A Survey on Pre-Processing Educational Data

  • Cristóbal Romero
  • José Raúl Romero
  • Sebastián Ventura
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 524)

Abstract

Data pre-processing is the first step in any data mining process, being one of the most important but less studied tasks in educational data mining research. Pre-processing allows transforming the available raw educational data into a suitable format ready to be used by a data mining algorithm for solving a specific educational problem. However, most of the authors rarely describe this important step or only provide a few works focused on the pre-processing of data. In order to solve the lack of specific references about this topic, this paper specifically surveys the task of preparing educational data. Firstly, it describes different types of educational environments and the data they provide. Then, it shows the main tasks and issues in the pre-processing of educational data, Moodle data being mainly used in the examples. Next, it describes some general and specific pre-processing tools and finally, some conclusions and future research lines are outlined.

Keywords

Educational data mining process Data pre-processing Data preparation Data transformation 

Abbreviations

AIHS

Adaptive and intelligent hypermedia system

ARFF

Attribute-relation File Format

CBE

Computer-based education

CSV

Comma-separated values

DM

Data mining

EDM

Educational data mining

HTML

Hypertext Markup language

ID

Identifier

IP

Internet Protocol

ITS

Intelligent tutoring system

KDD

Knowledge discovery in databases

LMS

Learning management system

MCQ

Multiple choice question

MIS

Management information system

MOOC

Massive Open Online Course

OLAP

Online Analytical Processing

SQL

Structured Query Language

WUM

Web Usage Mining

WWW

World Wide Web

XML

Extensible Markup Language

References

  1. 1.
    Romero, C., Ventura, S.: Data mining in education. WIREs Data Min. Knowl. Disc. 1(3), 12–27 (2013)CrossRefGoogle Scholar
  2. 2.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2006)Google Scholar
  3. 3.
    Miksovsky, P., Matousek, K., Kouba, Z.: Data Pre-processing support for data mining. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 208–212, Hammamet, Tunisia (2002)Google Scholar
  4. 4.
    Pyle, D.: Data Preparation for Data Mining. Morgan Kaufmann, San Francisco (1999)Google Scholar
  5. 5.
    Gonçalves, P.M., Barros, R.S.M., Vieria, D.C.L: On the use of data mining tools for data preparation in classification problems. In: 11th International Conference on Computer and Information Science, pp. 173–178, IEEE, Washington (2012)Google Scholar
  6. 6.
    Bohanec, M., Moyle, S., Wettschereck, D., Miksovsk, P.: A software architecture for data pre-processing using data mining and decision support models. In: ECML/PKDD’01 Workshop on Integrating Aspects of Data Mining, Decision Support and Meta-Learning, pp. 13–24 (2001)Google Scholar
  7. 7.
    Sael, N., Abdelaziz, A., Behja, H.: Investigating and advanced approach to data pre-processing in Moodle platform. Int. Rev. Comput. Softw. 7(3), 977–982 (2012)Google Scholar
  8. 8.
    Marquardt, C.G., Becker, K., Ruiz, D.D.: A Pre-processing tool for web usage mining in the distance education Domain. In: International Database Engineering and Applications Symposium, pp. 78–87. IEEE Computer Society, Washington (2004)Google Scholar
  9. 9.
    Wettschereck, D.: Educational data pre-processing. In: ECML’02 Discovery Challenge Workshop, pp. 1–6. University of Helsinki, Helsinki (2002)Google Scholar
  10. 10.
    Simon, J.: Data preprocessing using a priori knowledge. In: D’Mello, S.K., Calvo, R.A., Olney, A. (eds.) 6th International Conference on Educational Data Mining, pp. 352–353. International Educational Data Mining Society, Memphis (2013)Google Scholar
  11. 11.
    Rice, W.H.: Moodle E-learning Course Development. A Complete Guide to Successful Learning Using Moodle. Packt publishing, Birmingham (2006)Google Scholar
  12. 12.
    Ma, Y., Liu, B., Wong, C., Yu, P., Lee, S.: Targeting the right students using data mining. In: Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data mining, pp. 457–464. ACM, New York (2000)Google Scholar
  13. 13.
    Silva, D., Vieira, M.: Using data warehouse and data mining resources for ongoing assessment in distance learning. In: IEEE International Conference on Advanced Learning Technologies, pp. 40–45. IEEE Computer Society, Kazan (2002)Google Scholar
  14. 14.
    Clow, D.: MOOCs and the funnel of participation. In: Suthers, D., Verbert, K., Duval, E., Ochoa, X. (eds.) International Conference on Learning Analytics and Knowledge, pp. 185–189. ACM New York, NY (2013)Google Scholar
  15. 15.
    Anderson, J., Corbett, A., Koedinger, K.: Cognitive tutors. J. Learn. Sci. 4(2), 67–207 (1995)CrossRefGoogle Scholar
  16. 16.
    Mostow, J., Beck, J.: Some useful tactics to modify, map and mine data from intelligent tutors. J. Nat. Lang. Eng. 12(2), 95–208 (2006)Google Scholar
  17. 17.
    Brusilovsky, P., Peylo, C.: Adaptive and intelligent web-based educational systems. Int. J. Artif. Intell. Educ. 13(2–4), 159–172 (2003)Google Scholar
  18. 18.
    Merceron, A., Yacef, K.: Mining student data captured from a web-based tutoring tool: initial exploration and results. J. Interact. Learn. Res. 15(4), 319–346 (2004)Google Scholar
  19. 19.
    Brusilovsky, P., Miller, P.: Web-based testing for distance education. In: De Bra, P., Leggett, J. (eds.) WebNet’99, World Conference of the WWW and Internet, pp. 149–154. AACE, Honolulu (1999)Google Scholar
  20. 20.
    Hanna, M.: Data mining in the e-learning domain. Campus-Wide Inf. Syst. 21(1), 29–34 (2004)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Romero, C., Ventura, S., Salcines, E.: Data mining in course management systems: moodle case study and tutorial. Comput. Educ. 51(1), 368–384 (2008)CrossRefGoogle Scholar
  22. 22.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: Eleventh International Conference on Data Engineering, pp. 3–4. IEEE, Washington (1995)Google Scholar
  23. 23.
    Romero, C., Ventura, S., Zafra, A., De Bra, P.: Applying web usage mining for personalizing hyperlinks in web-based adaptive educational systems. Comput. Educ. 53(3), 828–840 (2009)CrossRefGoogle Scholar
  24. 24.
    Feldman, R., Sanger, J.: The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press, Cambridge (2006)CrossRefGoogle Scholar
  25. 25.
    Dringus, L.P., Ellis, T.: Using data mining as a strategy for assessing asynchronous discussion forums. Comput. Educ. J. 45(1), 141–160 (2005)CrossRefGoogle Scholar
  26. 26.
    Petrushin, V., Khan, L. (eds.): Multimedia Data Mining and Knowledge Discovery. Springer, London (2007)MATHGoogle Scholar
  27. 27.
    Bari, M., Lavoie, B.: Predicting interactive properties by mining educational multimedia presentations. In: International Conference on Information and Communications Technology, pp. 231–234. Bangladesh University of Engineering and Technology, Dhaka (2007)Google Scholar
  28. 28.
    Srivastava, J., Cooley, R., Deshpande, M., Tan, P.: Web usage mining: discovery and applications of usage patterns from web data. SIGKDD Explor. 1(2), 12–23 (2000)CrossRefGoogle Scholar
  29. 29.
    Romero, C., Ventura, S.: Educational data mining: a survey from 1995 to 2005. Expert Syst. Appl. 33(1), 135–146 (2007)CrossRefGoogle Scholar
  30. 30.
    Vranic, M., Pintar, D., Skocir, Z.: The use of data mining in education environment. In: 9th International Conference on Telecommunications, pp. 243–250. IEEE, Zagreb (2007)Google Scholar
  31. 31.
    Gibert, K., Izquierdo, J., Holmes, G., Athanasiadis, I., Comas, J., Sanchez, M.: On the role of pre and post processing in environmental data mining. In: Sánchez-Marré, M., Béjar, J., Comas, J., Rizzoli, A. E., Guariso, G. (eds.) iEMSs Fourth Biennial Meeting: International Congress on Environmental Modelling and Software (iEMSs 2008), pp. 1937–1958. International Environmental Modelling and Software Society, Barcelona (2008)Google Scholar
  32. 32.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2005)Google Scholar
  33. 33.
    Zhu, F., Ip, H., Fok, A., Cao, J.: PeRES: A Personalized Recommendation Education System Based on Multi-Agents & SCORM. In: Leung, H., Li, F., Lau, R., Li, Q. (eds.) Advances in Web Based Learning—ICWL 2007. LNCS, vol. 4823, pp. 31–42. Springer, Heidelberg (2007)Google Scholar
  34. 34.
    Avouris, N., Komis, V., Fiotakis, G., Margaritis, M., Voyiatzaki, E.: Why logging of fingertip actions is not enough for analysis of learning activities. In: Workshop on Usage Analysis in Learning Systems, pp. 1–8. AIED Conference, Amsterdam (2005)Google Scholar
  35. 35.
    Chanchary, F.H., Haque, I., Khalid, M.S.: Web usage mining to evaluate the transfer of learning in a web-based learning environment. In: International Workshop on Knowledge Discovery and Data Mining, pp. 249–253. IEEE, Washington (2008)Google Scholar
  36. 36.
    Spacco, J., Winters, T., Payne, T.: Inferring use cases from unit testing. In: AAAI Workshop on Educational Data Mining, pp. 1–7, AAAI Press, New York (2006)Google Scholar
  37. 37.
    Zhang, L, Liu, X., Liu, X.: Personalized instructing recommendation system based on web mining. In: International Conference for Young Computer Scientists, pp. 2517–2521. IEEE Computer Society Washington (2008)Google Scholar
  38. 38.
    Barnes, T.: The Q-matrix method: mining student response data for knowledge. In: AAAI-2005 Workshop on Educational Data Mining, pp. 1–8, AAAI Press, Pittsburgh (2005)Google Scholar
  39. 39.
    Chen, C., Chen, M., Li, Y.: Mining key formative assessment rules based on learner profiles for web-based learning systems. In: Spector, J.M., Sampson D.G., Okamoto, T., Kinshuk, Cerri, S.A., Ueno, M., Kashihara, A. (eds.) IEEE International Conference on Advanced Learning Technologies, pp. 1–5. IEEE Computer Society, Los Alamitos (2007)Google Scholar
  40. 40.
    Wang, F.H.: A fuzzy neural network for item sequencing in personalized cognitive scaffolding with adaptive formative assessment. Expert Syst. Appl. J. 27(1), 11–25 (2004)CrossRefGoogle Scholar
  41. 41.
    Markham, S., Ceddia, J., Sheard, J., Burvill, C., Weir, J., Field, B.: Applying agent technology to evaluation tasks in e-learning environments. In: International Conference of the Exploring Educational Technologies, pp. 1–7. Monash University, Melbourne (2003)Google Scholar
  42. 42.
    Medvedeva, O., Chavan, G., Crowley, R.: A data collection framework for capturing its data based on an agent communication standard. In: 20th Annual Meeting of the American Association for Artificial Intelligence, pp. 23–30, AAAI, Pittsburgh (2005)Google Scholar
  43. 43.
    Shen, R., Han, P., Yang, F., Yang, Q., Huang, J.: Data mining and case-based reasoning for distance learning. J. Distance Educ. Technol. 1(3), 46–58 (2003)CrossRefGoogle Scholar
  44. 44.
    Lenzerini, M.: Data integration: a theoretical perspective. In: International Conference on ACM SIGMOD/PODS, pp. 233–246. ACM, New York (2002)Google Scholar
  45. 45.
    Ingram, A.: Using web server logs in evaluating instructional web sites. J. Educ. Technol. Syst. 28(2), 137–157 (1999)CrossRefGoogle Scholar
  46. 46.
    Peled, A., Rashty, D.: Logging for success: advancing the use of WWW logs to improve computer mediated distance learning. J. Educ. Comput. Res. 21(4), 413–431 (1999)Google Scholar
  47. 47.
    Talavera, L., Gaudioso, E.: Mining student data to characterize similar behavior groups in unstructured collaboration spaces. In: Workshop on Artificial Intelligence in CSCL, pp. 17–23. Valencia (2004)Google Scholar
  48. 48.
    Romero, C., Ventura, S., Bra, P.D.: Knowledge discovery with genetic programming for providing feedback to courseware author. User modeling and user-adapted interaction. J. Personalization Res. 14(5), 425–464 (2004)Google Scholar
  49. 49.
    Mostow, J., Beck, J.E.: Why, what, and how to log? Lessons from LISTEN. In: Barnes, T., Desmarais, M., Romero, R., Ventura, S. (eds.) 2nd International Conference on Educational Data Mining, pp. 269–278. International Educational Data Mining Society, Cordoba (2009)Google Scholar
  50. 50.
    Binli, S.: Research on data-preprocessing for construction of university information systems. In: International Conference on Computer Application and System Modeling, pp. 459–462. IEEE, Taiyuan (2010)Google Scholar
  51. 51.
    Dierenfeld, H., Merceron, A.: Learning analytics with excel pivot tables. In: Moodle Research Conference, pp. 115–121. University of Piraeus, Heraklion (2012)Google Scholar
  52. 52.
    Solodovnikova, D., Niedrite, L.: Using data warehouse resources for assessment of e-earning influence on university processes. In: Eder, J., Haav, H.M., Kalja, A., Penjam, J. (eds.) 9th East European Conference, ADBIS 2005. Advances in Databases and Information Systems. LNCS, vol. 3631, pp. 233-248. Springer, Heidelberg (2005)Google Scholar
  53. 53.
    Merceron, A., Yacef, K.: Directions to Enhance Learning Management Systems for Better Data Mining. Personal Communication (2010)Google Scholar
  54. 54.
    Yan, S., Li, Z.: Commercial decision system based on data warehouse and OLAP. Microelectron. Comput. 2, 64–67 (2006)Google Scholar
  55. 55.
    Zorrilla, M.E., Menasalvas, E., Marin, D., Mora, E., Segovia, J.: Web usage mining project for improving web-based learning sites. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) Computer Aided Systems Theory—EUROCAST 2005. LNCS, vol. 3643, pp. 205–210. Springer, Heidelberg (2005)Google Scholar
  56. 56.
    Yin, C., Luo, Q.: Personality mining system in e-learning by using improved association rules. In: International Conference on Machine Learning and Cybernetics, pp. 4130–4134. IEEE, Hong Kong (2007)Google Scholar
  57. 57.
    Heiner, C., Beck, J.E., Mostow, J.: Lessons on using ITS data to answer educational research questions. In: Lester, J.C., Vicari, R.S., Paraguaçu, F. (eds.) Intelligent Tutoring Systems, 7th International Conference, ITS 2004. LNCS, vol. 3220, pp. 1–9. Springer, Heidelberg (2004)Google Scholar
  58. 58.
    Rubin, D.B., Little, R.J.A.: Statistical Analysis with Missing Data. Wiley, New York (2002)MATHGoogle Scholar
  59. 59.
    Salmeron-Majadas, S., Santos, O., Boticario, J.G., Cabestrero, R., Quiros, P.: Gathering emotional data from multiple sources. In: D’Mello, S.K., Calvo, R.A., Olney, A. (eds.) 6th International Conference on Educational Data Mining, pp. 404–405. International Educational Data Mining Society, Memphis (2013)Google Scholar
  60. 60.
    Shuangcheng, L., Ping, W.: Study on the data preprocessing of the questionarie based on the combined classification data mining model. In: International Conference on e-Learning, Enterprise Information Systems and E-Goverment, pp. 217–220. Las Vegas (2009)Google Scholar
  61. 61.
    García, E., Romero, C., Ventura, S., Castro, C.: An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering. User Model. User-Adap. Inter. 19(1–2), 99–132 (2009)CrossRefGoogle Scholar
  62. 62.
    Huang, C., Lin, W., Wang, S., Wang, W.: Planning of educational training courses by data mining: using China Motor Corporation as an example. Expert Syst. Appl. J. 36(3), 7199–7209 (2009)CrossRefGoogle Scholar
  63. 63.
    Kantardzic, M.: Data Mining: Concepts, Models, Methods, and Algorithms. Wiley, New York (2003)Google Scholar
  64. 64.
    Beck, J.E.: Using learning decomposition to analyze student fluency development. In: Workshop on Educational Data Mining at the 8th International Conference on Intelligent Tutoring Systems, pp. 21–28. Jhongli (2006)Google Scholar
  65. 65.
    Redpath, R., Sheard, J.: Domain knowledge to support understanding and treatment of outliers. In: International Conference on Information and Automation, pp. 398–403. IEEE, Colombo (2005)Google Scholar
  66. 66.
    Sunita, S.B., Lobo, L.M.: Data preparation strategy in e-learning system using association rule algorithm. Int. J. Comput. Appl. 41(3), 35–40 (2012)Google Scholar
  67. 67.
    Ivancsy, R., Juhasz, S.: Analysis of web user identification methods. World Acad. Sci. Eng. Technol. J. 34, 338–345 (2007)Google Scholar
  68. 68.
    Rahkila, M., Karjalainen, M.: Evaluation of learning in computer based education using log systems. In: ASEE/IEEE Frontiers in Education Conference, pp. 16–21. IEEE, San Juan (1999)Google Scholar
  69. 69.
    Wang, F.H.: Content recommendation based on education-contextualized browsing events for web-based personalized learning. Educ. Technol. Soc. 11(4), 94–112 (2008)Google Scholar
  70. 70.
    Munk, M., Drlík, M.: Impact of Different pre-processing tasks on effective identification of users’ behavioral patterns in web-based educational system. Procedia Comput. Sci. 4, 1640–1649 (2011)CrossRefGoogle Scholar
  71. 71.
    Heraud, J.M., France, L., Mille, A.: Pixed: an ITS that guides students with the help of learners’ interaction log. In: Lester, J.C., Vicari, R.S., Paraguaçu, F. (eds.) Intelligent Tutoring Systems, 7th International Conference, ITS 2004. LNCS, vol. 3220, pp. 57–64. Springer, Heidelberg (2004)Google Scholar
  72. 72.
    Sheard, J., Ceddia, J., Hurst, J., Tuovinen, J.: Inferring student learning behaviour from website interactions: a usage analysis. J. Educ. Inf. Technol. 8(3), 245–266 (2003)CrossRefGoogle Scholar
  73. 73.
    Petersen, R.J.: Policy dimensions of analytics in higher education. Educause Rev. 47, 44–49 (2012)Google Scholar
  74. 74.
    Bienkowski, M., Feng, M., Means, B.: Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief. U.S. Department of Education, Office of Educational Technology, pp. 1–57 (2012)Google Scholar
  75. 75.
    Liu, H., Motoda, H.: Computational Methods of Feature Selection. Chapman & Hall/CRC, Boca Raton (2007)MATHGoogle Scholar
  76. 76.
    Delavari, N., Phon-Amnuaisuk, S., Beikzadeh, M.: Data mining application in higher learning institutions. Inf. Educ. J. 7(1), 31–54 (2008)Google Scholar
  77. 77.
    Kotsiantis, B., Kanellopoulos, D., Pintelas, P.: Data pre-processing for supervised learning. Int. J. Comput. Sci. 1(2), 111–117 (2006)Google Scholar
  78. 78.
    Mihaescu, C., Burdescu, D.: Testing attribute selection algorithms for classification performance on real data. In: International IEEE Conference Intelligent Systems, pp. 581–586. IEEE, London (2006)Google Scholar
  79. 79.
    Márquez-Vera, C., Cano, A., Romero, C., Ventura, S.: Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data. Appl. Intell. 38(3), 315–330 (2013)CrossRefGoogle Scholar
  80. 80.
    Wong, S.K., Nguyen, T.T., Chang, E., Jayaratnal, N.: Usability metrics for e-learning. In: Meersman, R., Tari, Z. (eds.) On the Move to Meaningful Internet Systems 2003: OTM 2003 Workshops, LNCS, vol. 2889, pp. 235–252. Springer, Heidelberg (2003)Google Scholar
  81. 81.
    Hershkovitz, A. Nachmias, R.: Consistency of students’ pace in online learning. In: Barnes, T., Desmarais, M., Romero, R., Ventura, S. (eds.) 2nd International Conference on Educational Data Mining, pp. 71–80. International Educational Data Mining Society, Cordoba (2009)Google Scholar
  82. 82.
    Mor, E., Minguillón, J.: E-learning personalization based on itineraries and long-term navigational behavior. In: Thirteenth World Wide Web Conference, pp. 264–265. ACM, New York (2004)Google Scholar
  83. 83.
    Nilakant, K., Mitrovic, A.: Application of data mining in constraint based intelligent tutoring systems. In: International Conference on Artificial Intelligence in Education, pp. 896–898. Amsterdam (2005)Google Scholar
  84. 84.
    Baker, R., Carvalho, M.: A labeling student behavior faster and more precisely with text replays. In: Baker, R.S.J.d, Barnes, T., Beck, J.E. (eds.) 1st International Conference on Educational Data Mining, pp. 38–47. International Educational Data Mining Society, Montreal (2008)Google Scholar
  85. 85.
    Zhou, M., Xu, Y., Nesbit., J.C., Winne, P.H.: Sequential pattern analysis of learning logs: methodology and applications. In: Romero, C., Ventura, S., Pechenizkiy, M., Baker, R.S. J.D. (eds.) Handbook of Educational Data Mining, Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, pp. 107–120. CRC Press, Boca Raton (2010)Google Scholar
  86. 86.
    Liu, B.: Web Data Mining: Exploring Hyperlinks, Contents and Usage Data. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  87. 87.
    Thai, D., Wu, H., Li, P.: A hybrid system: neural network with data mining in an e-learning environment. In: Jain, L., Howlett, R.J., Apolloni, B. (eds.) Knowledge-Based Intelligent Information and Engineering Systems, 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks. LNCS, vol. 4693, pp. 42–49. Springer, Heidelberg (2007)Google Scholar
  88. 88.
    Hien, N.T.N., Haddawy, P.: A decision support system for evaluating international student applications. In: Frontiers in Education Conference, pp. 1–6. IEEE, Piscataway (2007)Google Scholar
  89. 89.
    Kosheleva, O., Kreinovich, V., Longrpre, L.: Towards interval techniques for processing educational data. In: International Symposium on Scientific Computing, Computer Arithmetic and Validated Numerics, pp. 1–28. IEEE Computer Society, Washington (2006)Google Scholar
  90. 90.
    Hämäläinen, W., Vinni, M.: Classifiers for educational data mining. In: Romero, C., Ventura, S., Pechenizkiy, M., Baker, R.S.J.d. (eds.) Handbook of Educational Data Mining, Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, pp. 57–71. CRC Press, Boca Raton (2010)Google Scholar
  91. 91.
    Cocea, M., Weibelzahl, S.: Can log files analysis estimate learners’ level of motivation? In: Workshop week Lernen—Wissensentdeckung—Adaptivität, pp. 32–35. Hildesheim (2006)Google Scholar
  92. 92.
    Tanimoto, S.L.: Improving the prospects for educational data mining. In: Track on Educational Data Mining, at the Workshop on Data Mining for User Modeling, at the 11th International Conference on User Modeling, pp. 1–6. User Modeling Inc., Corfu (2007)Google Scholar
  93. 93.
    Werner, L., McDowell, C., Denner, J.: A first step in learning analytics: pre-processing low-level Alice logging data of middle school students. J. Educ. Data Min. (2013, in press)Google Scholar
  94. 94.
    Alcalá, J., Sanchez, L., García, S., Del Jesus, M.J., Ventura, S., Garrell, J.M., Otero, J., Romero, C., Bacardit, J., Rivas, V., Fernández, J.C., Herrera, F.: KEEL: a software tool to assess evolutionary algorithms to data mining problems. Soft. Comput. 13(3), 307–318 (2009)CrossRefGoogle Scholar
  95. 95.
    Gonçalves, P.M., Barros, R.S.M.: Automating data preprocessing with DMPML and KDDML. In: 10th IEEE/ACIS International Conference on Computer and Information Science, pp. 97–103. IEEE, Washington (2011)Google Scholar
  96. 96.
    Zaïne, O.R., Luo, J.: Towards evaluating learners’ behaviour in a web-based distance learning environment. In: IEEE International Conference on Advanced Learning Technologies, pp. 357–360. Madison, WI (2001)Google Scholar
  97. 97.
    Ceddia, J., Sheard, J., Tibbery, G.: WAT: a tool for classifying learning activities from a log file. In: Ninth Australasian Computing Education Conference, pp. 11–17. Australian Computer Society, Darlinghurst (2007)Google Scholar
  98. 98.
    Rodrigo, M.T., Baker, R., McLaren, B.M., Jayme, A., Dy, T. : Development of a workbench to address the educational data mining bottleneck. In: Yacef, K., Zaïane, O., Hershkovitz, A., Yudelson, M., Stamper, J. (eds.) 5th International Conference on Educational Data Mining, pp. 152–155. International Educational Data Mining Society, Chania (2012)Google Scholar
  99. 99.
    Koedinger, K., Cunningham, K., Skogsholm, A., LEBER, B.: An open repository and analysis tools for fine-grained, longitudinal learner data. In: Baker, R.S.J.d, Barnes, T., Beck, J.E. (eds.) 1st International Conference on Educational Data Mining, pp. 157–166. International Educational Data Mining Society, Montreal (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Cristóbal Romero
    • 1
  • José Raúl Romero
    • 1
  • Sebastián Ventura
    • 1
  1. 1.Department of Computer Science and Numerical AnalysisUniversity of Córdoba Campus de RabanalesCórdobaSpain

Personalised recommendations