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10.Blanchard, E., Frasson, C.: An autonomy-oriented system design for enhancement of learner’s motivation in e-learning.9.Pappas, C.: Top 8 eLearning Barriers that Inhibit Online Learner Engagement with eLearning Content.Kim KJ Frick T Changes in student motivation during online learning J. In: Aptitude, Learning, and Instruction: Conative and Affective Process Analyses, pp. 7.Malone, T.W., Lepper, M.R.: Making learning fun: a taxonomy of intrinsic motivations for learning.Keller JM Motivational Design for Learning and Performance 2010 Boston Springer US 10.1007/978-1-4419-1250-3 Google Scholar Cross Ref ACM Press, New York (2013) Google Scholar In: ACM International Conference Proceeding Series, pp. 5.Priego, R.G., Peralta, A.G.: Engagement factors and motivation in e-learning and blended-learning projects.Bauer M Bräuer C Schuldt J Niemann M Krömker H Ahram TZ Application of wearable technology for the acquisition of learning motivation in an adaptive e-learning platform Advances in Human Factors in Wearable Technologies and Game Design 2019 Cham Springer 29 40 10.1007/978-9-1_4 Google Scholar Granić A Nakić J Leitner G Hitz M Holzinger A Enhancing the learning experience: preliminary framework for user individual differences HCI in Work and Learning, Life and Leisure 2010 Heidelberg Springer 384 399 10.1007/978-7-5_26 Google Scholar Digital Library Nakic J Granic A Glavinic V Anatomy of student models in adaptive learning systems: a systematic literature review of individual differences from 2001 to 2013 J. Afini Normadhi NB Shuib L Md Nasir HN Bimba A Idris N Balakrishnan V Identification of personal traits in adaptive learning environment: systematic literature review Comput. The approach addresses learner knowledge and motivational states to improve learning and sustain the learner’s motivation. A new approach for adapting and increasing motivation through the use of machine learning techniques and persuasive technology is proposed. This paper reviews the literature on modelling of motivational states and adaptation to motivation on ITSs, mapping research progress in terms of techniques and strategies for modelling motivational states and adapting to motivation. According to research, motivation is essential in the knowledge building process and in fostering high academic performance. However, present ITSs predominantly emphasize the role of instructional content adjustment to the modelled cognitive processes of a learner, disregarding the significance of motivation in learning processes. For example, intelligent tutoring systems (ITSs) provide adaptive instruction to a learner based on his/her learning needs by tailoring learning materials and teaching methods to each learner based on information available in the learner’s model. Adaptive learning systems support and enhance learning through monitoring important learner characteristics in the learning process and making appropriate adjustments in the process and the environment.

Adaptation and personalization of learning systems are promising approaches aiming to enhance learners’ experience and achievement of learning objectives.

Fidelia juarez