Skip to main content
SpringerLink
Log in

Learning Analytics: The Role of Information Technology for Educational Process Innovation

  • Conference paper
  • First Online:
Innovations in Bio-Inspired Computing and Applications (IBICA 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1180))

Abstract

Today, trends and advancements in information and communication technologies have proven to be indispensable towards achieving the goals of modern educational models and processes. On the one hand, there is a need for educators to adopt new technologies in support of different activities that constitute the educational processes; ranging from the changing higher institutional labour market to the rapid renovation of information systems and tools used to support the learners. Moreover, such a requirement also relates to the several educational communities that are expected to include a more proactive and creative learning strategies and experiences for the stakeholders (e.g. teachers and students). On the other hand, this paper shows that to meet those needs, Learning Analytics (LA) which implies measurement, collection, analysis, and reporting of data about the progress of stakeholders and contexts in which learning takes place is of importance. To this end, this paper proposes a Learning Analytics Educational Process Innovation model (LAEPI) that leverages the ever-increasing amount of data that are recorded and stored about the different learning activities and digital footprints of users within the educational settings. This is done in order to provide a method that shows to be useful towards maintaining a continuous improvement and monitoring of the educational processes or platforms. Thus, the term Educational Process Innovation. Technically, the work illustrates implication of the method in real-time using dataset about online learning activities of university students for its experimental analysis and results.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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

Notes

  1. 1.

    http://www.mirlabs.net/ibica19/speakers.php

References

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

    Article  Google Scholar 

  2. Papamitsiou, Z., Economides, A.A.: Learning analytics and educational data mining in practice: a systematic literature review of empirical evidence. J. Educ. Technol. Soc. 17(4), 49–64 (2014)

    Google Scholar 

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

    Article  Google Scholar 

  4. UNESCO: Competency Based Education. Learning Portal - Planning education for improved learning outcome. Tecnologico de Monterrey, Educational Innovation Observatory, Mexico (2015). https://learningportal.iiep.unesco.org/en/library/competency-based-education. Accessed 03 Sept 2019

  5. Bogarín, A., Romero, C., Cerezo, R., Sánchez-Santillán, M.: Clustering for improving educational process mining, pp. 11–15. ACM, New York (2014)

    Google Scholar 

  6. Trčka, N., Pechenizkiy, M., van der Aalst, W.M.P.: Process mining from educational data. In: Romero, C., Ventura, S., Pechenizkiy, M., Baker, R.S.J.D. (eds.) Handbook of Educational Data Mining. Data Mining and Knowledge Discovery Series, pp. 123–142. Chapman & Hall/CRC, Boca Raton (2010)

    Google Scholar 

  7. Bogarín, A., Cerezo, R., Romero, C.: A survey on educational process mining. Wiley Interdisc. Rev.: Data Min. Knowl. Discovery (WIRES) 8(1), e1230 (2018)

    Google Scholar 

  8. Van der Aalst, W.M.P.: Process Mining: Data Science in Action, 2nd edn. Springer, Heidelberg (2016)

    Book  Google Scholar 

  9. Cairns, A.H., Ondo, J.A., Gueni, B., Fhima, M., Schwarcfeld, M., Joubert, C., Khelifa, N.: Using semantic lifting for improving educational process models discovery and analysis. In: SIMPDA, CEUR Workshop Proceedings. CEUR-WS.org, vol. 1293, pp. 150–161 (2014)

    Google Scholar 

  10. Okoye, K., Islam, S., Naeem, U., Sharif, M.S., Azam, M.A., Karami, A.: The application of a semantic-based process mining framework on a learning process domain. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) Intelligent Systems and Applications. Advances in Intelligent Systems and Computing, vol. 868, pp. 1381–1403. Springer Cham (2019)

    Google Scholar 

  11. Sclater, N.: What is learning analytics and how can it help your institution? (2016) https://sclater.com/blog/what-is-learning-analytics-and-how-can-it-help-your-institution/. Accessed 03 Sept 2019

  12. Ley, T.: Educational innovation and learning analytics. In: 7th Int. Conference on Concept Mapping, Tallinn, Estonia (2016). http://cmc.ihmc.us/cmc2016papers/TobiasLey-Keynote-CMC2016.pdf. Accessed 16 Sept 2019

  13. Shibani, A., Knight, S., Shum, S.: Contextualizable learning analytics design: a generic model and writing analytics evaluations. In: ACM International Conference Proceedings Series, pp. 210–219 (2019)

    Google Scholar 

  14. Pardo, A., Jovanovic, J., Dawson, S., Gasevic, D., Mirriahi, N.: Using learning analytics to scale the provision of personalised feedback. Br. J. Educ. Technol. 50(1), 128–138 (2019)

    Article  Google Scholar 

  15. Nganji, J.: Towards learner-constructed e-learning environments for effective personal learning experiences. Behav. Inf. Technol. 37(7), 647–657 (2018)

    Article  Google Scholar 

  16. Okoye, K., Tawil, A.R.H., Naeem, U., Bashroush, R., Lamine, E.: A semantic rule-based approach supported by process mining for personalised adaptive learning. Procedia Comput. Sci. 37(C), 203–210 (2014)

    Article  Google Scholar 

  17. Prieto, L., Rodríguez-Triana, M., Martínez-Maldonado, R., Dimitriadis, Y., Gašević, D.: Orchestrating learning analytics (OrLA): Supporting inter-stakeholder communication about adoption of learning analytics at the classroom level. Australas. J. Educ. Technol. 35(4), 14–33 (2019)

    Article  Google Scholar 

  18. edX program, MOOC unit Tecnologico de Monterrey. https://www.edx.org/school/tecnologico-de-monterrey. Accessed 10 Aug 2019

  19. Leemans, S.J.J., Fahland, D., Van der Aalst, W.M.P.: Process and Deviation Exploration with Inductive Visual Miner (2014). http://www.processmining.org/_media/blogs/pub2014/bpmdemoleemans.pdf. Accessed 09 Sept 2019

  20. Leemans, S.J.J., Poppe, E., Wynn, M.T.: Directly follows-based process mining: exploration & a case study. In: 2019 International Conference on Process Mining (ICPM), Aachen, pp. 25–32 (2019)

    Google Scholar 

  21. Verbeek, H., et al.: Process Mining Research Group. Math&CS department, Eindhoven University of Technology (2016). http://www.processmining.org/prom/start. Accessed 13 Sept 2019

  22. Verbeek, H., Buijs, J., van Dongen, B., Van der Aalst, W.M.P.: XES, XESame, and ProM 6. In: Soffer, P., Proper, E. (ed.) Information Systems Evolution. CAiSE Forum 2010. Lecture Notes in Business Information Processing, pp. 60–75. Springer, Heidelberg (2011)

    Google Scholar 

  23. Toyawanit, T., Premchaiswadi, W.: Applying inductive Visual Miner technique to analyze and detect problems in procedures of a hospital in Thailand. In: 2016 14th International Conference on ICT and Knowledge Engineering (ICT&KE), Bangkok, pp. 98–104 (2016)

    Google Scholar 

  24. Ganesha, K., Soundarya, M., Supriya, K.V.: The best fit process model for the utilization of the physical resources in hospitals by applying inductive visual miner. In: International Conference on Inventive Communication & Computational Technologies, Coimbatore, pp. 318–322 (2017)

    Google Scholar 

  25. Kelleher, S.: From bricks to clicks: the potential of data and analytics in higher education. Policy Connect. Higher Education Commission, Technical report (2016). https://www.policyconnect.org.uk/hec/research/report-bricks-clicks-potential-data-and-analytics-higher-education. Accessed 10 Sept 2019

  26. Johnson, L., Becker, S.A., Estrada, V., Freeman, A.: The NMC Horizon Report: 2015 Higher Education Edition. The New Media Consortium, p. 12 (2015). http://cdn.nmc.org/media/2015-nmc-horizon-report-HE-EN.pdf. Accessed 03 Sept 2019

Download references

Acknowledgment

The authors would like to acknowledge the technical and financial support of Writing Lab, TecLabs, Tecnologico de Monterrey, in the publication of this work. We would also like to acknowledge The MOOC’s, Alternative Credentials Unit of the TecLabs for the provision of the datasets used for the analysis in this paper.

Author information

Authors and Affiliations

  1. Tecnologico de Monterrey, Writing Lab, TecLabs, Vicerrectoría de Investigación y Transferencia de Tecnología, 64849, Monterrey, NL, Mexico

    Kingsley Okoye & Samira Hosseini

  2. Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, Canada

    Julius T. Nganji

  3. School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, Nuevo Leon, Mexico

    Samira Hosseini

Authors
  1. Kingsley Okoye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Julius T. Nganji
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Samira Hosseini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kingsley Okoye .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR), Auburn, WA, USA

    Ajith Abraham

  2. Utkal University, Bhubaneswar, Odisha, India

    Mrutyunjaya Panda

  3. Department of Electronics Engineering, GIET University, Gunupur, Odisha, India

    Subhrajit Pradhan

  4. Area of Project Engineering, University of Cordoba, Córdoba, Córdoba, Spain

    Laura Garcia-Hernandez

  5. School of Information Science and Engineering, University of Jinan, Jinan, Shandong, China

    Kun Ma

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Okoye, K., Nganji, J.T., Hosseini, S. (2021). Learning Analytics: The Role of Information Technology for Educational Process Innovation. In: Abraham, A., Panda, M., Pradhan, S., Garcia-Hernandez, L., Ma, K. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2019. Advances in Intelligent Systems and Computing, vol 1180. Springer, Cham. https://doi.org/10.1007/978-3-030-49339-4_28

Download citation

Publish with us

Policies and ethics

Search

Navigation