About

Yael Gertner is a Teaching Associate Professor at the Siebel School of Computing and Data Science at the University of Illinois Urbana-Champaign. Her research areas include Computers and Education, with recent courses taught covering topics such as Discrete Structures, Algorithms, and Excursions in Computing. Gertner has been recognized for her innovative teaching, receiving The Grainger College of Engineering's 2024 Collins Award for Innovative Teaching. She is actively involved in mentoring students and developing educational programs within the field of computer science.

Research topics

• Computer Science
• Mathematics
• Artificial Intelligence
• Mathematics education
• Natural Language Processing
• Machine Learning
• Engineering
• Psychology
• Discrete mathematics
• Programming language
• Linguistics

Selected publications

• Measuring Students' Perceptions of an Autograded Scaffolding Tool for Students Performing at All Levels in an Algorithms Class

  2026-02-13

  articleOpen access1st authorCorresponding

  Algorithms courses are a foundational part of an undergraduate computer science degree that require abstract thinking and creativity and are known to be challenging for many students. Recently researchers have been developing auto-graded tools to scaffold students through the problem-solving process. We examine student's perceptions of such a tool in a required upper-division Algorithms course at a R1 University. The goal of the tool is to improve student experience in three ways: (1) help students break down the problem-solving process into clear steps; (2) increase students' self-efficacy by raising their confidence and understanding of the material; (3) have low ''cost'', by being easy to use, enjoyable, and a good use of students' time. The tool itself is designed to provide these benefits to students at every level of mastery through instantaneous feedback over increasingly challenging problems. It is designed as an addition to and not complete replacement of the written homework in the course. Based on a survey of almost 1000 students across four semesters, each with a different instructor, we examine whether student feedback is favorable over all four offerings, and for groups of students with different course outcomes. Using qualitative and quantitative methods, we found that across each of the four semesters and across letter grades A, B, C, and D students favored the tool as compared to written homework.

  PublisherDOI

• AI-Supported Grading and Rubric Refinement for Free Response Questions

  2026-02-13

  articleOpen access

  Manually grading free response questions remains a persistent challenge in education. While such questions offer valuable opportunities for student learning and critical thinking, their evaluation often requires substantial time and effort from instructors or teaching assistants. In addition to the grading workload, open-ended responses are susceptible to inconsistencies in scoring and may reflect unclear expectations, both of which can undermine the effectiveness and fairness of the assessment process. To address these challenges, we employed an AI-based grading system integrated in PrairieLearn to automatically evaluate student submissions to free response questions using a predefined set of rubric items. This approach not only streamlines the grading process but also enables direct comparison between AI-generated rubric applications and human judgments, providing insight into alignment and potential discrepancies. These discrepancies provided valuable insight, allowing us to iteratively revise and clarify the rubric items. Our experiences with using the AI grading system across several computing courses suggest that even experienced educators face difficulties articulating rubrics that are both specific and interpretable. We furthermore argue that more attention should be given to the iterative development and evaluation of rubrics.

  PublisherDOI

• Using Kotter’s Eight-Stage Model to Understand Departmental Efforts to Broaden Participation in Computing

  ACM Transactions on Computing Education · 2026-03-13

  articleOpen access

  Background : Motivated by the need for a diverse technological workforce, broadening participation in computing (BPC) efforts aim to increase the representation of people who identify as women, African American or Black, Hispanic or Latinx/a/o/e, Native American, Indigenous, persons from economically disadvantaged backgrounds, and persons with disabilities. Research on BPC efforts has highlighted exemplar institutions and activities, but tends to focus on what initiatives computing departments have undertaken and the outcomes of these initiatives. Purpose : Given this prior focus on what initiatives computing departments are undertaking, we propose refocusing on how change happens to increase our collective capacity for impactful change efforts. We apply a well-known organizational change framework, John Kotter’s eight-stage process of leading change, to examine catalysts for change in computing departments, who contributes to this work, and what motivates the work. Doing so can deepen our understanding of BPC efforts and how to enhance them. Theoretical Framework : Kotter’s framework for leading change includes the following eight stages: (1) establishing a sense of urgency, (2) creating the guiding coalition, (3) developing a vision and strategy, (4) communicating the change vision, (5) empowering broad-based action, (6) generating short-term wins, (7) consolidating gains and producing more change, and (8) anchoring new approaches in the culture. Methods : Using a practitioner research approach, we conducted interviews with 13 faculty and staff members across R1 and R2 U.S. institutions via Zoom. Participants were recruited based on their involvement in BPC efforts at their respective institutions. We used inductive and deductive analytic coding approaches to capture how the Kotter framework illuminated participants’ experiences leading BPC efforts. Findings : Kotter’s stages provide a useful breakdown of processes with which to understand and illuminate catalysts for change in computing departments. Across our participants, we see examples of how each stage not only takes shape in different departments, but also how subsequent stages build upon the one(s) before it. Findings reveal a variety of factors that motivate the urgency for computer science (CS) departments to engage in BPC work, along with the importance of top-down leadership and institutional resources. Implications : Kotter’s organizational change model provides an appropriate frame to guide BPC efforts and may be useful to practitioners. Findings from this study illuminate several areas for CS departments to address in order to build capacity for organizational change efforts that support BPC goals. These include examining a variety of departmental data, effectively using meetings and other communications mechanisms, and revising hiring and promotion policies.

  PublisherDOI

• A TA Training Lesson for Problem-Solving: How to Explain A Solution and Meet Students Where They Are

  2026-02-13

  articleOpen accessSenior author

  Teaching assistants (TAs) are essential to support growing Computer Science (CS) programs. In our university's TA training program, we taught 53 CS TAs a framework for how to develop solutions that focus on helping students with the problem-solving process. The framework provides TAs with tools to create a solution narrative that finds common ground with students, explicitly points out how to get started, emphasizes the trial and error process of problem-solving, and suggests how to recover from errors. In this poster we share our framework and preliminary findings that participants positively rated the quality of the lesson content and their narratives prior to our lesson do not already include this content. We suggest steps for future work.

  PublisherDOI

• Teaching Algorithm Design: A Literature Review

  ACM Transactions on Computing Education · 2025-04-04 · 7 citations

  review

  Algorithm design is a vital skill developed in most undergraduate Computer Science (CS) programs, but few research studies focus on pedagogy related to algorithms coursework. To understand the work that has been done in the area, we present a systematic survey and literature review of CS Education studies. We search for research that is both related to algorithm design (as described by the ACM Curricular Guidelines) and evaluated on post-secondary-level students. Across all venues we searched prior to July 2024, we only find 102 such papers. We first classify these papers by topic, evaluation metric, evaluation methods, and intervention target. Through our classification, we find a broad sparsity of papers which indicates that many open questions remain about teaching algorithm design. We also note the need for papers using rigorous research methods, as only 43 out of 92 papers presenting quantitative data use statistical tests, and only 16 out of 47 papers presenting qualitative data follow a coding scheme. Only 18 papers report controlled trials. In addition, almost all authors only contribute to one publication, an indication that few groups are specializing on these topics. We then synthesize the results of the existing literature to give insights into what the corpus reveals about how we should teach algorithms. Broadly, we find that much of the literature explores implementing well-established practices, such as active learning or automated assessment, in the algorithms classroom. However, there are algorithms-specific results as well: A number of papers find that students may under-utilize certain algorithmic design techniques, and studies describe a variety of ways to select algorithms problems that increase student engagement and learning. The results we present, along with the publicly available set of papers collected, provide a detailed representation of the current corpus of CS Education work related to algorithm design and can orient further research in the area.

  PublisherDOI

• BOARD # 95: WIP: Students’ reflections on their attitude and how it affects their performance in a CS Discrete Math course.

  2025-08-21

  articleSenior author
  PublisherDOI

• Measuring the Impact of Distractors on Student Learning Gains while Using Proof Blocks

  2025-02-12

  articleOpen access

  Background: Proof Blocks is a software tool that enables students to construct proofs by assembling prewritten lines and gives them automated feedback. Prior work on learning gains from Proof Blocks has focused on comparing learning gains from Proof Blocks against other learning activities such as writing proofs or reading.

  PublisherDOI

• Integrating a CS+Social Science Project into STEM and non-STEM High School Courses

  2025-02-12 · 2 citations

  article

  In this paper, we describe a CS+Social Science Python project that can be integrated directly into high school classrooms, enabling students to explore social science questions using computer science. The project uses the pandas library and Google Colab to give students an authentic experience with data science tools. We present teachers' experiences and students feedback from implementing the project in three high school classes, one non-STEM class and two AP CS classes. The project is designed to be simple enough for students with no CS background to succeed, but creative and open-ended enough to allow students with experience to develop their skills further. Students from both courses report the project was interesting and useful. Our work builds upon the body of literature examining ways to include CS in non-STEM high school courses, but also appears to fit well into CS curricula.

  PublisherDOI

• An Innovative One-Year Pathway to a Master’s in Computer Science for Non-Computing College Graduates

  2025-08-21

  article1st authorCorresponding
  PublisherDOI

• WIP: Students’ metacognition and how it relates to their performance in conceptual problem-solving introductory Engineering courses.

  2025-08-21

  articleSenior author
  PublisherDOI

Frequent coauthors

• Benjamin Cosman

  University of Illinois Urbana-Champaign

  90 shared

• Jennifer Amos

  University of Illinois Urbana-Champaign

  80 shared

• Juan Alvarez

  California University of Pennsylvania

  80 shared

• Jonathan Katz

  Google (United States)

  38 shared

• Rosario Gennaro

  City University of New York

  38 shared

• Luca Trevisan

  Bocconi University

  36 shared

• Tal Malkin

  12 shared

• Cynthia Fisher

  University of Illinois Urbana-Champaign

  8 shared

Labs

• Siebel School of Computing and Data SciencePI

Awards & honors

• The Grainger College of Engineering's 2024 Collins Award for…