Block-based programming environments such as Scratch, App Inventor, and Alice are a key part of introductory K-12 computer science (CS) experiences. Free-choice, open-ended projects are encouraged to promote learner agency and leverage the affordances of these novice-programming environments that also support creative engagement in CS. This mixed methods research examines what we can learn about student learning from such programming artifacts. Using an extensive rubric created to evaluate these projects along several dimensions, we coded a sample of ~80 Scratch and App Inventor projects randomly selected from 20 middle school classrooms in a diverse urban school district in the US. We present key elements of our rubric, and report on noteworthy trends including the types of artifacts created and which key programming constructs are or are not commonly used. We also report on how factors such as students' gender, grade, and teachers' teaching experience influenced students' projects. We discuss differences between programming environments in terms of artifacts created, use of computing constructs, complexity of projects, and use of features of the environment for creativity, interactivity, and engagement. Our findings will help educators of introductory computing be more cognizant of how best to leverage the programming environments they are using, and what aspects they need to focus on as they attempt to address the learning needs of all in "CS For All."

Domain experts are essential for successful software development, but these experts may not recognize their ideas when abstracted into Unified Modeling Language (UML) or ontologies. We describe a Web-based tool for modeling that creates and manipulates a simple data model without representing it in UML, while promoting collaboration and the use of examples to compare and validate the model. The open-source tool, "NEMo," is a by-product of a team effort to invent and refine a complex data model and library of examples.

Scatter/Gather is a cluster-based browsing technique for large text collections.Users are presented with automatically computed summaries of the contents of clusters of similar documents and provided with a method for navigating through these summaries at different levels of granularity.The aim of the technique is to communicate information about the topic structure of very large collections.We tested the effectiveness of Scatter/Gather as a simple pure document retrieval tool, and studied its effects on the incidental learning of topic structure.When compared to interactions involving simple keyword-based search, the results suggest that Scatter/Gather induces a more coherent conceptual image of a text collection, a richer vocabulary for constructing search queries, and communicates the distribution of relevant documents over clusters of documents in the collection.