Drill-type adaptive debugging to boost programming skills

This article presents a theoretical framework for enhancing manufacturing productivity and safety by integrating cognitive ergonomics and artificial intelligence (AI). The framework addresses the critical gap in current research for a holistic approach to managing cognitive workload by proposing an integrated approach to managing cognitive workload and individual differences in modern manufacturing contexts. Despite the growing recognition of cognitive ergonomics, existing frameworks often fail to provide a unified model that incorporates AI and adaptive systems to optimize worker performance without negatively impacting mental workload or well-being. This paper proposes a comprehensive five-step theoretical framework integrating cognitive ergonomics principles, AI, and adaptive systems to optimize worker performance and productivity in manufacturing environments. The framework begins with assessing manufacturing operations and evaluates mental resource allocation to prevent overload. It optimizes cognitive and physical workload through real-time monitoring, integrates AI for dynamic task allocation, and establishes continuous feedback loops to adapt task demands, reduce errors, and enhance safety. This paper aims to contribute to developing predictive models for cognitive workload and tailored technologies for cognitive load reduction, filling a significant gap in existing research. The proposed framework provides a standardized approach to improving manufacturing operations by combining cognitive ergonomics with adaptive automation systems. Ultimately, it aims to promote worker well-being and performance, ensuring that AI and adaptive systems complement, rather than detract from, cognitive capacity while reducing cognitive workload, mental pressure, and performance declines in high-workload environments.

This study investigates the integration of adaptive e-learning and gamification through a platform called NgodingSeru.com to improve problem-solving skills in programming among vocational high school students. The adaptive system offers personalized learning by adjusting task difficulty to student’s proficiency levels, while gamification elements such as missions, points, rankings, and rewards aim to sustain student engagement. A mixed-methods design was applied using an explanatory sequential approach, combining quantitative and qualitative data. Quantitative findings show a statistically significant improvement in student’s problem-solving performance, with average scores increasing from 61.7 to 74.2—an improvement of approximately 20.2%. The effect size was large, with a Cohen’s d of 0.90, indicating a substantial practical impact of the adaptive gamification-based e-learning system. The qualitative phase revealed that students perceived the system as motivating, enjoyable, and supportive of their learning needs. Key gamification features and adaptive feedback mechanisms contributed significantly to student’s engagement and persistence in completing programming challenges. In particular, case-based challenges and logic-based problems effectively strengthen student’s analytical thinking and the application of programming concepts. These results highlight the effectiveness of integrating adaptive and gamified learning environments in fostering essential problem-solving skills in programming education.

Although there have been previous publications on curriculum innovations in teaching O&G to medical students, especially utilizing simulation-based education, there have been none, as far as we know, incorporating and evaluating the outcomes using cognitive load theory. The aim of this article was to describe the introduction, implementation, and evaluation of an innovative teaching program in O&G, incorporating simulation-based education, underpinned by cognitive load theory. Cognitive load is defined as the amount of information a working memory can hold at any one time and incorporates three types of cognitive load-intrinsic, extraneous, and germane. To optimize learning, educators are encouraged to manage intrinsic cognitive load, minimize extraneous cognitive load, and promote germane cognitive load. In these sessions, students were encouraged to prepare in advance of each session with recommended reading materials; to limit intrinsic cognitive load and promote germane cognitive load, faculty were advised ahead of each session to manage intrinsic cognitive load, an open-book MCQ practice session aimed to reduce anxiety, promote psychological safety, and minimize extraneous cognitive load. For the simulation sessions, the faculty initially demonstrated the role-play situation or clinical skill first, to manage intrinsic cognitive load and reduce extraneous cognitive load. The results of the evaluation showed that the students perceived that they invested relatively low mental effort in understanding the topics, theories, concepts, and definitions discussed during the sessions. There was a low extraneous cognitive load. Measures of germane cognitive load or self-perceived learning were high. The primary message is that we believe this teaching program is a model that other medical schools globally might want to consider adopting, to evaluate and justify innovations in the teaching of O&G to medical students. The secondary message is that evaluation of innovations to teaching and facilitation of learning using cognitive load theory is one way to contribute to the high-quality training of competent future healthcare workers required to provide the highest standard of care to women who are crucial to the overall health and wellbeing of a nation.

Keyword recognition represents a fundamental skill in programming, yet little research has examined how novices develop this ability or how language background affects keyword learning. This study investigated cognitive load and keyword recognition accuracy amongst 27 novice programming students (15 English as an additional language [EAL] and 12 English as a native language [ENL]) during an intensive six-week Python course. Students completed a keyword recognition task at Weeks 1 and 6, identifying and classifying 23 Python keywords while reporting cognitive load using the Klepsch instrument. The results revealed no significant improvement in identification accuracy (Week 1: 39.80%; Week 6: 48.16%) or classification accuracy (40% at both time points) despite intensive instruction. The reported extraneous cognitive load significantly increased from Week 1 to Week 6 (p = 0.039, d = 0.99), contradicting Cognitive Load Theory predictions that schema automation reduces extraneous load with experience. EAL students reported a significantly higher intrinsic cognitive load (p = 0.030, d = 0.91) and a marginally lower keyword identification accuracy (p = 0.058, d = −0.54) than ENL students. All students (100%) who identified keywords also missed duplicate instances, indicating universal incomplete processing. These findings challenge assumptions about schema development timelines in programming education and document measurable linguistic barriers that persist even after substantial instruction, with implications for inclusive computing pedagogy.

In today's project-based work environment, project managers are expected to manage multiple projects at once, which increases the level of multitasking and creates cognitive difficulties. This research, based on Cognitive Load Theory (CLT) explores the effects of multi-tasking across multiple projects on cognitive load, decision-making efficiency and project performance of project managers in Pakistan. This includes cognitive load as a mediator and task complexity as moderator. The study used a quantitative cross-sectional approach and surveyed project managers from various industries in Pakistan such as information technology, construction, telecommunications, banking and development sectors. Through survey instruments and statistical techniques (SPSS and Structural Equation Modeling - SEM), the results indicated that multitasking significantly contributes to cognitive load and this, in turn, negatively impacts decision-making efficiency and project performance. Further analysis showed that cognitive load mediated the multitasking-managerial outcomes link, and task complexity had a significant moderating effect on the multitasking-cognitive load link. These results offer robust empirical evidence for Cognitive Load Theory and task switching research, suggesting the cognitive effects of managing multiple projects. This research advances the understanding of project management by bridging the gap between cognitive theories and managerial practices and has implications for organizations in terms of managing workloads, managerial performance and project performance. The findings stress the need for project management structures in line with cognitive abilities for optimal productivity and success.

The acquisition of programming skills is often complex and poses challenges that impede students’ progress and understanding. This study aimed to assess the effectiveness of the flipped project-based learning (FPBL) model, implemented via Moodle LMS, in enhancing students’ communication and problem-solving abilities in programming education. The study employed a quasi-experimental design with a control group following a blended learning approach and an experimental group utilizing the FPBL model. Purposive sampling was used. Data collection involved pre- and post-tests assessing communication and problem-solving skills, analyzed through paired and independent sample t-tests to evaluate the significance of the observed improvements. These findings demonstrated significantly positive outcomes in the experimental group. For problem-solving skills, the paired sample t-test showed a mean difference of 16.000, a t-value of 5.852, and a significance level of 0.000. Communication skills analysis revealed a mean difference of 7.400, a t-value of 10.418, and a significance level of 0.000. Independent sample t-tests corroborated these results, indicating notable enhancements in both skills in the experimental group compared to the control group. he FPBL model based on the Moodle LMS markedly improved students’ communication and problem-solving skills in programming education. Future research should investigate the long-term retention of these skills and the applicability of the model across different disciplines and educational levels.

Hypothesis A common belief in healthcare simulation is that higher realism results in improved learning.1-2 Predicated on the concept of context dependency (CD),3 proponents of this view advocate for simulations that recreate the contextual realities of clinical practice,4 arguing such environments help learners develop, coordinate and transfer constituent skills from simulation to clinical care.5 However, the evidence supporting this belief is mixed.6 A potential explanation for these disparate findings is that although higher realism may improve transfer through the CD effect, for novices it may also lead to increased task complexity. In turn, higher complexity may increases novices’ cognitive load (CL) to a point that is detrimental for learning, resulting in reduced transfer.7 The purpose of this study was to test these competing hypotheses by examining the effect of scenario complexity and CD on performance and CL during procedural skill acquisition, retention and transfer. Methods Novice medical students (n = 38) were randomly assigned to training on a simple or complex Lumbar Puncture (LP) simulation (Fig 1A and 1B), consisting of four practice trials interspersed with controlled feedback (acquisition phase). After 10 days, novices completed one trial on their training scenario (retention phase). Finally, to test the competing effects of CD vs. high task complexity, novices completed one trial on a very complex hybrid simulation (Fig 1C)8 contextually similar to the complex scenario (transfer phase). On all trials, LP performance was assessed using a global rating scale (GRS)9 and number of sterility breaches, while CL was measured using subjective ratings of mental effort (SRME) and reaction time (RT) to a vibrotactile secondary task.10-11 Acquisition and retention scores were analyzed using mixed 2x4 and 2x2 ANOVA; transfer scores were analyzed using t-tests. Effect sizes are reported as Cohen’s f or d, p<0.05 was considered statistically significant. Results Both groups demonstrated improved LP performance and fewer sterility breaches from the beginning to the end of the acquisition phase (p = 0.001, f = 0.96 for GRS; p = 0.001, f = 0.50 for sterility) and maintained this at retention. Novices in the simple group demonstrated superior LP performance and fewer sterility breaches compared to those in the complex group during acquisition (p = 0.002, d = 1.13 for GRS; p = 0.001, d = 1.60 for sterility) and at retention (p = 0.001, d = 1.25 for GRS; p = 0.001, d = 1.72 for sterility). CL decreased faster for novices in the simple group during acquisition (p = 0.005, f = 0.36 for SRME and p = 0.011, f = 0.35 for RT) and the simple group maintained lower CL at retention (p = 0.001, d = 1.39 for SRME and p = 0.021, d = 0.81 for RT). On transfer to the very complex simulation scenario, no differences in CL or GRS scores were observed between the two groups, however the simple group continued to make fewer sterility breaches (p = 0.023, d = 0.80). Conclusion This study provides empirical data explaining why higher realism may result in equivocal transfer among novice learners. Lower task complexity was associated with improved procedural performance during skill acquisition and faster decline in CL, which was retained after a 1-week delay. However, with the exception of fewer sterility breaches, the benefits of training on a simpler scenario were eliminated upon transfer to a very complex hybrid simulation. The equivalent performance of both groups raises the possibility that both CD and CL can impact transfer of procedural skills during simulation-based training. These competing effects should be balanced by simulation instructional designers, for instance by using a progressive sequence of training that reduces cognitive load in the early phases of learning and exposes the learner to more complex scenarios later in training.[5]

The effectiveness of gamification in programming education: Evidence from a meta-analysis

Online courses are becoming ubiquitous and increasingly tend to use authentic learning tasks as the driving force for teaching and learning. Nevertheless, designing online courses that incorporate real– world tasks is more challenging as these problems require more cognitive processes (van Merriënboer Sluijsmans, 2009). This phenomenon can be explained by Cognitive Load Theory (CLT) introduced by Sweller (1994). CLT distinguishes three types of cognitive load: intrinsic, extraneous and germane load. The level of intrinsic load is assumed to be determined by the level of element interactivity. An element can be a definition, concept, formula and procedure that needs to be or has been learned. Extraneous load is mainly imposed by instructional procedures that are suboptimal, whereas germane load refers to the learners’ working memory resources available to deal with the complexity of the task or learning material (Sweller, 2010). Accordingly, the experienced cognitive load is mainly dependent of students’ prior knowledge. Nevertheless, cognitive load can also be determined by students’ motivation (Feldon, Franco, Chao, Peugh, Maahs-Fladung, 2018; Verhoeven, Schnotz, Paas, 2009). As a consequence, when designing an online course for complex tasks, it is important to understand how the different types of cognitive load are affected by students’ cognitive and motivational characteristics. Therefore, in the current study, a high and low complex task was developed relating to the learning and teaching of geometry. The complexity of the task was manipulated by increasing the element interactivity for the high complex task (Sweller, 2010). In the low complex task one element was questioned each time, and consequently students had to apply a single rule, formula or procedure. By contrast, the high complex task was based on a real-life context (e.g., teaching geometry), and had higher element interactivity. Subsequently, the high complex task required learners to engage in a series of cognitive activities such as analysing, decision making, implementing and evaluating, while holding several procedures and rules in mind. Accordingly, we expected the high complex task to induce more cognitive load. The same amount of support containing the same content, was provided during both tasks. Consequently, in this context, students could take initiative in diagnosing their learning needs by identifying appropriate support. Since students could consult different amounts of support, this self-directed learning strategy could also influence the perceived cognitive load (van Merriënboer Sluijsmans, 2009). Accordingly, the amount of consulted support was also taken into account during the analyses. The aim of the study was twofold. First, as a manipulation check of task complexity, we investigated differences in the experienced cognitive load while solving a high and low complex task. Secondly, we examined whether students’ cognitive and motivational characteristics influence the different types of perceived cognitive load, when taking into account the amount of consulted support for both the high and low complex task. A multivariate approach was chosen to assess the degree of interplay that may exist among students’ cognitive, motivational characteristics, consultation of support and the different types of perceived cognitive load. By conducting this study, we wanted to gain insight into whether the cognitive, motivational characteristics and consultation of support influence the perceived cognitive load differently for a high and low complex task.

Driving in monotonous road environments may cause drivers to become bored and impair their alertness. This inattention may increase the risk of road accidents. Drivers usually respond to visual signals on the road. A large body of research has shown that the level of a driver’s visual attention is closely related to traffic accidents. Visual attention is often studied by tracking drivers’ eye movements. However, few studies to date have explored eye-movement patterns in different road environments such as open roads and tunnel expressways. This dissertation aims to address that research gap by studying drivers’ eye movements and visual characteristics in different driving conditions in real and simulated environments. In the pilot study, drivers’ eye movement patterns were recorded during high-speed driving in tunnels and on expressways in Singapore. Twenty-two drivers participated in the study; they drove a total of 55 km, which included a 9-km section of tunnel. The results of the study showed that drivers experienced longer fixation duration when driving in tunnels than on the open road but experienced a higher number of fixations, higher instantaneous velocity, acceleration, and dispersion on open roads than in tunnels. These findings suggest that drivers’ eye movements are more concentrated and have longer fixation durations in tunnels than in open road driving, indicating that drivers have a greater mental workload when driving in tunnels. However, testing drivers in a real environment has limitations. For example, weather and road characteristics cannot be manipulated. To address this limitation, Experiment 1 was conducted in a driving simulator using virtual reality simulations in UC-win/road. Experiment 1 sought to investigate drivers’ eye movements in various driving environments. Eighteen drivers participated in the experiment and drove on normal roads, expressways, and through underground tunnels in both sunny and rainy weather. The comparisons of their eye-movement patterns (e.g., saccadic parameters and pupil size) in the simulated environments were consistent with those in real open-road environments and tunnels in sunny conditions. The findings suggest that the eye-movement patterns of drivers in a simulator are comparable to those in real environments, indicating its ecological validity. Previous literature has suggested a relationship between eye movement patterns and mental workload. To verify that relationship, mental workloads in Experiments 2 and 3 were directly measured using questionnaires. Dynamic and static hazardous scenarios were simulated to induce different levels of mental workload. Experiment 2 investigated eye movements, driving performance, and mental workload of drivers who encountered static and dynamic obstacles while manual driving. The results from 23 drivers found that their mental workload in dynamic conditions was higher than in static hazardous conditions; it was also higher before encountering hazards than afterwards. The results indicated that pupil size and saccade numbers are effective indicators of mental workload. Experiment 3 was conducted under conditionally automated driving conditions, where participants were involved in tasks related to or not related to driving and requested to resume control manually when their vehicle was in semi-automated driving mode. Drivers’ eye movements and mental workloads were measured when they were driving, especially during the transitions from semi-automated to manual driving. Similar to Experiment 2, the results from 32 drivers found that mental workload was higher for dynamic hazards than for static hazards and higher before hazards were encountered than after. The results also indicated that pupil size is the only effective indicator of mental workload. Taken together, the results of these experiments reveal that pupil size is the most reliable way to measure drivers’ mental workload. Furthermore, in line with the load theory of attention and De Waard (1996)’s theory of workload and performance, it can be concluded that mental workload (indicated by pupil size) and visual workload (by saccadic dispersion) are the key determinants of driving performance and that drivers can only respond to emergencies during periods of medium levels of mental and visual workload (not underload or overload) with available attentional capacity. The findings from this thesis on eye movement and mental workload may be used to monitor drivers’ real-time attention and workload and thus provide guidance for the design of safe-driving autonomous vehicles that consider drivers’ capacity for information processing when driving. Knowledge about the limitations and capacity of human drivers in tandem with automated systems may also help improve user acceptance of autonomous vehicles in the future.

As interactive systems in cars are on the rise, driver distraction emerges as an important issue for the automotive industry. When car drivers operate different devices while driving, less attention is attributed to the primary task of driving safely. Therefore, various systems are being created to improve driving security. One of them is an adaptive system that predicts the driver’s mental workload. Data from various origins can be taken into account to estimate the workload. There are physiological sensors (e.g. heart rate, skin temperature), car sensors (e.g. steering angle, rain sensors), data from the environment (e.g. traffic, weather) and real-time data on the condition of the road’s surface. This thesis presents a conceptual system for workload estimation based on a selection of various parameters. A field study with ten test persons was conducted to find out whether different driving environments (e.g. highway, inner-city roads, roundabouts) produce a measurably varying mental workload. For the measurements, various physiological data was collected. In addition, a video-rating served as a subjective measure for later comparison with the physiological data. According to the evaluated field data, the driver’s skin conductance, known as a reliable indicator for mental workload, correlates the most with the results of the video rating. Speed-limited roads (30 km/h) and roundabouts proved to be the driving environments provoking the highest mental workload. Based on this knowledge, a paper-prototype system was developed which takes into account the mental workload scores of the field test data. Several other data sources such as weather and traffic information were added as supplementary parameters to predict the driver’s mental workload. Finally, two cases are presented as examples of applying mental workload estimates to increase driving safety. In the first case, a prototype application adjusts the information density on the GPS navigation screen according to the predicted mental workload. The second case describes an adaptive communication system which restricts or blocks means of communication (e.g. incoming telephone calls) based on the driver’s estimated workload. By making use of such adaptive systems, driver distraction can be reduced leading to increased driving safety.

Purpose of the study. The aim of this study is to substantiate the criteria and factors for the quality of screen interface design for digital educational resources from the perspective of visual complexity and cognitive load, as well as to identify their impact on learning outcomes in a digital and bilingual educational environment. Particular attention is paid to establishing the relationship between the structure of the visual presentation of educational material, the level of learners’ cognitive load, and the effectiveness of knowledge acquisition. This goal is aimed at developing scientifically based recommendations for optimizing the design of digital educational resources, ensuring that the visual structure of educational content matches the cognitive capabilities of learners and improving learning effectiveness in a multilingual educational environment. Materials and methods. The study is based on an analysis of domestic and international scientific works in the field of perception psychology, cognitive ergonomics, cognitive load theory, instructional design, and bilingual education. The methodological framework utilized concepts of visual complexity, cognitive load theory, adaptive cognitive control principles, as well as the results of empirical studies conducted using behavioral methods and eye tracking. For the analytical section, typical screen pages of digital educational resources, varying in visual complexity, were examined. Interfaces were evaluated based on criteria such as quantitative richness, structural organization, color and graphic complexity, semantic richness, and dynamic characteristics. Results. The study found that the visual complexity of a screen interface directly impacts learners’ cognitive load. Increasing the number of elements, semantic density, color variability, and dynamic components leads to the growth of the external cognitive load, a decrease in the information retrieval speed, and an increase in the likelihood of errors. In bilingual learning, cognitive load becomes complex, integrating subject-specific and linguistic information processing. It has been shown that high proficiency in a second language reduces the load on working memory and executive control, while frequent code-switching simultaneously increases linguistic complexity and develops cognitive flexibility. A “visual complexity – cognitive load – performance” model is proposed, describing the cause-and-effect relationship between interface design and learning outcomes. Conclusion. The obtained results confirm the need for a systematic assessment of visual complexity and cognitive load in the design of digital educational interfaces. Well-founded evaluation criteria enable comparable and reproducible analysis of interface quality, as well as the development of design solutions that align with learners’ cognitive abilities. The proposed approach has high practical significance, as it can be used in the creation, examination, and adaptation of e-learning courses, multimedia lectures, and interactive educational platforms, ensuring improved e-learning effectiveness and the sustainability of educational outcomes in the digital environment.

Measurement and Management of Cognitive Load in Surgical Education: A Narrative Review

When designing simulation for novices, educators aim to design tasks and environments that are complex enough to promote learning but not too complex to compromise task performance and cause cognitive overload. This study aimed to determine the impact of modulating task and environment complexity on novices' performance and cognitive load during simulation. Second-year pharmacy students (N = 162) were randomly assigned to one of four conditions (2 × 2 factorial design) in simulation: simple task in simple environment, complex task in simple environment, simple task in complex environment and complex task in complex environment. Using video recordings, two raters assessed students' performance during the simulation. We measured intrinsic cognitive load (ICL) and extraneous cognitive load (ECL) with questionnaires after the task and tested knowledge after task and debriefing. Mean performance scores in simple environment were 28.2/32 (SD = 3.8) for simple task and 25.8/32 (SD = 4.2) for complex task. In complex environment, mean performance scores were 24.6/32 (SD = 5.2) for simple task and 25.6/32 (SD = 5.3) for complex task. We found significant interaction effects between task and environment complexity for performance. In simple environment, mean ICL scores were 4.2/10 (SD = 2.2) for simple task and 5.7/10 (SD = 1.5) for complex task. In complex environment, mean ICL scores were 4.9/10 (SD = 1.8) for simple task and 5.1/10 (SD = 1.9) for complex task. There was a main effect of task complexity on ICL. For ECL, we found neither an interaction effect nor main effects of task and environment complexity. There was a main effect of task complexity on knowledge test after task and main effects of both task and environment complexity on knowledge after debriefing. Performance was good, and cognitive load remained reasonable in all conditions, which suggests that, despite increased complexity, students seemed to strategically manage their own cognitive load and learn from the simulations. Our findings also indicate that environmental complexity contributes to ICL.

Purpose Construction workers process complex information, make decisions and coordinate tasks under deadlines. These cognitive demands can overwhelm workers, leading to errors and inefficiencies. While task complexity (TC) influences construction performance, prior research lacks a structured approach to assessing and managing cognitive load. This study introduces a scalable framework integrating cognitive load theory (CLT), Lean thinking and physiological metrics to evaluate TC and its impact on worker performance. Design/methodology/approach A design science research approach was used to assess TC and cognitive load in construction. Through literature reviews and expert consultations, a structured framework integrating cognitive load metrics and TC indicators was developed. The framework was validated through a controlled experiment simulating visual complexity using Object Speed (OS). A structural equation modeling (SEM) was developed to model TC as a latent construct using OS and cognitive load metrics while predicting performance errors. Findings The SEM model demonstrated relationships between TC, cognitive load and performance, confirming OS as a key determinant. The results support the framework’s ability to capture complexity-performance dynamics with high model fit indices and validate its use for interpreting cognitive responses to visual task variation. Research limitations/implications It supports human-centered task design to enhance productivity, safety and worker well-being. Future research should incorporate other complexity metrics and validate it in real-world construction. Originality/value This study applies CLT to construction and integrates TC concepts from behavioral science to provide structured TC assessment.