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The disfluency effect postulates that intentionally inserted desirable difficulties can have a beneficial effect on learning. Nevertheless, there is an ongoing discussion about the emergence of this effect since studies could not replicate this effect or even found opposite effects. To clarify boundary effects of the disfluency effect and to investigate potential social effects of disfluency operationalized through handwritten material, three studies (N 1 = 97; N 2 = 102; N 3 = 103) were carried out. In all three experiments, instructional texts were manipulated in terms of disfluency (computerized font vs. handwritten font). Learning outcomes and cognitive load were measured in all experiments. Furthermore, metacognitive variables (Experiment 2 and 3) and social presence (Experiment 3) were measured. Results were ambiguous, indicating that element interactivity (complexity or connectedness of information within the learning material) of the learning material is a boundary condition that determines the effects of disfluency. When element interactivity is low, disfluency had a positive effect on learning outcomes and germane processes. When element interactivity increases, disfluency had negative impacts on learning efficiency (Experiment 2 and 3) and extraneous load (Experiment 3). In contrast to common explanations of the disfluency effect, a disfluent font had no metacognitive benefits. Social processes did not influence learning with disfluent material as well.
Introduction:
Results from experimental research in instructional psychology
imply that a deep menu structure of a e-learning website may provide useful segmentation. However, menu depth also increases the need for navigation and thus, might have impairing eects on learning. Furthermore, instructional support can be provided by including a checklist, to ensure that learners reflect on their
study progress. The study aimed at investigating which menu structure is beneficial for e-learning websites and whether a checklist could compensate the negative effects of an unfavorable menu structure.
Methods:
Therefore, in an online experiment, we let 101 students learn facts about rocks from an e-learning website with either a deep or a flat menu structure. We further manipulated whether metacognitive support through a checklist was provided or not. Learning outcomes, cognitive load, metacognitive factors as well
as learning time were measured.
Results:
Results show no main eects of the menu depth or the presence of
a checklist on retention and transfer performance. Learning achievements in percent for retention were 37.31 (deep menu/checklist), 31.10 (deep menu/no checklist), 36.07 (flat menu/checklist), 38.13 (flat menu, no checklist) and for transfer were 35.19 (deep menu/checklist), 34.40 (deep menu/no checklist), 37.78 (flat menu/checklist), 33.23 (flat menu, no checklist). Yet, there are hints that the deeper menu structure had a negative eect on learning processes: The deep menu structure led to an enhanced extraneous cognitive load (ECL) and reduced
learning efficiency. However, providing a checklist had beneficial eects mainly when learning with a deep menu structure but not overall. Unexpectedly, the presence of the checklist did not influence metacognitive measures.
Discussion:
Our study suggests that possible costs of a deep menu structure
should be considered when designing instructional checklists. However, the study also provides a way in which these costs can be compensated, which is by using a checklist. Implications for instructional research and e-learning are discussed.