Memory-related cognitive load effects in an interrupted learning task: A model-based explanation

How Society Anxiety Influences Attention Control in College Students: The Moderated Mediation Effect of Cognitive Flexibility and Resting-state Electroencephalography Activity

Social anxiety is a prevalent issue among college students, adversely affecting their overall well-being. Drawing from the cognitive model of social anxiety and attention control theory, heightened levels of social anxiety may correspond to poorer attention control ability. However, little is known about the underlying cognitive mechanisms of the relationship between social anxiety and attention control. To address this research gap, the current study recruited a sample of 156 college students (56 women) who underwent self-report measures of social anxiety, cognitive flexibility, and attention control, followed by a resting-state EEG recording. The results revealed a significant negative predictive effect of social anxiety on attention control, with cognitive flexibility partially mediating this relationship. Furthermore, resting-state theta power emerged as a significant moderator, accentuating the negative impact of social anxiety on cognitive flexibility among individuals with lower theta power. In addition, frontal alpha asymmetry (FAA) demonstrated a moderating effect, with lower FAA intensifying the predictive influence of cognitive flexibility on attention control. Taken together, these results suggested that social anxiety can predict attention control either directly or indirectly via the mediating role of cognitive flexibility, and lower theta power and FAA has a risk amplification effect, which provide novel insights into the treatment and prevention of social anxiety and its negative impact on college students.

Profiling cognitive workload in an unmanned vehicle control task with cognitive models and physiological metrics

In the present study, we use Cognitive Metrics Profiling (CMP) to capture variance in cognitive load within a complex unmanned vehicle control task. We aim to demonstrate convergent validity with existing workload measurement methods, and to decompose workload into constituent cognitive resources to aid in diagnosing causes of workload. A cognitive model of the task was developed and examined to determine the extent to which it could predict behavioral performance, subjective workload, and validated physiological workload metrics. We also examined model activity to draw insights regarding loaded cognitive capacities. We found that composite workload from the model predicted physiological metrics, performance, and subjective workload. Moreover, the model indicates that differences in workload were driven largely by procedural, declarative, and temporal memory demands. We have found preliminary evidence of correspondence between workload predictions of a CMP model and physiological measures of workload. This suggests our approach captures interesting aspects of workload in a complex task environment and may provide a theoretical link between behavioral, physiological, and subjective metrics. This approach may provide a means to design effective workload mitigation interventions and improve decision-making about personnel tasking and automation.

Modeling with Real-Time Informative Feedback: Implementing and Evaluating a New Massive Open Online Course Component

As part of the design, development, and deployment of a massive open online course (MOOC) on model-based systems engineering, we introduced MORTIF-Modeling with Real-Time Informative Feedback, a new learning-by-doing feature that enables the learner to model, receive detailed feedback, and resubmit improved solutions. We examined the pedagogical usability of MORTIF by investigating characteristics of participants working with it, and their perceived contribution, preferred question type, and learning style. The research included 295 participants and applied the mixed-methods approach, using MOOC server data and online questionnaires. Analyzing 12,095 submissions, we found increasing frequency of using the model resubmitting option. Students ranked MORTIF as the highest of six question types in terms of preference and perceived contribution level. Nine learning style categories were identified and classified based on students' verbal explanations regarding their preference of MORTIF over the other question types. MORTIF has been effective in promoting meaningful learning, supporting our hypothesis that the combination of active learning with real-time informative feedback is a learning mode that students eagerly embrace and benefit from. The benefits we identified for using MORTIF include active learning, provision of meaningful immediate feedback to the learner, the option to use the feedback on the spot and resubmitting an improved model, and its suitability for a variety of learning styles.

Understanding Cognitive Load in Digital and Online Learning: a New Perspective on Extraneous Cognitive Load

Cognitive load theory has been a major influence for the field of educational psychology. One of the main guidelines of the theory is that extraneous cognitive load should be reduced to leave sufficient cognitive resources for the actual learning to take place. In recent years, research regarding various design factors, in particular from the field of digital and online learning, have challenged this assumption. Interactive learning media, immersion, disfluency, realism, and redundant elements constitute five major challenges, since these design factors have been shown to induce task-irrelevant cognitive load, i.e., extraneous load, while still promoting motivation and learning. However, currently there is no unified approach to integrate such effects into cognitive load theory. By including aspects of constructive alignment, an approach aimed at fostering deep forms of learning in order to achieve specific learning outcomes, we devise a strategy to balance cognitive load in digital learning. Most importantly, we suggest considering both the positive and negative effects on cognitive load that certain design factors of digital learning can cause. In addition, a number of research results highlight that some types of positive effects of digital learning can only be detected using a suitable assessment method. This strategy of aligning cognitive load with desired learning outcomes will be useful for formulating theory-guided and empirically testable hypotheses, but can be particularly helpful for practitioners to embrace emerging technologies while minimizing potential extraneous drawbacks.