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Recently, there has been a surge in developing curricula and tools that integrate computing (C) into Science, Technology, Engineering, and Math (STEM) programs. These environments foster authentic problem-solving while facilitating students’ concurrent learning of STEM+C content. In our study, we analyzed students’ behaviors as they worked in pairs to create computational kinematics models of object motion. We derived a domain-specific metric from students’ collaborative dialogue that measured how they integrated science and computing concepts into their problem-solving tasks. Additionally, we computed social metrics such as equity and turn-taking based on the students’ dialogue. We identified and characterized students’ planning, enacting, monitoring, and reflecting behaviors as they worked together on their model construction tasks. This study in-vestigates the impact of students’ collaborative behaviors on their performance in STEM+C computational modeling tasks. By analyzing the relationships between group synergy, turn-taking, and equity measures with task performance, we provide insights into how these collaborative behaviors influence students’ ability to construct accurate models. Our findings underscore the importance of synergistic discourse for overall task success, particularly during the enactment, monitoring, and reflection phases. Conversely, variations in equity and turn-taking have a minimal impact on segment-level task performance. 

Collaborative problem-solving (CPS) in STEM+C education involves cognitive coordination and emotional regulation during joint tasks. Prior research has examined discrete affective states in learning environments but less is known about how these emotions evolve over time and affect CPS behavior. This study investigates the temporal dynamics of five emotions—engagement, confusion, boredom, delight, and frustration—using Markov Chain analysis of data from high school pairs building computational models in the C2STEM environment. Emotional transitions aligned with cognitive processes, seen in interaction patterns like PLAY, ADJUST, and BUILD, to analyze affect during modeling. Results show that emotional trajectories closely relate to cognitive actions, including construction, simulation testing, and debugging. Transitions that maintained engagement linked to productive collaboration and stronger performance, while ongoing frustration and boredom indicated disengagement progress. 

Abstract Although the “eye-mind link” hypothesis posits that eye movements provide a direct window into cognitive processing, linking eye movements to specific cognitions in real-world settings remains challenging. This challenge may arise because gaze metrics such as fixation duration, pupil size, and saccade amplitude are often aggregated across timelines that include heterogeneous events. To address this, we tested whether aggregating gaze parameters across participant-defined events could support the hypothesis that increased focal processing, indicated by greater gaze duration and pupil diameter, and decreased scene exploration, indicated by smaller saccade amplitude, would predict effective task performance. Using head-mounted eye trackers, nursing students engaged in simulation learning and later segmented their simulation footage into meaningful events, categorizing their behaviors, task outcomes, and cognitive states at the event level. Increased fixation duration and pupil diameter predicted higher student-rated teamwork quality, while increased pupil diameter predicted judgments of effective communication. Additionally, increased saccade amplitude positively predicted students’ perceived self-efficacy. These relationships did not vary across event types, and gaze parameters did not differ significantly between the beginning, middle, and end of events. However, there was a significant increase in fixation duration during the first five seconds of an event compared to the last five seconds of the previous event, suggesting an initial encoding phase at an event boundary. In conclusion, event-level gaze parameters serve as valid indicators of focal processing and scene exploration in natural learning environments, generalizing across event types. 

This paper explores the design of two types of pedagogical agents—teaching and peer—in a collaborative STEM+C learning environment, C2STEM, where high school students learn physics (kinematics) and computing by building computational models that simulate the motion of objects. Through in-depth case study interviews with teachers and students, we identify role-based features for these agents to support collaborative learning in open-ended STEM+C learning environments. We propose twelve design principles—four for teaching agents, four for peer agents, and four shared by both—contributing to foundational guidelines for developing agents that enhance collaborative learning through computational modeling. 

Embodied learning represents a natural and immersive approach to education, where the physical engagement of learners plays a critical role in how they perceive and internalize concepts. This allows students to actively embody and explore knowledge through interaction with their environment, significantly enhancing retention and understanding of complex subjects. However, researchers face significant challenges in exploring children's learning in these physically interactive spaces, particularly due to the complexity of tracking multiple students' movements and dynamic interactions in real-time. To address these challenges, this paper introduces a Double Diamond design thinking process for developing an AI-enhanced timeline aimed at assisting researchers in visualizing and analyzing interactions within embodied learning environments. We outline key considerations, challenges, and lessons learned in this user-centered design process. Our goal is to create a timeline that employs state-of-the-art AI techniques to help researchers interpret complex datasets, such as children's movements, gaze directions, and affective states during learning activities, thereby simplifying their tasks and augmenting the process of interaction analysis. 

The incorporation of technology into primary and secondary education has facilitated the creation of curricula that utilize computational tools for problem-solving. In Open-Ended Learning Environments (OELEs), students participate in learning-by- modeling activities that enhance their understanding of (Science, technology, engineering, and mathematics) STEM and computational concepts. This research presents an innovative multimodal emotion recognition approach that analyzes facial expressions and speech data to identify pertinent learning-centered emotions, such as engagement, delight, confusion, frustration, and boredom. Utilizing sophisticated machine learning algorithms, including High-Speed Face Emotion Recognition (HSEmotion) model for visual data and wav2vec 2.0 for auditory data, our method is refined with a modality verification step and a fusion layer for accurate emotion classification. The multimodal technique significantly increases emotion detection accuracy, with an overall accuracy of 87%, and an Fl -score of 84%. The study also correlates these emotions with model building strategies in collaborative settings, with statistical analyses indicating distinct emotional patterns associated with effective and ineffective strategy use for tasks model construction and debugging tasks. These findings underscore the role of adaptive learning environments in fostering students' emotional and cognitive development.