We analyze detailed mobility data sets to explore changes in the usage of parks, forests, and other public green space during the COVID-19 pandemic. We seek to identify trends in patterns of access to public green space based on granular, county-level data in the United States. We look also at broader global trends, comparing park access patterns between countries. We find large changes in park usage during the pandemic in the United States. These changes are especially pronounced between regions with high income and comparable regions with low income. These disparities are not as evident when analyzing the data at a country level.

Debugging involves the simultaneous application of a number of programming skills - reading code, writing code, problem comprehension, etc. This makes it a challenging activity for novice programmers. Unfortunately, debugging is rarely taught explicitly in introductory programming courses, and is often learned as an implicit goal through programming assignments. In this experience report we explore the impact of a cognitive awareness scaffold to help students monitor their progress as they debug their code. We created a simple form that students used to document their debugging process when they ran into bugs. The form asks questions that students are likely to be asked by course staff during office hours, e.g., "What have you tried so far?''. This act of verbalizing errors and enumerating successful and unsuccessful strategies to fix them is meant to help students monitor their own debugging progress. We examined the cognitive awareness demonstrated in form responses, finding that responses were more superficial on projects of higher difficulty. Additionally, we gave students an exit survey to measure the perceived impact of the debugging form on students' ability to regulate their debugging process and their confidence while debugging. Students indicated that the form helped them better verbalize errors in their programs, and helped them surmount problems with which they would otherwise have needed help.

Teaching computer science to low-income populations is critical to building a more diverse workforce. However, it has added challenges including limited resources and greater diversity in academic backgrounds. You'll hear a variety of perspectives ranging from experiences building programs for low-income K-12 students to teaching incarcerated adults at county jails.

Computer Science and programming are changing the world, but not everyone has equal access to education about this field. In California, Juvenile hall students typically lack opportunities to learn computer programming. In this paper, we present an experience report about the course we created to address the needs of this population. Specifically, we created and taught an introductory computing course focused on engagement. This project-based game design curriculum was launched to a small cohort of the juvenile hall in spring 2019. This course focused on engaging students while introducing computing programming concepts such as variables, logic and function. Student surveys and reports from their teacher showed this class had a positive impact and was well received by students and staff. We hypothesize and show initial positive indication that creative, game-oriented curriculum had a positive impact on the demographic. We also present some of the challenges encountered when working within the juvenile hall system and our solutions and general recommendations for these types of classes.

2048 is a simple and intriguing sliding block puzzle game that has been studied for several years. Many complex solvers, often developed using neural nets are available and capable of achieving very high scores. We are, however, interested in using only basic heuristics, the kind that could be conceivably employed by human players without the aid of computation. A common way to implement a 2048 solver involves searching the game tree for the best moves, choosing a move and scoring the game board using some evaluation functions. The choice in heuristic evaluation function can dramatically affect the moves chosen by the solver. Furthermore, two or more possible moves can frequently produce the same score as evaluated by the same heuristic function, requiring either a random choice, or the use of a secondary or back up evaluation function which itself in turn may produce a tie. In this paper, we test the effectiveness of several basic heuristics in a simple 2048 solver. In order to test these, we create a system that takes basic predefined heuristic evaluation functions as input parameters, generates compositions from these functions with certain rules, and automatically tests all of them with a specified number of games. We find that compositions of evaluation functions that maximize empty spaces and monotonicity of tiles on the board-especially those that prioritize high numbers of empty spaces above prioritizing higher monotonicity- perform the best out of all compositions that we test.