Analogies and metaphors are ubiquitous in computing education, helping instructors break down complex or abstract ideas by connecting them to familiar experiences. This Birds of a Feather session invites educators from across the CS curriculum—from introductory through advanced courses—to share ''sticky'' analogies: explanations that clarify challenging concepts and remain memorable for students. Facilitators will begin with a small set of example analogies that illustrate how to articulate a learning goal, map each component of the analogy to the target concept, and identify any needed cultural or background knowledge. The floor will then open for attendees to contribute analogies that have either worked well for them or fallen flat, enabling collective reflection on what makes an analogy effective or ineffective. A shared document will be collaboratively built during the session so attendees can take away new analogies, refine existing ones, and continue contributing after the symposium.

Transformers with causal attention can solve tasks that require positional information without using positional encodings. In this work, we propose and investigate a new hypothesis about how positional information can be stored without using explicit positional encoding. We observe that nearby embeddings are more similar to each other than faraway embeddings, allowing the transformer to potentially reconstruct the positions of tokens. We show that this pattern can occur in both the trained and the randomly initialized Transformer models with causal attention and no positional encodings over a common range of hyperparameters.

When you start out reading C code, reading code can feel like reading a foreign language: you get lost in a sea of asterisks, dots, arrows, and ampersands. Experienced C programmers write pointer code the way fluent users of math and language write: they do not necessarily always need to reason from first principles. We propose learning to use pointers the way students learn algebra in school: doing a large number of small exercises. John von Neumann famously said that ''[i]n mathematics, you don't understand things. You just get used to them.'' We hope that our drill exercises enable both getting used to pointers and understanding them. Our exercises and solutions are available at https://CPointerDrills.github.io/ .

We demonstrate an AI agent for the card game of Gin Rummy. The agent uses simple heuristics in conjunction with a model that predicts the probability of each card's being in the opponent's hand. To estimate the probabilities for cards' being in the opponent's hand, we generate a dataset of Gin Rummy games using self-play, and train a random forest on the game information states. We explore the random forest classifier we trained and study the correspondence between its outputs and intuitively correct outputs. Our agent wins 61% of games against a baseline heuristic agent that does not use opponent hand estimation.

We delivered a large Introduction to Data Science course with a team of undergraduate Teaching Assistant-Researchers (TARs) who both helped students in the lab and collected qualitative observations about student learning. The TARs were concurrently participating in a senior-level Pedagogy of Data Science seminar.

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of nine AI assignments from the 2020 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.

We present an assignment in which students apply predictive modelling to build a model that predicts re-arrest of criminal defendants using real data. Students assess the algorithmic fairness of a real-world criminal risk assessment tool (RAT), and reproduce results from an impactful story in ProPublica and a 2018 Science Advances paper. Students explore different measures of algorithmic fairness, and adjust the model they build to satisfy the false positive parity measure.

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of ten AI assignments from the 2019 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http: //modelai.gettysburg.edu.

Several institutions have recently introduced Introduction to Data Science courses that involve a substantial programming component and do not require CS1 as a prerequisite. Programming and computational thinking are central to the emerging discipline of data science, and so there is overlap between traditional CS1 courses and Introduction to DS.

I suspect that students learn more from our programming assignments than from our much worried-over lectures, with their slide transitions and attempts at live coding in lecture. A great assignment is deliberate about where the student hours go, concentrating the student's attention on material that is interesting and useful. The best assignments solve a problem that is topical and entertaining, providing motivation for the whole stack of work. Unfortunately, creating great programming assignments is both time consuming and error prone. The Nifty Assignments special session is all about promoting and sharing the ideas and ready-to-use materials of successful assignments.

I suspect that students learn more from our programming assignments than from our much sweated-over lectures, with their slide transitions, clip art, and joke attempts. A great assignment is deliberate about where the student hours go, concentrating the student's attention on material that is interesting and useful. The best assignments solve a problem that is topical and entertaining, providing motivation for the whole stack of work. Unfortunately, creating great programming assignments is both time consuming and error prone. The Nifty Assignments special session is all about promoting and sharing the ideas and ready-to-use materials of successful assignments.