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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. 

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. 

Open-ended learning environments (OELEs) have become an important tool for promoting constructivist STEM learning. OELEs are known to promote student engagement and facilitate a deeper understanding of STEM topics. Despite their benefits, OELEs present significant challenges to novice learners who may lack the self-regulated learning (SRL) processes they need to become effective learners and problem solvers. Recent studies have revealed the importance of the relationship between students' affective states, cognitive processes, and performance in OELEs. Yet, the relations between students' use of cognitive processes and their corresponding affective states have not been studied in detail. In this paper, we investigate the relations between studentsż˝f affective states and the coherence in their cognitive strategies as they work on developing causal models of scientific processes in the XYZ OELE. Our analyses and results demonstrate that there are significant differences in the coherence of cognitive strategies used by high- and low-performing students. As a result, there are also significant differences in the affective states of the high- and low-performing students that are related to the coherence of their cognitive activities. This research contributes valuable empirical evidence on studentsż˝f cognitive-affective dynamics in OELEs, emphasizing the subtle ways in which students' understanding of their cognitive processes impacts their emotional reactions in learning environments.