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TunePad is a free, online platform designed with the goal of empowering diverse communities of learners to create and share music through code. We are interested in the idea of music as a pervasive form of literacy with abundant connections to concepts of computer programming. Over the past three years we have developed and refined successive prototypes with over 500 middle school and high school students in a variety of learning spaces including schools, libraries, summer camps, and other out-of-school programs. This paper shares the current TunePad design along with data from three summer camps for middle school students that involved daily work with the platform. Through these camps we saw significant gains in learners’ attitudes around computer programming as measured through pre-post surveys. We also share a theoretical perspective on music and coding as an intersection of literacies that we reflect on through student-created artifacts. 

We review an array of experimental methodological factors that either contribute to or detract from the measurement of pragmatic implicatures in child language. We carry out a truth value judgment task to measure children’s interpretations of the Spanish existential quantifier algunos in implicature-consistent and implicature-inconsistent contexts. Independently, we take measures of children’s inhibition, working memory, attention, approximate number ability, phrasal syntax, and lexicon. We model the interplay of these variables using a piecewise structural equation model (SEM), common in the life sciences, but not in the social and behavioral sciences. By 6 years of age, the children in our sample were not statistically different from adults in their interpretations. Syntax, lexicon, and inhibition significantly predict implicature generation, each accounting for unique variance. The approximate number system and inhibition significantly predict lexical development. The statistical power of the piecewise SEM components, with a sample of 64 children, is high, in comparison to a traditional, globally estimated SEM of the same data.