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2014 | OriginalPaper | Buchkapitel

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

verfasst von : Diego García-Saiz, Camilo Palazuelos, Marta Zorrilla

Erschienen in: Educational Data Mining

Verlag: Springer International Publishing

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Abstract

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.

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Vorheriges Kapitel Maximizing the Value of Student Ratings Through Data Mining

Nächstes Kapitel Collaborative Learning of Students in Online Discussion Forums: A Social Network Analysis Perspective

See http://congresoestilosdeaprendizaje.blogspot.com..

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)

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)CrossRef

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)CrossRef

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)CrossRef

Rice, W.: Moodle e-learning Course Development. A Complete Guide to Successful Learning Using Moodle. Packet Publishing, Birmingham (2006)

Southworth, H., Cakici, K., Vovides, Y., Zvacek, S.: Blackboard for Dummies. Wiley, New York (2006)

Korcuska, M., Berg, A.M.: Sakai Courseware Management: The Official Guide. Packt Publishing, Birmingham (2009)

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)

Romero, C., Ventura, S.: Data mining in education. Wiley Interdiscip. Rev.: Data Min. Knowl. Disc. 3(1), 12–27 (2013)CrossRef

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)CrossRef

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)CrossRef

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)CrossRef

16.

Watts, D., Strogatz, S.: Collective dynamics of small-world networks. Nature 393(6684), 440–442 (1998)CrossRef

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)CrossRef

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)CrossRef

20.

Kleinberg, J.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–632 (1999)MathSciNetCrossRefMATH

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)CrossRef

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)CrossRef

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)CrossRef

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)CrossRef

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)CrossRef

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)CrossRef

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)CrossRef

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)CrossRef

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)CrossRef

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)

Titel: Data Mining and Social Network Analysis in the Educational Field: An Application for Non-Expert Users
verfasst von: Diego García-Saiz
Camilo Palazuelos
Marta Zorrilla
Verlag: Springer International Publishing
Buch: Educational Data Mining
Print ISBN: 978-3-319-02737-1

Electronic ISBN: 978-3-319-02738-8

Copyright-Jahr: 2014
DOI: https://doi.org/10.1007/978-3-319-02738-8_15

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