Mental Effort and Performance as Determinants for the Dynamic Selection of Learning Tasks in Air Traffic Control Training

The differential effects of four task selectionmethods on training efficiency and transfer incomputer-based training for Air Traffic Controlwere investigated. A non-dynamic condition, inwhich the learning tasks were presented to theparticipants in a fixed, predeterminedsequence, was compared to three dynamicconditions, in which learning tasks wereselected on the basis of performance, mentaleffort, and a combination of both (i.e., mentalefficiency). Using the 3-factor mentalefficiency formula of Tuovinen and Paas (2004, this issue), the hypothesis that dynamic taskselection leads to more efficient training thannon-dynamic task selection was confirmed.However, the hypothesis that dynamic taskselection based on mental efficiency leads tomore efficient training than dynamic taskselection based on performance or mental effortalone was not supported. The results arediscussed in light of the theoretical frameworkand suggestions are given for futureresearch.     

The expertise reversal effect and worked examples in tutored problem solving

Prior research has shown that tutored problem solving with intelligent software tutors is an effective instructional method, and that worked examples are an effective complement to this kind of tutored problem solving. The work on the expertise reversal effect suggests that it is desirable to tailor the fading of worked examples to individual students’ growing expertise levels. One lab and one classroom experiment were conducted to investigate whether adaptively fading worked examples in a tutored problem-solving environment can lead to higher learning gains. Both studies compared a standard Cognitive Tutor with two example-enhanced versions, in which the fading of worked examples occurred either in a fixed manner or in a manner adaptive to individual students’ understanding of the examples. Both experiments provide evidence of improved learning results from adaptive fading over fixed fading over problem solving. We discuss how to further optimize the fading procedure matching each individual student’s changing knowledge level.     

Accounting for Beneficial Effects of Worked Examples in Tutored Problem Solving

Recent studies have tested the addition of worked examples to tutored problem solving, a more effective instructional approach than the untutored problem solving used in prior worked example research. These studies involved Cognitive Tutors, software designed to support problem solving while minimizing extraneous cognitive load by providing prompts for problem sub-goals, step-based immediate feedback, and context-sensitive hints. Results across eight studies in three different domains indicate that adding examples to Cognitive Tutors is beneficial, particularly for decreasing the instructional time needed and perhaps also for achieving more robust learning outcomes. These studies bolster the practical importance of examples in learning, but are also of theoretical interest. By using a stronger control condition than previous studies, these studies provide a basis for refining Cognitive Load Theory explanations of the benefits of examples. Perhaps, in addition to other reasons, examples may help simply because they more quickly provide novices with information needed to induce generalized knowledge.