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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.
360° videos raised the attention of educators, as they can mediate complex environments in educational settings. However, learning irrelevant cognitive strains might be imposed because it is necessary to navigate through spherical material. These downsides could be compensated by using signalling techniques. In a two (macro‐level vs. no signalling) × two (micro‐level vs. no signalling) factorial between‐subjects design plus control group, 203 students watched a video about visual and behavioural characteristics of animals. Learning outcomes, cognitive load, disorientation, and presence were investigated. Results revealed that macro‐level signalling enhanced learning outcomes. Descriptively, the control group outperformed all experimental groups except the condition with macro‐level signalling regarding retention performance. According to an exploratory path model, extraneous load moderated the effects of signalling on learning outcomes. Results are discussed considering cognitive load and spatial presence induced by using 360° videos as learning material.
Emotional design of pedagogical agents: the influence of enthusiasm and model-observer similarity
(2023)
Pedagogical agents were found to enhance learning but studies on the emotional effects of such agents are still missing. While first results show that pedagogical agents with an emotionally positive design might especially foster learning, these findings might depend on the gender of the agent and the learner. This study investigated whether emotional expressions performed by an on-screen instructor were able to increase learning outcomes while considering differences the gender of the agent and the learner. In a 2 (neutral vs. enthusiastic expressions) × 2 (female vs. male agent) between-subject design with additional consideration of the gender of the learner, data of 129 participants was collected. Results revealed that the manipulation of enthusiasm lead to higher perceptions of positive emotions. In addition, a pedagogical agent who performed enthusiastic expressions led to a higher retention but not transfer performance. In terms of the gender of the agent and the learner, male learners retained knowledge better when they watched the agent performing enthusiastic expression irrespective of the persona gender. Female learners, however, retained knowledge only better when a female agent performed enthusiastic expressions. Results are discussed in the light of the positivity principle, model-observer similarity hypotheses and current theories on social cues in multimedia learning.
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.
Abstract
To explain successful subject matter learning with digital tools, the specification of mediating cognitive processes is crucial for any empirical investigation. We introduce a cognitive process framework for the mechanisms of learning with digital tools (CoDiL) that combines core ideas from the psychology of instruction (utilization-of-learning-opportunity framework), cognitive psychology (knowledge-learning-instruction framework), and domain-specific research on learning and instruction. This synthesizing framework can be used to theoretically ground, firstly, the design of digital tools for learning, and secondly, the empirical analysis of students’ learning activities in digitally enriched educational settings via the analysis of specific student-tool interactions.
Abstract
In formal educational settings, such as online university lectures, instructional videos often consist of PowerPoint slides accompanied by a video or audio explanation from the instructor. It has been assumed that the social cues provided by the instructor’s video may facilitate affective processes and affect learning outcomes. Research on instructor presence in instructional videos has focused primarily on laboratory and online studies that are not embedded in the courses in which learners are enrolled. Therefore, we present three field studies examining instructor presence in instructional videos embedded in higher education courses to strengthen external validity (exam-relevant topic, > 30 min long, personally known instructor). The results of these studies show positive effects of a visible instructor compared to no visible instructor on some affective measures: social presence in Study 1 ( n = 18, d = .85) and well-being in Study 3 ( n = 38, d = 1.01), but not on others (well-being in Studies 1 & 2 ( n = 53); motivation in Studies 1–3, social presence in Studies 2 & 3). They also show no effects on extraneous processing or learning outcomes (Studies 1–3). Thus, no general effect of instructor presence can be shown for instructional videos embedded in university courses in higher education, but there are also no detrimental effects. This leads to implications for future research, teaching, and design practice.
Learning from interactive video: the influence of self-explanations, navigation, and cognitive load
(2025)
Abstract
Recent research has shown that enhancing instructional videos with questions, such as self-explanation prompts, and thus shifting the process from receptive to constructive learning, is beneficial to learning. However, the inclusion of questions is often confounded with the implementation of learner pacing through navigation features. Furthermore, previous studies have often not controlled for learning time. To address these shortcomings, an experiment ( N = 128) was conducted. Participants watched an instructional video about cloud formation and lightning, with learning time controlled. In a 2 × 2 between-subjects design, navigation features (learner pacing vs. system pacing) and self-explanation prompts (prompts vs. no prompts) were manipulated. The results showed no effects of navigation features and self-explanation prompts on learning performance. While navigation features did not affect cognitive load, self-explanation prompts increased both intrinsic and extraneous cognitive load. Overall, the quality of responses to prompts was low but positively related to comprehension. The results are discussed in terms of the Interactive-Constructive-Active-Passive framework and Cognitive Load Theory. They highlight the importance of boundary conditions when investigating the effects of interactive features in instructional videos.
Abstract
In research practice, it is common to measure cognitive load after learning using self-report scales. This approach can be considered risky because it is unclear on what basis learners assess cognitive load, particularly when the learning material contains varying levels of complexity. This raises questions that have yet to be answered by educational psychology research: Does measuring cognitive load during and after learning lead to comparable assessments of cognitive load depending on the sequence of complexity? Do learners rely on their first or last impression of complexity of a learning material when reporting the cognitive load of the entire learning material after learning? To address these issues, three learning units were created, differing in terms of intrinsic cognitive load (low, medium, or high complexity) as verified by a pre-study ( N = 67). In the main-study ( N = 100), the three learning units were studied in two sequences (increasing vs. decreasing complexity) and learners were asked to report cognitive load after each learning unit and after learning as an overall assessment. The results demonstrated that the first impression of complexity is the most accurate predictor of the overall cognitive load associated with the learning material, indicating a primacy effect. This finding contrasts with previous studies on problem-solving tasks, which have identified the most complex task as the primary determinant of the overall assessment. This study suggests that, during learning, the assessment of the overall cognitive load is influenced primarily by the timing of measurement.
In the context of climate change, the implementation of education for sustainable development (ESD) is of particular importance in schools. Developing key competencies, such as systems thinking to understand complex relationships, is essential in teaching students how to solve sustainability-related problems through ESD. Previous research has shown that using system models in the classroom can foster systems thinking. This study investigated the effects of using alternative system models with varying visualization of temporal developments in developing different facets of systems thinking based on a heuristic structural competence model. Overall, 293 elementary school students were assigned to one of four groups (a qualitative system model without time representation vs. a qualitative system model with time representation vs. a quantitative system model with time representation vs. the control group) and participated in a five-lesson classroom intervention in pre-post-test design. The results showed a large effect of fostering systems thinking in all experimental groups compared to the control group. The qualitative system models led to higher learning gains than the quantitative system model, especially with system modeling and solving complex problems, which requires higher systems thinking skills. The results were interpreted by referring to situational interest in interacting with the respective system model.