Exploring the Relationship Between Self-Esteem and Mental Workload Among University Professors

Background: Academic staff have different roles which may impose a high mental workolad on them and affect their cognitive function. This study was designed to assess different subscales of mental worklaod and cognitive function in university academic staff. Methods: This was a cross-sectional study on 86 faculty members of Shahid Sadoughi University of Medical Sciences. Stratified sampling was used to select participants from different schools. The participants were divided into basic sciences and clinical faculty members. Mental workload was assessed by National Aeronautics and Space Administration-Task Load Index (NASA-TLX). Cognitive function was assessed by Stroop test. Data were analyzed by SPSS 26 using Shapiro-Wilks test, Student’s T-test, Mann Whitney U test, univariate ANOVA, and Kruskal-Wallis test. The level of significance was 0.05. Results: Mean age and work history of the participants was 43.6±8.6 yr. and 11.7±9.1 yr., respectively. Mental workload, especially two aspects of mental demand and performance, was high and it was significantly higher in the participants from school of medicine and among those with executive/administrative responsibilities. From subscales of mental workload, physical demand was significantly higher among clinical than basic sciences faculty members (p<0.05). Among different aspects of cognitive function, only numbers of congruent errors were significantly higher among clinical faculties. Mental workload and cognitive function were not significantly correlated. Conclusion: This study showed a high level of mental workload in university academic staff, especially in clinical faculty members, but this high mental workload did not affect their cognitive function.

Background:Advancing patient safety during handoffs remains a public health priority. The application of cognitive load theory offers promise, but is currently limited by the inability to measure cognitive load types.Objective:To develop and collect validity evidence for a revised self-report inventory that measures cognitive load types during a handoff.Methods:Based on prior published work, input from experts in cognitive load theory and handoffs, and a think-aloud exercise with residents, a revised Cognitive Load Inventory for Handoffs was developed. The Cognitive Load Inventory for Handoffs has items for intrinsic, extraneous, and germane load. Students who were second- and sixth-year students recruited from a Dutch medical school participated in four simulated handoffs (two simple and two complex cases). At the end of each handoff, study participants completed the Cognitive Load Inventory for Handoffs, Paas’ Cognitive Load Scale, and one global rating item for intrinsic load, extraneous load, and germane load, respectively. Factor and correlational analyses were performed to collect evidence for validity.Results:Confirmatory factor analysis yielded a single factor that combined intrinsic and germane loads. The extraneous load items performed poorly and were removed from the model. The score from the combined intrinsic and germane load items associated, as predicted by cognitive load theory, with a commonly used measure of overall cognitive load (Pearson’s r = 0.83, p < 0.001), case complexity (beta = 0.74, p < 0.001), level of experience (beta = −0.96, p < 0.001), and handoff accuracy (r = −0.34, p < 0.001).Conclusion:These results offer encouragement that intrinsic load during handoffs may be measured via a self-report measure. Additional work is required to develop an adequate measure of extraneous load.

In the field of neuroeconomics, the assessment of cognitive workload is a crucial issue with significant implications for real-world applications. Previous research has made progress in task-based germane cognitive load classification, but decentralized studies focusing on task-independent assessment have often produced less than optimal results. In this study, we present a stacked graph attention convolutional networks (SGATCNs) model to tackle the challenges related to task-independent cognitive workload assessment using EEG spatial information. The model employs the differential entropy (DE) and power spectral density (PSD) features of each EEG channel across four frequency bands (delta, theta, alpha, and beta) as node information. For the construction of the network structure, phase-locked values (PLVs), phase-lag indices (PLIs), Pearson correlation coefficients (PCCs), and mutual information (MI) are utilized and evaluated to generate a functional brain network. Specifically, the model aggregates spatial information on the dynamic map by stacking the graph attention layers and utilizes the convolution module to extract the frequency domain information from between the networks under each frequency band. We conducted a cognitive workload experiment with 15 subjects and selected three representative psychological experimental task paradigms (N-back, mental arithmetic, and Sternberg) to induce different levels of cognitive workload (low, medium, and high). Our framework achieved an average accuracy of 65.11% in recognizing the task-independent cognitive workload across the three scenarios.

This study aims to explore the effect of using 3D Augmented Reality (AR) on cognitive load, collaboration tendency, and enjoyment in engineering education. A quasi-experimental design was conducted involving 111 students, divided into three groups: a control group (conventional), an experimental group with hints (AR + scaffolding), and an experimental group without hints (AR only). Data were collected through pretest-posttest scores and post-intervention questionnaires measuring Cognitive Load (CL), Collaboration Tendency (CT), and Enjoyment (EJ). Data analysis included normality tests (Shapiro–Wilk and Kolmogorov–Smirnov), Wilcoxon Signed-Rank Test, Kruskal–Wallis test, One-Way ANOVA, and Multiple Linear Regression. Results showed that all groups experienced learning improvement, with the highest gains in the AR with hints group. Effect size analysis using Cohen’s d indicated a large impact of AR with hints ( d = 1.10) and moderate impact of AR without hints ( d = 0.63) compared to the control group. Regression analysis revealed that EJ ( p = 0.012) and CT ( p = 0.034) significantly predicted learning performance, while CL had a negative effect ( p = 0.035). Two-Way ANOVA showed no significant gender effect on CL or CT, but found a significant interaction between gender and group on EJ ( p = 0.028), suggesting gender-based differences in emotional engagement with AR. Female students reported higher EJ without hints, while male students preferred guided AR learning. These findings highlight that 3D AR enhances learning outcomes not only cognitively but also socially and emotionally. The integration of scaffolding into AR environments increases effectiveness by reducing cognitive overload and fostering collaboration. Designing adaptive, gender-responsive AR learning environments can further optimize educational impact in technical disciplines.

The use of multimedia learning is increasing in modern education. On the other hand, it is crucial to design multimedia contents that impose an optimal amount of cognitive load, which leads to efficient learning. Objective assessment of instantaneous cognitive load plays a critical role in educational design quality evaluation. Electroencephalography (EEG) has been considered a potential candidate for cognitive load assessment among neurophysiological methods. In this study, we experiment to collect EEG signals during a multimedia learning task and then build a model for instantaneous cognitive load measurement. In the experiment, we designed four educational multimedia in two categories to impose different levels of cognitive load by intentionally applying/violating Mayer’s multimedia design principles. Thirty university students with homogenous English language proficiency participated in our experiment. We divided them randomly into two groups, and each watched a version of the multimedia followed by a recall test task and filling out a NASA-TLX questionnaire. EEG signals are collected during these tasks. To construct the load assessment model, at first, power spectral density (PSD) based features are extracted from EEG signals. Using the minimum redundancy - maximum relevance (MRMR) feature selection approach, the best features are selected. In this way, the selected features consist of only about 12% of the total number of features. In the next step, we propose a scoring model using a support vector machine (SVM) for instantaneous cognitive load assessment in 3s segments of multimedia. Our experiments indicate that the selected feature set can classify the instantaneous cognitive load with an accuracy of 84.5 ± 2.1%. The findings of this study indicate that EEG signals can be used as an appropriate tool for measuring the cognitive load introduced by educational videos. This can be help instructional designers to develop more effective content.

The article highlights the experience of internal quality assurance of educational activities and the quality of higher education of the Faculty of Social and Psychological Education of Uman State Pedagogical University named after Pavel Tychyna. The current system of internal quality assurance of educational activity and quality of higher education at the Faculty of Social and Psychological Education of Uman State Pedagogical University named after Pavel Tychyna is analyzed. It is established that the internal quality assurance of educational activities and the quality of higher education at the faculty has a systemic nature and is carried out in certain areas that are logically and consistently interconnected, namely: periodic review and updating of educational programs (updating educational programs; preparation of new and improvement of current curricula, monitoring the quality of working programs of academic disciplines, development of syllabuses, informing students about the disciplines of free choice, analysis of the availability of educational and methodological and information support); assessment of applicants for higher education (assessment of students, periodic surveys of participants in the educational process); promoting the principles of academic integrity; monitoring the quality of teaching staff (analysis of the timeliness of internships and in-service training by teachers, monitoring the quality of training sessions by teachers, analysis of compliance with the licensing conditions of staffing); ensuring the availability of necessary resources; ensuring publicity of information. Ensuring the quality of educational activities and the quality of higher education at the Faculty of Social and Psychological Education is ensured by a holistic systematic interaction of all participants in the educational process, faculty and university in the implementation of appropriate internal measures and procedures aimed at improving the quality of education.

This study analyzes the function of procedural controllers and its influence on workload at the Area Control Centre (ACC) within the Jakarta Air Traffic Service Centre (JATSC). Unlike radar controllers who operate with real-time visual aids, procedural controllers rely on communication, coordination, and pilot reports, resulting in a higher mental and cognitive workload. The research aims to identify how procedural control functions contribute to workload levels and how these functions can be optimized to maintain operational safety and efficiency. The study adopts a quantitative method with a descriptive-correlation approach. Data were collected through observations, semi-structured interviews, and closed-ended questionnaires using a five-point Likert scale. Respondents consisted of active Air Traffic Controllers (ATCs) at ACC JATSC, selected using purposive sampling with at least one year of work experience. Data analysis was performed using multiple linear regression to evaluate both simultaneous and partial effects of procedural control functions on workload. The findings show that high adherence to standard operating procedures (SOP), consistent application of procedures, and effective communication significantly reduce perceived workload. However, procedural controllers continue to face moderate-to-high levels of mental and physical workload due to sustained cognitive demands, communication intensity, and time pressure. The study concludes that optimizing procedural control functions serves as a protective mechanism against excessive workload, supporting safety, efficiency, and the well-being of ATC personnel at JATSC.

&lt;p&gt;The article deals with the problems of the quality of education. It has been&lt;br&gt; determined that the quality of education is considered as the degree of compliance of&lt;br&gt; a set of characteristics with the current requirements. The quality of education is&lt;br&gt; aimed at meeting the requirements of several stake holders: consumers, owners,&lt;br&gt; employees of institutions, organizations and the society as a whole. In connection&lt;br&gt; with this, the assessment of the quality is a complex and controversial social process&lt;br&gt; which cannot be reduced to control only. A significant regulatory framework which&lt;br&gt; should regulate and ensure the quality of educational processes, the quality of&lt;br&gt; providing educational services, the quality of specialists&rsquo; level of competence has&lt;br&gt; been analyzed. The legal acts which guarantee the specified processes include: the&lt;br&gt; Law of Ukraine &ldquo;On Higher Education&rdquo;, the Regulations &ldquo;On the Educational&lt;br&gt; Process Organization in Higher Educational Institutions&rdquo;, the Regulations on&lt;br&gt; Graduate Education, the Regulations of the Ministry of Education and Science of&lt;br&gt; Ukraine &ldquo;On the Organization of Research Work of Students in Higher Educational&lt;br&gt; Institutions&rdquo; and others. Higher educational institutions of Ukraine have the&lt;br&gt; authority to develop their own normative documents which take into account the&lt;br&gt; specifics of the university and the peculiarities of the organization of the educational&lt;br&gt; process (for example, the Regulations on the Сertification for the Educational&lt;br&gt; Degrees &ldquo;Bachelor&rdquo; and &ldquo;Master&rdquo; of H.S. Skovoroda Kharkiv National Pedagogical&lt;br&gt; University, Regulations on Teaching Practice of Students of the H.S. Skovoroda&lt;br&gt; Kharkiv National Pedagogical University). The guideline for ensuring the quality of&lt;br&gt; education is the international standard ISO 8402:1994 (which complies with the state&lt;br&gt; standard of Ukraine DSTU 3230-95). It has been noted that other standards also&lt;br&gt; function successfully. The issues of the quality are important not only for universities,&lt;br&gt; state institutions, public organizations, but also for all stake holders of the&lt;br&gt; educational process in Ukraine (prospective students, their parents, students,&lt;br&gt; teaching staff, employers). In order to monitor the quality of providing educational&lt;br&gt; services and the level of education in general, the article describes innovative&lt;br&gt; approaches to the implementation of this process. It has been determined that it is&lt;/p&gt; &lt;p&gt;necessary to take into account the achievements of native and foreign pedagogy,&lt;br&gt; international experience in assessing the quality of higher education, to provide a&lt;br&gt; permanent communication between students and teachers, to understand employers&rsquo;&lt;br&gt; demands, to continuously improve the scientific and methodological level of teachers,&lt;br&gt; to create conditions for mobility of teachers and students, to build up the material&lt;br&gt; and technical base.&lt;/p&gt;

Objective structured clinical examinations (OSCEs) are commonly used to assess the clinical skills of health professional students. Examiner judgement is one acknowledged source of variation in candidate marks. This paper reports an exploration of examiner decision making to better characterise the cognitive processes and workload associated with making judgements of clinical performance in exit-level OSCEs. Fifty-five examiners for exit-level OSCEs at five Australian medical schools completed a NASA Task Load Index (TLX) measure of cognitive load and participated in focus group interviews immediately after the OSCE session. Discussions focused on how decisions were made for borderline and clear pass candidates. Interviews were transcribed, coded and thematically analysed. NASA TLX results were quantitatively analysed. Examiners self-reported higher cognitive workload levels when assessing a borderline candidate in comparison with a clear pass candidate. Further analysis revealed five major themes considered by examiners when marking candidate performance in an OSCE: (a) use of marking criteria as a source of reassurance; (b) difficulty adhering to the marking sheet under certain conditions; (c) demeanour of candidates; (d) patient safety, and (e) calibration using a mental construct of the 'mythical [prototypical] intern'. Examiners demonstrated particularly higher mental demand when assessing borderline compared to clear pass candidates. Examiners demonstrate that judging candidate performance is a complex, cognitively difficult task, particularly when performance is of borderline or lower standard. At programme exit level, examiners intuitively want to rate candidates against a construct of a prototypical graduate when marking criteria appear not to describe both what and how a passing candidate should demonstrate when completing clinical tasks. This construct should be shared, agreed upon and aligned with marking criteria to best guide examiner training and calibration. Achieving this integration may improve the accuracy and consistency of examiner judgements and reduce cognitive workload.

The study aimed to examine educational funding systems and quality service delivery in the University of Calabar, Calabar, Cross River State, Nigeria. To achieve the aim of this study, two research questions were raised and two hypotheses were formulated to guide the study. A survey research design was adopted for the study. A total sample of 116 Heads of Department in the University of Calabar, Calabar, in Cross River State was selected for the study. The selection was done through the census sampling technique. The instrument used for the data collection was a questionnaire titled: Educational Funding Systems and Quality Service Delivery Questionnaire (EFSQSDQ) was developed by the researchers and was validated by experts in administration in higher education and measurement and evaluation. Data collected were subjected to statistical analysis using simple linear regression analysis (r) of the Statistical Package for Social Science (SPSS) version 25. The result revealed that quality service delivery in universities is related to educational funding systems. It was recommended that management of the universities should make adequate provision for effective funding of the university in order to encourage quality service delivery in the university.

BackgroundMental workload is an abstract concept that perceives cognition as the brain having a small and finite capacity to process information, with high levels of workload associated with poor performance and error. While an individual may be able to complete two different tasks individually, a combination of tasks may lead to cognitive overload and poor performance. In many high-risk industries, it is common to measure mental workload and then to redesign tasks until cognitive overload is avoided. This study aimed to measure the effect of multitasking on the mental workload and performance of medical students completing single and combined clinical tasks.MethodsMedical students who had completed basic clinical skills training in a single undergraduate Medical School completed four standardised tasks for a total of four minutes each, consisting of: inactivity, listening, venepuncture and a combination of listening and venepuncture. Task performance was measured using standard binary checklists and with mental workload measured using a secondary task method.ResultsThe tasks were successfully completed by 40 subjects and as expected, mental workload increased with task complexity. Combining the two tasks showed no difference in the associated mental workload and performance at venepuncture (p = 0.082) However, during the combined task, listening appeared to deteriorate (p < 0.001).ConclusionsIf staff are expected to simultaneously complete multiple tasks then they may preferentially shed communication tasks in order to maintain their performance of physical tasks, leading to the appearance of poor communication skills. Although this is a small-scale study in medical students it suggests that the active assessment and management of clinician workload in busy clinical settings may be an effective strategy to improve doctor-patient communication.

Multiple-choice questions (MCQs) are a common form of assessment in medical science education. The traditional MCQ format involves students picking a single best answer (SBA) from four or five options. There are concerns about the ability of SBA formats to reward partial knowledge and their susceptibility to guessing. An alternative to SBA is elimination testing (ET), wherein students eliminate all the incorrect answer options, with negative marking to deter guessing. Cognitive load theory (CLT) is an approach to education that prioritises strategies to minimise the amount of unnecessary ‘load’ placed upon working memory. The cognitive load imposed by assessment design has received little attention. We evaluated the cognitive load of SBA and ET MCQ formats, using an online participant pool and a survey of students at a UK Medical School. We found that partial knowledge was rewarded with the ET format. However, students strongly preferred the SBA format and reported both that it was easier and imposed a lower cognitive load. Removing negative marking reduced the cognitive load of ET style questions and improved the student experience, but the improvement was insufficient to change student preference for SBA questions.

Self Esteem as a Factor in the Mathematics Anxiety of Grade Six Pupils of Santolan Elementary School

Introduction Self-esteem refers to one’s positive or negative attitude towards oneself and the evaluation of one’s thoughts and feelings overall about oneself. It is a vital element in a person’s life as it affects the overall human behaviours and relationships. The level of self-esteem has an impact on the way one accepts responsibilities, their communication skills, and the way one cooperates with others, human response to stressful life events, and how to deal with positive and negative emotions. Purpose: To assess levels of self-esteem and demographic factors associated with self-esteem levels among undergraduate nursing students of Mbarara University of Science and Technology. Methodology The study used a cross-sectional study design and recruited 139 students who are undergraduate nurses at Mbarara University of Science and Technology. The self-esteem of the participants was measured using the Rosenberg Self-esteem Scale (RSES), and data were analyzed using SPSS version 23. Results 59.70% of the participants had moderate levels of self-esteem, 40.3% had low self-esteem, and no one had high self-esteem. Of all the assessed demographic factors, age, sex, year of study, program of study, marital status, sponsorship program, and religion, year of study was the only factor significantly associated with levels of self-esteem. Conclusion There are no undergraduate nursing students with high self-esteem at Mbarara University of Science and Technology. Recommendations Additional research on self-esteem and associated demographic factors ought to be done using much larger populations so as to have enough data to make adequate statistical analysis, probably a study involving nursing students from different nursing institutions all at once. A qualitative study, as well as to further describe the relation between self-esteem and associated demographic factors, is key in further understanding this concept.

Purpose Educational assignments are pivotal for student learning, yet educators face the dual burden of creating meaningful questions and providing timely, individualized feedback. This study introduces IntelliA, a generative AI-powered platform designed to address these challenges by holistically automating the entire assignment lifecycle, from pedagogically aware question generation to concept-centric evaluation, thereby alleviating instructor workload and enhancing student learning outcomes. Design/methodology/approach IntelliA’s core innovation is a synergistic architecture that operationalizes pedagogical principles directly into the AI-driven assignment workflow. For question generation, its pedagogical primitive-driven system allows educators to design assessments at a conceptual level – by selecting instructional goals – rather than manually crafting prompts. For evaluation, a novel three-stage, rubric-driven methodology is designed to produce pedagogically valuable feedback, grounding its supportive guidance in objective, evidence-based scoring. These subsystems are unified by a unified quality augmentation module, creating a self-reinforcing loop that continuously refines the system’s pedagogical alignment. This architecture’s ability to enhance both educational quality and operational efficiency was rigorously validated through a semester-long university deployment, assessed via a multi-faceted framework of instructor interviews, student surveys and cross-model benchmarking. Findings The results demonstrate that IntelliA significantly reduces assignment creation time for instructors while maintaining high pedagogical quality in generated questions. Student surveys revealed high satisfaction with the grading accuracy and the personalized, actionable feedback, which they reported as instrumental to their learning. The findings confirm that an architecturally sophisticated AI system can successfully move beyond simple automation to enhance both operational efficiency and educational quality. Research limitations/implications Our study’s validation was conducted within a single university STEM course, which limits the generalizability of our findings. Future research should expand the library of pedagogical primitives to diverse domains (e.g. humanities and K-12) and validate their effectiveness in these new contexts. Additionally, a more rigorous, controlled experiment is needed to quantitatively measure efficiency gains in terms of time-on-task and cognitive load. This work signals a shift in AI-in-education research, moving from single-task automation toward designing synergistic, architecturally aware systems. Practical implications IntelliA offers a practical, scalable solution for educators to significantly reduce the workload associated with assignment creation and feedback personalization. By automating routine tasks, the platform empowers instructors to shift their focus from content generation to higher-value pedagogical activities, such as refining learning objectives and providing nuanced student support. This model demonstrates how AI can be integrated into educational workflows as a collaborative partner, enhancing both operational efficiency and the quality of pedagogical practice. Social implications The widespread adoption of synergistic AI systems like IntelliA could democratize access to high-quality, personalized learning support, potentially narrowing educational equity gaps. However, it also raises critical social questions about the future of the teaching profession and the risk of over-reliance on automation. The key social implication is the need for a public discourse on responsible AI integration in education, ensuring that technology serves to amplify educator agency and enhance human-centric learning, rather than deskilling teachers or creating uniform, passive learning pathways. Originality/value This study presents a significant leap forward by proposing a novel, synergistic system architecture for AI in education. Unlike prior systems that address isolated tasks, IntelliA offers a holistically integrated and self-improving ecosystem. By demonstrating how to embed pedagogical principles directly into an AI’s core logic, IntelliA provides a scalable and flexible blueprint for a new generation of educational tools. This approach moves beyond simple automation, fostering a new dynamic where AI functions not as a mere instrument but as a genuine partner in the pedagogical process.