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  3. The demand is growing for a populace that is AI literate; such literacy centers on enabling individuals to evaluate, collaborate with, and effectively use AI. Because the middle school years are a critical time for developing youths’ perceptions and dispositions toward STEM, creating engaging AI learning experiences for middle grades students (ages 11 to 14) is paramount. The need for providing enhanced access to AI learning opportunities is especially pronounced in rural areas, which are typically underserved and underresourced. Inspired by prior research that game design holds significant potential for cultivating student interest and knowledge in computer science, we are designing, developing, and iteratively refining an AI-centered game development environment that infuses AI learning into game design activities. In this work, we review design principles for game design interventions focused on middle grades computer science education and explore how to introduce AI learning experiences into interactive game-design activities. We also discuss results from our initial co-design sessions with middle grades students and teachers in rural communities.
  4. In successful collaborative paradigms such as pair programming, students engage in productive dialogue and work to resolve con- flicts as they arise. However, little is known about how elementary students engage in collaborative dialogue for computer science learning. Early findings indicate that these younger students may struggle to manage conflicts that arise during pair programming. To investigate collaborative dialogue that elementary learners use and the conflicts that they encounter, we analyzed videos of twelve pairs of fifth grade students completing pair programming activities. We developed a novel annotation scheme with a focus on collab- orative dialogue and conflicts. We found that student pairs used best-practice dialogue moves such as self-explanation, question generation, uptake, and praise in less than 23% of their dialogue. High-conflict pairs antagonized their partner, whereas this behav- ior was not observed with low-conflict pairs. We also observed more praise (e.g., “We did it!”) and uptake (e.g., “Yeah and. . . ”) in low-conflict pairs than high-conflict pairs. All pairs exhibited some conflicts about the task, but high-conflict pairs also engaged in conflicts about control of the computer and their partner’s con- tributions. The results presented here provide insights into the collaborative process of young learners in CSmore »problem solving, and also hold implications for educators as we move toward building learning environments that support students in this context.« less
  5. Background and Context: Researchers and practitioners have begun to incorporate collaboration in programming because of its reported instructional and professional benefits. However, younger students need guidance on how to collaborate in environments that require substantial interpersonal interaction and negotiation. Previous research indicates that feedback fosters students’ productive collaboration. Objective: This study employs an intervention to explore the role instructor-directed feedback plays on elementary students’ dyadic collaboration during 2-computer pair programming. Method: We used a multi-study design, collecting video data on students’ dyadic collaboration. Study 1 qualitatively explored dyadic collaboration by coding video transcripts of four dyads which guided the design of Study 2 that examined conversation of six dyads using MANOVA and non-parametric tests. Findings: Result from Study 2 showed that students receiving feed- back used productive conversation categories significantly higher than the control condition in the sample group considered. Results are discussed in terms of group differences in specific conversation categories. Implications: Our study highlights ways to support students in pair programming contexts so that they can maximize the benefits afforded through these experiences.
  6. With the increased demand for introducing computational thinking (CT) in K-12 classrooms, educational researchers are developing integrated lesson plans that can teach CT fundamentals in non- computing specific classrooms. Although these lessons reach more students through the core curriculum, proper evaluation methods are needed to ensure the quality of the design and integration. As part of a research practice partnership, we work to infuse research- backed curricula into science courses. We find a three-pronged approach of evaluation can help us make better decisions on how to improve experimental curricula for active classrooms. This CEO model uses three data sources (student code traces, exit ticket responses, and field observations) as a triangulated approach that can be used to identify programming behavior among novice developers, preferred task ordering for the assignment, and scaffolding recommendations to teachers. This approach allows us to evaluate the practical implementations of our initiative and create a focused approach for designing more effective lessons.
  7. As computing skills become necessary for 21st-century students, infused computational thinking (CT) lessons must be created for core courses to truly provide computing education for all. This will bring challenges as students will have widely varying experience and programming ability. Additionally, STEM teachers might have little experience teaching CT and instructing using unfamiliar technology might create discomfort. We present a design pattern for infused CT assignments that scaffold students and teachers into block-based programming environments. Beginning with existing code, students and teachers work together 'Using' and comprehending code before 'Modifying' it together to fix their programs. The activity ends with students 'Choosing' their own extensions from a pre-set list. We present a comparison of two implementations of a simulation activity, one ending with student choosing how to extend their models and one having all students create the same option. Through triangulating data from classroom observations, student feedback, teacher interviews, and programming interaction logs, we present support for student and teacher preference of the 'Student-Choice' model. We end with recommended strategies for developing curricula that follow our design model.