This work-in-progress research paper aims to explore students' dropout behavior during video engagement in online learning platforms. As online learning becomes increasingly popular, analyzing how students engage with video content provides important insights into their learning behaviors. This study explores multiple factors influencing K-12 students' in-video dropout rates in online math education. We examined 34,666,481 log entries from Math Nation, covering 1313 videos and 14,251 students. Using survival analysis, we evaluated how 27 variables, including demographic details, video interaction behaviors, and video characteristics(e.g. length, category), affect in-video dropout. Our findings reveal that video length significantly predicts dropout, with each additional minute increasing the dropout rate by 1.26%. Videos with higher dropout rates often feature more frequent pauses, jumps, and rewatches. The study also highlights that the quality of video content, the creators of the videos, and how students interact with the videos are crucial factors affecting dropout rates. Further research is needed to determine the specific causes of video dropout.

This work-in-progress innovative practice paper builds on existing studies highlighting the significant lack of diversity in the field of artificial intelligence (AI),with a particular emphasis on the role of identity in shaping biases, inequalities, and ethical considerations within AI systems. As AI becomes increasingly integrated into society, it is essential to critically examine its impact on disabled individuals. This paper advocates for the adoption of an anti-ableist framework, emphasizing the inclusion of disability perspectives throughout the AI development and deployment processes. Central to this position is a critical examination of the AI identity ecosystem, which includes the creators of AI, the technologies they produce, and the societal implications of these technologies-all viewed through a lens that prioritizes disability rights and perspectives. We introduce a conceptual framework designed to center disability within the AI ecosystem, particularly in educational settings. This framework aims to teach students the importance of accessibility and anti-ableism in AI, equipping them with the tools to integrate these principles throughout their work. By promoting an anti-ableist approach and integrating accessibility education, this paper seeks to inform future policies and initiatives in human-centered AI. It contributes to the ongoing discourse on ethical AI, urging a reevaluation of how disability is integral to AI's identity and its future trajectory.

While virtual learning environments (VLEs) are widely used in K-12 education for classroom instruction and self-study, young students’ success in VLEs highly depends on their self-regulated learning (SRL) skills. Therefore, it is important to provide personalized support for SRL. One important precursor of designing personalized SRL support is to understand students’ SRL behavioral patterns. Extensive studies have clustered SRL behaviors and prescribed personalized support for each cluster. However, limited attention has been paid to the algorithm bias and fairness of clustering results. In this study, we “fairly” clustered the behavioral patterns of SRL using fair-capacitated clustering (FCC), an algorithm that incorporates constraints to ensure fairness in the assignment of data points. We used data from 14,251 secondary school learners in a virtual math learning environment. The results of FCC showed that it could capture six clusters of SRL behaviors in a fair way; three clusters belonging to high-performing (i.e., H-1. Help-provider, H-2) Active SRL learner, H-3) Active onlooker), and three clusters in low-performing groups (i.e., L-1) Quiz-taker, L-2) Dormant learner, and L-3) Inactive onlooker). The findings provide a better understanding of SRL patterns in online learning and can potentially guide the design of personalized support for SRL.

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.

As artificial intelligence permeates our lives through various tools and services, there is an increasing need to consider how to teach young learners about AI in a relevant and engaging way. One way to do so is to leverage familiar and pervasive technologies such as conversational AIs. By learning about conversational AIs, learners are introduced to AI concepts such as computers’ perception of natural language, the need for training datasets, and the design of AI-human interactions. In this experience report, we describe a summer camp curriculum designed for middle school learners composed of general AI lessons, unplugged activities, conversational AI lessons, and project activities in which the campers develop their own conversational agents. The results show that this summer camp experience fostered significant increases in learners’ ability beliefs, willingness to share their learning experience, and intent to persist in AI learning. We conclude with a discussion of how conversational AI can be used as an entry point to K-12 AI education.

Because of the structural parallelisms between music and computing, it has long been suggested that coding music could be a good way for young children to engage in and learn about computer science (CS). Despite these suggestions, coding music has not reached a wider audience of young children, and the approach’s potential to engage them has not been thoroughly demonstrated. To facilitate the adoption of coding music activities, we created M-flow, a flow-based programming platform that allows young children to code music intuitively from the outset. Then, we developed a standards-aligned curriculum that teachers applied in their fourth-grade classrooms. Surveys indicate that children were greatly engaged, the experience successfully exposed them to and increased their self-efficacy toward programming. Our results indicate that with the appropriate coding platform, coding music can be a powerful way to engage children in CS.

Mathematical discussions have become a popular educational strategy to promote math literacy. While some studies have associated math literacy with linguistic factors such as verbal ability and phonological skills, no studies have examined the relationship between linguistic synchrony and math literacy. In this study, we modeled linguistic synchrony and students’ math literacy from 20,776 online mathematical discussion threads between students and facilitators. We conducted Cross-Recurrence Quantification Analysis (CRQA) to calculate linguistic synchrony within each thread. The statistical testing result comparing CRQA indices between high and low math literacy groups shows that students with high math literacy have a significantly higher Recurrence Rate (RR), Number of Recurrence Lines (NRLINE), and the average Length of lines (L), but lower Determinism (DET) and normalized Entropy (rENTR). This result implies that students with high math literacy are more likely to share common words with facilitators, but they would paraphrase them. On the other hand, students with low math literacy tend to repeat the exact same phrases from the facilitators. The findings provide a better understanding of mathematical discussions and can potentially guide teachers in promoting effective mathematical discussions.

The ubiquity of AI-based conversational apps such as Siri, Alexa and Google Assistant means more young users are interacting with these apps. The increasing popularity of these conversational applications brings a potential opportunity to attract learners to AI, CS and STEM fields. CS Education researchers need to explore how to leverage this opportunity, in particular to serve learners who are underrepresented in CS and STEM. This experience report describes the design and iterative refinement of a series of two-week summer camps in which 62 predominantly Black students participated in hands-on AI-based learning experiences to design and develop their own conversational AI apps. We discuss the organization of this summer camp experience, including strategies for recruiting from and building trust within the target community, designing professional development for camp facilitators, structuring the camp activities, and encouraging projects that are personally and socially relevant. We share challenges and lessons learned from this AI summer camp in the hopes that they will inform other researchers and practitioners who are interested in designing and deploying similar experiences.