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Effective researcher-coach relationships need reciprocal learning, which allows practitioners to share valuable contextual knowledge while researchers share evidence-based ideas. Nevertheless, these collaborations encounter obstacles due to power imbalances, which frequently establish researchers as authorities and reduce the role of practitioners as co-creators. Therefore, this study examines power dynamics in researcher-coach partnerships within educational contexts, emphasizing equitable collaboration strategies. Using Cultural-Historical Activity Theory (CHAT) as a framework, this study analyzes video data from a writing intensive to explore interactions between two participants, Ashley and Russell. Findings reveal that initial tensions foster deeper understanding through negotiated power exchanges. The study underscores that openness, mutual trust, and reflective dialogue are essential for sustainable partnerships, advancing the understanding of power dynamics in researcher-coach collaborations. 

Predicting students' performance early in programming courses is crucial because it allows instructors to intervene early, improving learning outcomes. Currently, no existing platforms can effectively forecast student performance in programming activities based on students' developed code. Forecasting student scores based on their programming activities is challenging because the accuracy of different predictive models often varies throughout these activities. To address this challenge, we introduce a novel framework utilizing Mixture of Experts (MoE). The MoE method combines insights from various neural networks and dynamically picks the most accurate predictions. This system significantly enhances the reliability of forecasting each student's performance within the first 15 minutes of a 30-minute programming session. By enabling early predictions, the MoE provides instructors with a powerful mechanism to understand and support the student learning process in real-time. 

PurposeThere is a need for precollege learning designs that empower youth to be epistemic agents in contexts that intersect burgeoning areas of computing, big data and social media. The purpose of this study is to explore how “sandbox” or open-inquiry data science with social media supports learning. Design/methodology/approachThis paper offers vignettes from an illustrative youth study case that highlights the pedagogical prospects and obstacles tied to designing for open-ended inquiry with computational data science to access or “scrape” Twitter/X. The youth case showcases how social media can be taken up productively and in ways that facilitate epistemological agency, an approach where individuals actively shape understanding and knowledge-creation processes, highlighting the potentially transformative impact this approach might have in empowering learners to engage productively. FindingsThe authors identify three key affordances for learning that emerged from the illustrative case: (1) flexible opportunities for content-specific domain mastery, (2) situated inquiry that embodies next-generation science practices and (3) embedded computational skill development. The authors discuss these findings in relation to contemporary education needs to broaden participation in data science and computing. Originality/valueTo address challenges in current data science education associated with supporting sustained and productive engagement in computing-based data science, the authors leverage a “sandbox” approach – an original pedagogical framework to support open inquiry with precollege groups. The authors demonstrate how “big data” drawn from social media with high school-aged youth supports learning designs and outcomes by emphasizing learner interests and authentic practice. 

Informal learning environments play a critical role in science, technology, engineering, and mathematics learning across the lifespan and are consequential in informing public understanding and engagement. This can be difficult to accomplish in life science where expertise thresholds and logistics involved with handling biological materials can restrict access. Community laboratories are informal learning environments that provide access to the resources necessary to carry out pursuits using enabling biotechnologies. We investigate a group of these spaces in order to ascertain how this occurs—with specific attention to how material and intellectual resources are structured and shape learning. Using surveys and focus group interviews, we explore a group of these spaces located in the United States. We found that the spaces examined offer learning activities that are sufficiently scaffolded and flexible as to promote personalized and community-driven practice. We discuss these findings in relation to informal learning environment design and learning. 

Biodesign is a relatively new interdisciplinary field, which has grown rapidly over the last decade (as evidenced for example by the growth in student teams entering the Biodesign Challenge from 9 in 2016 to 52 in 2024).