Skip to main content
SpringerLink
Log in

Towards Evidence-Based Academic Advising Using Learning Analytics

  • Conference paper
  • First Online:
Computers Supported Education (CSEDU 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 865))

Included in the following conference series:

Abstract

Academic advising is a process between the advisee, adviser and the academic institution which provides the degree requirements and courses contained in it. Content-wise planning and management of the student’ study path, guidance on studies and academic career support is the main joint activity of advising. The purpose of this article is to propose the use of learning analytics methods, more precisely robust clustering, for creation of groups of actual study profiles of students. This allows academic advisers to provide evidence-based information on the study paths that have actually happened similarly to individual students. Moreover, academic institutions can focus on management and updates of course schedule having an effect of clearly characterized and recognized group of students. Using this approach a model of automated academic advising process, which can determine the study profiles, is presented. The presented model shows the whole automated process, where the learners will be profiled regularly, and where the proper study path will be suggested.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://www.jyu.fi/itp/en/korppi-guide.

References

  1. Agrawal, R., Srikant, R.: Mining sequential patterns. In: 1995 Proceedings of the Eleventh International Conference on Data Engineering, pp. 3–14. IEEE (1995)

    Google Scholar 

  2. Al-Ansari, A., El Tantawi, M., AbdelSalam, M., Al-Harbi, F.: Academic advising and student support: help-seeking behaviors among Saudi dental undergraduate students. Saudi Dental J. 27(2), 57–62 (2015)

    Article  Google Scholar 

  3. Al-Sarem, M.: Building a decision tree model for academic advising affairs based on the algorithm C 4–5. arXiv preprint arXiv:1511.04026 (2015)

  4. Auvinen, T.: Educational technologies for supporting self-regulated learning in online learning environments. Aalto University (2015)

    Google Scholar 

  5. Auvinen, T., Paavola, J., Hartikainen, J.: STOPS: a graph-based study planning and curriculum development tool. In: Proceedings of the 14th Koli Calling International Conference on Computing Education Research, pp. 25–34. ACM (2014)

    Google Scholar 

  6. Äyrämö, S.: Knowledge Mining Using Robust Clustering. Jyväskylä Studies in Computing, vol. 63. University of Jyväskylä, Jyväskylä (2006)

    Google Scholar 

  7. Biletskiy, Y., Brown, J.A., Ranganathan, G.: Information extraction from syllabi for academic e-Advising. Expert Syst. Appl. 36(3), 4508–4516 (2009)

    Article  Google Scholar 

  8. Brusilovsky, P.: Adaptive educational systems on the world-wide-web: a review of available technologies. In: Proceedings of Workshop “WWW-Based Tutoring” at 4th International Conference on Intelligent Tutoring Systems (ITS 1998), San Antonio, TX (1998)

    Google Scholar 

  9. Chatti, M.A., Dyckhoff, A.L., Schroeder, U., Thüs, H.: A reference model for learning analytics. Int. J. Technol. Enhanc. Learn. 4(5–6), 318–331 (2012)

    Article  Google Scholar 

  10. Chen, C.M.: Intelligent web-based learning system with personalized learning path guidance. Comput. Educ. 51(2), 787–814 (2008)

    Article  MathSciNet  Google Scholar 

  11. Chen, C.M.: Ontology-based concept map for planning a personalised learning path. Br. J. Educ. Technol. 40(6), 1028–1058 (2009)

    Article  Google Scholar 

  12. Chen, C.M.: Personalized e-learning system with self-regulated learning assisted mechanisms for promoting learning performance. Expert Syst. Appl. 36(5), 8816–8829 (2009)

    Article  Google Scholar 

  13. Chen, C.M., Lee, H.M., Chen, Y.H.: Personalized e-learning system using item response theory. Comput. Educ. 44(3), 237–255 (2005)

    Article  Google Scholar 

  14. Chen, C.M., et al.: Ontology-based concept map for planning personalized learning path. In: IEEE International Conferences on Cybernetics & Intelligent Systems (2008)

    Google Scholar 

  15. Cohoon, J.M., Aspray, W.: Women and Information Technology: Research on Underrepresentation, vol. 1. The MIT Press, Cambridge (2006)

    Book  Google Scholar 

  16. Daniel, B.: Big data and analytics in higher education: opportunities and challenges. Br. J. Educ. Technol. 46(5), 904–920 (2015)

    Article  Google Scholar 

  17. Dougherty, S.B.: Academic advising for high-achieving college students. High. Educ. Rev. 4, 63–82 (2007)

    Google Scholar 

  18. Drezner, Z., Hamacher, H.W.: Facility Location: Applications and Theory. Springer, Heidelberg (2001). https://doi.org/10.1007/978-3-642-56082-8

    Book  MATH  Google Scholar 

  19. Drozd, D.S.: Student preferences for academic advisors as transformational leaders. Ph.D. thesis, Texas A&M University (2010)

    Google Scholar 

  20. Feghali, T., Zbib, I., Hallal, S.: A web-based decision support tool for academic advising. Educ. Technol. Soc. 14(1), 82–94 (2011)

    Google Scholar 

  21. Ferguson, R.: Learning analytics: drivers, developments and challenges. Int. J. Technol. Enhanc. Learn. 4(5–6), 304–317 (2012)

    Article  Google Scholar 

  22. de Freitas, S., Gibson, D., Du Plessis, C., Halloran, P., Williams, E., Ambrose, M., Dunwell, I., Arnab, S.: Foundations of dynamic learning analytics: using university student data to increase retention. Br. J. Educ. Technol. 46(6), 1175–1188 (2015)

    Article  Google Scholar 

  23. Fung, A., Yeung, J.: An object model for a web-based adaptive educational system. In: Proceedings of the IFIP International Conference on Educational Use of Technologies (ICEUT 2000) (2000)

    Google Scholar 

  24. Gavriushenko, M., Saarela, M., Kärkkäinen, T.: Supporting institutional awareness and academic advising using clustered study profiles. In: Proceedings of the 9th International Conference on Computer Supported Education: CSEDU, vol. 1, pp. 35–46. INSTICC, ScitePress (2017)

    Google Scholar 

  25. Henderson, L.K., Goodridge, W.: AdviseMe: an intelligent web-based application for academic advising. Int. J. Adv. Comput. Sci. Appl. 6(8), 233–243 (2015)

    Google Scholar 

  26. Hershkovitz, A., Knight, S., Dawson, S., Jovanović, J., Gašević, D.: About “learning” and “analytics”. J. Learn. Anal. 3(2), 1–5 (2016)

    Article  Google Scholar 

  27. Huang, M.J., Huang, H.S., Chen, M.Y.: Constructing a personalized e-learning system based on genetic algorithm and case-based reasoning approach. Expert Syst. Appl. 33(3), 551–564 (2007)

    Article  MathSciNet  Google Scholar 

  28. Huber, P.J.: Robust Statistics. Springer, Heidelberg (2011)

    Book  Google Scholar 

  29. Hwang, G.J., Kuo, F.R., Yin, P.Y., Chuang, K.H.: A heuristic algorithm for planning personalized learning paths for context-aware ubiquitous learning. Comput. Educ. 54(2), 404–415 (2010)

    Article  Google Scholar 

  30. Idris, N., Yusof, N., Saad, P., et al.: Adaptive course sequencing for personalization of learning path using neural network. Int. J. Adv. Soft Comput. Appl. 1(1), 49–61 (2009)

    Google Scholar 

  31. Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31(8), 651–666 (2010)

    Article  Google Scholar 

  32. Kaihlavirta, A., Isomöttönen, V., Kärkkäinen, T.: A self-ethnographic investigation of continuing education program in engineering arising from economic structural change. Stud. Contin. Educ. 37(1), 99–114 (2015)

    Article  Google Scholar 

  33. Kärkkäinen, T., Saarela, M.: Robust principal component analysis of data with missing values. In: Perner, P. (ed.) MLDM 2015. LNCS (LNAI), vol. 9166, pp. 140–154. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21024-7_10

    Chapter  Google Scholar 

  34. Kinnunen, P., Marttila-Kontio, M., Pesonen, E.: Getting to know computer science freshmen. In: Proceedings of the 13th Koli Calling International Conference on Computing Education Research, Koli Calling 2013, pp. 59–66. ACM, New York (2013). https://doi.org/10.1145/2526968.2526975

  35. Latorre-Navarro, E.M., Harris, J.G.: An intelligent natural language conversational system for academic advising. Editor. Preface 6(1) (2015)

    Google Scholar 

  36. Lee, M.G.: Profiling students’ adaptation styles in web-based learning. Comput. Educ. 36(2), 121–132 (2001)

    Article  Google Scholar 

  37. Leung, C.M., Tsang, E.Y., Lam, S., Pang, D.C.: Intelligent counseling system: a 24 x 7 academic advisor. Educause Q. 33(4), n4 (2010)

    Google Scholar 

  38. Liu, C.L., Wu, S., Chang, M., Heh, J.: Guiding students to do remedial learning in school campus with learning objects’ spatial relations. In: 16th International Conference on Computers in Education Conference, pp. 249–256. Citeseer (2008)

    Google Scholar 

  39. Minaei-Bidgoli, B.: Data mining for a web-based educational system. Ph.D. thesis, Michigan State University (2004)

    Google Scholar 

  40. Mostafa, L., Oately, G., Khalifa, N., Rabie, W.: A case based reasoning system for academic advising in Egyptian educational institutions. In: 2nd International Conference on Research in Science, Engineering and Technology (ICRSET 2014), pp. 21–22, March 2014

    Google Scholar 

  41. Nguyen, T.B., Nguyen, D.N., Nguyen, H.S., Tran, H., Hoang, T.A.D.: An integrated approach for an academic advising system in adaptive credit-based learning environment. J. Sci. Nat. Sci. Technol. 24, 110–121 (2008)

    Google Scholar 

  42. Noaman, A.Y., Ahmed, F.F.: A new framework for e academic advising. Procedia Comput. Sci. 65, 358–367 (2015)

    Article  Google Scholar 

  43. Papanikolaou, K.A., Grigoriadou, M., Magoulas, G.D., Kornilakis, H.: Towards new forms of knowledge communication: the adaptive dimension of a web-based learning environment. Comput. Educ. 39(4), 333–360 (2002)

    Article  Google Scholar 

  44. Papastergiou, M.: Are computer science and information technology still masculine fields? High school students’ perceptions and career choices. Comput. Educ. 51(2), 594–608 (2008)

    Article  Google Scholar 

  45. Rogers, T.: Critical realism and learning analytics research: epistemological implications of an ontological foundation. In: Proceedings of the 5th International Conference on Learning Analytics and Knowledge, pp. 223–230. ACM, New York (2015). https://doi.org/10.1145/2723576.2723631

  46. Saarela, M., Kärkkäinen, T.: Discovering gender-specific knowledge from Finnish basic education using PISA scale indices. In: Proceedings of the 7th International Conference on Educational Data Mining, pp. 60–68 (2014)

    Google Scholar 

  47. Saarela, M., Kärkkäinen, T.: Analysing student performance using sparse data of core bachelor courses. JEDM-J. Educ. Data Min. 7(1), 3–32 (2015). http://www.educationaldatamining.org/JEDM/index.php/JEDM/article/view/JEDM056

  48. Saarela, M., Kärkkäinen, T.: Do country stereotypes exist in PISA? A clustering approach for large, sparse, and weighted data. In: Proceedings of the 8th International Conference on Educational Data Mining, pp. 156–163 (2015)

    Google Scholar 

  49. Saarela, M., Kärkkäinen, T.: Weighted clustering of sparse educational data. In: Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015), pp. 337–342 (2015)

    Google Scholar 

  50. Saarela, M.: Automatic Knowledge Discovery from Sparse and Large-scale Educational Data: Case Finland. Jyväskylä Studies in Computing, vol. 262. University of Jyväskylä, Jyväskylä(2017)

    Google Scholar 

  51. Saarela, M., Hämäläinen, J., Kärkkäinen, T.: Feature ranking of large, robust, and weighted clustering result. In: Kim, J., et al. (eds.) PAKDD 2017. LNCS, vol. 10234, pp. 96–109. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57454-7_8

    Chapter  Google Scholar 

  52. Saarela, M., Kärkkäinen, T.: Knowledge discovery from the programme for international student assessment. In: Peña-Ayala, A. (ed.) Learning Analytics: Fundaments, Applications, and Trends. SSDC, vol. 94, pp. 229–267. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52977-6_8

    Chapter  Google Scholar 

  53. Sandvig, J., Burke, R.: AACORN: a CBR recommender for academic advising. Technical report TR05-015, DePaul University (2005)

    Google Scholar 

  54. Schiaffino, S., Garcia, P., Amandi, A.: eTeacher: providing personalized assistance to e-learning students. Comput. Educ. 51(4), 1744–1754 (2008)

    Article  Google Scholar 

  55. Sprent, P., Smeeton, N.C.: Applied Nonparametric Statistical Methods. CRC Press, Boca Raton (2016)

    Google Scholar 

  56. Study Amanuensis: Private Communication, 2 November 2016

    Google Scholar 

  57. Tang, T.Y., McCalla, G.: Smart recommendation for an evolving e-learning system: architecture and experiment. Int. J. eLearning 4(1), 105 (2005)

    Google Scholar 

  58. The Daily Telegraph: University degree subjects with the highest dropout rates (2017). http://www.telegraph.co.uk/education/educationpicturegalleries/11002595/University-degree-subjects-with-the-highest-dropout-rates.html?frame=2824912. Accessed 01 Mar 2017

  59. Tuttle, K.N.: Academic advising. New Dir. High. Educ. 2000(111), 15–24 (2000)

    Article  Google Scholar 

  60. Wallden, L.J.: Kansainvälisten koulutusarvioiden vertailu koulutuksellisen tiedonlouhinnan keinoin. University of Jyväskylä, Jyväskylä Studies in Computing (2016)

    Google Scholar 

  61. Wen, F.L.S.L.D., McGreal, F.Z.K.R.: e-Advisor: a multi-agent system for academic advising. Int. J. Adv. Comput. Sci. Appl. 6(8) (2015)

    Google Scholar 

  62. Werghi, N., Kamoun, F.K.: A decision-tree-based system for student academic advising and planning in information systems programmes. Int. J. Bus. Inf. Syst. 5(1), 1–18 (2009)

    Google Scholar 

  63. Zaki, M.J., Meira Jr., W.: Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, Cambridge (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Information Technology, University of Jyväskylä, P.O. Box 35, 40014, Jyväskylä, Finland

    Mariia Gavriushenko, Mirka Saarela & Tommi Kärkkäinen

Authors
  1. Mariia Gavriushenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mirka Saarela
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tommi Kärkkäinen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mariia Gavriushenko .

Editor information

Editors and Affiliations

  1. Instituto Superior de Engenharia do Porto, Porto, Portugal

    Paula Escudeiro

  2. University of Salerno, Fisciano, Italy

    Gennaro Costagliola

  3. University of Denver, Denver, Colorado, USA

    Susan Zvacek

  4. School of Engineering, University of Ulster, Newtownabbey, United Kingdom

    James Uhomoibhi

  5. Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Bruce M. McLaren

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gavriushenko, M., Saarela, M., Kärkkäinen, T. (2018). Towards Evidence-Based Academic Advising Using Learning Analytics. In: Escudeiro, P., Costagliola, G., Zvacek, S., Uhomoibhi, J., McLaren, B. (eds) Computers Supported Education. CSEDU 2017. Communications in Computer and Information Science, vol 865. Springer, Cham. https://doi.org/10.1007/978-3-319-94640-5_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-94640-5_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-94639-9

  • Online ISBN: 978-3-319-94640-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation