Context Introducing Computer Science (CS) into formal education can be challenging, notably when considering the numerous stakeholders involved which include the students, teachers, schools, and policy makers. We believe these perspectives should be considered conjointly, which is possible within Research Practice Partnerships (RPPs) . RPPs look to bridge research-practice gaps and have seen an increase in the field of education and CS-education. Unfortunately, RPPs are considered to be under-researched, in addition to presenting their own challenges. Objectives To the purpose of assessing how RPPs may support the successful introduction of CS into formal education, we investigate three perspectives (students, teachers, and RPP stakeholders) and their interplay within the context of a multi-institution RPP conducting a pilot program to introduce CS to secondary school students. Methods A mixed methods analysis was employed to triangulate data in a concurrent triangulation design. The data included (i) three surveys distributed over the semester to 106 grade 9 students (ages 12-14), (ii) four teacher-journals, (iii) two interviews and four focus groups with the teachers and representatives of the partner institutions. Findings From the students’ perspective , while their self-efficacy increased, their motivation decreased throughout the semester due to a miss-match between their expectations and the course. The findings also indicate that gender biases and heterogeneity are already present in grade 9. From the teachers’ perspective , co-constructing the study plan, having access to regular support and collaborating within a community of practice when starting to teach CS all facilitated the teachers’ experience. Finally, from the RPP’s perspective the collaboration between stakeholders and having researchers evaluate the program were considered to be key elements in the pilot program. However, there appears to be a research-practice gap, in large part due to limited interactions between researchers and curriculum designers, and researchers and the teachers in the field. Conclusions From the students’ perspective it appears relevant to introduce CS (i) prior to secondary school to address motivation and bias-related issues early on, and (ii) to all students to avoid participation being motivation-, stereotype-, or belief-driven, and risk broadening the gap between students, (iii) all the while being attentive to course format and content to ensure that the course meets students’ expectations and fosters autonomous motivation. From the teachers’ perspective , while the support provided met the teachers’ needs, it is essential to find means of scaling such approaches when looking to deploy CS-curricular reforms to entire administrative regions. Finally, from the RPP’s perspective (i) teachers’ should be given a voice in the RPP to better align with the field, and (ii) researchers’ roles should be reconsidered to move beyond being only evaluators, and towards having a more co-constructive role in setting up the curricular reform. Recommendations are provided for researchers and practitioners involved in CS curricular reforms.

Causal structure-discovery techniques usually assume that all causes of more than one variable are observed. This is the so-called causal sufficiency assumption. In practice, it is untestable, and often violated. In this paper, we present an efficient causal structure-learning algorithm, suited for causally insufficient data. Similar to algorithms such as IC* and FCI, the proposed approach drops the causal sufficiency assumption and learns a structure that indicates (potential) latent causes for pairs of observed variables. Assuming a constant local density of the data-generating graph, our algorithm makes a quadratic number of conditional-independence tests w.r.t. the number of variables. We show with experiments that our algorithm is comparable to the state-of-the-art FCI algorithm in accuracy, while being several orders of magnitude faster on large problems. We conclude that MBCS* makes a new range of causally insufficient problems computationally tractable.