This tutorial will guide participants in how to use the CS2Mulch project, which provides engaging manipulatives specifically developed for physically demonstrating concepts in advanced data structures. Instructors can use these tools to support lessons on sorting algorithms, binary search trees, red-black trees, heaps, sets, and hash tables, including cuckoo hashing and bloom filters. Two original decks of cards have been designed and tested that can form the basis for interactive classroom games and collaborative peer exercises in CS2. The Acorns deck consists of 70 cards, numbered with integers from 00-69. The leaves on these cards incorporate visual cues to reinforce proper sorting through a progression from green to red as the numbers increase. The Menagerie deck facilitates hashed data structures. This deck consists of 110 cards. 64 of these cards display an animal, along with two integers that are the result of passing the name through two simple hash functions, Murmur3 and FNV, modulo 8. Additional cards are numbered 0 to 7, to be used as markers for arrays within various hash table implementations. Twenty-six circular chits are also available, red on one side, black on the other, labeled with each letter of the alphabet. Students can use these cards to physically manipulate data according to algorithms. Classroom teachers can use these cards in interactive demonstrations, either physically for small classes or virtually for larger audiences. The CS2Mulch decks, along with lesson plans and supporting materials, are available at https://mgoadric.github.io/cs2mulch/, by PDF download, purchase, or virtually.

The organization of stacks and queues are often familiar to students because they have previously stacked plates and have stood in many lines. However, it is less frequent for them to have an innate mental model of higher-level tree and hash-based data structures. This demo will highlight the CS2Mulch project, which provides engaging manipulatives for demonstrating concepts in advanced data structures to build these mental models. Instructors can use these tools to support lessons on sorting algorithms, binary search trees, heaps, sets, and hash tables. Two original decks of cards form the basis for interactive classroom games and collaborative peer exercises in CS2. The Acorns deck consists of cards numbered with integers 00-69. These cards can be overlapped on the corners to demonstrate insertion and removal algorithms for binary trees or heaps. The Menagerie deck consists of cards that display an animal, along with two integers computed by passing the name through two simple hash functions, Murmur3 and FNV, modulo 8. Additional cards are numbered 0 to 7, to be used as markers for arrays within hash table implementations. The CS2Mulch decks, along with lesson plans and supporting materials, are freely available at https://mgoadric.github.io/cs2mulch/, either for PDF download, virtually through the online board game software Tabletopia, or for purchase through on-demand printing at The Game Crafter. This work is supported through a 2019 SIGCSE Special Project Grant.

Challenges for physical solitaire puzzle games are typically designed in advance by humans and limited in number. Alternatively, some games incorporate rules for stochastic setup, where the game board is randomly configured before the challenge is solved. These setup rules greatly increase the number of possible challenges, but can often generate unsolvable or uninteresting challenges. To better understand the compromises involved in minimizing undesirable challenges, we examine three games where component design choices can influence the stochastic nature of the resulting challenge generation algorithms. We evaluate the effect of these components and algorithms on challenge solvability and challenge engagement. We find that algorithms which control randomness through entangling components based on subelements of the puzzle mechanics can generate interesting challenges with a high probability of being solvable.

As outlined in the ACM Computer Science Curricula 2013 Guidelines section on Social Issues and Professional Practice, "Students must also be exposed to the larger societal context of computing to develop an understanding of the relevant social [and] ethical ... issues." In this panel, we demonstrate diverse approaches used to achieve this goal with respect to civic engagement. Drawing from experiences with non-major, introductory computing, mobile applications, software engineering, and interdisciplinary courses, we discuss how to move beyond surface-level discussions of ethical case studies toward an integration of civic engagement activities and personal reflection into standard computing curriculum.

The Learn Java in N Games project (LJING) aims to teach topics in CS1, CS2, and other courses by having students implement a variety of games. Each game is presented as a stand-alone module, easily incorporated into an existing course without committing to major curricular changes. Modules include unusually clear solution code, skeleton code for students to complete, JUnit tests for quick feedback to students and automated grading, and clear assignment statements with learning objectives. Program code strictly separates game logic from graphic user interfaces (GUIs), allowing the same code to be used in both conventional and mobile Android environments. In this three-hour tutorial, participants will complete two assignments. These assignments are ready to be used in classes the next day.

Current research in evolutionary robotics is largely focused on creating controllers by either evolving neural networks or refining genetic programs based on grammar trees. We propose the use of the parallel, dataflow languages for the construction of effective robotic controllers and the evolution of new controllers using genetic programming techniques. These languages have the advantages of being built on concurrent execution frameworks that lend themselves to formal verification along with being visualized as a dataflow graph. In this paper, we compare and contrast the development and subsequent evolution of one such process-oriented control algorithm. Our control software was built from composable, communicating processes executing in parallel, and we tested our solution in an annual fire-fighting robotics competition. Subsequently, we evolved new controllers in a virtual simulation of this parallel dataflow domain, and in doing so discovered and quantified more efficient solutions. This research demonstrates the effectiveness of using process networks as the basis for evolutionary robotics.

This workshop is designed for faculty, considering teaching a course in mobile app development, who are unsure as to whether they should use iOS, Android, or both. To help them make an educated decision, in this workshop participants will build one app, to implement the game Pig, in both platforms. By so doing, they will be able to make a head-to-head comparison of the respective development environments, languages, and frameworks, guided by experienced instructors.

In a remarkably short timeframe, developing apps for smartphones has gone from an arcane curiosity to an essential skill set. Employers are scrambling to find developers capable of transforming their ideas into apps. Educators interested in filling that void are likewise trying to keep up, and face difficult decisions in designing a meaningful course. There are a plethora of development platforms, but two stand out because of their popularity and divergent approaches - Apple's iOS, and Google's Android. In this paper, we will compare the two, and address the question: which should faculty teach?

Receiver Operator Characteristic (ROC) curves are commonly used to present results for binary decision problems in machine learning. However, when dealing with highly skewed datasets, Precision-Recall (PR) curves give a more informative picture of an algorithm's performance. We show that a deep connection exists between ROC space and PR space, such that a curve dominates in ROC space if and only if it dominates in PR space. A corollary is the notion of an achievable PR curve, which has properties much like the convex hull in ROC space; we show an efficient algorithm for computing this curve. Finally, we also note differences in the two types of curves are significant for algorithm design. For example, in PR space it is incorrect to linearly interpolate between points. Furthermore, algorithms that optimize the area under the ROC curve are not guaranteed to optimize the area under the PR curve.