With the increasing integration of Augmented Reality (AR) in education, learners can investigate scientific phenomena through embodied and interactive experiences, while collaborating in shared perceptual spaces. Although prior research has highlighted the conceptual and motivational benefits of AR, less is known about how students’ behavioral and emotional processes unfold during AR-supported collaboration. This study investigates 80 middle school students’ collaborative inquiry in an AR-based collaborative activity using a multimodal learning analytics approach. We combined video-based coding of verbal and non-verbal behaviors with audio-based emotion detection across three dimensions: arousal, dominance, and valence. Cluster analysis revealed three distinct collaboration patterns, which we further examined in relation to students’ emotional trajectories and learning outcomes. Findings show that groups in the reciprocal–balanced pattern engaged in active and coordinated behaviors, accompanied by more synchronized trajectories, and achieved higher learning gains. Dominant groups displayed high activity but uneven participation and imbalanced emotional dynamics. By contrast, passive–disengaged groups demonstrated limited coordination and unstable affect. This work advances understanding of how collaboration unfolds in AR-supported inquiry by linking behavioral and affective dimensions. Our results provide implications for the design of AR learning environments and analytics-driven supports for productive and emotionally balanced AR learning experiences.

Augmented Reality (AR) has demonstrated significant potential in enhancing inquiry-based learning in K-12 classrooms. However, challenges such as communication barriers and unequal participation during collaboration highlight the need for structured support when conducting AR-based collaborative learning activities. This study introduces collaboration scripts in AR-based collaborative inquiry settings to examine their effects on learning outcomes and student experiences. A quasi-experimental study was conducted with 78 sixth-grade students, divided into an experimental group using collaboration scripts and a control group engaging in unscripted AR inquiry. Key findings indicate that collaboration scripts significantly enhance knowledge acquisition and reduce cognitive load. Observation and interviews further explained the effect of collaborative scripts in facilitating a more structured and effective collaborative inquiry process. The results highlight the importance of well-defined scripts and clear guidelines in improving AR-based collaboration.

Despite extensive research on code plagiarism detection in higher education and for programming languages like Java and Python, limited work has focused on K-12 settings, particularly for pseudocode. This study aims to address this gap by building explainable machine learning models for pseudocode plagiarism detection in online programming education. To achieve this, we construct a comprehensive dataset comprising 7,838 pseudocode submissions from 2,578 high school students enrolled in an online programming foundations course, along with 6,300 pseudocode samples generated by three versions of generative pre-trained transformer (GPT) models. Utilizing this dataset, we develop an explainable model to detect AI-generated pseudocode across various assessments. The model not only identifies AI-generated content but also provides insights into its predictions at both the student and problem levels, thus enhancing our understanding of AI-generated pseudocode in K-12 education. Furthermore, we analyzed SHAP values and key features of the model to pinpoint student submissions that closely resemble AI-generated pseudocode. This research offers implications for developing robust educational technologies and methodologies to uphold academic integrity in online programming courses.

Preschool children have difficulty learning and comprehending abstract concepts, and the cognition of numbers has always been the key to mathematical enlightenment for young children. Our research aims to help preschool children build their cognition of cardinal and ordinal numbers, comprehend simple logical relationships, and master simple digital addition. We developed an Augmented Reality learning tool based on theories related to number cognition and a theoretical framework of software design for preschool children. We also conducted a teaching experiment in a kindergarten, and interviewed the teachers of the kindergarten to learn about their attitudes towards the application of AR in preschool education. Through data analysis, interviews, and discussions, we conclude that (a) AR application can positively influence children’s cognitive digital skills; (b) children have positive attitudes and positive evaluations toward AR application use, but there are some unavoidable problems in children’s attention allocation; (c) proficiency in operating AR tools has a large impact on children’s learning effects.

Augmented Reality (AR) has great potential in science education, and Collaborative Inquiry-based Learning (CIBL) in the AR environment is of great significance. However, there is a problem of low collaborative performance in technology-based CIBL. This study applied the strategy of role assignment to AR-based CIBL, aiming to explore the effect of role assignment on students’ collaboration. Forty-seven sixth-grade students in elementary school were randomly divided into Group A (without role assignment) and Group B (with role assignment) to participate in AR-based collaborative scientific inquiry activities. Data on students’ scientific knowledge achievement, attitudes toward science learning, cognitive load, and flow experience were collected. In addition, interviews were conducted to investigate students’ opinions on role assignments. It is found that the strategy of role assignment could significantly improve students’ science knowledge achievement. The interview results revealed how role assignments facilitate students’ collaboration from three aspects.