Towards motivation-based adaptation of difficulty in e-learning programs
The 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.
Australasian Journal of Educational Technology
