Towards motivation-based adaptation of difficulty in e-learning programs
AbstractThe objective of this study was to investigate if an e-learning environment may use measurements of the user's current motivation to adapt the level of task difficulty for more effective learning. In the reported study, motivation-based adaptation was applied randomly to collect a wide range of data for different adaptations in a variety of motivational states. This data was then utilised to extract rules for an adequate motivation-based adaptation to maximise expected learning success. A learning classifier system was used for the data analysis, generating rules for suitable and unsuitable adaptations based on current user motivation data. We extracted a set of twelve rules which suggest particular adaptation strategies based on real-world data. These rules could generally be embedded into existing psychological theories, namely the Zone of Proximal Development and the Yerkes-Dodson Law. In future research, we intend to evaluate these rules on further studies and develop concrete sets of adaptation strategies based on user motivation measurements.
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How to Cite
Endler, A., Rey, G. D., & Butz, M. V. (2012). Towards motivation-based adaptation of difficulty in e-learning programs. Australasian Journal of Educational Technology, 28(7). https://doi.org/10.14742/ajet.792
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