Building teams that foster equitable interaction provides the foundation for a positive collaborative learning experience.Existing literature shows that many context-specific algorithms exist to help instructors form teams automatically in large classes, but the field lacks general guidelines for selecting a suitable algorithm in a given pedagogical context and lacks a general evaluation approach that allows for the methodological comparison of these algorithms.This paper presents a general-purpose team formation algorithm that considers diversity and inclusion in its design.We also describe an evaluation framework with diversity metrics to assess team compositions using synthetically generated student data and real class data.Our simulation and classroom experiments show that our algorithm performs competitively against three state-of-theart algorithms.We hope this work contributes to building a more equitable and collaborative learning environment for students. CCS Concepts• Human-centered computing → Collaborative and social computing design and evaluation methods; • General and reference → Metrics; • Social and professional topics → User characteristics.

JUnit++ is an extension to the JUnit 5 test framework designed specifically for testing student submissions in CS1 classes. JUnit++ simplifies the test writing process by eliminating repetitive code and letting test writers focus on the logical correctness and structure of the solution. Although everything our extension does can be achieved by using only the base JUnit framework, we have found that using our extension speeds up the efficiency of the test development process and makes it easier to review and maintain tests.

Many educational resources are available that teach children computational thinking and visual programming. As part of this initiative, we develop computational puzzles from well-known computational thinking activities. Inspired by the idea of geocaching, we made our puzzles accessible and fun for the whole family by embedding them into a scavenger hunt. We describe our approach that frames computational puzzles as an outdoor family activity. Our project was launched in 2021 and has received informal positive feedback.

Students often find CS1 to be difficult and the workload too demanding. Those who are struggling may lose marks on assessments due to knowledge gaps, slips, or simply a lack of time. Meanwhile, other students demand more practice questions to better prepare for upcoming exams. To mitigate these differences, we developed an open learning platform that combines ideas from mastery learning, concept mapping, and gamification. The main feature of this work is an open question bank with over 1,500 multiple-choice questions, Parsons problems, and programming exercises for CS1. Using our platform, students can earn tokens by solving questions in multiple ways, including free-form practice, setting personal goals, and completing challenges individually or in teams. Our platform also has a leaderboard to promote engagement and personal analytics to foster self-regulated learning and deliberate practice. Initial feedback from over 500 students has been encouraging. Ultimately, we hope that our system can accommodate different student preferences in how they want to learn and practice programming.