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.
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References
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)
Greany, K.: Adaptive learning: how to personalize your learning strategy (2018)
Shelle, G., Earnesty, D., Pilkenton, A., Powell, E.: Adaptive learning: an innovative method for online teaching and learning. J. Extension 56 (2018)
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)
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)
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)
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)
Hattie, J.: Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement. Edición, 1 edn. Routledge, London; New York (2008)
Hattie, J.: Visible Learning for Teachers: Maximizing Impact on Learning. Routledge (2013)
Hattie, J., Anderman, E.M. (eds.).: International Guide to Student Achievement, 1 edn. Routledge, New York (2012)
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)
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)
Gardner, H.: Intelligence Reframed: Multiple Intelligences for the 21st century. Basic Books, New York (2000) OCLC: 247819868
Sternberg.: Beyond IQ Paperback: A Triarchic Theory of Human Intelligence. Cambridge University Press, Cambridge (2009)
Gardner, H.: Multiple Intelligences: Reflections After Thirty Years. National Association of Gifted Children Parent and Community Network Newsletter (2011)
Quenk, N.L.: Essentials of Myers-Briggs Type Indicator Assessment, 2nd edn. Essentials of Psychological Assessment Series. Wiley, Hoboken (2009) OCLC: ocn320494042
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)
OECD.: Student Engagement at School: A Sense of Belonging and Participation: Results from PISA 2000. PISA. OECD (2003)
Csikszentmihalyi, M.: Flow: The Psychology of Optimal Experience. Perennial Modern Classics. Harper & Row (1990)
Castejón Costa, J.L.: Introducción a la psicología de la instrucción. Editorial Club Universitario (1997)
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)
Schmeck, R.R. (ed.).: Learning Strategies and Learning Styles. Perspectives on Individual Differences. Springer, US (1988)
Kolb, D.A.: Facilitator’s Guide to Learning. Hay Group Transforming Learning (2000)
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)
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)
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)
Bishop, C.M.: Pattern Recognition and Machine Learning. Information Science and Statistics. Springer, New York (2006)
Siemens, G.: Learning analytics: the emergence of a discipline. Am. Behav. Sci. 57(10), 1380–1400 (2013)
Niles-Hofmann, L.: Data-Driven Learning Design
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)
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)
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)
Fröschl, C., Nguyen, L., Do, P.: Learner Model in Adaptive Learning, vol. 35. Paris (2008)
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)
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)
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)
Alonso, C.M., Gallego, D., Honey, P.: Los Estilos de Aprendizaje: Procedimientos de diagnóstico y mejora, 7th edn. Ediciones Mensajero, S.A., Bilbao (2007)
Gallego, D.: Diagnosticar los estilos de aprendizaje (2019)
Gallego Rodríguez, A., Martínez Caro, E.: Estilos de aprendizaje y e-learning. Hacia un mayor rendimiento académico. Rev. de Educación a Distancia (7) (2003)
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)
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)
Canfield, A.A.: Western Psychological Services (Firm): Canfield Learning Styles Inventory (LSI) Manual. Western Psychological Services, Los Angeles (1988)
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)
Grasha, A.F.: Teaching with Style : A Practical Guide to Enhancing Learning by Understanding Teaching and Learning Styles. Alliance Publishers (1996)
Rezler, A.G., Rezmovic, V.: The learning preference inventory. J. Allied Health 10(1), 28–34 (1981)
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)
Entwistle, N., Tait, H.: Approaches and Study Skills Inventory for Students (ASSIST) (Incorporating the Revised Approaches to Studying Inventory—RASI) (2013)
Kolb, D., Kolb, A.: The Kolb learning style inventory 4.0: guide to theory. Psychometr. Res. Appl. (2013)
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)
Aggarwal, C.C.: Machine Learning for Text, 1st edn. Springer (2018)
Long, P., Siemens, G.: Penetrating the fog: analytics in learning and education. Educase Rev. (2011)
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This research is partially supported by Unidad Científica de Innovación Empresarial “Ars Innovatio”, Agència Valenciana d’Innovació and University of Alicante, Spain.
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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
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