Is There a (Dis-)Fluency Effect in Learning With Handwritten Instructional Texts? Evidence From Three Studies

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 (N1 = 97; N2 = 102; N3 = 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.

The Influence of Signaling on the Disfluency Effect in Multimedia Learning

Do students learn better with texts that are slightly harder-to-read (i.e., disfluent)? Previous research has yielded conflicting findings. The present study identified the boundary condition that determines when disfluent texts benefit learning. We used eye-tracking to examine the joint influence of text legibility (fluent vs. disfluent) and signaling (signaling vs. non-signaling) on multimedia learning. The results revealed that both disfluent text and signaling led to better transfer test performance, and there was also an interaction between them. Specifically, the disfluent text led to better learning outcomes with or without signaling; however, in the fluent text condition, only signaling facilitated learning. Eye movement analyses indicated that signaling guided learners to pay more attention to important content in the learning materials. The current results suggest that signaling can enhance individuals’ perceived fluency or familiarity to the material and guide the attention during multimedia learning, and the positive impact of disfluency on multimedia learning seems to be more stable and ubiquitous. We discuss these under the framework of disfluency effect and attention-guiding effect.

What’s New to You? Preschoolers’ Partner-Specific Online Processing of Disfluency

Speech disfluencies (e.g., “Point to thee um turtle”) can signal that a speaker is about to refer to something difficult to name. In two experiments, we found evidence that 4-year-olds, like adults, flexibly interpret a particular partner’s disfluency based on their estimate of that partner’s knowledge, derived from the preceding conversation. In entrainment trials, children established partner-specific shared knowledge of names for tangram pictures with one or two adult interlocutors. In each test trial, an adult named one of two visible tangrams either fluently or disfluently while children’s eye-movements were monitored. We manipulated speaker knowledge in the test trials. In Experiment 1, the test-trial speaker was the same speaker from entrainment or a naïve experimenter; in Experiment 2, the test-trial speaker had been one of the child’s partners in entrainment and had seen half of the tangrams (either animal or vehicle tangrams). When hearing disfluent expressions, children looked more at a tangram that was unfamiliar from the speaker’s perspective; this systematic disfluency effect disappeared in Experiment 1 when the speaker was entirely naïve, and depended on each speaker’s entrainment experience in Experiment 2. These findings show that 4-year-olds can keep track of two different partners’ knowledge states, and use this information to determine what should be difficult for a particular partner to name, doing so efficiently enough to guide online interpretation of disfluent speech.

Testing Expectancy, but not Judgements of Learning, Moderate the Disfluency Effect

Do students learn better with material that is perceptually harder-to-process? Previous research has been equivocal concerning this question. To clarify these discrepancies, the present study examined two potential boundary conditions to determine when disfluent text is, and is not, beneficial to learning. The two boundary conditions examined were: type of judgement of learning (JOLs) and testing expectancy. Boundary conditions were examined in separate Group (incidental aggregate JOLs vs. intentional aggregate JOLs vs. item-by-item JOLs) by Disfluency (Masked vs. Nonmasked) mixed ANOVAs. Results revealed that type of JOL did not moderate the disfluency effect, but testing expectancy did. These results bring forth questions pertaining to the utility of disfluency on learning.