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Since the 1960s, the ACM has provided routinely updated guidelines for what concepts constitute a computer science curriculum, with the latest version currently in development in 2023. These guidelines have traditionally provided a model curriculum from which universities can choose to adopt or modify for their own purposes. What is unclear, however, is to what degree schools follow the curriculum recommendations that the ACM provides. While most faculty and students likely have knowledge of their own institution's curriculum, as well as what courses are offered at a small selection of other schools, the goal of the work presented in this poster is to distill a cohesive view of what computer science curriculums in their second and third years look like across a broad range of universities across a range of institutions. Our goal with this work was to answer the following question: What do computer science course requirements look like at a wide range of different institutions? We believe the work will help those who are trying to develop curriculum changes within their own institutions and aims to provide a more cohesive view of what trends and patterns exist in course offerings and degree requirements for computer science in the second and third years across a wide range of universities. 

The field of computer science has a problem of representation-many groups are not represented in our classroom at levels approaching their composition in society. Unfortunately, the representation issue is a larger societal issue and begins well before students enter our institutions. Though we acknowledge that building inclusive and equitable classroom environments cannot increase representation by itself, it can have an impact on retention and inclusion for members of marginalized communities. Current grading policies overemphasize the gaming aspect of points (e.g., goal is to maximize points) in ways that distract students from paying attention to learning. Alternatives to traditional grading, such as standards or competency-based grading, specifications-based grading, and ungrading, allow instructors to change the conversation and redirect the focus on learning. The goal of this Birds of a Feather is to foster the creation of a community of like-minded educators interested in exploring alternative grading methodologies in computer science. The goal is to make computing classrooms more accessible and equitable for all students. 

WIP Research Paper: The ability to identify threshold concepts within a discipline recognizes key components within a curriculum which, when learned, enables an individual to demonstrate as a member of that community. Within computer science, potential threshold concepts have sparked debates among researchers. According to the original definition by Meyer and Land, threshold concepts result in an individual being placed into a state of uncertainty or liminality and successful traversal of this liminal state results in a potentially irreversible transformation. When seeking to identify threshold concept, researchers often search for what concepts students feel are ‘troublesome’ or ‘difficult to learn’ since this state is often difficult to describe or understand when currently inside it or just past it. While threshold concepts have been identified for the beginning stages of programming, there is little to no work on the intermediate years of university computing education (years 2 & 3) and what potential threshold concepts exist during that time for computer science students. Our goal with this research is to help address this gap by answering the following research question: What concepts do intermediate students identify as being troublesome and/or ‘uncomfortable to learn’ within their computer science coursework? 

Subgoal labeling is an instructional design framework for breaking down problems into pieces that are small enough for novices to grasp, and often difficult for instructors (i.e., experts) to articulate. Subgoal labels have been shown to improve student performance during problem solving in many disciplines, including computing. Improved student performance occurs because subgoal labels improve student transfer and retention of knowledge. With support from NSF (DUE-1712025, 1712231, 1927906, 2110156, 2111578), subgoal labels have been previously identified and integrated into a CS1 course (variables, expressions, conditionals, loops, arrays, classes) and an e-book has been created on the Runestone platform to enable students to complete practice problems using the subgoals. The initial implementation focused on Java, but within the past year, the development of subgoals for CS1 courses in Python have been created. Subsequently, course materials have been created as well. This workshop will introduce participants to the new materials (in Python) and demonstrate how the subgoal labels and worked examples are integrated throughout the course. Materials include worked examples and practice problems that increase in complexity and difficulty within each topic. The materials are designed to be integrated into CS1 courses as homework or classroom examples and activities. Assessment of topics using subgoal labels will also be discussed. Participants will also engage in an activity where they create an example for their own course using subgoal labels. 

Subgoal labeling is an instructional design framework for breaking down problems into pieces that are small enough for novices to grasp, and often difficult for instructors (i.e., experts) to articulate. Subgoal labels have been shown to improve student performance during problem solving in disciplines both in and out of computing. Improved student performance occurs because subgoal labels improve student transfer and retention of knowledge. With support from NSF (DUE-1712025, #1712231), subgoal labels have been identified and integrated into a CS1 course (variables, expressions, conditionals, loops, arrays, classes). This workshop will introduce participants to the materials and demonstrate how the subgoal labels and worked examples are integrated throughout the course. Materials include over 100 worked examples and practice problem pairs that increase in complexity and difficulty within each topic. The materials are designed to be integrated into CS1 courses as homework or classroom examples and activities. Assessment of topics using subgoal labels will also be discussed. Participants will also engage in an activity where they create an example for their own course using subgoal labels.