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Novice programmers often face challenges in designing computational artifacts and fixing code errors, which can lead to task abandonment and over-reliance on external support. While research has explored effective meta-cognitive strategies to scaffold novice programmers' learning, it is essential to first understand and assess students' conceptual, procedural, and strategic/conditional programming knowledge at scale. To address this issue, we propose a three-model framework that leverages Large Language Models (LLMs) to simulate, classify, and correct student responses to programming questions based on the SOLO Taxonomy. The SOLO Taxonomy provides a structured approach for categorizing student understanding into four levels: Pre-structural, Uni-structural, Multi-structural, and Relational. Our results showed that GPT-4o achieved high accuracy in generating and classifying responses for the Relational category, with moderate accuracy in the Uni-structural and Pre-structural categories, but struggled with the Multi-structural category. The model successfully corrected responses to the Relational level. Although further refinement is needed, these findings suggest that LLMs hold significant potential for supporting computer science education by assessing programming knowledge and guiding students toward deeper cognitive engagement. 

Abstract Music and computer science (CS) have profound historical and structural connections, with programming music offering a promising avenue for engaging children in CS through creative expression. To foster this engagement, our team developed M-Flow, a flow-based music programming platform designed to introduce students to CS via music. Despite extensive existing research in music and CS education, experience reports and empirical studies on K-12 teachers' implementation and its impact on young kids' learning are limited. Therefore, we recruit elementary school teachers and students with no or limited prior programming experience, introducing them to M-Flow and its curriculum through a professional development workshop, a semester's job embedded support, and classroom implementation. We describe the experiences of teachers as they attempt to integrate music and CS, the challenges they face, and the influence on students' attitudes toward learning computing concepts. Specifically, we reflect on our intervention by conducting a sequential mixed-method evaluation. During the qualitative phase, we collected multiple sources of data from three teachers through focus groups and debriefings after a semester of classroom implementation. Thematic analysis of workshop activities, interviews, and debrief videos revealed three themes with seven sub-themes on teachers' integration of flow-based music programming and two themes with five sub-themes on challenges faced by the teachers. In the quantitative phase, we gathered data on attitudes and self-efficacy from 75 students taught by these teachers. Results indicate that the flow-based music programming environment provided an engaging programming experience for students and significantly increased their self-efficacy towards learning programming. 

Question-asking is a crucial learning and teaching approach. It reveals different levels of students' understanding, application, and potential misconceptions. Previous studies have categorized question types into higher and lower orders, finding positive and significant associations between higher-order questions and students' critical thinking ability and their learning outcomes in different learning contexts. However, the diversity of higher-order questions, especially in collaborative learning environments. has left open the question of how they may be different from other types of dialogue that emerge from students' conversations, To address these questions, our study utilized natural language processing techniques to build a model and investigate the characteristics of students' higher-order questions. We interpreted these questions using Bloom's taxonomy, and our results reveal three types of higher-order questions during collaborative problem-solving. Students often use Why, How and What If' questions to I) understand the reason and thought process behind their partners' actions: 2) explore and analyze the project by pinpointing the problem: and 3) propose and evaluate ideas or alternative solutions. In addition. we found dialogue labeled 'Social'. 'Question - other', 'Directed at Agent', and 'Confusion/Help Seeking' shows similar underlying patterns to higher-order questions, Our findings provide insight into the different scenarios driving students' higher-order questions and inform the design of adaptive systems to deliver personalized feedback based on students' questions. 

K-12 Computer Science (CS) education has seen remarkable growth recently, driven by the increasing focus on CS and Computational Thinking (CT) integration. Despite the abundance of Professional development (PD) programs designed to prepare future CS teachers with the required knowledge and skills, there is a lack of research on how teachers' perceptions and attitudes of CS and CT evolve before and after participating in these programs. To address this gap, our exploratory study aims to study the dynamics of pre-and in-service teachers' experiences, attitudes, and perceptions towards CS and CT through their participation in a K-12 CS education micro-credential program. In this study, we employed topic modeling to identify topics that emerged from teachers' written pre- and post-CS autobiographies, conducted statistical analysis to explore how these topics evolve over time and applied regression analysis to investigate the factors influencing these dynamics. We observed a shift in teachers' initial feelings of fear, intimidation, and stress towards confidence, fun, and feeling competent in basic CS, reflecting a positive transformation. Regression analysis revealed that features, such as experienced teacher status and CT conceptual understanding, correlate with participants' evolving views. These observed relationships highlight the micro-credential's role in not only enhancing technical competency but also fostering an adaptive, integrative pedagogical mindset, providing new insights for course design. 

As artificial intelligence (AI) becomes more prominent in children’s lives, an increasing number of researchers and practitioners have underscored the importance of integrating AI as learning content in K-12. Despite the recent efforts in developing AI curricula and guiding frameworks in AI education, the educational opportunities often do not provide equally engaging and inclusive learning experiences for all learners. To promote equality and equity in society and increase competitiveness in the AI workforce, it is essential to broaden participation in AI education. However, a framework that guides teachers and learning designers in designing inclusive learning opportunities tailored for AI education is lacking. Universal Design for Learning (UDL) provides guidelines for making learning more inclusive across disciplines. Based on the principles of UDL, this paper proposes a framework to guide the design of inclusive AI learning. We conducted a systematic literature review to identify AI learning design-related frameworks and synthesized them into our proposed framework, which includes the core component of AI learning content (i.e., five big ideas), anchored by the three UDL principles (the “why,” “what,” and “how” of learning), and six praxes with pedagogical examples of AI instruction. Alongside this, we present an illustrative example of the application of our proposed framework in the context of a middle school AI summer camp. We hope this paper will guide researchers and practitioners in designing more inclusive AI learning experiences. 

As conversational AI apps such as Siri and Alexa become ubiquitous among children, the CS education community has begun leveraging this popularity as a potential opportunity to attract young learners to AI, CS, and STEM learning. However, teaching conversational AI to K-12 learners remains challenging and unexplored due in part to the abstract and complex nature of some conversational AI concepts, such as intents and training phrases. One promising approach to teaching complex topics in engaging ways is through unplugged activities, which have been shown to be highly effective in fostering CS conceptual understanding without using computers. Research efforts are underway toward developing unplugged activities for teaching AI, but few thus far have focused on conversational AI. This experience report describes the design and iterative refinement of a series of novel unplugged activities for a conversational AI summer camp for middle school learners. We discuss learner responses and lessons learned through our implementation of these unplugged activities. Our hope is that these insights support CS education researchers in making conversational AI learning more engaging and accessible to all learners. 

Summer camps have become popular for introducing K-12 learners to computer science (CS) and artificial intelligence (AI) in informal learning environments. Facilitators play crucial roles in guiding and engaging learners in these contexts, but there is limited research on their roles in informal AI learning. This paper examines facilitators’ dialogues with campers in a middle school AI summer camp, identifying eight major facilitator roles. The roles differed depending on group dynamics and project phase. The paper provides empirical grounding to define facilitators’ roles in AI learning and guide the design of professional development for camp facilitators. 

Intelligent systems to support collaborative learning rely on real-time behavioral data, including language, audio, and video. However, noisy data, such as word errors in speech recognition, audio static or background noise, and facial mistracking in video, often limit the utility of multimodal data. It is an open question of how we can build reliable multimodal models in the face of substantial data noise. In this paper, we investigate the impact of data noise on the recognition of confusion and conflict moments during collaborative programming sessions by 25 dyads of elementary school learners. We measure language errors with word error rate (WER), audio noise with speech-to-noise ratio (SNR), and video errors with frame-by-frame facial tracking accuracy. The results showed that the model’s accuracy for detecting confusion and conflict in the language modality decreased drastically from 0.84 to 0.73 when the WER exceeded 20%. Similarly, in the audio modality, the model’s accuracy decreased sharply from 0.79 to 0.61 when the SNR dropped below 5 dB. Conversely, the model’s accuracy remained relatively constant in the video modality at a comparable level (> 0.70) so long as at least one learner’s face was successfully tracked. Moreover, we trained several multimodal models and found that integrating multimodal data could effectively offset the negative effect of noise in unimodal data, ultimately leading to improved accuracy in recognizing confusion and conflict. These findings have practical implications for the future deployment of intelligent systems that support collaborative learning in actual classroom settings.