With the increasing prevalence of large language models (LLMs) such as ChatGPT, there is a growing need to integrate natural language processing (NLP) into K-12 education to better prepare young learners for the future AI landscape. NLP, a sub-field of AI that serves as the foundation of LLMs and many advanced AI applications, holds the potential to enrich learning in core subjects in K-12 classrooms. In this experience report, we present our efforts to integrate NLP into science classrooms with 98 middle school students across two US states, aiming to increase students' experience and engagement with NLP models through textual data analyses and visualizations. We designed learning activities, developed an NLP-based interactive visualization platform, and facilitated classroom learning in close collaboration with middle school science teachers. This experience report aims to contribute to the growing body of work on integrating NLP into K-12 education by providing insights and practical guidelines for practitioners, researchers, and curriculum designers.

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.

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.

Artificial Intelligence (AI) and Natural Language Processing (NLP) have become increasingly relevant across multiple fields, creating a necessity for young learners to understand these concepts. However, resources enabling learners to apply AI and NLP, particularly in middle school science, remain limited. To address this gap, we present the early development of NLP4Science, an interactive visualization application facilitating the integration of NLP concepts such as sentiment analysis and keyword extraction into middle school science. We adopted an iterative co-design process starting with a professional development workshop with four teachers, followed by a 2-day pilot study with 48 eighth graders, and concluding with a 5-day study involving 50 sixth graders. This poster presents an overview of NLP4Science, highlighting its key features, and sharing insights gained from the iterative design process, demonstrating the potential of NLP4Science to transform AI and NLP learning within middle school science classrooms.

Intelligent virtual agents have tremendous potential for facilitating collaborative learning by modeling and reinforcing desirable collaborative practices. Despite recent work in this area, the extent to which intelligent virtual agents can facilitate improvements in the collaborative behavior of children is largely unknown. This study employed a wizard-of-oz study design and investigated elementary children's collaborative behavior after interacting with virtual agents. These agents model exploratory talk for upper elementary school dyads, such as asking higher-order questions and listening to their partners. The findings uncover associations between elementary learner dyads' positive changes in collaboration after agent interventions, the dyads' affective reactions to interventions, and their attentiveness to the agents. Our results also reveal associations between positive changes in collaboration and the timing of interventions: for example, earlier interventions had a higher occurrence of positive changes, and positive changes in collaboration typically happened within five seconds of interventions. The results suggest ways in which intelligent virtual agents may be used to promote effective collaborative learning practices for children.

Collaborative problem solving has numerous benefits for learners, such as improving higher-level reasoning and developing critical thinking. While learners engage in collaborative activities, they often experience impasse, a potentially brief encounter with differing opinions or insufficient ideas to progress. Impasses provide valuable opportunities for learners to critically discuss the problem and re-evaluate their existing knowledge. Yet, despite the increasing research efforts on developing multimodal modeling techniques to analyze collaborative problem solving, there is limited research on detecting impasse in collaboration. This paper investigates multimodal detection of impasse by analyzing 46 middle school learners’ collaborative dialogue—including speech and facial behaviors—during a coding task. We found that the semantics and speaker information in the linguistic modality, the pitch variation in the audio modality, and the facial muscle movements in the video modality are the most significant unimodal indicators of impasse. We also trained several multimodal models and found that combining indicators from these three modalities provided the best impasse detection performance. To the best of our knowledge, this work is the first to explore multimodal modeling of impasse during the collaborative problem solving process. This line of research contributes to the development of real-time adaptive support for collaboration.

The COVID-19 pandemic has demonstrated that learning remotely is a crucial skill for K-12 students. However, remote instruction and collaboration bring a new set of challenges for these students, especially in the context of pair programming. An important goal for the CS education community is to understand these younger learners' experiences during remote programming activities. This experience report describes a three-day learning experience in which 18 middle school students engaged in remote pair programming activities by modeling scientific processes in a block-based programming language. After three remote pair programming sessions, we conducted individual interviews to understand middle school students' experiences during remote pair programming activities as well as comparing these new experiences to their previous co-located pair programming experiences. The results from these interviews suggest that the majority of the students (72%) enjoyed the remote activities despite many (55%) experiencing some form of technical difficulty. The interviews revealed important opportunities and challenges that being remote brought to pair programming within themes of changes in communication and focus, pair programming dynamics, and available resources. Students also identified issues with remote collaboration such as technical difficulties from software that impaired their ability to work and to communicate. These observations inform new efforts to adapt CS education to the increased demand for remote collaborative work and reveal patterns that may increase success in this new work style.

The COVID-19 pandemic shifted many U.S. schools from in-person to remote instruction. While collaborative CS activities had become increasingly common in classrooms prior to the pandemic, the sudden shift to remote learning presented challenges for both teachers and students in implementing and supporting collaborative learning. Though some research on remote collaborative CS learning has been conducted with adult learners, less has been done with younger learners such as elementary school students. This experience report describes lessons learned from a remote after-school camp with 24 elementary school students who participated in a series of individual and paired learning activities over three weeks. We describe the design of the learning activities, participant recruitment, group formation, and data collection process. We also provide practical implications for implementation such as how to guide facilitators, pair students, and calibrate task difficulty to foster collaboration. This experience report contributes to the understanding of remote CS learning practices, particularly for elementary school students, and we hope it will provoke methodological advancement in this important area.

Discussion forums are invaluable resources when scaling up undergraduate CS courses to larger class sizes. However, passive incorporation of discussion forums is not a silver bullet, as these platforms tend to devolve into places of shallow engagement. To aid our understanding of the factors that influence the nature of these interactions, we collected data from three CS1/CS2 forums. We obtained survey responses from the course instructors and performed a content analysis of the question-response pairs across all the courses. The results suggest that students' help-seeking patterns are influenced by the course curriculum, mode of delivery, and the existence of other help-seeking avenues. The findings also shed light on common strategies used by instructors to incentivize productive student-teaching staff and student-student interactions (e.g., instructing students to describe their debugging questions in detail, asking teaching staff to respond with hints/questions instead of direct answers). This poster presents a series of takeaways that can inform CS educators' choices around discussion forums.

More young people are interacting with smart conversational agents such as Alexa and Google Assistant. These platforms are extensible, providing, in principle, a compelling opportunity for young users to create and tinker with their own conversational agents. However, to date the interfaces for conversational app development are adult-focused. This paper presents the early design process for AMBY (AI Made by You), which we are building to empower young learners to create their own conversational agents. We first conducted a contextual inquiry with 14 middle school students (aged 11-13) in an AI summer camp, followed by two other usability studies. The system design has been refined after each study. Key features of AMBY include a visual dialogue management panel, testing panel with a diverse avatar, and a voice input modality. AMBY is designed to serve as a pedagogically-robust resource for K-12 AI education and as an engaging and creative way for middle schoolers to explore AI.

In recent years, there has been a dramatic increase in teaching CS in the context of other disciplines such as science. However, learning CS in an interdisciplinary context may be particularly challenging for students. An important goal for CS education researchers is to develop a deep understanding of the student experience when integrating CS into science classrooms in K-12. This paper presents the results of a mixed-methods study in which 75 middle school students engaged in a series of computationally rich science activities by creating simulations and models in a block-based programming language. After two semesters, students reported their experiences on in-class computer science activities through reflection essays. The quantitative results show that both experienced and novice students increased their CS knowledge significantly after several weeks, and a majority of students (72%) had positive sentiment toward the integration of CS into their science class. Deeper qualitative analysis of students' reflections revealed positive themes centered around the visualization and gamification of science concepts, the hands-on nature of the coding activities, and showing science from a different angle. On the other hand, students expressed negative sentiments on weaknesses in the activity design, lack of CS/science background/interest, and failing to make connections between CS and science concepts. These findings inform efforts to infuse CS education into different disciplines and reveal patterns that may foster success of K-12 classroom implementations.

As efforts to integrate computer science into K-8 teaching in the US are dramatically rising, professional development workshops for teachers are becoming widespread. An open challenge for the CS education community is to understand teachers' needs and develop empirically grounded best practices for professional development. This experience report describes a five-day professional development workshop in which 22 third through eighth grade teachers learned about fundamental CS concepts, practiced coding, and created lesson plans for integrating what they learned into their classroom. We describe the professional development workshop including its modules and sequencing, and present teachers' perception of CS and how to integrate it into their classrooms. Teachers achieved significant gains in technical knowledge and improvements in attitude toward computer science. In initial focus groups, teachers reported that limited exposure to CS, time constraints, and lack of understanding of CS are barriers to integrating it into their classrooms. After the workshop, focus group feedback indicated that the workshop provided teachers a clearer sense of the potential of CS to enhance their classroom plans. Teachers noted that they felt able to use CS to help students learn critical thinking, prepare them for their futures, and address their individual needs. The results of this experience can inform future workshops that address the needs of teachers and students.

As efforts to integrate computer science into K-8 teaching in the US are dramatically rising, professional development workshops for teachers are becoming widespread. An open challenge for the CS education research community is to understand teachers' needs and develop empirically grounded best practices for professional development. This poster displays our findings from a five-day professional development workshop in which 22 third through eighth grade teachers from various subjects learned about fundamental CS concepts, practiced coding, and created lesson plans for integrating what they learned into their classroom. We present quantitative and qualitative outcomes, with emphasis on teachers' perception of CS and how to integrate CS into their classrooms. The quantitative results, based on pre- and post-tests, reveal that the teachers gained significant technical knowledge and improved their attitudes toward CS. The qualitative results, based on the focus groups conducted before and after the workshop, indicated that teachers' limited exposure to CS can lead to misconceptions and some negative attitudes. Moreover, limitations such as time constraints and lack of understanding of CS are barriers to integrating CS into classrooms. On the other hand, the workshop provided teachers a clearer sense of the potential of CS to enhance their classroom plans. Teachers noted that they felt able to use CS to help students learn critical thinking, prepare them for their futures, and address their individual needs. These results can inform future workshops that address the needs of teachers and students.

This study explored the relationships between eye tracking and traditional usability testing data in the context of analyzing the usability of Algebra Nation™, an online system for learning mathematics used by hundreds of thousands of students. Thirty-five undergraduate students (20 females) completed seven usability tasks in the Algebra Nation™ online learning environment. The participants were asked to log in, select an instructor for the instructional video, post a question on the collaborative wall, search for an explanation of a mathematics concept on the wall, find information relating to Karma Points (an incentive for engagement and learning), and watch two instructional videos of varied content difficulty. Participants’ eye movements (fixations and saccades) were simultaneously recorded by an eye tracker. Usability testing software was used to capture all participants’ interactions with the system, task completion time, and task difficulty ratings. Upon finishing the usability tasks, participants completed the System Usability Scale. Important relationships were identified between the eye movement metrics and traditional usability testing metrics such as task difficulty rating and completion time. Eye tracking data were investigated quantitatively using aggregated fixation maps, and qualitative examination was performed on video replay of participants’ fixation behavior. Augmenting the traditional usability testing methods, eye movement analysis provided additional insights regarding revisions to the interface elements associated with these usability tasks.

Collaborative learning frameworks such as pair programming have been shown to be highly effective for computer science learning. Skeptics of this approach often refer to the risk of one student relying on a stronger partner to solve the problem. Lending weight to this skepticism, many theories emphasize the importance of learner autonomy. Therefore, it is reasonable to hypothesize that a hybrid pair programming paradigm-one in which partners work together side-by-side at two separate computers and produce their own versions of the code-may be even more effective than traditional pair programming. To investigate this hypothesis, we conducted a study in which 200 introductory programming students were paired and then placed in either a pair-programming condition (two students at one computer) or a hybrid condition (two students at two computers). The results show that traditional pair programming fostered comparable learning gains as measured on an individual post-test, and significantly higher student satisfaction, than the hybrid approach. These findings highlight the importance of not just collaborating, but working together on shared code, for novice computer science learners.

Pair programming is a successful approach for improving student performance, retention, and motivation toward computer science. However, not all students benefit equally from this approach. An open challenge for researchers is to develop a deep understanding of the student experience in pair programming, particularly for novices. This paper reports on a study of the cognitive, affective, and social experiences of students in an introductory programming course in which pair programming was utilized throughout the term. Students reported their experience through reflection essays written at the end of the semester. We analyzed 137 student reflection papers in a mixed-methods study. The quantitative results show that overall, students have a positive attitude toward pair programming. Looking more deeply at the reflection essays, thematic analysis revealed themes centered around cognitive, affective, and social dimensions. In the cognitive dimension, students expressed the importance of exposure to different ideas and developing deeper understanding. Affectively, students reported that working with a partner reduced their frustration and increased their confidence. Students also pointed out the social benefits of forming friendships and helpful connections. These results highlight the powerful benefits of pair programming and point to ways in which this collaborative approach could be adapted to better meet student needs.