Recent interest in alternative grading strategies has been increasing in the Computer Science Education community. The umbrella term ungrading has been used to refer to a variety of practices that de-emphasize numeric grades. In this paper we present the results of implementing an ungrading scheme that eliminates numeric grades, allows resubmission of assignments, and encourages student input into their final assigned letter grade. We administered surveys measuring student attitudes and motivation at the start and end of three different upper level Computer Science elective courses using the new grading scheme and found a significant increase in students' feelings of intrinsic goal orientation (valuing coursework for its own sake), self-efficacy (feeling able to be successful), and control of learning (taking responsibility for their own learning). We observed that, given the opportunity, most students chose to redo only a small number of assignments, and most students requested final grades within a half-letter of the instructor's estimate. Overall, compared with prior iterations of the courses that were graded traditionally, the final grade point average did not significantly increase, while students' reported level of effort did significantly increase. Comments on post-course surveys indicate that students liked the new grading scheme, and they reported improved learning and reduced anxiety.

https://dl.acm.org/doi/pdf/10.1145/3545945.3569747

Improving Student Motivation by Ungrading

We implemented a standards-based grading scheme in an upperdivision course on algorithm design taken by non-CS-majors. The alternate grading system allows students to submit multiple attempts at the same algorithm design problem, while managing grading load by replacing standard point-based scales with a 4- possibility-scale for all problems. The simplified grading system created flexibility in the course structure that allowed us to give students more problems each week than we expected them to complete, covering different aspects of the given topics (e.g., both theoretical and practical approaches to algorithm design). The additional problems allowed for students with different goals and backgrounds to choose different problems and tailor it to their needs. The availability of resubmissions created incentives for students to master difficult topics throughout the term without a final exam. We argue that the simplified grading system is particularly well-suited to courses in algorithm design and courses for students with varying backgrounds and goals.

/pdf/using-alternative-grading-in-a-non-major-algorithms-course-1kghq8g8.pdf

Using Alternative Grading in a Non-Major Algorithms Course

The COVID-19 pandemic caused the flaws of traditional grading systems to become even more apparent. In response, a growing number of educators are transitioning their classrooms to focus on alternative methods of assessment. These subversive methods promote more equitable assessments, as they provide a more accurate picture of what a student has learned, cultivate students' intrinsic motivation, and do not privilege students from certain backgrounds. This article details how alternative grading, specifically "ungrading," was integrated into an introductory data science course. I detail how the course components align with the principles of alternative grading, students' responses to the course structure, and the lessons I learned along the way. Finally, I close with a discussion of how infusing alternative methods of assessment into the classroom stands to cultivate the diversity continually lacking in computer science and data science.

https://dl.acm.org/doi/pdf/10.1145/3587102.3588816

Human Centered Data Science: Ungrading in an Introductory Data Science Course

We report our experience with a novel course on binary reverse engineering, a university computer science course that was offered at the second-year level to both computer science majors as well as non-majors, with minimal prerequisites. While reverse engineering has known, important uses in computer security, this was pointedly not framed as a security course, because reverse engineering is a skill that has uses outside computer science and can be taught to a more diverse audience. The original course design intended students to perform hands-on exercises during an in-person class; we describe the systems we developed to support that, along with other online systems we used, which allowed a relatively easy pivot to online learning and back as necessitated by the pandemic. Importantly, we detail our application of "ungrading" within the course, an assessment philosophy that has gained some traction primarily in non-STEM disciplines but has seen little to no discussion in the context of computer science education. The combination of pedagogical methods we present has potential uses in other courses beyond reverse engineering.

https://dl.acm.org/doi/pdf/10.1145/3587102.3588790

Binary Reverse Engineering for All

Mounting evidence shows that distribution enforcing grading schemes that rank students undermine learning. Alternative assessment schemes such as specifications grading, ungrading, and competency-based grading center learning and create a more equitable learning environment. However, to implement such a shift in a course requires a bigger lift from instructors. Students enter our courses expecting certain things; the long-term benefits of more equitable, learning-centered grading schemes can be clouded by the initial anxiety about encountering something new. In this BoF, we will create a space for instructors to discuss their fears, challenges, and solutions in adopting assessment practices and strategies, from the assignment level to the course level, that aligns with learning and equity. In particular, we will focus on discussing how to communicate the need for and benefits of said practices to students, and how these practices can help shift students' attention away from their grades and toward their learning.

Communicating Alternative Grading Schemes: How to Shift Students' Attention to Their Learning from Grades

This panel will highlight the experiences of Black, Asian, and Latinx women and non-binary instructors as junior faculty. Despite efforts to broaden participation, CS departments are often quite homogeneous, forcing faculty of marginalized identities to attenuate certain aspects of, or entire identities, to better "fit in" with their colleagues. We will discuss the impacts on faculty and engage with the audience to identify avenues of individual (i.e., colleagues) and systemic (i.e., department culture and policies) allyship.

Creating Safe Spaces for Instructor Identity in Computing

A students what the hardest CS courses are, and they will likely mention data structures and algorithms (DSA) amongst the top three. In fact, DSA courses are included thrice in the current top 5 video results for “hardest CS classes” on YouTube. Students often bemoan about their nightmarish experiences and the high failure rates. How can we make CS2-DSA courses more joyful? How can we build on the incredible work being done in AP CS and CS0 courses through efforts such as “Passion, Beauty, Joy and Awe” (PBJA) of computing [4,5] and “Beauty and Joy of Computing” (BJC) [5]? In this piece, I reflect on my own experiences teaching a DSA course for the first and second time and highlight some of the ways my students and I were able to (re)-discover the joy of computing in CS2.

How My Students and I (Re)Discovered the Joy of Computing in CS2

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Communicating Alternative Grading Schemes: How to Shift Students' Attention to Their Learning from Grades

Creating Safe Spaces for Instructor Identity in Computing

How My Students and I (Re)Discovered the Joy of Computing in CS2