Skip to main content
SpringerLink
Log in

Definition of a Feature Vector to Characterise Learners in Adaptive Learning Systems

  • Conference paper
  • First Online:
Research & Innovation Forum 2019 (RIIFORUM 2019)

Part of the book series: Springer Proceedings in Complexity ((SPCOM))

Included in the following conference series:

Abstract

Adaptive learning can be defined as a learning model based on technology that can detect the students individual situation, context, learning needs and style, and the state of their learning process dynamically, and act according to them. So, it is necessary to define a student or learner model, that is, the set of information obtained and retained by the learning system about the learner so that the learner is characterised, and the learning process is adapted. In this work, we propose a learner model made of three main types of information: behavioural features, performance features and personal features. For this model to be useful in automatic learning systems, a formal feature vector must be then obtained. The features in the vector must be meaningful, discriminating and independent so that effective machine learning algorithms can be applied.

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
Hardcover Book
USD 169.99
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

Institutional subscriptions

References

  1. Hwang, G.J.: Definition, framework and research issues of smart learning environments–a context-aware ubiquitous learning perspective. Smart Learn. Environ. 1(1), 4 (2014)

    Article  Google Scholar 

  2. Greany, K.: Adaptive learning: how to personalize your learning strategy (2018)

    Google Scholar 

  3. Shelle, G., Earnesty, D., Pilkenton, A., Powell, E.: Adaptive learning: an innovative method for online teaching and learning. J. Extension 56 (2018)

    Google Scholar 

  4. Molina-Carmona, R., Villagrá-Arnedo, C.: Smart learning. In: Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality—TEEM’18, pp. 645–647. ACM Press, Salamanca, Spain (2018)

    Google Scholar 

  5. Real-Fernández, A., Molina-Carmona, R., Llorens-Largo, F.: Aprendizaje adaptativo basado en competencias y actividades–(Adaptive learning based on competences and activities). La innovación docente como misión del profesorado : Congreso Internacional Sobre Aprendizaje. Innovación y Competitividad, pp. 1–6. Servicio de Publicaciones Universidad, Zaragoza, Spain (2017)

    Google Scholar 

  6. Real-Fernández, A., Molina-Carmona, R., Llorens-Largo, F.: Smart system based on adaptive learning itineraries. In: Poster Presentation in the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality—TEEM’18, pp. 654–659. ACM Press, Salamanca, Spain (2018)

    Google Scholar 

  7. Ahn, S., Ames, A.J., Myers, N.D.: A review of meta-analyses in education: methodological strengths and weaknesses. Rev. Educ. Res. 82(4), 436–476 (2012)

    Article  Google Scholar 

  8. Hattie, J.: Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement. Edición, 1 edn. Routledge, London; New York (2008)

    Book  Google Scholar 

  9. Hattie, J.: Visible Learning for Teachers: Maximizing Impact on Learning. Routledge (2013)

    Google Scholar 

  10. Hattie, J., Anderman, E.M. (eds.).: International Guide to Student Achievement, 1 edn. Routledge, New York (2012)

    Google Scholar 

  11. Castejon, J.L., Perez, A.M., Gilar, R.: Confirmatory factor analysis of project spectrum activities. A second-order g factor or multiple intelligences? Intelligence 38(5), 481–496 (2010)

    Article  Google Scholar 

  12. Sternberg, R.J., Castejón, J.L., Prieto, M.D., Hautamäki, J., Grigorenko, E.L.: Confirmatory factor analysis of the Sternberg Triarchic Abilities Test in three international samples: an empirical test of the triarchic theory of intelligence. Eur. J. Psychol. Assess. 17(1), 1–16 (2001)

    Article  Google Scholar 

  13. Gardner, H.: Intelligence Reframed: Multiple Intelligences for the 21st century. Basic Books, New York (2000) OCLC: 247819868

    Google Scholar 

  14. Sternberg.: Beyond IQ Paperback: A Triarchic Theory of Human Intelligence. Cambridge University Press, Cambridge (2009)

    Google Scholar 

  15. Gardner, H.: Multiple Intelligences: Reflections After Thirty Years. National Association of Gifted Children Parent and Community Network Newsletter (2011)

    Google Scholar 

  16. Quenk, N.L.: Essentials of Myers-Briggs Type Indicator Assessment, 2nd edn. Essentials of Psychological Assessment Series. Wiley, Hoboken (2009) OCLC: ocn320494042

    Google Scholar 

  17. Webb, T.L., Sheeran, P.: Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental evidence. Psychol. Bull. 132(2), 249–268 (2006)

    Article  Google Scholar 

  18. OECD.: Student Engagement at School: A Sense of Belonging and Participation: Results from PISA 2000. PISA. OECD (2003)

    Google Scholar 

  19. Csikszentmihalyi, M.: Flow: The Psychology of Optimal Experience. Perennial Modern Classics. Harper & Row (1990)

    Google Scholar 

  20. Castejón Costa, J.L.: Introducción a la psicología de la instrucción. Editorial Club Universitario (1997)

    Google Scholar 

  21. Messick, S.J.: Structural relationships across cognition, personality, and style. In: Snow, R.E., Farr, M.J., Farr, M.J. (eds.) Aptitude, Learning, and Instruction: Volume 3: Cognitive and Affective Process Analyses, vol. 3. Routledge, Hillsdale (1987)

    Google Scholar 

  22. Schmeck, R.R. (ed.).: Learning Strategies and Learning Styles. Perspectives on Individual Differences. Springer, US (1988)

    Google Scholar 

  23. Kolb, D.A.: Facilitator’s Guide to Learning. Hay Group Transforming Learning (2000)

    Google Scholar 

  24. Ruffing, S., Hahn, E., Spinath, F.M., Brünken, R., Karbach, J.: Predicting students’ learning strategies: the contribution of chronotype over personality. Pers. Individ. Differ. 85, 199–204 (2015)

    Article  Google Scholar 

  25. Jensen, A.R.: The g Factor: The Science of Mental Ability. The Science of Mental Ability. Praeger Publishers/Greenwood Publishing Group, Westport, The g Factor (1998)

    Google Scholar 

  26. Carberry, S., Carbonell, J.G., Chin, D.N., Cohen, R., Lehman, J.F., Finin, T.W., Jameson, A., Jones, M., Kass, R., Kobsa, A., McCoy, K.F., Morik, K., Paris, C.L., Quilici, A.E., Rich, E., Jones, K.S., Wahlster, W.: User Models in Dialog Systems. Softcover reprint of the original, 1st edn. (1989 edn.) Springer (2011)

    Google Scholar 

  27. Bishop, C.M.: Pattern Recognition and Machine Learning. Information Science and Statistics. Springer, New York (2006)

    Google Scholar 

  28. Siemens, G.: Learning analytics: the emergence of a discipline. Am. Behav. Sci. 57(10), 1380–1400 (2013)

    Article  Google Scholar 

  29. Niles-Hofmann, L.: Data-Driven Learning Design

    Google Scholar 

  30. Villagrá-Arnedo, C., Gallego-Duraìn, F.J., Compañ-Rosique, P., Llorens-Largo, F., Molina-Carmona, R.: Predicting academic performance from behavioural and learning data. Int. J. Des. Nat. Ecodyn. 11(3), 239–249 (2016)

    Article  Google Scholar 

  31. Villagrá-Arnedo, C.J., Gallego-Durán, F.J., Llorens-Largo, F., Compañ-Rosique, P., Satorre-Cuerda, R., Molina-Carmona, R.: Improving the expressiveness of black-box models for predicting student performance. Comput. Hum. Behav. 72, 621–631 (2017)

    Article  Google Scholar 

  32. Molina-Carmona, R., Villagrá-Arnedo, C., Gallego-Durán, F., Llorens-Largo, F.: Analytics-driven redesign of an instructional course. In: Proceedings of the 5th International Conference on Technological Ecosystems for Enhancing Multiculturality—TEEM 2017, pp. 1–7. ACM Press, Cádiz, Spain (2017)

    Google Scholar 

  33. Fröschl, C., Nguyen, L., Do, P.: Learner Model in Adaptive Learning, vol. 35. Paris (2008)

    Google Scholar 

  34. Bull, S., Kay, J.: Open learner models. In: Kacprzyk, J., Nkambou, R., Bourdeau, J., Mizoguchi, R. (eds.) Advances in Intelligent Tutoring Systems, vol. 308, pp. 301–322. Springer, Berlin Heidelberg (2010)

    Google Scholar 

  35. Macfadyen, L.P., Dawson, S.: Mining LMS data to develop an “early warning system” for educators: a proof of concept. Comput. Educ. 54(2), 588–599 (2010)

    Article  Google Scholar 

  36. Fröschl, C.: User Modeling and User Profiling in Adaptive E-learning Systems: An Approach for a Service-Based Personalization Solution for the Research Project AdeLE. VDM Verlag Dr, Müller (2008)

    Google Scholar 

  37. Alonso, C.M., Gallego, D., Honey, P.: Los Estilos de Aprendizaje: Procedimientos de diagnóstico y mejora, 7th edn. Ediciones Mensajero, S.A., Bilbao (2007)

    Google Scholar 

  38. Gallego, D.: Diagnosticar los estilos de aprendizaje (2019)

    Google Scholar 

  39. Gallego Rodríguez, A., Martínez Caro, E.: Estilos de aprendizaje y e-learning. Hacia un mayor rendimiento académico. Rev. de Educación a Distancia (7) (2003)

    Google Scholar 

  40. García Cué, J.L., Santizo Rincón, J.A., Alonso García, C.M.A.: IInstrumentos de medición de estilos de aprendizaje. J. Learn. Styles 2(4) (2009)

    Google Scholar 

  41. Palomino Hawasly, M.A., Strefezza, M., Contreras Bravo, L.E.: Sistema Difuso Para la Detección Automática de Estilos de Aprendizaje en Ambientes de Formación Web. Ciencia, Docencia y Tecnología 27(52), 9 (2016)

    Google Scholar 

  42. Canfield, A.A.: Western Psychological Services (Firm): Canfield Learning Styles Inventory (LSI) Manual. Western Psychological Services, Los Angeles (1988)

    Google Scholar 

  43. Price, G.E., Dunn, R., Dunn, K.J.: Productivity Environmental Preference Survey: An Inventory for the Identification of Individual Adult Preferences in a Working Or Learning Environment. Peps Manual, Price Systems (1991)

    Google Scholar 

  44. Grasha, A.F.: Teaching with Style : A Practical Guide to Enhancing Learning by Understanding Teaching and Learning Styles. Alliance Publishers (1996)

    Google Scholar 

  45. Rezler, A.G., Rezmovic, V.: The learning preference inventory. J. Allied Health 10(1), 28–34 (1981)

    Google Scholar 

  46. Biggs, J., Kember, D., Leung, D.Y.: The revised two-factor study process questionnaire: R-SPQ-2f. Br. J. Educ. Psychol. 71(1), 133–149 (2001)

    Article  Google Scholar 

  47. Entwistle, N., Tait, H.: Approaches and Study Skills Inventory for Students (ASSIST) (Incorporating the Revised Approaches to Studying Inventory—RASI) (2013)

    Google Scholar 

  48. Kolb, D., Kolb, A.: The Kolb learning style inventory 4.0: guide to theory. Psychometr. Res. Appl. (2013)

    Google Scholar 

  49. Kagan, J., Rosman, B.L., Day, D., Albert, J., Phillips, W.: Information processing in the child: significance of analytic and reflective attitudes. Psychol. Monogr. Gen. App. 78(1), 1–37 (1964)

    Article  Google Scholar 

  50. Aggarwal, C.C.: Machine Learning for Text, 1st edn. Springer (2018)

    Google Scholar 

  51. Long, P., Siemens, G.: Penetrating the fog: analytics in learning and education. Educase Rev. (2011)

    Google Scholar 

Download references

Acknowledgements

This research is partially supported by Unidad Científica de Innovación Empresarial “Ars Innovatio”, Agència Valenciana d’Innovació and University of Alicante, Spain.

Author information

Authors and Affiliations

  1. Unidad Científica de Innovación Empresarial “Ars Innovatio”, University of Alicante, San Vicente del Raspeig, Spain

    Alberto Real-Fernández & Rafael Molina-Carmona

  2. Developmental and Educational Psychology Department, University of Alicante, San Vicente del Raspeig, Spain

    María L. Pertegal-Felices

  3. Cátedra Santander-UA de Transformación Digital, University of Alicante, San Vicente del Raspeig, Spain

    Faraón Llorens-Largo

Authors
  1. Alberto Real-Fernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rafael Molina-Carmona
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. María L. Pertegal-Felices
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Faraón Llorens-Largo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rafael Molina-Carmona .

Editor information

Editors and Affiliations

  1. Research & Innovation Institute (Rii), Warsaw, Poland

    Anna Visvizi

  2. Deree College, The American College of Greece, Athens, Greece

    Miltiadis D. Lytras

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Real-Fernández, A., Molina-Carmona, R., Pertegal-Felices, M.L., Llorens-Largo, F. (2019). Definition of a Feature Vector to Characterise Learners in Adaptive Learning Systems. In: Visvizi, A., Lytras, M. (eds) Research & Innovation Forum 2019. RIIFORUM 2019. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-30809-4_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-30809-4_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30808-7

  • Online ISBN: 978-3-030-30809-4

  • eBook Packages: EducationEducation (R0)

Publish with us

Policies and ethics

Search

Navigation