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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. 

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. 

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. 

In this study, we explore the impact of incorporating a virtual instructor with realistic lip-syncing in an augmented reality (AR) learning environment. The study is particularly focused on understanding if this enhancement can reduce students’ mental workload and improve system usability and performance in AR learning. The research stems from previous feedback indicating that a virtual instructor without facial movements was perceived as “creepy” and “distracting.” The updated virtual instructor includes facial animations, such as blinking and synchronized lip movements, especially during lecture explanations. The study aims to determine if there are significant changes in mental workload and usability differences between the AR systems with and without the enhanced virtual instructor. The study found significant differences in the usability scores in some questions. However, there was no significant difference in the mental workload between them. 

Augmented Reality (AR) technology offers the possibility of experiencing virtual images with physical objects and provides high quality hands-on experiences in an engineering lab environment. However, students still need help navigating the educational content in AR environments due to a mismatch problem between computer-generated 3D images and actual physical objects. This limitation could significantly influence their learning processes and workload in AR learning. In addition, a lack of student awareness of their learning process in AR environments could negatively impact their performance improvement. To overcome those challenges, we introduced a virtual instructor in each AR module and asked a metacognitive question to improve students’ metacognitive skills. The results showed that student workload was significantly reduced when a virtual instructor guided students during AR learning. Also, there is a significant correlation between student learning performance and workload when they are overconfident. The outcome of this study will provide knowledge to improve the AR learning environment in higher education settings. 

This study aims to develop an interactive learning solution for engineering education by combining augmented reality (AR), Near-Field Electromagnetic Ranging (NFER), and motion capture technologies. We built an instructional system that integrates AR devices and real-time positioning sensors to improve the interactive experience of learners in an immersive learning environment, while the motion, eye-tracking, and location-tracking data collected by the devices applied to learners enable instructors to understand their learning patterns. To test the usability of the system, two AR-based lectures were developed with different difficulty levels (Lecture 1 - Easy vs. Lecture 2 - Hard), and the System Usability Scale (SUS) was collected from thirty participants. We did not observe a significant usability difference between Lecture 1 and Lecture 2. Through the experiment, we demonstrated the robustness of this AR learning system and its unique promise in integrating AR teaching with other technologies. 

Being conscious of your thought processes is known as metacognition. It supports students in being more aware of their actions, motivations, and the potential applications of the skills [1]. This study investigates how different metacognitive judgment questions affect students’ metacognitive awareness in an augmented reality (AR) environment. The outcomes of this study will help us to understand what metacognitive monitoring method is more effective in the AR learning environment. According to the literature, students with high knowledge about cognition have higher test performance, while students with low regulation have a challenge during planning, organizing, and elaborating strategies. The dependent variables of the study are student learning performance and metacognitive awareness inventory (MAI) score, and one independent variable is the metacognitive judgment question Retrospective Confidence Judgment (RCJ) and Judgment of Learning (JOL). We hypothesized that the students with high performance would have improved MAI scores in both groups. The experiment was done with two groups (RCJ and JOL). Both groups responded to the pre-post metacognitive awareness inventory questionnaire. During the experiment, the MAI questionnaire was asked two times. In round one, the MAI questionnaire was asked at the beginning of lecture one; however, in round two, the questionnaire was asked at the end of lecture two. Results indicated significant differences in RCJ low performers. In RCJ, the participants whose performance was significantly reduced in lecture 2 had a higher improvement on MAI both regulation and knowledge about cognition. Overall, the result of our study could advance our understanding of how to design an advanced instructional strategy in an AR environment.