Ron J. C. M. Salden

The current research investigates a combination of two instructional approaches, tutored problem solving and worked examples. Tutored problem solving with automated tutors has proven to be an effective instructional method. Worked‐out examples have been shown to be an effective complement to untutored problem solving, but it is largely unknown whether they are an effective complement to tutored problem solving. Further, while computer‐based learning environments offer the possibility of adaptively transitioning from examples to problems while tailoring to an individual learner, the effectiveness of such machine‐adapted example fading is largely unstudied. To address these research questions, one lab and one classroom experiment were conducted. Both studies compared a standard Cognitive Tutor with two example‐enhanced Cognitive Tutors, in which the fading of worked‐out examples occurred either in a fixed way or adaptively. Results indicate that the adaptive fading of worked‐out examples leads to higher transfer performance on delayed posttests than the other two methods.

The effects of 3 task selection methods on test performance and training efficiency in a computer-based flight management system (FMS) training were investigated. A fixed condition was compared to a learner control condition and a condition using the participants' self-rated performance and mental effort. Although the experimental conditions revealed more positive training effects, no differences were found for training efficiency and test performance. A follow-up study did not confirm the alternative hypothesis that these results were caused by the higher amount of tasks in the fixed condition. Extra analyses suggested that the quality of self-rating needs to be considered in future research.