Skip to main content
SpringerLink
Log in
Book cover

Radical Solutions and Learning Analytics pp 1–15Cite as

Learning Analytics as a Breakthrough in Educational Improvement

  • Chapter
  • First Online:

Part of the book series: Lecture Notes in Educational Technology ((LNET))

Abstract

Learning analytics has become a reference area in the field of Learning Technologies as the mixture of different technical and methodological approaches in the capture, treatment and representation of educational data for later use in decision-making processes. With approximately ten years of development, it can be considered that learning analytics have abandoned their stage of dispersion and are heading towards a state of maturity that will position them as a fundamental piece in educational practice mediated by technology. However, it cannot be ignored that the power and goodness of these analytics must be channelled to improve learning itself and, therefore, the learning-teaching process, always acting from an ethical sense and preserving the privacy of the people who participate because it is straightforward to invade personal spaces in favour of the objectives sought. This chapter presents, from a conceptual perspective, the reference models that support the creation of educational strategies based on learning analytics that integrate the most current trends in the field, defined from a critical perspective that balances the undoubted benefits with the potential risks.

This is a preview of subscription content, 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   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

References

  • Agudo-Peregrina, Á. F., Iglesias-Pradas, S., Conde-González, M. Á., & Hernández-García, Á. (2014). Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior, 31, 542–550. https://doi.org/10.1016/j.chb.2013.05.031.

    Article  Google Scholar 

  • Alier Forment, M., Amo Filvà, D., García-Peñalvo, F. J., Fonseca Escudero, D., & Casañ, M. J. (2018). Learning analytics’ privacy on the blockchain. In F. J. García-Peñalvo (Ed.), TEEM’18 Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality (Salamanca, Spain, October 24th–26th, 2018) (pp. 294–298). New York, NY, USA: ACM.

    Google Scholar 

  • Almosallam, E. A., & Ouertani, H. C. (2014). Learning analytics: definitions, applications and related fields. A study for future challenges. In T. Herawan, M. M. Deris, & J. Abawajy (Eds.), Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013) (pp. 721–730). Singapore: Springer Science + Business Media.

    Google Scholar 

  • Amo-Filvà, D. A., Alier Forment, M., García-Peñalvo, F. J., Fonseca-Escudero, D., & Casañ, M. J. (2019). Clickstream for learning analytics to assess students’ behavior with scratch. Future Generation Computer Systems, 93, 673–686. https://doi.org/10.1016/j.future.2018.10.057.

    Article  Google Scholar 

  • Andres, J. M. L., ¡Baker, R. S., Gašević, D., Siemens, G., Crossley, S. A., & Joksimović, S. (2018). Studying MOOC completion at scale using the MOOC replication framework. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge—LAK′18 (Sydney, New South Wales, Australia—March 07–09, 2018) (pp. 71–78). New York, NY, USA: ACM.

    Google Scholar 

  • Bach, C. (2010). Learning analytics: Targeting instruction, curricula and student support. Paper presented at the Education and Information Systems, Technologies and Applications Conference, Orlando, FL, USA.

    Google Scholar 

  • Berlanga, A. J., & García-Peñalvo, F. J. (2008). Learning design in adaptive educational hypermedia systems. Journal of Universal Computer Science, 14(22), 3627–3647. https://doi.org/10.3217/jucs-014-22-3627.

    Article  Google Scholar 

  • Burgos, D., Tattersall, C., & Koper, R. (2007). Representing adaptive and adaptable units of learning. In B. Fernández-Manjón, J. M. Sánchez-Pérez, J. A. Gómez-Pulido, M. A. Vega-Rodríguez, & J. Bravo-Rodríguez (Eds.), Computers and education: E-learning, from theory to practice (pp. 41–56). Dordrecht: Springer, Netherlands.

    Chapter  Google Scholar 

  • Campbell, J. P., DeBlois, P. B., & Oblinger, D. G. (2007). Academic analytics. A new tool for a new era. Educause Review, 42(4), 40–42,44,46,48,50,52,54,56–57.

    Google Scholar 

  • Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5/6), 318–331. https://doi.org/10.1504/ijtel.2012.051815.

    Article  Google Scholar 

  • Clow, D. (2012). The learning analytics cycle: closing the loop effectively. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, LAK ′12 (Vancouver, BC, Canada, 29 April–2 May 2012) (pp. 134–138). New York, NY, USA: ACM.

    Google Scholar 

  • Cobos, R., & Macías Palla, V. (2017). edX-MAS: Model analyzer system. In Proceedings of the 5th International Conference on Technological Ecosystems for Enhancing Multiculturality―TEEM 2017 (Cádiz, Spain—October 18–20, 2017). New York, NY, USA: ACM.

    Google Scholar 

  • Conde-González, M. Á., & Hernández-García, Á. (2015). Learning analytics for educational decision making. Computers in Human Behavior, 47, 1–3. https://doi.org/10.1016/j.chb.2014.12.034.

    Article  Google Scholar 

  • Conde-González, M. Á., Hernández-García, Á., García-Peñalvo, F. J., & Sein-Echaluce, M. L. (2015). Exploring student interactions: Learning analytics tools for student tracking. In P. Zaphiris & I. Ioannou (Eds.), Learning and Collaboration Technologies. Second International Conference, LCT 2015, Held as Part of HCI International 2015 (Los Angeles, CA, USA, August 2–7, 2015, Proceedings) (pp. 50–61). Switzerland: Springer International Publishing.

    Google Scholar 

  • Doroudi, S., & Brunskill, E. (2019). Fairer but not fair enough on the equitability of knowledge tracing. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge—LAK’19 (Tempe, AZ, USA—March 04–08, 2019) (pp. 335–339). New York, NY, USA: ACM.

    Google Scholar 

  • Elias, T. (2011). Learning analytics: Definitions, processes and potential. Retrieved from https://bit.ly/2mh3EKD.

  • Fancsali, S. E., Zheng, G., Tan, Y., Ritter, S., Berman, S. R., & Galyardt, A. (2018). Using embedded formative assessment to predict state summative test scores. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge—LAK’18 (Sydney, New South Wales, Australia—March 07–09, 2018) (pp. 161–170). New York, NY, USA: ACM.

    Google Scholar 

  • Farhan, M., Munwar, I. M., Aslam, M., Martínez Enríquez, A. M., Farooq, A., Tanveer, S., & Mejia, A. P. (2012). Automated reply to students’ queries in e-learning environment using Web-BOT. In Proceedings of the 2012 11th Mexican International Conference on Artificial Intelligence (San Luis Potosi, Mexico - 27 Oct.-4 Nov. 2012) (pp. 63–65). USA: IEEE.

    Google Scholar 

  • Feild, J., Lewkow, N., Burns, S., & Gebhardt, K. (2018). A generalized classifier to identify online learning tool disengagement at scale. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge—LAK′18 (Sydney, New South Wales, Australia—March 07–09, 2018) (pp. 61-70). New York, NY, USA: ACM.

    Google Scholar 

  • Felten, P. (2008). Visual Literacy. Change: The Magazine of Higher Learning, 40(6), 60–64. https://doi.org/10.3200/chng.40.6.60-64.

  • Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6), 304–317. https://doi.org/10.1504/ijtel.2012.051816.

    Article  Google Scholar 

  • Fidalgo-Blanco, Á., Sein-Echaluce, M. L., & García-Peñalvo, F. J. (2016). From massive access to cooperation: Lessons learned and proven results of a hybrid xMOOC/cMOOC pedagogical approach to MOOCs. International Journal of Educational Technology in Higher Education (ETHE), 13, 24. https://doi.org/10.1186/s41239-016-0024-z.

    Article  Google Scholar 

  • Fidalgo-Blanco, Á., Sein-Echaluce, M. L., García-Peñalvo, F. J., & Conde-González, M. Á. (2015). Using learning analytics to improve teamwork assessment. Computers in Human Behavior, 47, 149–156. https://doi.org/10.1016/j.chb.2014.11.050.

    Article  Google Scholar 

  • Frank, M., Roehrig, P., & Pring, B. (2017). What to do when machines do everything: How to get ahead in a world of AI, algorithms, bots, and big data. Hoboken, New Jersey, USA: Wiley.

    Google Scholar 

  • García-Holgado, A., & García-Peñalvo, F. J. (2019). Validation of the learning ecosystem metamodel using transformation rules. Future Generation Computer Systems, 91, 300–310. https://doi.org/10.1016/j.future.2018.09.011.

    Article  Google Scholar 

  • García-Peñalvo, F. J. (2018). Ecosistemas tecnológicos universitarios. In J. Gómez (Ed.), UNIVERSITIC 2017. Análisis de las TIC en las Universidades Españolas (pp. 164–170). Madrid, España: Crue Universidades Españolas.

    Google Scholar 

  • García-Peñalvo, F. J., Cruz-Benito, J., Martín-González, M., Vázquez-Ingelmo, A., Sánchez-Prieto, J. C., & Therón, R. (2018a). Proposing a machine learning approach to analyze and predict employment and its factors. International Journal of Interactive Multimedia and Artificial Intelligence, 5(2), 39–45. https://doi.org/10.9781/ijimai.2018.02.002.

    Article  Google Scholar 

  • García-Peñalvo, F. J., Fidalgo-Blanco, Á., & Sein-Echaluce, M. L. (2018b). An adaptive hybrid MOOC model: Disrupting the MOOC concept in higher education. Telematics and Informatics, 35, 1018–1030. https://doi.org/10.1016/j.tele.2017.09.012.

    Article  Google Scholar 

  • García-Peñalvo, F. J., & Seoane-Pardo, A. M. (2015). An updated review of the concept of eLearning. Tenth anniversary. Education in the Knowledge Society, 16(1), 119–144. http://dx.doi.org/10.14201/eks2015161119144.

  • Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71. https://doi.org/10.1007/s11528-014-0822-x.

    Article  Google Scholar 

  • Goldstein, P. J., & Katz, R. N. (2005). Academic analytics: The uses of management information and technology in higher education. ECAR Research Study, 8. Retrieved from https://bit.ly/2kP3I41.

  • Gómez-Aguilar, D. A., García-Peñalvo, F. J., & Therón, R. (2014). Analítica visual en eLearning. El Profesional de la Información, 23(3), 236–245. https://doi.org/10.3145/epi.2014.may.03.

    Article  Google Scholar 

  • Gómez-Aguilar, D. A., Hernández-García, Á., García-Peñalvo, F. J., & Therón, R. (2015). Tap into visual analysis of customization of grouping of activities in eLearning. Computers in Human Behavior, 47, 60–67. https://doi.org/10.1016/j.chb.2014.11.001.

    Article  Google Scholar 

  • Gómez-Aguilar, D. A., Therón, R., & García-Peñalvo, F. J. (2009). Semantic spiral timelines used as support for e-Learning. Journal of Universal Computer Science, 15(7), 1526–1545. https://doi.org/10.3217/jucs-015-07-1526.

    Article  Google Scholar 

  • Graham, C. R. (2006). Blended learning systems: Definition, current trends, and future directions. In C. J. Bonk & C. R. Graham (Eds.), The handbook of blended learning: Global perspectives, local designs (pp. 3–21). San Francisco, USA: JosseyBass/Pfeiffer.

    Google Scholar 

  • Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Journal of Educational Technology & Society, 15(3), 42–57.

    Google Scholar 

  • Gros, B., & García-Peñalvo, F. J. (2016). Future trends in the design strategies and technological affordances of e-learning. In M. Spector, B. B. Lockee, & M. D. Childress (Eds.), Learning, design, and technology. An international compendium of theory, research, practice, and policy (pp. 1–23). Switzerland: Springer International Publishing.

    Google Scholar 

  • Jiang, W., Pardos, Z. A., & Wei, Q. (2019). Goal-based course recommendation. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge—LAK19 (Tempe, AZ, USA—March 04–08, 2019) (pp. 36-45). New York, NY, USA: ACM.

    Google Scholar 

  • Johnson, L., Smith, R., Willis, H., Levine, A., & Haywood, K. (2011). NMC horizon report: 2011 higher education edition. Retrieved from https://bit.ly/2kpMie8.

  • Keim, D. A., Andrienko, G., Fekete, J., Görg, C., Kohlhammer, J., & Melançon, G. (2008). Visual analytics: Definition, process, and challenges. In A. Kerren, J. Stasko, J. Fekete, & C. North (Eds.), Information visualization (pp. 154–175). Berlin, Heidelberg: Springer.

    Chapter  Google Scholar 

  • Keim, D. A., Kohlhammer, J., Ellis, G., & Mansmann, F. (Eds.). (2010). Mastering the information age solving problems with visual analytics. Goslar, Germany: Eurographics Association.

    Google Scholar 

  • Keim, D. A., & Zhang, L. (2011). Solving problems with visual analytics: challenges and applications. In Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies—i-KNOW ′11 (Graz, Austria—September 07–09, 2011). New York, NY, USA: ACM.

    Google Scholar 

  • Leony, D., Pardo, A., de la Fuente Valentín, L., de Castro, D. S., & Kloos, C. D. (2012). GLASS: A learning analytics visualization tool. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge—LAK′12 (Vancouver, British Columbia, Canada—April 29–May 02, 2012) (pp. 162–163). New York, NY, USA: ACM.

    Google Scholar 

  • Lerís, D., & Sein-Echaluce, M. L. (2011). La personalización del aprendizaje: Un objetivo del paradigma educativo centrado en el aprendizaje. Arbor, 187(Extra_3), 123–134. https://doi.org/10.3989/arbor.2011.extra-3n3135.

  • Lerís, D., Sein-Echaluce, M. L., Hernández, M., & Bueno, C. (2017). Validation of indicators for implementing an adaptive platform for MOOCs. Computers in Human Behavior, 72, 783–795. https://doi.org/10.1016/j.chb.2016.07.054.

    Article  Google Scholar 

  • Lerís, D., Sein-Echaluce, M. L., Hernández, M., & Fidalgo-Blanco, Á. (2016). Participantes heterogéneos en MOOCs y sus necesidades de aprendizaje adaptativo. Education in the Knowledge Society, 17(4), 91–109. https://doi.org/10.14201/eks201617491109.

  • Liz-Domínguez, M., Caeiro-Rodríguez, M., Llamas-Nistal, M., & Mikic-Fonte, F. (2019). Predictors and early warning systems in higher education—A systematic literature review. In M. Caeiro-Rodríguez, Á. Hernández-García, & P. J. Muñoz-Merino (Eds.), Proceedings of LASI-SPAIN 2019. Learning Analytics Summer Institute Spain 2019: Learning Analytics in Higher Education (Vigo, Spain, June 27–28, 2019) (pp. 84–99). Aachen, Germany: CEUR-WS.org.

    Google Scholar 

  • Long, P. D., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30–32.

    Google Scholar 

  • Lu, Y., & Hsiao, I.-H. (2019). Exploring programming semantic analytics with deep learning models. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge—LAK′19 (Tempe, AZ, USA—March 04–08, 2019) (pp. 155–159). New York, NY, USA: ACM.

    Google Scholar 

  • Mangaroska, K., Vesin, B., & Giannakos, M. (2019). Cross-platform analytics: A step towards personalization and adaptation in education. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge—LAK′19 (Tempe, AZ, USA—March 04–08, 2019) (pp. 71–75). New York, NY, USA: ACM.

    Google Scholar 

  • Munce, S. E. P., & Archibald, M. M. (2016). “The future of mixed methods: A five year projection to 2020”: An early career perspective. Journal of Mixed Methods Research, 11(1), 11–14. https://doi.org/10.1177/1558689816676659.

    Article  Google Scholar 

  • Palomo Duarte, M., Balderas, A., Dodero, J. M., Reinoso, A. J., Caballero, J. A., & Delatorre, P. (2018). Integrating quantitative and qualitative data in assessment of wiki collaborative assignments. In F. J. García-Peñalvo (Ed.), Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality—TEEM 2018 (Salamanca, Spain—October 24–26, 20189 (pp. 328–332). New York, NY, USA: ACM.

    Google Scholar 

  • Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), 135–146. https://doi.org/10.1016/j.eswa.2006.04.005.

    Article  Google Scholar 

  • Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 40(6), 601–618. https://doi.org/10.1109/tsmcc.2010.2053532.

    Article  Google Scholar 

  • Sein-Echaluce, M. L., Fidalgo-Blanco, Á., & García-Peñalvo, F. J. (2017). Adaptive and cooperative model of knowledge management in MOOCs. In P. Zaphiris & A. Ioannou (Eds.), Learning and Collaboration Technologies. Novel Learning Ecosystems. 4th International Conference, LCT 2017. Held as Part of HCI International 2017, Vancouver, BC, Canada, July 9–14, 2017. Proceedings, Part I (pp. 273–284). Switzerland: Springer International Publishing.

    Google Scholar 

  • Sein-Echaluce, M. L., Fidalgo-Blanco, Á., García-Peñalvo, F. J., & Conde-González, M. Á. (2016). iMOOC platform: Adaptive MOOCs. In P. Zaphiris & I. Ioannou (Eds.), Learning and Collaboration Technologies. Third International Conference, LCT 2016, Held as Part of HCI International 2016, Toronto, ON, Canada, July 17–22, 2016, Proceedings (pp. 380–390). Switzerland: Springer International Publishing.

    Google Scholar 

  • Shneiderman, B. (1996). The eyes have it: A task by data type taxonomy for information visualizations. In Proceedings of 1996 IEEE Symposium on Visual Languages (3–6 Sept. 1996, Boulder, CO, USA, USA) (pp. 336–343). EEUU: IEEE.

    Google Scholar 

  • Siemens, G. (2010). What are learning analytics? Retrieved from https://bit.ly/2PcZKQE.

  • Siemens, G. (2013). Learning analytics. The emergence of a discipline. American Behavioral Scientist, 57(10), 1380–1400. https://doi.org/10.1177/0002764213498851.

  • Tanes, Z., Arnold, K. E., King, A. S., & Remnet, M. A. (2011). Using signals for appropriate feedback: Perceptions and practices. Computers & Education, 57(4), 2414–2422. https://doi.org/10.1016/j.compedu.2011.05.016.

    Article  Google Scholar 

  • van Barneveld, A., Arnold, K. E., & Campbell, J. P. (2012). Analytics in higher education: Establishing a common language. EDUCAUSE Learning Initiative, 1, 1–11.

    Google Scholar 

  • Vázquez-Ingelmo, A., García-Peñalvo, F. J., & Therón, R. (2019). Information dashboards and tailoring capabilities—A systematic literature review. IEEE Access, 7, 109673–109688. https://doi.org/10.1109/access.2019.2933472.

  • Vázquez-Ingelmo, A., García-Peñalvo, F. J., Therón, R., & Conde, M. Á. (2019). Extending a dashboard meta-model to account for users’ characteristics and goals for enhancing personalization. In M. Caeiro-Rodríguez, Á. Hernández-García, & P. J. Muñoz-Merino (Eds.), Proceedings of LASI-SPAIN 2019. Learning Analytics Summer Institute Spain 2019: Learning Analytics in Higher Education (Vigo, Spain, June 27–28, 2019) (pp. 35–42). Aachen, Germany: CEUR-WS.org.

    Google Scholar 

  • Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning analytics dashboard applications. American Behavioral Scientist, 57(10), 1500–1509. https://doi.org/10.1177/0002764213479363.

    Article  Google Scholar 

  • Villamañe, M., Álvarez, A., Larrañaga, M., Caballero, J., & Hernández-Rivas, O. (2018). Using visual learning analytics to support competence-based learning. In Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality—TEEM 2018 (Salamanca, Spain—October 24–26, 2018) (pp. 333–338). New York, NY, USA: ACM.

    Google Scholar 

  • Worsley, M. (2018). (Dis)engagement matters: identifying efficacious learning practices with multimodal learning analytics. Proceedings of the 8th International Conference on Learning Analytics and Knowledge—LAK′18 (Sydney, New South Wales, Australia—March 07–09, 2018) (pp. 365–369). New York, NY, USA: ACM.

    Google Scholar 

  • Yu, H., Miao, C., Leung, C., & White, T. J. (2017). Towards AI-powered personalization in MOOC learning. npj Science of Learning, 2(1), 15. https://doi.org/10.1038/s41539-017-0016-3.

Download references

Acknowledgements

This work was supported in part by the Spanish Ministry of Science, Innovation, and Universities throughout the DEFINES Project under Grant TIN2016-80172-R.

Author information

Authors and Affiliations

  1. GRIAL Research Group, Computer Science Department, Research Institute for Educational Sciences, University of Salamanca, Facultad de Ciencias. Plaza de Los Caídos, 37008, Salamanca, S/N, Spain

    Francisco José García-Peñalvo

Authors
  1. Francisco José García-Peñalvo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Francisco José García-Peñalvo .

Editor information

Editors and Affiliations

  1. Research Institute for Innovation & Technology in Education (UNIR iTED), Universidad Internacional de La Rioja (UNIR), Logroño, La Rioja, Spain

    Daniel Burgos

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

García-Peñalvo, F.J. (2020). Learning Analytics as a Breakthrough in Educational Improvement. In: Burgos, D. (eds) Radical Solutions and Learning Analytics. Lecture Notes in Educational Technology. Springer, Singapore. https://doi.org/10.1007/978-981-15-4526-9_1

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-4526-9_1

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-4525-2

  • Online ISBN: 978-981-15-4526-9

  • eBook Packages: EducationEducation (R0)

Publish with us

Policies and ethics

Search

Navigation