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Improving Student Mobility Through Automated Mapping of Similar Courses

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Domain-Specific Conceptual Modeling

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Abstract

Internationalization is a global trend of society development. In order to get on board with this trend, universities are making great efforts to create possibilities for students to participate in mobility and exchange programs and learn from others. Students’ decision to take this great step get out of their comfort zone and manage their stay in an unknown environment depends on many influencing factors, the most important one being a smooth recognition process of the study period and credits earned abroad. SCoRe4Mobility is a tool supposed to help students and responsible people at the home university to find similar courses at other universities and make the selection of the most compatible destination for mobility easier.

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References

  1. Teichler, U.: Internationalisation trends in higher education and the changing role of international student mobility. J. Int. Mobil. 5, 177 (2017). https://doi.org/10.3917/jim.005.0179

    Article  Google Scholar 

  2. Courtois, A.: ‘It doesn’t really matter which university you attend or which subject you study while abroad.’ The massification of student mobility programmes and its implications for equality in higher education. Eur. J. High. Educ. 8, 99–114 (2018). https://doi.org/10.1080/21568235.2017.1373027

    Article  Google Scholar 

  3. Roy, A., Newman, A., Ellenberger, T., Pyman, A.: Outcomes of international student mobility programs: a systematic review and agenda for future research. Stud. High. Educ. 44, 1630–1644 (2019). https://doi.org/10.1080/03075079.2018.1458222

    Article  Google Scholar 

  4. Zimmermann, J., Greischel, H., Jonkmann, K.: The development of multicultural effectiveness in international student mobility. High. Educ. (2020). https://doi.org/10.1007/s10734-020-00509-2

  5. Bartha, Z., Gubik, A.S.: Institutional determinants of higher education students’ international mobility within the Erasmus Programme countries. Theory Methodol. Pract. 14, 3–13 (2018). https://doi.org/10.18096/TMP.2018.02.01

    Article  Google Scholar 

  6. Pagani, R.N., Ramond, B., Da Silva, V.L., Zammar, G., Kovaleski, J.L.: Key factors in university-to-university knowledge and technology transfer on international student mobility. Knowl. Manag. Res. Pract. 18, 405–423 (2020). https://doi.org/10.1080/14778238.2019.1678415

    Article  Google Scholar 

  7. Bartha, Z., Gubik, A.S., Rethi, G.: Management of innovations in Hungarian HEIs: enhancing the Erasmus mobility Programme. Mark. Manag. Innov. 84–95 (2019). https://doi.org/10.21272/mmi.2019.1-07

  8. European Ministers in charge of Higher Education. The Bologna Declaration of 19 June 1999: Joint Declaration of the European Ministers of Education (1999).

    Google Scholar 

  9. Council of the EU. Erasmus + 2021 2027: Council Reaches a Provisional Agreement with the European Parliament. In: Council of the European Union. https://www.consilium.europa.eu/en/press/press-releases/2020/12/11/erasmus-2021-2027-council-reaches-a-provisional-agreement-with-the-european-parliament/ (2020). Accessed 22 Mar 2021

  10. Chopra, A., Prashar, A., Sain, C.: Natural language processing. Int. J. Technol. Enhanc. Emerg. Eng. Res. 1, 131–134 (2013)

    Google Scholar 

  11. Jain, A., Kulkarni, G., Shah, V.: Natural language processing. Int. J. Comput. Sci. Eng. 6, 161–167 (2018). https://doi.org/10.26438/ijcse/v6i1.161167

    Article  Google Scholar 

  12. Manning, C.D., Raghavan, P., Schutze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge, UK (2008)

    Book  Google Scholar 

  13. Luhn, H.P.: A statistical approach to mechanized encoding and searching of literary information. IBM J. Res. Dev. 1, 309–317 (1957). https://doi.org/10.1147/rd.14.0309

    Article  MathSciNet  Google Scholar 

  14. spaCy. ExplosionAI GmbH (2021)

    Google Scholar 

  15. ExplosionAI GmbH. Linguistic Features: Word Vectors and Semantic Similarity. In: SpaCy Usage Doc. https://spacy.io/usage/linguistic-features#vectors-similarity (2021). Accessed 17 Mar 2021

  16. Landauer, T.K., Foltz, P.W., Laham, D.: An introduction to latent semantic analysis. Discourse Process. 25, 259–284 (1998). https://doi.org/10.1080/01638539809545028

    Article  Google Scholar 

  17. Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41, 391–407 (1990)

    Article  Google Scholar 

  18. Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. ArXiv181004805 Cs (2019)

    Google Scholar 

  19. Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall, Hoboken, NJ (2010)

    MATH  Google Scholar 

  20. Landauer, T.K.: LSA as a theory of meaning. In: Landauer, T.K., McNamara, D.S., Dennis, S., Kintsch, W. (eds.) Handbook of latent semantic analysis, pp. 3–34. Routledge, New York (2011)

    Google Scholar 

  21. Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. ArXiv13104546 Cs Stat (2013)

    Google Scholar 

  22. Martin, D.I., Berry, M.W.: Mathematical foundations behind latent semantic analysis. In: Landauer, T.K., McNamara, D.S., Dennis, S., Kintsch, W. (eds.) Handbook of latent semantic analysis, pp. 35–56. Routledge, New York (2011)

    Google Scholar 

  23. Nayak, P.: Understanding searches better than ever before. In: Google. https://blog.google/products/search/search-language-understanding-bert/ (2019). Accessed 24 Feb 2021

  24. Guberović, E., Turčinović, F., Relja, Z., Bosnić, I.: In search of a syllabus: comparing computer science courses. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0588–0592. IEEE, Opatija (2018)

    Chapter  Google Scholar 

  25. Fu, Q., Zhuang, Y., Gu, J., Zhu, Y., Guo, X.: Agreeing to disagree: Choosing among eight topic-modeling methods. Big Data Res. 23, 100173 (2021). https://doi.org/10.1016/j.bdr.2020.100173

    Article  Google Scholar 

  26. Miller, T.: Essay assessment with latent semantic analysis. J. Educ. Comput. Res. 29, 495–512 (2003). https://doi.org/10.2190/W5AR-DYPW-40KX-FL99

    Article  Google Scholar 

  27. Evangelopoulos, N.E.: Latent semantic analysis. Wiley Interdiscip. Rev. Cogn. Sci. 4, 683–692 (2013). https://doi.org/10.1002/wcs.1254

    Article  Google Scholar 

  28. Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM. 18, 613–620 (1975). https://doi.org/10.1145/361219.361220

    Article  MATH  Google Scholar 

  29. Řehůřek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 workshop on new challenges for NLP frameworks, pp. 45–50. University of Malta, Valletta, MT (2010)

    Google Scholar 

  30. Schatten, M., Tomičić, I., Okreša Ðurić, B.: Orchestration platforms for hybrid artificial intelligence in computer games -- a conceptual model. In: Strahonja, V., Steingartner, W., Kirinić, V. (eds.) Central European conference on information and intelligent systems, pp. 3–8. Varaždin, Faculty of Organization and Informatics, University of Zagreb (2020)

    Google Scholar 

  31. Karagiannis, D., Buchmann, R.A., Burzynski, P., Reimer, U., Walch, M.: Fundamental conceptual modeling languages in OMiLAB. In: Karagiannis, D., Mayr, H.C., Mylopoulos, J. (eds.) Domain-Specific Conceptual Modeling, 1st edn, pp. 3–30. Springer, Cham (2016)

    Chapter  Google Scholar 

  32. Karagiannis, D., Kühn, H.: Metamodelling platforms. In: Bauknecht, K., Tjoa, A.M., Quirchmayr, G. (eds.) E-commerce and web technologies, pp. 182–182. Springer, Aix-en-Provence (2002)

    Chapter  Google Scholar 

  33. BOC Gmbh. ADOxx. BOC Gmbh, Vienna, AT. (2016)

    Google Scholar 

  34. Grinberg, M.: Flask web development: developing web applications with python, 2nd edn. O’Reilly Media, Sebastopol, CA (2018)

    Google Scholar 

  35. Lewis J, Fowler M.: Microservices. In: martinfowler.com. https://martinfowler.com/articles/microservices.html. Accessed 27 Mar 2021

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Acknowledgments

This work has been supported in part by Croatian Science Foundation under project number IP-2019-04-5824.

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Authors and Affiliations

  1. Faculty of Organization and Informatics, University of Zagreb, Zagreb, Croatia

    Martina Tomičić Furjan, Bogdan Okreša Djuric & Tomislav Peharda

Authors
  1. Martina Tomičić Furjan
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  2. Bogdan Okreša Djuric
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  3. Tomislav Peharda
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Corresponding author

Correspondence to Martina Tomičić Furjan .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science, Research Group Knowledge Engineering, University of Vienna, Vienna, Austria

    Dimitris Karagiannis

  2. College of Engineering, Chonbuk National University, Jeonju, Korea (Republic of)

    Moonkun Lee

  3. School of Business, University of Applied Sciences and Arts Northwestern Switzerland, Olten, Switzerland

    Knut Hinkelmann

  4. OMiLAB gGmbH, Berlin, Germany

    Wilfrid Utz

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Tomičić Furjan, M., Okreša Djuric, B., Peharda, T. (2022). Improving Student Mobility Through Automated Mapping of Similar Courses. In: Karagiannis, D., Lee, M., Hinkelmann, K., Utz, W. (eds) Domain-Specific Conceptual Modeling. Springer, Cham. https://doi.org/10.1007/978-3-030-93547-4_22

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  • DOI: https://doi.org/10.1007/978-3-030-93547-4_22

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  • Print ISBN: 978-3-030-93546-7

  • Online ISBN: 978-3-030-93547-4

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