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Debugging process plays a crucial role in helping students pinpoint their specific learning weaknesses, allowing them to modify their strategies for enhanced academic performance. Notably, changes in pupil dilation serve as an indicator of arousal associated with confronting learning challenges. This physiological response acts as a “physiological footprint” that reflects cognitive engagement, facilitating internally focused cognitive processes such as insight generation and mind-wandering. In this study, we proposed that pupil dilation could be a valuable predictor of students’ metacognitive awareness throughout the debugging process, specifically within an augmented reality (AR) learning environment. The findings revealed significant differences in pupil dilation among students categorized by their varying levels of debugging, which represents a specific dimension of the Metacognitive Awareness Inventory. 

This study investigates the method for measuring cognitive workload in augmented reality-based biomechanics lectures by analyzing pupil dilation. Using Dikablis Glasses 3 and Microsoft HoloLens, we recorded physiological and subjective data across learning and problem-solving phases. Pupil dilation was normalized and segmented, enabling a comparison of cognitive demands between phases. The results indicated significant correlations between pupil dilation and NASA TLX cognitive demand, particularly in lectures that primarily involved procedural knowledge. These findings suggest that instructional design and content complexity have a significant impact on cognitive load, providing valuable insights for optimizing AR-based learning environments to support cognitive efficiency and student engagement. 

There is an increasing demand for developing new metrics that can effectively measure the physical demand experienced by users in augmented reality (AR) environments. In this study, we evaluated one of the recent metrics, called “slouching score,” in an AR-based biomechanics lecture. This study aims to uncover the correlation between the AR interaction and the physical demand of users in a different setup compared to the earlier study. The slouching score, which evaluates posture changes that may indicate fatigue during AR interactions, is measured using Xsens motion capture equipment. These calculated scores are compared with responses to physical demand assessments surveyed using NASA-TLX questionnaires. One of the key differences between the current study and earlier ones is that participants had to physically move to access the next AR module in earlier studies. In contrast, this time, participants simply needed to click a virtual arrow button to view the next AR modules, eliminating the need for physical movement. Our preliminary findings show correlations between the slouching score from some modules and the NASA-TLX physical demand ratings. 

In the context of student learning, investigating effective feedback mechanisms within augmented reality (AR) learning systems is crucial for better understanding and optimizing study behaviors. This study examines the influence of metacognitive monitoring feedback within an AR setting. Our hypothesis suggests that regularly providing students with feedback on their metacognitive monitoring within an AR learning environment has a beneficial effect on their metacognitive state. The results of the study confirm that frequent exposure to such feedback significantly improves scores on the Metacognitive Awareness Inventory. Essentially, there was a marked increase in the inventory scores of participants who received ongoing feedback, compared to those who only received metacognitive monitoring feedback once after the lecture, particularly in the areas of planning, monitoring comprehension, and debugging strategies. This enhancement is achieved by influencing student calibration by directly impacting their metacognitive state. 

This study explores the application of slouching scores to assess ergonomic posture in augmented reality (AR) environments. Employing Microsoft HoloLens 2 with Xsens motion capture technology, participants engaged in interactive biomechanics tasks, including a practical luggage-lifting exercise. Real-time feedback guided users towards safe posture, emphasizing spinal alignment and reducing physical strain. Slouching scores functioned as quantitative measures of posture quality, establishing a connection between unsafe postures and the requisite postural adjustments. The results illustrate how AR-integrated systems can enhance posture awareness, improve user ergonomics, and promote active learning in both educational and professional settings. 

With the growing need for augmented reality (AR) technology, understanding and optimizing study behaviors in AR learning environments has become crucial. However, one major drawback of AR learning is the absence of effective feedback mechanisms for students. To overcome this challenge, we introduced metacognitive monitoring feedback. Additionally, we created a location-based AR learning environment utilizing a real-time indoor tracking system to further enhance student learning. This study focuses on the positive impact of metacognitive monitoring feedback in a location-based AR learning environment. Our hypothesis posits that regularly providing students with feedback on their metacognitive monitoring within this new AR learning system positively influences their metacognitive awareness. The study's findings confirm that frequent exposure to such feedback significantly enhances the Metacognitive Awareness Inventory (MAI) scores. Participants who received continuous feedback demonstrated a significant increase in MAI scores compared to those who received feedback only once after the lecture. This improvement is achieved by influencing student calibration and directly enhancing their metacognitive awareness. 

Augmented Reality revolutionises education by enhancing learning with interactive, immersive experiences. However, the impact of long-term AR use, particularly in terms of physical demand, within educational environments remains poorly understood. This study investigates the relationship between AR engagement and physical demand, utilising motion capture technology, NASA Task Load Index, and HoloLens eye-tracking to quantify user posture, engagement, and perceived workload. We hypothesise that prolonged AR interaction results in a change in slouching scores, indicating increased fatigue. The results show a strong correlation between the slouching score and the NASA-TLX physical demand score. Our study lays the groundwork for incorporating predictive modelling to develop proactive physical demand measures. 

This study examines the ergonomic impact of augmented reality (AR) technologies in educational contexts, with a focus on understanding how prolonged AR engagement affects postural dynamics and physical demands on users. By analyzing slouching scores alongside NASA Task Load Index (TLX) Physical Demand (PD) values, we assess the physical strain experienced by participants during the initial modules of an AR-based lecture series. Our findings demonstrate a notable decline in slouching scores as participants progress through the lecture modules, indicating increased postural deviations. To quantify these effects, we developed a regression model that effectively predicts the physical demands imposed by various AR modules, based on the observed slouching scores. 

This research investigates fatigue’s impact on arm gestures within augmented reality environments. Through the analysis of the gathered data, our goal is to develop a comprehensive understanding of the constraints and unique characteristics affecting the performance of arm gestures when individuals are fatigued. Based on our findings, prolonged engagement in full-arm movement gestures under the influence of fatigue resulted in a decline in muscle strength within upper body segments. Thus, this decline led to a notable reduction in the accuracy of gesture detection in the AR environment, dropping from an initial 97.7% to 75.9%. We also found that changes in torso movements can have a ripple effect on the upper and forearm regions. This valuable knowledge will enable us to enhance our gesture detection algorithms, thereby enhancing their precision and accuracy, even in fatigue-related situations. 

This research aims to explore the prediction of student learning outcomes in Augmented Reality (AR) educational settings, focusing on engineering education, by analyzing pupil dilation and problem-solving time as key indicators. In this research, we have created an innovative AR learning platform through the incorporation of eye-tracking technology into the Microsoft HoloLens 2. This enhanced learning platform enables the collection of data on pupil dilation and problem-solving duration as students engage in AR-based learning activities. In this study, we hypothesize that pupil dilation and problem-solving time could be significant predictors of student performance in the AR learning environment. The results of our study suggest that problem-solving time may be a critical factor in predicting student learning success for materials involving procedural knowledge at low difficulty levels. Additionally, both pupil dilation and problem-solving time are predictive indicators of student learning outcomes when dealing with predominantly procedural knowledge at high difficulty levels.