Jen Hwang,et al.  A Computer-Assisted Approach to Diagnosing Student Learning Problems in Science courses , 2003, J. Inf. Sci. Eng..

[73]  Osmar R. Zaïane,et al.  Web Usage Mining for a Better Web-Based Learning Environment , 2001 .

[74]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[75]  Nikolaos Avouris,et al.  A Survey of Web-Usage Mining: Techniques for Building Web-Based Adaptive Hypermedia Systems , 2005 .

[76]  Mihaela Cocea,et al.  Can Log Files Analysis Estimate Learners' Level of Motivation? , 2006, LWA.

[77]  Edward A. Fox,et al.  Digital libraries , 1995, CACM.

[78]  VenturaSebastián,et al.  Knowledge Discovery with Genetic Programming for Providing Feedback to Courseware Authors , 2005 .

[79]  Gordon I. McCalla,et al.  Smart Recommendation for an Evolving E-Learning System: Architecture and Experiment , 2005 .

[80]  John Scott Social Network Analysis , 1988 .

[81]  John F. Roddick,et al.  Association mining , 2006, CSUR.

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

[83]  R. Crowley,et al.  Mining Student Learning Data to Develop High Level Pedagogic Strategy in a Medical ITS , 2006 .

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

[85]  Wei Wang,et al.  Learning portfolio analysis and mining in SCORM compliant environment , 2004, 34th Annual Frontiers in Education, 2004. FIE 2004..

[86]  Timothy K. Shih,et al.  A New Courseware Diagram for Quantitative Measurement of Distance Learning Courses , 2003, J. Inf. Sci. Eng..

[87]  Kalina Yacef,et al.  Mining Student Data Captured from a Web-Based Tutoring Tool: Initial Exploration and Results , 2004 .

[88]  C.J.H. Mann,et al.  Handbook of Data Mining and Knowledge Discovery , 2004 .

[89]  Jack Mostow,et al.  Some useful tactics to modify, map and mine data from intelligent tutors , 2006, Natural Language Engineering.

[90]  William F. Punch,et al.  Using Genetic Algorithms for Data Mining Optimization in an Educational Web-Based System , 2003, GECCO.

[91]  Samuel Pierre,et al.  E-Learning Networked Environments and Architectures: A Knowledge Processing Perspective , 2006 .

[92]  Jason Cole Using moodle , 2005 .

[93]  Francisco Herrera,et al.  Proyecto KEEL: Desarrollo de una herramienta para el análisis e implementación de algoritmos de extracción de conocimiento evolutivos , 2004 .

[94]  Brian W. Junker,et al.  Predicting end-of-year accountability assessment scores from monthly student records in an online tutoring system , 2006 .

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

[96]  Maomi Ueno Online Outlier Detection System for Learning Time Data in E-Learning and Its Evaluation , 2004, CATE.

[97]  Sebastián Ventura,et al.  Using Rules Discovery for the Continuous Improvement of e-Learning Courses , 2006, IDEAL.

[98]  Duncan Dubugras Alcoba Ruiz,et al.  Ontology-Based Filtering Mechanisms for Web Usage Patterns Retrieval , 2005, EC-Web.

[99]  Antonija Mitrovic,et al.  Applications of Data Mining in Constraint-based Intelligent Tutoring Systems , 2005, AIED.