Motivational Design in an Intelligent Tutoring System that Helps Students Make Good Task Selection Decisions

Abstract

Making effective problem selection decisions is an important yet challenging self-regulated learning (SRL) skill. Although efforts have been made to scaffold students’ problem selection in intelligent tutoring systems (ITS), little work has tried to support students’ learning of the transferable problem selection skill that can be applied when the scaffolding is not in effect. The current work uses a user-centered design approach to extend an ITS for equation solving, Lynnette, so the new designs may motivate and help students learn to apply a general, transferable rule for effective problem selection, namely, to select problem types that are not fully mastered (“Mastery Rule”). We conducted user research through classroom experimentation, interviews and storyboards. We found that the presence of an Open Learner Model significantly improves students’ problem selection decisions, which has not been empirically established by prior work; also, lack of motivation, especially lack of a mastery-approach orientation, may cause difficulty in applying the Mastery Rule. Based on our user research, we designed prototypes of tutor features that aim to foster a mastery-approach orientation as well as transfer of the learned Mastery Rule when the scaffolding is faded. The work contributes to the research of supporting SRL in ITSs through a motivational design perspective, and lays foundation for future controlled experiments to evaluate the transfer of the problem selection skill in new tutor units where there is no scaffolding.