Data mining and social network analysis in the educational field: An application for non-expert users

Studies in Computational Intelligence

Volumn 524, Issue , 2014, Pages 411-439

  García Saiz, Diego ^a   Palazuelos, Camilo ^a   Zorrilla, Marta ^a  

^a UNIVERSITY OF CANTABRIA   (Spain)

Author keywords

Data mining; Educational data mining; Learning analytics; Social network analysis

Indexed keywords

EID: 84958542823     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-319-02738-8_15     Document Type: Article

References (55)

1. Capra, R., Arguello, J., Chen, A., Hawthorne, K., Marchionini, G., Shaw, L.: The results space collaborative search environment. In: 12th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 435-436. ACM, New York (2012
2. Lin, C.C., Tsai, C.C.: Participatory learning through behavioral and cognitive engagements in an online collective information searching activity. Int. J. Comput. Support. Collaborative Learn. 7(4), 543-566 (2012
3. McNely, B.J., Gestwicki, P., Hill, J.H., Parli-Horne, P., Johnson, E.: Learning analytics for collaborative writing: A prototype and case study. In: Dawson, S., Haythornthwaite, C., Shum, S.B., Gasevic, D., Ferguson, R. (eds.) Second International Conference on Learning Analytics and Knowledge, pp. 222-225. ACM, New York (2012
4. Joubert, M., Wishart, J.: Participatory practices: Lessons learnt from two initiatives using online digital technologies to build knowledge. Comput. Educ. 59(1), 110-119 (2012
5. Rice, W.: Moodle E-learning Course Development. A Complete Guide to Successful Learning Using Moodle. Packet Publishing, Birmingham (2006
6. Southworth, H., Cakici, K., Vovides, Y., Zvacek, S.: Blackboard for Dummies. Wiley, New York (2006
7. Korcuska, M., Berg, A.M.: Sakai Courseware Management: The Official Guide. Packt Publishing, Birmingham (2009
8. Johnson, L., Adams-Becker S., Cummins, M., Estrada, V., Freeman, A., Ludgate, H.: NMC Horizon Report: Higher Education Edition. Report. The New Media Consortium (2013
9. Romero, C., Ventura, S.: Data mining in education. Wiley Interdiscip. Rev.: Data Min. Knowl. Disc. 3(1), 12-27 (2013
10. Macfadyen, L.P., Dawson, S.: Mining LMS data to develop an early warning system for educators: A proof of concept. Comput. Educ. 54(2), 588-599 (2010
11. Zorrilla, M., García-Saiz, D.: A service oriented architecture to provide data mining services for non-expert data miners. Decis. Support Syst. 55(1), 399-411 (2013
12. Palazuelos, C., Zorrilla, M.E.: FRINGE: A new approach to the detection of overlapping communities in graphs. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2011. LNCS, vol. 6784, pp. 638-653. Springer, Heidelberg (2011
13. 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 (2012
14. Scott, J.: Social Network Analysis: A Handbook. SAGE Publications, London (2000
15. Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Structural Analysis in the Social Sciences. Cambridge University Press, Cambridge (1994
16. Watts, D., Strogatz, S.: Collective dynamics of small-world networks. Nature 393(6684), 440-442 (1998
17. Klovdahl, A., Potterat, J., Woodhouse, D., Muth, J., Muth, S., Darrow, W.: Social networks and infectious disease: The colorado springs study. Soc. Sci. Med. 38(1), 79-88 (1994
18. Krebs, V.: Mapping networks of terrorist cells. Connections 24(3), 43-52 (2002
19. Freeman, L.: A set of measures of centrality based on betweenness. Sociometry 40(1), 35-41 (1977
20. Kleinberg, J.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604-632 (1999
21. Brewe, E., Kramer, L.H., Sawtelle, V.: Investigating student communities with network analysis of interactions in a physics learning center. Phys. Rev. Spec. Top. Phys. Educ. Res. 8(1), 1-9 (2012
22. Crespo, P.M.T., Antunes, C.: Social networks analysis for quantifying students performance in teamwork. In: Yacef, K., Zaïane, O., Hershkovitz, A., Yudelson, M., Stamper, J. (eds.) 5th International Conference on Educational Data Mining, pp. 234-235. International Educational Data Mining Society, Chania (2012
23. Cuéllar, M.P., Delgado, M., Pegalajar, M.C.: Improving learning management through semantic web and social networks in e-learning environments. Expert Syst. Appl. 38(4), 4181-4189 (2011
24. Rabbany, R., Takaffoli, M., Zaïane, O.R.: Social network analysis and mining to support the assessment of on-line student participation. SIGKDD Explor. 13(2), 20-29 (2011
25. Dawson, S., Tan, J.P.L., McWilliam, E.: Measuring creative potential: Using social network analysis to monitor a learners' creative capacity. Australas. J. Educ. Technol. 27(6), 924-942 (2011
26. Dawson, S.: Seeing the learning community: An exploration of the development of a resource for monitoring online student networking. Br. J. Educ. Technol. 41(5), 736-752 (2010
27. Obsivac, T., Popelinsky, L., Bayer, J., Geryk, J., Bydzovska, H.: Predicting drop-out from social behaviour of students. In: Yacef, K., Zaïane, O., Hershkovitz, A., Yudelson, M., Stamper, J. (eds.) 5th International Conference on Educational Data Mining, pp. 103-109. International Educational Data Mining Society, Chania (2012
28. Palazuelos, C., García-Saiz, D., Zorrilla, M.: Social network analysis and data mining: An application to the e-learning context. In: International Conference on Computational Collective Intelligence Technologies and Applications (2013, in press
29. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. J. Artif. Intell. 17(1), 37-54 (1996
30. García-Saiz, D., Zorrilla, M.E.: Towards the development of a classification service for predicting students' performance. In: D'Mello, S.K., Calvo, R.A., Olney, A. (eds.) 6th International Conference on Educational Data Mining, pp. 318-319. International Educational Data Mining Society, Memphis (2013
31. Kotsiantis, S.B.: Use of machine learning techniques for educational proposes: A decision support system for forecasting students' grades. J. Artif. Intell. 37(4), 331-344 (2012
32. Romero, C., Ventura, S., Espejo, P.G., Hervás, C.: Data mining algorithms to classify students. In: Baker, R.S.J.D., Barnes, T., Beck, J.E. (eds.) 1st International Conference on Educational Data Mining, pp. 8-17. International Educational Data Mining Society, Montreal (2008
33. Zafra, A., Romero, C., Ventura, S.: Predicting academic achievement using multiple instance genetic programming. In: Ninth International Conference on Intelligent Systems Design and Applications, pp. 1120-1125, IEEE, Washington (2009
34. Dekker, G., Pechenizkiy, M., Vleeshouwers, J.: Predicting students drop out: A case study. In: Barnes, T., Desmarais, M., Romero, C., Ventura, S. (eds.) 2nd International Conference on Educational Data Mining, pp. 41-50. International Educational Data Mining Society, Cordoba (2009
35. Kotsiantis, S.B., Pierrakeas, C., Pintelas, P.E.: Preventing student dropout in distance learning using machine learning techniques. In: Palade, V., Howlett, R.J., Jain, L.C. (eds.) KES. LNCS, vol. 2773, pp. 267-274. Springer, Heidelberg (2003
36. John, G., Langley, P.: Estimating continuous distributions in bayesian classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 338-345. Morgan Kaufmann, San Francisco (1995
37. Hämäläinen, W., Vinni, M.: Comparison of machine learning methods for intelligent tutoring systems. In: Ikeda, M., Ashley, K., Chan, T.W. (eds.) Intelligent Tutoring Systems. LNCS, vol. 4053, pp. 525-534. Springer, Heidelberg (2006
38. Zafra, A., Ventura, S.: G3P-MI: A genetic programming algorithm for multiple instance learning. Inf. Sci. 180(23), 4496-4513 (2010
39. Friedman, N., Geiger, D., Goldszmidt, M., Provan, G., Langley, P., Smyth, P.: Bayesian network classifiers. Mach. Learn., 131-163. Kluwer Academic Publishers, Boston (1997
40. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Francisco (1993
41. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005
42. Romero, C., Ventura, S.: Educational data mining: A review of the state of the art. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 40(6), 601-618 (2010
43. García-Saiz, D., Zorrilla, M.E.: E-learning web miner: A data mining application to help instructors involved in virtual courses. In: Pechenizkiy, M., Calders, T., Conati, C., Ventura, S., Romero, C., Stamper, J. (eds.) 4th International Conference on Educational Data Mining, pp. 323-324. International Educational Data Mining Society, Eindhoven (2011
44. Benchaffai, M., Debord, G., Merceron, A., Yacef, K.: TADA-ED, a tool to visualize and mine students' online work. In: McKay, E., Collis, B. (eds.) International Conference on Computers in Education, pp. 1891-1897. RMIT, Melbourne (2004
45. Romero, C., Ventura, S., García, E.: Data mining in course management systems: Moodle case study and tutorial. Comput. Educ. 51(1), 368-384 (2008
46. García, E., Romero, C., Ventura, S., de Castro, C.: A collaborative educational association rule mining tool. Internet High. Educ. 14(2), 77-88 (2011
47. Holmes G., Hall, M., Frank, E.: Generating rule sets from model trees. In: 12th A.J.C. on Artificial Intelligence. LNCS, vol. 1747, pp. 1-12. Springer, Heidelberg (1999
48. 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
49. Balcázar, J.L., Tîrnauca, C., Zorrilla, M.E.: Filtering association rules with negations on the basis of their confidence boost. In: International Conference on Knowledge Discovery and Information Retrieval, pp. 263-268, INSTICC, Valencia (2010
50. Zorrilla, M.E., García, D.: A data mining service to assist instructors involved in virtual education. In: Zorrilla, M., Mazón, J., Ferrández, Ó., Garrigós, I., Daniel, F., Trujillo, J. (eds.) Business Intelligence Applications and the Web: Models, Systems and Technologies, pp. 222-243. Business Science Reference, Hershey (2012
51. Zorrilla, M.E., García-Saiz, D., Balcázar, J.L.: Towards parameter-free data mining: Mining educational data with Yacaree. In: Pechenizkiy, M., Calders, T., Conati, C., Ventura, S., Romero, C., Stamper, J. (eds.) 4th International Conference on Educational Data Mining, pp. 363-364. International Educational Data Mining Society, Eindhoven (2011
52. Balcázar, J.L.: Parameter-free association rule mining with Yacaree. In: Khenchaf, A., Poncelet, P. (eds.) Extraction et Gestion des Connaissances, pp. 251-254. Hermann, Brest (2011
53. Borgelt, C.: Efficient implementations of Apriori and Eclat. In: Goethals, B., Zaki, M.J. (eds.) ICDM Workshop of Frequent ItemSet Mining Implementations. CEUR-WS, Melbourne (2003
54. Park, H.S., Jun, C.H.: A simple and fast algorithm for K-medoids clustering. Expert Syst. Appl. 36(2), 3336-3341 (2009
55. García-Saiz, D., Zorrilla, M.: Comparing classification methods for predicting distance students' performance. In: Diethe, T., Balcázar, J.L., Shawe-Taylor, J., Tîrnauca, C. (eds.) Journal of Machine Learning Research, Workshop and Conference Proceedings. 2nd Workshop on Applications of Pattern Analysis, vol. 17, pp. 26-32 (2011