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International Conference on Human-Computer Interaction

HCII 2021: Adaptive Instructional Systems. Adaptation Strategies and Methods pp 79–92Cite as

Modelling and Quantifying Learner Motivation for Adaptive Systems: Current Insight and Future Perspectives

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12793))

Abstract

Adaptation and personalization of learning systems are promising approaches aiming to enhance learners’ experience and achievement of learning objectives. 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. 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. 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. According to research, motivation is essential in the knowledge building process and in fostering high academic performance. 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. A new approach for adapting and increasing motivation through the use of machine learning techniques and persuasive technology is proposed. The approach addresses learner knowledge and motivational states to improve learning and sustain the learner’s motivation.

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Authors and Affiliations

  1. University of Saskatchewan, Saskatoon, Canada

    Fidelia A. Orji & Julita Vassileva

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  1. Fidelia A. Orji
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  2. Julita Vassileva
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Correspondence to Julita Vassileva .

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  1. Soar Technology, Inc., Orlando, FL, USA

    Robert A. Sottilare

  2. Fraunhofer FKIE, Wachtberg, Germany

    Jessica Schwarz

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Orji, F.A., Vassileva, J. (2021). Modelling and Quantifying Learner Motivation for Adaptive Systems: Current Insight and Future Perspectives. In: Sottilare, R.A., Schwarz, J. (eds) Adaptive Instructional Systems. Adaptation Strategies and Methods. HCII 2021. Lecture Notes in Computer Science(), vol 12793. Springer, Cham. https://doi.org/10.1007/978-3-030-77873-6_6

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  • DOI: https://doi.org/10.1007/978-3-030-77873-6_6

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