Cognitive Load Research Articles

Enhancing Learning Engagement: A Study on Gamification’s Influence on Motivation and Cognitive Load

Research has demonstrated that engagement in any learning environment improves learning and may even boost performance. Consequently, numerous scholars have examined various approaches to raising student engagement in learning. Gamification is one such approach because it is thought to increase motivation and reduce cognitive load to ultimately improve learning outcomes. However, some studies have called into question its benefits, prompting more research to be conducted to properly understand the phenomenon. Hence, the study examined gamification’s influence on motivation and cognitive load for enhancing learning engagement. The study was underpinned by the integration of the Attention, Relevance, Confidence, and Satisfaction (ARCS) model, self-determination theory (SDT), and cognitive load theory (CLT) in developing a conceptual framework. A sample of 407 university students who participated in five gamified courses were involved in the study. The data collected through a questionnaire were analyzed using SmartPLS structural equation modeling software. According to the study’s findings, both motivation and cognitive load influence engagement in learning, with cognitive load being the stronger influence. The study contributes to our knowledge by elucidating the relationship between gamification and students’ cognitive load and motivation to learn, as well as how these elements ultimately engage students in the learning process.

Developing and evaluating an e-learning and e-assessment tool for organic chemistry in higher education

A convincing e-learning system for higher education should offer adequate usability and not add unnecessary (extraneous) cognitive load. It should allow teachers to switch easily from traditional teaching to flipped classrooms to provide students with more opportunities to learn and receive immediate feedback. However, an efficient e-learning and technology-enhanced assessment tool that allows generating digital organic chemistry tasks is yet to be created. The Universities of Bonn and Duisburg-Essen are currently developing and evaluating an e-learning and technology-enhanced assessment tool for organic chemistry. This study compares the effectiveness of traditional paper-pencil-based and digital molecule-drawing tasks in terms of student performance, cognitive load, and usability—factors that all contribute to learning outcomes. Rasch analysis, t-tests, and correlation analyses were used for evaluation, revealing that the developed system can generate digital organic chemistry tasks. Students performed equally well on simple digital and paper-pencil molecule-drawing tasks when they received an appropriate introduction to the digital tool. However, using the digital tool in two of three studies imposes a higher extraneous cognitive load than using paper and pencil. Nevertheless, the students rated the tool as sufficiently usable. A significant negative correlation between extraneous load and tool usability was found, suggesting room for improvement. We are currently concentrating on augmenting the functionality of the new e-learning tool to increase its potential for automatic feedback, even for complex tasks such as reaction mechanisms.

Using multivariate partial least squares on fNIRS data to examine load-dependent brain-behaviour relationships in aging.

Researchers implementing non-invasive neuroimaging have reported distinct load-dependent brain activity patterns in older adults compared with younger adults. Although findings are mixed, these age-related patterns are often associated with compensatory mechanisms of cognitive decline even in the absence of direct comparisons between brain activity and cognitive performance. This study investigated the effects of cognitive load on brain-behavior relationships in younger and older adults using a data-driven, multivariate partial least squares (PLS) analysis of functional near-infrared spectroscopy (fNIRS) data. We measured bilateral prefrontal brain activity in 31 older and 27 younger adults while they performed single and dual 2-back tasks. Behavioral PLS analysis was used to determine relationships between performance metrics (reaction time and error rate) and brain oxygenation (HbO) and deoxygenation (HbR) patterns across groups and task loads. Results revealed significant age-group differences in brain-behavior relationships. In younger adults, increased brain activity (i.e., increased HbO and decreased HbR) was associated with faster reaction times and better accuracy in the single task, indicating sufficient neural capacity. Conversely, older adults showed a negative correlation between HbR and error rates in the single task; however, in the dual task, they demonstrated a positive relationship between HbO and performance, indicative of compensatory mechanisms under the higher cognitive load. Overall, older adults' showed relationships with either HbR or HbO, but not both, indicating that the robustness of the relationship between brain activity and behavior varies across task load conditions. Our PLS approach revealed distinct load-dependent brain activity between age groups, providing further insight into neurocognitive aging patterns, such as compensatory mechanisms, by emphasizing the variability and complexity of brain-behavior relationships. Our findings also highlight the importance of considering task complexity and cognitive demands in interpreting age-related brain activity patterns.

Spontaneous eye blinking as a diagnostic and prognostic marker in disorders of consciousness: Protocol of an international multicentre longitudinal study.

Diagnostic and prognostic decision-making in patients with Disorders of Consciousness (DoC) is challenging. It has been suggested that spontaneous eye blink rate is an index of patients' level of consciousness easy to detect in clinical practice. Further blinking features (i.e., amplitude, duration, variability in intervals between blinks) may change as a function of cognitive load, but have not been investigated in patients with DoC. This multicentre, longitudinal study aims at exploring the diagnostic and prognostic value of spontaneous eye blinking features in DoC. Eight European medical institutions will enrol consecutively admitted adult patients with DoC. Within two weeks from study entry demographic, anamnestic and clinical data will be collected. Moreover, patients will undergo two 20-minute EEG-EOG recordings at rest, to collect blinking features and EEG activity. A clinical follow-up will be performed after 6 months. A group of healthy individuals will be enrolled for reference. Possible differences in blink features between patients and the reference group, differences across diagnostic sub-groups, and correlations between blinking features and clinical outcome will be investigated. The results of this study might help clinicians to reduce misdiagnosis rate in DoC and provide useful information for prognostication and care pathway plan.

Comparing the effects of a specific task instruction and prompts on pre-service teachers’ noticing of classroom management situations

AbstractAttending to relevant information in complex classroom situations can be a challenging task for pre-service teachers which is why teacher education programs often use authentic classroom videos to scaffold pre-service teachers’ professional vision. However, to date, it remains unclear which instructions can guide pre-service teachers’ attention toward classroom management situations to promote the early development of teacher professional vision. This mixed-methods study (n = 85 pre-service teachers) compared effects of (a) a specific task instruction before watching a classroom video and (b) prompts during the classroom video on noticing—indicated by the number, velocity, and relevance of identified classroom management situations. Controlling for mental effort and pedagogical-psychological knowledge, t-Tests showed both types of instruction to have a similar attention-guiding effect. Qualitative analyses of retrospective interviews revealed that pre-service teachers identified verbal reactive classroom management strategies more easily than nonverbal strategies when observing experienced teachers in the classroom videos. We discuss specific task instructions as an economic alternative to the use of prompts in video-based teacher education and—based on our qualitative findings—provide directions for future research.

Reaching the cognitive-motor interface: effects of cognitive load on arm choice and motor performance after stroke.

A vexing characteristic of motor disability after stroke is that many individuals fail to use their affected arm effectively despite having the capacity to do so, a phenomenon termed arm nonuse. Based on the hypothesis that nonuse is influenced by the competing cognitive demands of many daily activities, we examined the effects of cognitive load on arm choice and motor performance in individuals with stroke using a novel virtual reality paradigm that mimics the demands of real-life visual search, object selection, and reaching to targets. Twenty individuals with single left or right hemispheric chronic stroke (11 left cerebrovascular accident; 9 right cerebrovascular accident) and 10 age-matched neurotypical participants completed the Virtual Reality Arm Choice task, in which they reached for target objects in an array under varied cognitive demand. To manipulate cognitive demand, we varied the semantic similarity of objects in the reaching space and the presence or absence of a secondary task. The results showed reduced use of the paretic arm under increased demand. Under cognitive load, participants with stroke also showed slower reach initiation, slower movements, increased reach curvature, and increased performance differences between the paretic and non-paretic arms. The arm choice of neurotypical individuals was also modulated under cognitive load. These data indicate that cognitive factors influence arm choice and motor performance in naturalistic reaching tasks in individuals with chronic stroke. Performance decrements under cognitive load may in turn influence reduced paretic arm use during daily activities.

Predictive Gaze Analytics: A Comparative Case Study of the Foretelling Signs of User Performance during Interaction with Visualizations of Ontology Class Hierarchies

The current research landscape in ontology visualization has largely focused on tool development, yielding an extensive array of visualization tools. Although many existing solutions provide multiple ontology visualization layouts, there is limited research in adapting to an individual user’s performance, despite successful applications of adaptive technologies in related fields, including information visualization. In an effort to innovate beyond traditional one-size-fits-all visualizations, this paper contributes one step towards realizing user adaptive visualization by recognizing timely moments when users may potentially need intervention, as real-time adaptation can only occur if it is possible to correctly predict user success and failure during an interaction in the first place. In addition, an open-source, reusable, and extensible software: Beach Environment for the Analytics of Human Gaze (BEACH-Gaze) is made available to the broader scientific community interested in descriptive and predictive gaze analytics. Building on a wealth of research in eye tracking, this paper compares four approaches to predictive gaze analytics through a series of experiments that utilize scheduled gaze digests, irregular gaze events, the last known gaze status, as well as all gaze captured for a user at a given moment in time. The results from a set of experimental trials suggest that irregular gaze events are most informative of early predictions of user performance, whereas cognitive workload appears to be most indicative of overall user performance in the task scenario presented in this paper. These empirical findings highlight the importance of an analytical approach to gaze on user predictions and indicate careful consideration when applying.

A comparative analysis of face and object perception in 2D laboratory and virtual reality settings: insights from induced oscillatory responses.

In psychophysiological research, the use of Virtual Reality (VR) for stimulus presentation allows for the investigation of how perceptual processing adapts to varying degrees of realism. Previous time-domain studies have shown that perceptual processing involves modality-specific neural mechanisms, as evidenced by distinct stimulus-locked components. Analyzing induced oscillations across different frequency bands can provide further insights into neural processes that are not strictly phase-locked to stimulus onset. This study uses a simple perceptual paradigm presenting images of faces and cars on both a standard 2D monitor and in an immersive VR environment. To investigate potential modality-dependent differences in attention, cognitive load, and task-related post-movement processing, the induced alpha, theta and beta band responses are compared between the two modalities. No evidence was found for differences in stimulus-dependent attention or task-related post-movement processing between the 2D conditions and the realistic virtual conditions in electrode space, as posterior alpha suppression and re-synchronization of centro-parietal beta did not differ between conditions. However, source analysis revealed differences in the attention networks engaged during 2D and 3D perception. Midfrontal theta was significantly stronger in laboratory conditions, indicating higher cognitive load than in the VR environment. Exploratory analysis of posterior theta showed stronger responses in VR, possibly reflecting the processing of depth information provided only by the 3D material. In addition, the theta response seems to be generated by distinct neuronal sources under realistic virtual conditions indicating enhanced involvement of semantic information processing and social cognition.

A Framework for Enhancing Maintainability of CMS in Kenyan Universities' LMS

The maintainability of Content Management Systems (CMS) within Learning Management Systems (LMS) is critical in educational institutions to ensure efficient content management, system scalability, and security. This paper presents a framework aimed at enhancing the maintainability of CMS within LMS in Kenyan universities. The research utilizes Cognitive Load Theory and Software Evaluation Theory to address CMS challenges, focusing on perfective, corrective, adaptive, and preventive maintenance. This paper also discusses key factors influencing CMS maintainability, including system documentation, modularity, and error handling. Findings from this study contribute to the development of a framework and advancing CMS design in LMS, providing a robust, scalable, and secure platform for educators and administrators.

Human-Centric Virtual Museum: Redefining the Museum Experience Through Immersive and Interactive Environments

Virtual Museums present a novel approach for audiences to experience cultural content. This paper proposes two new concepts of virtual museums: Human-Centric Virtual Museum, which emphasizes audience interaction experiences, and Content-Centric Virtual Museum, which focuses more on the digitized display of real museum collections and content. With the widespread application of extended reality (XR) technology in this field, people are increasingly inclined towards experiencing highly interactive Human-Centric Virtual Museums. However, evaluating these from a user cognition perspective warrants further investigation. To address this, we design and implement three fundamental forms: Mobile Application (MA), Virtual Reality (VR), and Mixed Reality (MR), and evaluate comprehensive user experiences, including satisfaction, cognitive load, emotional involvement, presence, interactivity, degree of participation and potential negative impact. The experimental results show that in terms of satisfaction and cognitive load, users generally find the MR mode easier to understand and operate. Regarding emotional involvement, audiences in the VR and MR modes are more likely to focus on the current content. Concerning presence, the MR mode enables audiences to perceive the real spatial location, thereby increasing their immersion. Furthermore, compared to the MA mode, both MR and VR modes exhibit stronger interactivity, providing audiences with a more immersive exhibition experience. However, the VR mode may also cause users to feel discomfort during use.

Automated feedback on fluency and complexity measures for L2 academic paragraphs: Student perspectives and impacts on human-rater scores

While complexity, accuracy, and fluency (CAF) measures are known to correlate with L2 writers’ scores, less is known about the effectiveness of providing automated feedback based on these measures for improving writing performance. Furthermore, it remains unclear if improvements in specific CAF measures correspond to improvements in human-rater scores. Finally, the trade-off hypothesis predicts that all three components of CAF cannot be improved at once, so providing multiple CAF measures to students at the same time might cause cognitive overload and overwhelm students, reducing their ability to uptake the feedback. To examine these issues, a simple paragraph feedback tool was developed to provide input on number of words, vocabulary variety, supporting detail markers, and sentence length. The tool was implemented repeatedly in L2 academic paragraph writing lessons with 124 students. Improvement was assessed through complexity, accuracy, and fluency (CAF) measures and human-rater scores, while also gathering student opinions. Results showed significant improvements in number of words, sentence length, and supporting detail marker usage from pre- to post-treatment with improvements in number of words and vocabulary variety found to be the strongest predictors of improvements in human-rater scores. Students reported finding vocabulary variety feedback most helpful, while supporting detail marker feedback was perceived as most confusing. Notably, students reported very low cognitive load overall. This article also discusses pedagogical implications of these findings for L2 writing instruction and automated feedback implementation.

Question generation type and organizational cues in video learning: A study on their effects on online learning behaviors

This study examined the influence of question generation types and organizational cues on video learning. Participants were 100 undergraduate students randomly assigned to view videos that differed in terms of question generation type (instructor-generated vs. self-generated) and organizational cues (with vs. without). The results revealed that self-generated questions led to superior learning performance and higher cognitive load compared to instructor-generated questions in the absence of organizational cues, but when combined with organizational cues, there was no significant difference between the instructor and self-generated questions. Additionally, students who engaged with self-generated questions demonstrated a higher frequency of interaction and seeking behaviors and meta-cognition. Mediation analysis further indicated that self-generated questions, compared to instructor-generated questions, improved learning performance by increasing both the seeking frequency and cognitive load. These findings underscore the importance of question generation and organizational cues in video learning. They also have practical implications for design and use of video learning: if students are required to generate questions by themselves, it is better not to provide organizational cues; this will foster their engagement (more research behaviors and higher cognitive load) and thus their learning. In contrast, if they must answer questions generated by the instructor, the presence or absence of organizational cues does not matter.

Research on Evaluation and Prediction Methods of Cognitive Intentions for Product Morphological Features

The morphological characteristics of a product serve as essential carriers for conveying design intentions. These characteristics directly affect users’ comprehension of the product’s functions and proper usage, which are critical to the safety of product utilization and the overall comfort of the user experience. Incorporating prior experience to predict users’ cognitive intentions regarding product form characteristics can provide valuable evaluation and decision-making references for design. This approach effectively reduces product development risks and contributes to enhancing user acceptance and experience. The study established intention discrimination indicators for form characteristics, covering six dimensions: functional orientation, behavioral intention, recognizability, cognitive load, attention distribution, and experiential feeling. Combining multidimensional scaling (MDS) and systematic clustering, samples were screened, and the morphological decomposition method was used to categorize and extract form characteristic categories and feature factors. The entropy weight method was applied to assign weights to the feature categories, and a feedforward neural network (FNN) was employed to construct a prediction model for cognitive intentions regarding product form characteristics. The model was tested using leave-one-out cross-validation, yielding a mean squared error (MSE) of 0.0089 and an R correlation coefficient of 0.9998, indicating high reliability. Finally, the feasibility and effectiveness of this method were validated through a case study on earthquake science experience facilities.

Unlocking the power of immersive learning: The FAIRI instructional design proposition for adaptive immersive virtual reality

Immersive Virtual Reality (IVR) is a promising tool for providing immersive and adaptive learning experiences by simulating realistic work environments and gathering data on learner's interactions to offer personalized instructional content. However, IVR-supported instruction presents unique challenges due to its heightened immersion, including managing the learner's cognitive load and self-regulation. Incorporating adaptivity into IVR can mitigate these challenges by regulating the complexity of the virtual environment, reducing cognitive strain, and maintaining learner focus through real-time feedback for both learners and instructors. However, recent reviews highlight the underutilization of adaptivity within IVR research, making it difficult to inform the design of adaptive IVR instruction.This paper proposes FAIRI, an instructional design for adaptive IVR in domain-specific training. FAIRI synthesizes recent IVR research relevant to the design of adaptivity for IVR through a purposive literature review, guided by the instructional design elements of the Four-Component Instructional Design-model (4C/ID) and Intelligent Tutoring Systems (ITS). The instructional design elements of the 4C/ID-model and ITS are evaluated for their potential viability and further refined for adaptive IVR-supported instruction based on recent IVR studies. Two key design principles are identified for adaptive IVR: limiting IVR to tasks that benefit from its heightened realism and leveraging its adaptive features to manage the intensity of the experience. Additionally, the application of FAIRI's design principles and elements are demonstrated through a forklift operator training case in IVR. FAIRI aims to catalyse further research into adaptivity for IVR by proposing and aligning promising instructional design elements for adaptive IVR instruction.

From Play to Understanding: Large Language Models in Logic and Spatial Reasoning Coloring Activities for Children

Visual thinking leverages spatial mechanisms in animals for navigation and reasoning. Therefore, given the challenge of abstract mathematics and logic, spatial reasoning-based teaching strategies can be highly effective. Our previous research verified that innovative box-and-ball coloring activities help teach elementary school students complex notions like quantifiers, logical connectors, and dynamic systems. However, given the richness of the activities, correction is slow, error-prone, and demands high attention and cognitive load from the teacher. Moreover, feedback to the teacher should be immediate. Thus, we propose to provide the teacher with real-time help with LLMs. We explored various prompting techniques with and without context—Zero-Shot, Few-Shot, Chain of Thought, Visualization of Thought, Self-Consistency, logicLM, and emotional —to test GPT-4o’s visual, logical, and correction capabilities. We obtained that Visualization of Thought and Self-Consistency techniques enabled GPT-4o to correctly evaluate 90% of the logical–spatial problems that we tested. Additionally, we propose a novel prompt combining some of these techniques that achieved 100% accuracy on a testing sample, excelling in spatial problems and enhancing logical reasoning.

Mental load in University Teachers: Case study

Mental load among university teachers has gained importance in recent decades due to its impact on workers' health and well-being. This study focuses on the mental load of teachers from the Faculty of Engineering at the National University of Chimborazo, analyzing factors like the cognitive and emotional demands they face in their daily work. The aim is to determine mental load levels and their relationship with sociodemographic variables, identifying the need for preventive interventions. The Subjective Workload Assessment Technique (SWAT) was applied to a sample of 97 teachers who voluntarily completed the survey. Results showed that 80.4% of teachers exhibit medium mental load levels, while 18.6% report high levels. Significant correlations were identified between mental load and variables like age and gender. Stress and mental effort dimensions were particularly high, indicating a potential risk to teachers' health. It is concluded that implementing preventive measures and intervention programs is necessary to improve working conditions and teacher self-efficacy.

Syntax, stress and cognitive load, or on syntactic processing in simultaneous interpreting

Abstract This corpus-based study examines the effect of syntactic complexity in the source language on simultaneous interpreters’ cognitive load and stress. Previous studies show contrasting results regarding the source text syntax and cognitive load in interpreting, while the link between syntactic complexity and interpreters’ stress remains unexplored. Our research aims to fill this gap by measuring cognitive load through filled and silent pauses, and stress through fundamental frequency. We evaluated syntactic complexity by calculating dependency distance, i.e., the number of words between syntactically dependent elements. We used PINC, the Polish Interpreting Corpus (Chmiel et al. 2022), as our dataset. We found that higher syntactic complexity in the source text increases stress and cognitive load among interpreters. Additionally, when interpreters produce complex syntax themselves, their stress increases. The study provides empirical evidence for simplification in interpreting and shows that syntactic complexity in interpreters’ own output does not necessarily lead to increased cognitive load.

Implementation of Machine Learning in the Categorization of Improvement Areas for Kaizen Submissions

This paper explores the synergy between Kaizen methodologies and Machine Learning, with the goal of improving the efficiency of categorizing areas of improvement. The implementation of Machine Learning algorithms, utilizing the ML.NET tool, has proven to be highly advantageous. These algorithms automate the categorization of Kaizen proposals based on structured data and predefined labels. This automation significantly reduces manual workload, expedites the classification process, and ultimately accelerates decision-making within the realm of continuous improvement. Furthermore, the application of machine learning has improved the categorization accuracy, reduced the risk of human errors, and ensured a more precise assignment to the relevant categories. This, in turn, enhances the quality of the collected information, facilitating well-informed decision-making. Additionally, the automated categorization has streamlined the process for users submitting Kaizen proposals, eliminating the need for manual category selection. This reduces cognitive load and promotes active engagement in the continuous improvement process. Furthermore, the system has introduced a category-based incentive structure, where users accrue points corresponding to the category assigned to their Kaizen proposals. These points can be exchanged for items provided by the company, acting as an additional incentive for active employee participation in the continuous improvement process. In conclusion, the integration of Machine Learning with the ML.NET tool has become a valuable tool for optimizing the management of improvement proposals, resulting in a marked enhancement in efficiency, accuracy, and user engagement in the categorization process.

Comparative Analysis of Augmented Reality in Engineering Drawing Course: Assessing Spatial Visualization and Cognitive Load with Marker-Based, Markerless, and Web-Based Approaches

This study investigates the integration of augmented reality (AR) into engineering education, with a specific focus on its impact on spatial visualization abilities and the cognitive load in the context of an engineering drawing course. The research employs a structured approach, involving three experimental groups utilizing marker-based (MBAR), markerless (MLAR), and web-based AR (WBAR) applications, in comparison to a control group receiving traditional instruction. The primary objective is to rigorously assess the comparative effectiveness of these instructional approaches in enhancing spatial visualization skills and reducing the cognitive load of undergraduate engineering students. Spatial visualization, a vital cognitive skill for engineering disciplines, is evaluated using the Purdue Spatial Visualization Test: Rotations (PSVT-R) questionnaire. The cognitive load of students is assessed with the help of a questionnaire comprising items related to mental load and effort. This study not only quantifies the impact of AR applications but also highlights subtle differences among the various AR modalities. The findings suggested that the group that used marker-based AR had better improvement in their spatial ability and felt less cognitive load compared to those who utilized markerless AR and web-based AR.

Generative artificial intelligence tools for diverse learning styles in design education

The integration of Artificial Intelligence (AI) is transforming design education by automating processes and enhancing learning. Early exposure to AI offers students a competitive edge in critical thinking and problem-solving. AI tools can inspire space planning, improve workflow, reduce cognitive load, and foster creativity. This study explores how the VARK model, which categorizes learning styles into Visual, Auditory, Reading/Writing, and Kinesthetic, influences student engagement with AI tools. In an interior design classroom study involving 32 sophomore students, AI tools were used to create floor plans and generate 3D images. The study assessed the correlation between learning styles and technology acceptance. Results revealed no strong correlations between individual learning styles and perceived ease of use, perceived usefulness, or actual use of AI tools. However, visual and kinesthetic learners displayed higher engagement, suggesting that these modalities may benefit more from AI-enhanced design education. The study also confirmed that perceived ease of use and usefulness are critical factors in technology acceptance, aligning with the Technology Acceptance Model (TAM). The findings suggest that AI tools can be broadly effective across diverse learning styles, with a universal perception of their utility in design education. This research highlights the potential of AI to support creative problem-solving and prepare students for future challenges in a digital design landscape. While learning styles influence engagement, AI tools’ usability and educational benefits are consistently recognized, demonstrating their transformative potential in design education.