As computer science is integrated into a wider variety of fields, block-based programming languages like Snap!, which assemble code with visual blocks rather than text syntax, are increasingly used to teach computational thinking (CT) to students from diverse backgrounds. Although automated evaluators (autograders) for programming assignments usually focus on runtime efficiency and output accuracy, effective evaluation of a student's CT skills requires assessing coding best practices, such as decomposition, abstraction, and algorithm design. While autograders are commonplace for text languages like Python, we present a machine learning approach to assess how effectively block-based code demonstrates understanding of CT fundamentals. Our dataset consists of Snap! programs written by students new to coding and evaluated by instructors using a CT rubric. We explore how to best transform these programs into low-dimensional features to allow encapsulation and repetition patterns to emerge. Experimentation involves comparing the effectiveness of a suite of clustering models and similarity metrics by analyzing how directly automated feedback correlates to the course staff's manual evaluation. Lastly, we demonstrate the practical application of the autograder in a classroom setting and discuss scalability and feasibility in other domains of CS education.

To accelerate the adoption of computational thinking (CT), we have developed TuneScope, an online platform for introducing novices to programming in the context of music. TuneScope combines a sound analysis & synthesis tool with Snap!, a computing language developed at the University of California, Berkeley. This demo explores CT concepts such as decomposition, patterns, abstraction, and algorithms in TuneScope while also exploring the creation of four cascading musical components from (1) sequences of notes, (2) musical chords, (3) sampled sounds, and (4) synthesized sounds. The challenge is to design activities that include authentic music learning as well as genuine computational thinking. In this demo, we show concepts around sequence (the order in which musical notes appear in time; and the order of statements in a computer program) and repetition (includes repeats as well as the structure of melodies; and computing loops and recursion). The instructional activities in this demo have been piloted three times in an associated course at the University of Virginia. Data collected from the course suggest a positive effect on both the understanding of CT concepts and the comprehension of music. More detail on TuneScope can be found at https://maketolearn.org/tunescope/.

The rapid adoption of Maker Spaces in schools offers opportunities for students to design and fabricate their own inventions. Smithsonian Invention Kits provide a framework that allows students to explore and learn science and engineering through reconstruction of historic inventions.