Artificial intelligence (AI) and machine learning (ML) systems are ubiquitous across many fields ranging from medicine (e.g., tumor detection), to natural language processing (e.g., digital home assistants, auto-translate tools), and personalization (e.g., social media recommendations). The rise of these systems has also seen a rise in the ethical challenges resulting from how some of these systems are designed and implemented. Most problematic is their propagation and amplification of problems such as racism and sexism [4, 6], among others. Further, these challenges exist within a computing discipline that is already burdened by exclusive, marginalizing cultures and practices that lead to low participation by women and Black, Indigenous, and People of Color (BIPOC) [3]. Our project, AI + Dance, attends to these dimensions of inequity within AI/ML education through developing our understanding of how we can equip learners to recognize and rectify issues of AI and ML systems within an inclusive and culturally sustaining experience. We explore this in collaboration with STEM From Dance1 (SFD), a non-profit organization that supports girls of color in creative production with dance, CS, and STEM. In our prior work with SFD, we developed danceON, an open-access creative coding environment that enables learners to create code to engage authentically with dance and body motion [5]. With danceON, learners can code animations that can bind and respond to body positions and be statically and dynamically positioned in space. The system provides two ways to explore AI and ML. First, it integrates a pose detection machine learning model (PoseNet [8]) that consists of body points (i.e., Left Ear, Nose, etc.) and confidence scores for these key points, which are all accessible within the code. Second, danceON enables learners to import a model trained in Google’s Teachable Machine [2] to access the probabilistic classifier to manipulate and trigger animations. While we have explored the use of danceON to enable learners to build culturally relevant artistic artifacts [5], we have yet to explore how we can teach AI and ML concepts and ethics through the use of the system. We investigate two research questions through the co-design of culturally sustaining AI/ML resources with teachers: RQ1) How can we authentically build on learners’ identities, cultural knowledge, and practices with dance as they build, explore, and critique ML and AI models and systems? RQ2) How can we leverage learners’ creative, embodied experiences with ML and AI to facilitate reflection and critique of CS and AI/ML within their communities and society more broadly? We focus on co-design as a method that facilitates sustainability through centering teachers’ values, ownership, and authentic contexts in the realization, implementation, and evaluation of learning designs [7]. Through co-designing curricular resources, we will develop a set of modules aligned with the AI4K12 guidelines [1] that builds our understanding of creating culturally sustaining educational resources to teach about AI and ethical design in ways that leverage the culturally situated, collaborative, and embodied nature of dance.

Intuitively, it seems plausible that students who start their work earlier and work on more days than their peers should perform better in any course. But does the early bird really catch the worm? In this article, we examine introductory programming students' time management behavior as evidenced by data collected from a programming environment. We analyze: 1) the earliness of students' work, i.e. when they start working on their course assignments, 2) the number of days students work on course assignments, and 3) the relationship between earliness, the number of days worked, and course outcomes. Our results provide further support for the notion that, on average, students who start working on course assignments early perform slightly better in the course. At the same time, we found that starting early does not necessarily mean that students work on more days, and that starting early and working on many days does not necessarily mean that students get better grades. In addition, some students who start working early on the assignments in the first weeks of the course seem to start delaying when they begin working on assignments as the course progresses, while other students seem to be able to continue starting early throughout the course.

This experience report details pilot deployments of the Platform for Ethics and Responsible Computing Education (PEaRCE) in undergraduate computer science (CS) courses. PEaRCE is an online learning environment designed to immerse undergraduate CS students in realistic interactive work scenarios. The simulated work scenarios illustrate how technical CS course material can be readily exploited towards the creation of appealing systems, which nevertheless can be instruments of harm to others. Students take the role of an employee within a project and are given details about the project. Students then decide whether to continue with the project immediately, or to first talk to various project stakeholders who could provide additional information about the project and its impacts. At the end of the simulation, students are provided information about the project scenario, including the potential impacts of the project and feedback on stakeholder conversation choices. We report on preliminary findings from pilot deployments of PEaRCE in undergraduate CS courses, including student feedback about PEaRCE and the simulation scenarios and discuss future directions for the design of the system and its use in future CS courses.

\emphTime-on-task is one key contributor to learning. However, how time-on-task is measured often varies, and is limited by the available data. In this work, we study two different time-on-task metrics---derived from programming process data---for predicting performance in an introductory programming course. The first metric, coarse-grained time-on-task, is based on students' submissions to programming assignments; the second, fine-grained time-on-task, is based on the keystrokes that students take while constructing their programs. Both types of time-on-task metrics have been used in prior work, and are supposedly designed to measure the same underlying feature: time-on-task. However, previous work has found that the correlation between these two metrics is not as high as one might expect. We build on that work by analyzing how well the two metrics work for predicting students' performance in an introductory programming course. Our results suggest that the correlation between the fine-grained time-on-task metric and both weekly exercise points and exam points is higher than the correlation between the coarse-grained time-on-task metric and weekly exercise points and exam points. Furthermore, we show that the fine-grained time-on-task metric is a better predictor of students' future success in the course exam than the coarse-grained time-on-task metric. We thus propose that future work utilizing time-on-task as a predictor of performance should use as fine-grained data as possible to measure time-on-task if such data is available.