Six papers by CSE researchers at SIGCSE TS 2025

Six papers authored by researchers affiliated with CSE are being presented at the 2025 Technical Symposium on Computer Science Education (SIGCSE TS). The flagship conference of the ACM Special Interest Group on Computer Science Education, SIGCSE TS is a leading international venue in the computing education space, bringing together educators and researchers to share the latest developments related to computing programs, curricula, pedagogy, and more. This year’s conference is taking place February 26 to March 1 in Pittsburgh, PA.

New research by CSE authors covers a range of topics related to computer science education, including AI integration in learning environments, ethics in computing education, and strategies for teaching diverse student populations.

The papers being presented are as follows, with the names of authors affiliated with CSE in bold:

Personalized Parsons Puzzles as Scaffolding Enhance Practice Engagement Over Just Showing LLM-Powered Solutions
Xinying Hou, Zihan Wu, Xu Wang, Barbara Ericson

Abstract: As generative AI products could generate code and assist students with programming learning seamlessly, integrating AI into programming education contexts has driven much attention. However, one emerging concern is that students might get answers without learning from the LLM-generated content. In this work, we deployed the LLM-powered personalized Parsons puzzles as scaffolding to write-code practice in an intro-level Python classroom (PC condition) and conducted an 80-minute randomized between-subjects study. Both conditions received the same practice problems. The only difference was that when requesting help, the control condition showed students a complete AI-generated solution as scaffolding (CC condition), simulating the traditional LLM output. Results indicated that students who received personalized Parsons puzzles as scaffolding engaged in practicing significantly longer while maintaining the same high performance level as those who received complete AI-generated solutions as scaffolding.

Designing Courses for Liberal Arts and Sciences Students Contextualized around Creative Expression and Social Justice
Mark Guzdial, Tamara Nelson-Fromm

Abstract: The goal of teaching everyone computing (explicitly including programming) predates the definition of the computer science (CS) major. The rationale was not about becoming a software developer, since that job did not exist then in the same way. At our institution, we are creating courses for non-CS majors which are grounded in the computational practices of liberal arts and sciences faculty. These courses have no connection to the CS major curriculum or software development jobs. We focus here on two of the themes that those faculty valued (Computing for Expression and Computing for Justice) and the introductory courses that we designed around each theme. The courses emphasize gaining broad perspectives of computing, which serve the study of multiple disciplines. Student activities include readings, writing essays, classroom discussion, and open-ended programming homework assignments. This experience report describes our design process, the Creative Expression and Social Justice courses, and an initial evaluation of our design. Most of the programming assignments were written in the block-based programming language Snap!, with some in-class exercises using teaspoon languages. Several units ended with an ebook assignment to connect the Snap!~programming to equivalent programs in Python, Processing, and SQL. Interview and survey findings suggest that students found this sequence and the courses useful, despite not counting toward a CS major or focusing on early software development skills. Students described usefulness in terms of developing general computing knowledge, preparation for a range of future careers, and introducing them to other course choices.

The Development and Validation of the Critical Reflection and Agency in Computing Scale
Aadarsh Padiyath, Mark Guzdial, Barbara Ericson

Abstract: As computing’s societal impact grows, so does the need for computing students to recognize and address the ethical and sociotechnical implications of their work. While there are efforts to integrate ethics into computing curricula, we lack a standardized tool to measure those efforts, specifically, students’ attitudes towards ethical reflection and their ability to effect change. This paper introduces the novel framework of Critically Conscious Computing and reports on the development and content validation of the Critical Reflection and Agency in Computing Index, a novel instrument designed to assess undergraduate computing students’ attitudes towards practicing critically conscious computing. The resulting index is a theoretically grounded, expert-reviewed tool to support research and practice in computing ethics education. This enables researchers and educators to gain insights into students’ perspectives, inform the design of targeted ethics interventions, and measure the effectiveness of computing ethics education initiatives.

Teaching Computing to K-12 Emergent Bilinguals: Identified Challenges and Opportunities
Emma R. Dodoo, Tamara Nelson-Fromm, Mark Guzdial

Abstract: Emergent bilingual (EB) students are a growing population within the United States. An increasing number of these students are in K-12 computing courses. Since programming languages are primarily grounded in English, K-12 computing teachers have to balance and tailor their instruction to meet the needs of EB students. Teachers informed us of insufficient computing education resources for teaching EBs programming. Through a thematic analysis of semi-structured interviews with eight K-12 computing teachers who have EB students in their classes, we identified challenges they faced in teaching programming to EBs and emph{strategies} they use to support their EB students. Our analysis revealed three challenges: (1) students can experience cognitive overload from translating between English and their native language, (2) terminologies have different meanings across disciplines (e.g., `algorithm’ in Math and CS), and (3) educators’ low computing self-efficacy. Two strategies teachers implemented to address their students’ needs were: (1) providing multiple ways for EB students to engage with content focused on preventing them from becoming overwhelmed (2) offering multiple modalities for translating computing concepts. This study contributes to the ongoing discussion on inclusive computing education by offering insights into educators’ needs and potential solutions for supporting EB students’ computing education.

Instructor-Written Hints as Automated Test Suite Quality Feedback
James Perretta, Andrew DeOrio, Arjun Guha, Jonathan Bell

Abstract: Mutation testing measures a test suite’s ability to detect bugs by inserting bugs into the code and seeing if the tests behave differently. Mutation testing has recently seen increased adoption in industrial and open-source software but sees limited use in education. Some instructors use manually-constructed mutants to evaluate student tests and provide general automated feedback. Additional tutoring requires more intensive instructor interaction such as in office hours, which requires substantial resources at scale. Prior work suggests that students benefit from frequent, actionable feedback, and our work focuses on the challenge of leveraging automation to give students high-quality quality feedback when they need it.

We deployed an automated hint system that provides instructor-written hints related to mutants that student-written tests do not detect. We evaluated our hint system in a controlled experiment across four assignments in two introductory programming courses, comprising 4,122 students. We also analyzed student test suite revisions and conducted a mixed-methods analysis of student hint ratings and comments collected by the automated hint system.

We observed a small, statistically significant increase in the mean number of mutants detected by students who received hints (experiment group) compared to those who did not (control group). In 25% of instances where students received a hint, they detected the mutant in a single revision to their test suite. We conclude with recommendations based on our analysis as a starting point for instructors who wish to deploy this type of automated feedback.

Understanding Non-CS Students’ Motivations and Decision Making In A General Education Computing Course
Tamara Nelson-Fromm

Abstract: Undergraduate students are taking computer science (CS) courses, even if they are not planning to become computer science majors. However, recommendations for post-secondary computing curriculum presume that students are learning computer science for a career goal such as software engineering. Prior work about non-majors in introductory programming courses have revealed additional reasons why students learn programming: such as to become conversational programmers or computational scientists. These purposes are not well served by programming education built around the creation of software professionals, which can lead to students with non-technological career goals feeling unwelcome or uninterested in CS courses. At my institution, we have created new computing courses designed to meet the needs of liberal arts and humanities majors by teaching discipline-specific programming skills within an elective course. I conducted an interview study of 24 undergraduate students who enrolled in these elective, general education (not CS credit bearing) courses about computing and programming. I seek to understand why students not required to study computer science are motivated to learn about computing and programming, and why they chose to learn about computing through a general education elective which could not aid CS or technical degree progress. I additionally investigate how students in this context approach programming and debugging within their open-ended assignments. My findings build upon prior work to highlight additional reasons why non-CS majors might want to study computing, and how students’ computing-related goals affect their decision making when creating and refining programs.