Skip to main content
SpringerLink
Log in

OrgBR-M: a method to assist in organizing bibliographic material based on formal concept analysis—a case study in educational data mining

International Journal on Digital Libraries volume 21, pages 423–448 (2020)Cite this article

Abstract

For conducting a literature review is necessary a preliminary organization of the available bibliographic material. In this article, we present a novel method called OrgBR-M (method to organize bibliographic references), based on the formal concept analysis theory, to assist in organizing bibliographic material. Our method systematizes the organization of bibliography and proposes metrics to assist in guiding the literature review. As a case study, we apply the OrgBR-M method to perform a literature review of the educational data mining field of study.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Notes

  1. Entity: Anything that exists and has some understanding in a domain [16, 17].

  2. Conexp: http://conexp.sourceforge.net/.

  3. Conexp: http://conexp.sourceforge.net/.

References

  1. Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, New York (1997)

    MATH  Google Scholar 

  2. Ganter, B., Stumme, G., Wille, R.: Formal concept analysis: theory and applications. J. Univ. Comput. Sci. 10(8), 955 (2004). https://doi.org/10.3217/jucs-010-01

    Article  MathSciNet  Google Scholar 

  3. Wille, R.: In Formal Concept Analysis. In: Ganter, B., Stumme, G., Wille, R. (eds.) Lecture Notes in Computer Science, vol. 3626, pp. 1–33. Springer, Berlin (2005). https://doi.org/10.1007/11528784_1

  4. Ganter, B., Stumme, G., Wille, R.: Formal Concept Analysis, Foundations and Applications. Lecture Notes in Computer Science, vol. 3626. Springer, Berlin (2005)

    Book  Google Scholar 

  5. Altinel, B., Ganiz, M.C.: Semantic text classification: a survey of past and recent advances. Inf. Process. Manag. 54(6), 1129 (2018). https://doi.org/10.1016/j.ipm.2018.08.001

    Article  Google Scholar 

  6. Huynh, T., Takasu, A., Masada, T., Hoang, K.: Collaborator recommendation for isolated researchers, pp. 639–644 (2014). https://doi.org/10.1109/WAINA.2014.105

  7. Rodrigues M.W., Brandão, W.C., Zárate L.E.: Recommending scientific collaboration from researchgate. In: Proceedings of the 7th Brazilian Conference on Intelligent systems (Sociedade Brasileira de Computação (SBC)), BRACIS’18, pp. 1–6 (2018). https://doi.org/10.1109/BRACIS.2018.00065

  8. Gipp, B., Beel, J., Hentschel, C.: Scienstein: A research paper recommender. In: Proceedings of the International Conference on Emerging Trends in Computing (ICETiC’09), ICETiC’09, pp. 309–315 (2009)

  9. Galgani, F., Compton, P., Hoffmann, A.: Summarization based on bi-directional citation analysis. Inf. Process. Manag. 51(1), 1 (2015). https://doi.org/10.1016/j.ipm.2014.08.001

    Article  Google Scholar 

  10. Villegas, N.M., Sánchez, C., Díaz-Cely, J., Tamura, G.: Characterizing context-aware recommender systems: a systematic literature review. Knowl.-Based Syst. 140, 173 (2018). https://doi.org/10.1016/j.knosys.2017.11.003

    Article  Google Scholar 

  11. Raamkumar, A.S., Foo, S., Pang, N.L.S.: Rec4lrw-scientific paper recommender system for literature review and writing. Front. Artif. Intell. Appl. 275, 106 (2015). https://doi.org/10.3233/978-1-61499-503-6-106

    Article  Google Scholar 

  12. Raamkumar, A.S., Foo, S., Pang, N.: A framework for scientific paper retrieval and recommender systems. CoRR abs/1609.01415 (2016)

  13. Nart, D.D., Tasso, C.: A personalized concept-driven recommender system for scientific libraries. Procedia Comput. Sci. 38, 84 (2014). https://doi.org/10.1016/j.procs.2014.10.015

    Article  Google Scholar 

  14. Tho, Q.T., Hui, S., Fong, A.: A citation-based document retrieval system for finding research expertise. Inf. Process. Manag. 43(1), 248 (2007). https://doi.org/10.1016/j.ipm.2006.05.015

    Article  Google Scholar 

  15. Poelmans, J., Ignatov, D.I., Viaene, S., Dedene, G., Kuznetsov, S.O.: Text Mining Scientific Papers: A Survey on FCA-Based Information Retrieval Research, pp. 273–287. Springer, Berlin (2012). https://doi.org/10.1007/978-3-642-31488-9_22

    Book  Google Scholar 

  16. Alencar, R.O.d., Zarate, L., Song, M.: Sphere-m: An ontology capture method, pp. 353–358 (2012). https://doi.org/10.1109/ICSMC.2012.6377726

  17. Sowa, J.F.: Knowledge Representation: Logical, Philosophical, and Computational Foundations, vol. 27. Brooks Cole Publishing Co., Pacific Grove (1999). https://doi.org/10.1162/089120101750300544

    Book  Google Scholar 

  18. Yevtushenko, S.A.: System of data analysis concept explorer. In: Proceedings of the 7th National Conference on Artificial Intelligence KII-2000, pp. 127–134 (2000)

  19. Neto, S.M., Zárate, L.E., Song, M.A.: Handling high dimensionality contexts in formal concept analysis via binary decision diagrams. Inf. Sci. 429, 361 (2018). https://doi.org/10.1016/j.ins.2017.11.028

    Article  MathSciNet  MATH  Google Scholar 

  20. Rodrigues, M.W., Isotani, S., Zárate, L.E.: Educational data mining: a review of evaluation process in the e-learning. Telem. Inform. 35(6), 1701 (2018). https://doi.org/10.1016/j.tele.2018.04.015

    Article  Google Scholar 

  21. Kitchenham, B., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., Linkman, S.: Systematic literature reviews in software engineering - a systematic literature review. Inf. Software Technol. 51(1), 7 (2009). https://doi.org/10.1016/j.infsof.2008.09.009

    Article  Google Scholar 

  22. Chen, C.M., Chen, Y.Y., Liu, C.Y.: Learning performance assessment approach using web-based learning portfolios for e-learning systems. IEEE Trans. Syst. Man Cybern. C: Appl. Rev. 37(6), 1349 (2007). https://doi.org/10.1109/TSMCC.2007.900641

    Article  Google Scholar 

  23. Erosa, V., Arroyo, P.: Technology and knowledge: enhancing the education frontiers, pp. 1501–1507 (2007). https://doi.org/10.1109/PICMET.2007.4349471

  24. Liu, F.J., Shih, B.J.: Learning activity-based e-learning material recommendation system, pp. 343–348 (2007). https://doi.org/10.1109/ISM.Workshops.2007.64

  25. Romero, C., Ventura, S.: Educational data mining: a survey from 1995 to 2005. Expert Syst. Appl. 33(1), 135 (2007). https://doi.org/10.1016/j.eswa.2006.04.005

    Article  Google Scholar 

  26. Richardson, B., Davis, K., Beach, M.: Introducing data mining techniques and software engineering to high school science students, pp. 1–6 (2008). https://doi.org/10.1109/FIE.2008.4720456

  27. Hongxia, J., Yao, H.: Classroom teaching quality evaluation based on neuro-fuzzy id3 algorithm 1, 166 (2008). https://doi.org/10.1109/ISCID.2008.127

  28. Caballe, S., Xhafa, F., Abraham, A.: Towards an automatic real-time assessment of online discussions in computer-supported collaborative learning practices, pp. 470–475 (2008). https://doi.org/10.1109/ICDIM.2008.4746758

  29. Fernández-Luna, J.M., Huete, J.F., MacFarlane, A., Efthimiadis, E.N.: Teaching and learning in information retrieval. Inf. Retr. 12(2), 201 (2009). https://doi.org/10.1007/s10791-009-9089-9

    Article  Google Scholar 

  30. Hsieh, J.C., Chen, C.M., Lin, H.F.: Social interaction mining based on wireless sensor networks for promoting cooperative learning performance in classroom learning environment, pp. 219–221 (2010). https://doi.org/10.1109/WMUTE.2010.22

  31. Kechaou, Z., Ben Ammar, M., Alimi, A.: Improving e-learning with sentiment analysis of users’ opinions, pp. 1032–1038 (2011). https://doi.org/10.1109/EDUCON.2011.5773275

  32. Sachin, R., Vijay, M.: A survey and future vision of data mining in educational field, pp. 96–100 (2012). https://doi.org/10.1109/ACCT.2012.14

  33. Ivancevic, V., Celikovic, M., Lukovic, I.: The individual stability of student spatial deployment and its implications, pp. 1–4 (2012)

  34. Li, Y., Mei, L., Wang, J.: A personalized recommendation system in e-learning environment based on semantic analysis, pp. 802–807 (2012)

  35. Baradwaj, B.K., Pal, S.: Mining educational data to analyze students’ performance. CoRR abs/1201.3417 (2012). https://doi.org/10.13189/wjcat.2014.020203

  36. Liu, Q., Peng, Y.: A method of unstructured information process in computer teaching evaluation system based on data mining technology, pp. 688–692 (2013). https://doi.org/10.1109/CSNT.2013.147

  37. Leelathakul, N., Chaipah, K.: Quantitative effects of using facebook as a learning tool on students’ performance, pp. 87–92 (2013). https://doi.org/10.1109/JCSSE.2013.6567325

  38. Harfield, A., Jormanainen, I., Rungrattanaubol, J., Pattaranit, R.: An open monitoring environment for primary school children engaged in tablet-based learning, pp. 195–199 (2013). https://doi.org/10.1109/JCSSE.2013.6567344

  39. França, R.S.d., Amaral, H.J.C.d.: Mineraç ao de dados na identificaç ao de grupos de estudantes com dificuldades de aprendizagem no ensino de programaç ao, RENOTE: Revista Novas Tecnologias na Educaç ao 11(1) (2013). http://seer.ufrgs.br/renote/article/view/41634/26413

  40. Huebner, R.A.: A survey of educational data-mining research. Res. High. Educ. J. 19(4) (2013). http://www.aabri.com/manuscripts/121328.pdf

  41. Jindal, R., Borah, M.D.: A survey on educational data mining and research trends. Int. J. Database Manag. Syst. 5(3), 53 (2013). http://airccse.org/journal/ijdms/papers/5313ijdms04.pdf

  42. Mohamad, S.K., Tasir, Z.: Educational data mining: a review. In: The 9th International Conference on Cognitive Science, vol. 97, p. 320 (2013). https://doi.org/10.1016/j.sbspro.2013.10.240. Procedia - Social and Behavioral Sciences

  43. Gray, G., McGuinness, C., Owende, P.: Investigating the efficacy of algorithmic student modelling in predicting students at risk of failing in tertiary education, pp. 378–380 (2013)

  44. Liu, Y.E., Mandel, T., Butler, E., Andersen, E., O’Rourke, E., Brunskill, E., Popović, Z.: Predicting player moves in an educational game a hybrid approach, pp. 106–113 (2013)

  45. Pardos, Z.A., Bergner, Y., Seaton, D.T., Pritchard, D.E.: Adapting bayesian knowledge tracing to a massive open online course in edx. In: D’Mello, S.K., Calvo, R.A., Olney A. (eds.) Proceedings of the 6th International Conference on Educational Data Mining, International Educational Data Mining Society, pp. 137–144 (2013)

  46. Maldonado, R.M., Yacef, K., Kay, J.: Data mining in the classroom discovering groups strategies at a multi-tabletop environment. In: Proceedings of the 6th International Conference on Educational Data Mining (Interntional Educational Data Mining Society), pp. 121–128 (2013)

  47. Almeda, M.V., Scupelli, P., Baker, R.S., Weber, M., Fisher, A.: Clustering of design decisions in classroom visual displays, pp. 44–48 (2014). https://doi.org/10.1145/2567574.2567605

  48. Guleria, P., Sood, M.: Data mining in education: a review on the knowledge discovery perspective. Int. J. Data Min. Knowl. Manag. Process 4(5), 47 (2014). https://doi.org/10.5121/ijdkp.2014.4504

    Article  Google Scholar 

  49. Paiva, R., Borges, D., Santos, J., Bittencourt, I.I., da Silva, A.P.: Lessons learned from an online open course: a Brazilian case study, pp. 229–234 (2014). https://doi.org/10.1145/2554850.2555024

  50. Pena-Ayala, A.: Educational data mining: a survey and a data mining-based analysis of recent works. Expert Syst. Appl. 41(4), 1432 (2014). https://doi.org/10.1016/j.eswa.2013.08.042

    Article  Google Scholar 

  51. Tashakkori, R.M., Parry, R.M., Benoit, A., Cooper, R.A., Jenkins, J.L., Westveer, N.T.: Research experience for teachers: Data analysis & mining, visualization, and image processing, pp. 193–198 (2014). https://doi.org/10.1145/2538862.2538967

  52. Guarín, C.E.L., Guzmán, E.L., González, F.A.: A model to predict low academic performance at a specific enrollment using data mining. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje 10(3), 119 (2015). https://doi.org/10.1109/RITA.2015.2452632

    Article  Google Scholar 

  53. Heredia, D., Amaya, Y., Barrientos, E.: Student dropout predictive model using data mining techniques. IEEE Latin Am. Trans. 13(9), 3127 (2015). https://doi.org/10.1109/TLA.2015.7350068

    Article  Google Scholar 

  54. Ihantola, P., Vihavainen, A., Ahadi, A., Butler, M., Börstler, J., Edwards, S.H., Isohanni, E., Korhonen, A., Petersen, A., Rivers, K., Rubio, M.A., Sheard, J., Skupas, B., Spacco, J., Szabo, C., Toll, D.: Educational data mining and learning analytics in programming: Literature review and case studies, pp. 41–63 (2015). https://doi.org/10.1145/2858796.2858798

  55. London, A., Pelyhe, A., Holló, C., Németh, T.: Applying graph-based data mining concepts to the educational sphere, pp. 358–365 (2015). https://doi.org/10.1145/2812428.2812436

  56. In: O.C. Santos, J.G. Boticario, C. Romero, M. Pechenizkiy, A. Merceron, P. Mitros, J.M. Luna, C. Mihaescu, P. Moreno, A. Hershkovitz, S. Ventura, M. Desmarais (eds.) Proceedings of the 8th International conference on educational data mining (EDM’15), International Conference on Educational Data Mining (EDM) 2015 (International Educational Data Mining Society (IEDMS)), pp. 282–288

  57. Gramoli, V., Charleston, M., Jeffries, B., Koprinska, I., McGrane, M., Radu, A., Viglas, A., Yacef, K.: Mining autograding data in computer science education (2016). https://doi.org/10.1145/2843043.2843070

  58. Romero, C., Ventura, S.: Educational data mining: a review of the state of the art. IEEE Trans. Syst. Man Cybern. C 40(6), 601 (2010). https://doi.org/10.1109/TSMCC.2010.2053532

    Article  Google Scholar 

  59. Ricarte, I.L.M., Junior, G.R.F.: Uma metodologia para a mineraç ao de dados oriundos de ambientes de aprendizagem apoiados por computadores. Revista Informática na Educaç ao: Teoria e Prática 14(2), 83 (2011)

    Google Scholar 

  60. Vasconcelos, F.H.L., da Silva, T.E.V., Mota, J.C.M.: Multilinear educational data analysis for evaluation of engineering education. IEEE Latin Am. Trans. 13(8), 2785 (2015). https://doi.org/10.1109/TLA.2015.7332163

    Article  Google Scholar 

  61. Gobert, J.D., Kim, Y.J., Pedro, M.A.S., Kennedy, M., Betts, C.G.: Using educational data mining to assess students’ skills at designing and conducting experiments within a complex systems microworld. Think. Skills Creat. 18, 81 (2015). https://doi.org/10.1016/j.tsc.2015.04.008

    Article  Google Scholar 

  62. Allen, L., McNamara, D.: You are your words: modeling students’ vocabulary knowledge with natural language processing techniques, pp. 258–265 (2015)

  63. Pedro, M.O.S., Snow, E., Baker, R., McNamara, D., Heffernan, N.: Exploring dynamical assessments of affect, behavior, and cognition and math state test achievement, pp. 85–92 (2015)

  64. Shahiri, A.M., Husain, W., Rashid, N.A.: A review on predicting student’s performance using data mining techniques. Procedia Comput. Sci. 72, 414 (2015). https://doi.org/10.1016/j.procs.2015.12.157

    Article  Google Scholar 

  65. Campagni, R., Merlini, D., Sprugnoli, R., Verri, M.C.: Data mining models for student careers. Expert Syst. Appl. 42(13), 5508 (2015). https://doi.org/10.1016/j.eswa.2015.02.052

    Article  Google Scholar 

  66. Hung, Y.H., Chang, R.I., Lin, C.F.: Hybrid learning style identification and developing adaptive problem-solving learning activities. Comput. Hum. Behav. A 552, 55 (2016). https://doi.org/10.1016/j.chb.2015.07.004

    Article  Google Scholar 

  67. Sabourin, J., Kosturko, L., Fitzgerald, C., Mcquiggan, S.: Student privacy and educational data mining: perspectives from industry. In: Santos, O.C., Boticario, J.G., Romero, C., Pechenizkiy, M., Merceron, A., Mitros, P., Luna, J.M., Mihaescu, C., Moreno, P., Hershkovitz, A., Ventura, S., Desmarais, M. (eds.) Proceedings of the 8th International Conference on Educational Data Mining (EDM’15), (International Educational Data Mining Society (IEDMS), pp. 164–170 (2015)

  68. Pedro, M.O.Z.S., Baker, R.S., Bowers, A.J., Heffernan, N.T.: Predicting college enrollment from student interaction with an intelligent tutoring system in middle school. In: D’Mello, S.K., Calvo, R.A. Olney, A. (eds.) Proceedings of the 6th international conference on educational data Mining (EDM’15), International Educational Data Mining Society, pp. 137–144 (2013)

  69. Olsen, J., Aleven, V., Rummel, N.: Predicting student performance in a collaborative learning environment. In: Santos, O.C., Boticario, J.G., Romero, C., Pechenizkiy, M., Merceron, A., Mitros, P., Luna, J.M., Mihaescu, C., Moreno, P., Hershkovitz, A., Ventura, S., Desmarais, M. (eds.) Proceedings of the 8th international conference on educational data mining (EDM’15), International Conference on Educational Data Mining (EDM) 2015, (International Educational Data Mining Society (IEDMS), pp. 211–217 (2015)

  70. Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41(6), 391 (1990). https://doi.org/10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9

  71. Bellegarda, J.R.: Latent semantic mapping. IEEE Signal Process. Mag. 22(5), 70 (2005). https://doi.org/10.1109/MSP.2005.1511825

    Article  Google Scholar 

  72. Landauer, T.K.: A solution to plato’ s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychol Rev 104, 211 (1997). https://doi.org/10.1037/0033-295X.104.2.211 (1997)

  73. Recchia, G., Jones, M.N.: More data trumps smarter algorithms: Comparing pointwise mutual information with latent semantic analysis. Behavior Research Methods 41(3), 647 (2009). https://doi.org/10.3758/BRM.41.3.647

    Article  Google Scholar 

  74. Budiu, R., Royer, C., Pirolli, P.L.: Modeling information scent: a 1446 comparison of lsa, pmi and glsa similarity measures on common 1447 tests and corpora. In: 8th Annual Conference of the Recherche d’Information Assistee Par Ordinateur (RIAO’), Pittsburgh, PA, USA (2005)

Download references

Acknowledgements

The authors acknowledge the financial support received from the CNPq (Brazilian National Council for Scientific and Technological Development), CAPES (Coordination for the Improvement of Higher Education Personnel), FAPEMIG (Foundation for Research Support of the State of Minas Gerais), and Pontifical Catholic University of Minas Gerais, Brazil.

Author information

Authors and Affiliations

  1. Pontifical Catholic University of Minas Gerais, Belo Horizonte, MG, Brazil

    Marcos Wander Rodrigues & Luis Enrique Zárate

Authors
  1. Marcos Wander Rodrigues
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luis Enrique Zárate
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marcos Wander Rodrigues.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rodrigues, M.W., Zárate, L.E. OrgBR-M: a method to assist in organizing bibliographic material based on formal concept analysis—a case study in educational data mining. Int J Digit Libr 21, 423–448 (2020). https://doi.org/10.1007/s00799-020-00290-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00799-020-00290-8

Keywords

search

Navigation