@article{hoq_shi_leinonen_babalola_lynch_price_akram_2024, title={Detecting ChatGPT-Generated Code Submissions in a CS1 Course Using Machine Learning Models}, url={https://doi.org/10.1145/3626252.3630826}, DOI={10.1145/3626252.3630826}, abstractNote={The emergence of publicly accessible large language models (LLMs) such as ChatGPT poses unprecedented risks of new types of plagiarism and cheating where students use LLMs to solve exercises for them. Detecting this behavior will be a necessary component in introductory computer science (CS1) courses, and educators should be well-equipped with detection tools when the need arises. However, ChatGPT generates code non-deterministically, and thus, traditional similarity detectors might not suffice to detect AI-created code. In this work, we explore the affordances of Machine Learning (ML) models for the detection task. We used an openly available dataset of student programs for CS1 assignments and had ChatGPT generate code for the same assignments, and then evaluated the performance of both traditional machine learning models and Abstract Syntax Tree-based (AST-based) deep learning models in detecting ChatGPT code from student code submissions. Our results suggest that both traditional machine learning models and AST-based deep learning models are effective in identifying ChatGPT-generated code with accuracy above 90%. Since the deployment of such models requires ML knowledge and resources that are not always accessible to instructors, we also explore the patterns detected by deep learning models that indicate possible ChatGPT code signatures, which instructors could possibly use to detect LLM-based cheating manually. We also explore whether explicitly asking ChatGPT to impersonate a novice programmer affects the code produced. We further discuss the potential applications of our proposed models for enhancing introductory computer science instruction.}, journal={PROCEEDINGS OF THE 55TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, SIGCSE 2024, VOL. 1}, author={Hoq, Muntasir and Shi, Yang and Leinonen, Juho and Babalola, Damilola and Lynch, Collin and Price, Thomas and Akram, Bita}, year={2024}, pages={526–532} }
 @article{harred_barnes_fisk_akram_price_yoder_2023, title={Do Intentions to Persist Predict Short-Term Computing Course Enrollments? A Scale Development, Validation, and Reliability Analysis}, url={https://doi.org/10.1145/3545945.3569875}, DOI={10.1145/3545945.3569875}, abstractNote={A key goal of many computer science education efforts is to increase the number and diversity of students who persist in the field of computer science and into computing careers. Many interventions have been developed in computer science designed to increase students' persistence in computing. However, it is often difficult to measure the efficacy of such interventions, as measuring actual persistence by tracking student enrollments and career placements after an intervention is difficult and time-consuming, and sometimes even impossible. In the social sciences, attitudinal research is often used to solve this problem, as attitudes can be collected in survey form around the same time that interventions are introduced and are predictive of behavior. This can allow researchers to assess the potential efficacy of an intervention before devoting the time and energy to conduct a longitudinal analysis. In this paper, we develop and validate a scale to measure intentions to persist in computing, and demonstrate its use in predicting actual persistence as defined by enrolling in another computer science course within two semesters. We conduct two analyses to do this: First, we develop a computing persistence index and test whether our scale has high alpha reliability and whether our scale predicts actual persistence in computing using students' course enrollments. Second, we conduct analyses to reduce the number of items in the scale, to make the scale easy for others to include in their own research. This paper contributes to research on computing education by developing and validating a novel measure of intentions to persist in computing, which can be used by computer science educators to evaluate potential interventions. This paper also creates a short version of the index, to ease implementation.}, journal={PROCEEDINGS OF THE 54TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, VOL 1, SIGCSE 2023}, author={Harred, Rachel and Barnes, Tiffany and Fisk, Susan R. and Akram, Bita and Price, Thomas W. and Yoder, Spencer}, year={2023}, pages={1062–1068} }
 @article{hoq_chilla_ranjbar_brusilovsky_akram_2023, title={SANN: Programming Code Representation Using Attention Neural Network with Optimized Subtree Extraction}, DOI={10.1145/3583780.3615047}, abstractNote={Automated analysis of programming data using code representation methods offers valuable services for programmers, from code completion to clone detection to bug detection. Recent studies show the effectiveness of Abstract Syntax Trees (AST), pre-trained Transformer-based models, and graph-based embeddings in programming code representation. However, pre-trained large language models lack interpretability, while other embedding-based approaches struggle with extracting important information from large ASTs. This study proposes a novel Subtree-based Attention Neural Network (SANN) to address these gaps by integrating different components: an optimized sequential subtree extraction process using Genetic algorithm optimization, a two-way embedding approach, and an attention network. We investigate the effectiveness of SANN by applying it to two different tasks: program correctness prediction and algorithm detection on two educational datasets containing both small and large-scale code snippets written in Java and C, respectively. The experimental results show SANN's competitive performance against baseline models from the literature, including code2vec, ASTNN, TBCNN, CodeBERT, GPT-2, and MVG, regarding accurate predictive power. Finally, a case study is presented to show the interpretability of our model prediction and its application for an important human-centered computing application, student modeling. Our results indicate the effectiveness of the SANN model in capturing important syntactic and semantic information from students' code, allowing the construction of accurate student models, which serve as the foundation for generating adaptive instructional support such as individualized hints and feedback.}, journal={PROCEEDINGS OF THE 32ND ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, CIKM 2023}, author={Hoq, Muntasir and Chilla, Sushanth Reddy and Ranjbar, Melika Ahmadi and Brusilovsky, Peter and Akram, Bita}, year={2023}, pages={783–792} }
 @article{marwan_akram_barnes_price_2022, title={Adaptive Immediate Feedback for Block-Based Programming: Design and Evaluation}, volume={15}, ISSN={["1939-1382"]}, url={https://doi.org/10.1109/TLT.2022.3180984}, DOI={10.1109/TLT.2022.3180984}, abstractNote={Theories on learning show that formative feedback that is immediate, specific, corrective, and positive is essential to improve novice students’ motivation and learning. However, most prior work on programming feedback focuses on highlighting student's mistakes, or detecting failed test cases after they submit a solution. In this article, we present our adaptive immediate feedback (AIF) system, which uses a hybrid data-driven feedback generation algorithm to provide students with information on their progress, code correctness, and potential errors, as well as encouragement in the middle of programming. We also present an empirical controlled study using the AIF system across several programming tasks in a CS0 classroom. Our results show that the AIF system improved students’ performance, and the proportion of students who fully completed the programming assignments, indicating increased persistence. Our results suggest that the AIF system has potential to scalably support students by giving them real-time formative feedback and the encouragement they need to complete assignments.}, number={3}, journal={IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Marwan, Samiha and Akram, Bita and Barnes, Tiffany and Price, Thomas W.}, year={2022}, month={Jun}, pages={406–420} }
 @article{wang_le meur_bobbadi_akram_barnes_martens_price_2022, title={Exploring Design Choices to Support Novices' Example Use During Creative Open-Ended Programming}, DOI={10.1145/3478431.3499374}, abstractNote={Open-ended programming engages students by connecting computing with their real-world experience and personal interest. However, such open-ended programming tasks can be challenging, as they require students to implement features that they may be unfamiliar with. Code examples help students to generate ideas and implement program features, but students also encounter many learning barriers when using them. We explore how to design code examples to support novices' effective example use by presenting our experience of building and deploying Example Helper, a system that supports students with a gallery of code examples during open-ended programming. We deployed Example Helper in an undergraduate CS0 classroom to investigate students' example usage experience, finding that students used different strategies to browse, understand, experiment with, and integrate code examples, and that students who make more sophisticated plans also used more examples in their projects.}, journal={PROCEEDINGS OF THE 53RD ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION (SIGCSE 2022), VOL 1}, author={Wang, Wengran and Le Meur, Audrey and Bobbadi, Mahesh and Akram, Bita and Barnes, Tiffany and Martens, Chris and Price, Thomas}, year={2022}, pages={619–625} }
 @article{akram_fisk_yoder_hunt_price_battestilli_barnes_2022, title={Increasing Students' Persistence in Computer Science through a Lightweight Scalable Intervention}, url={http://dx.doi.org/10.1145/3502718.3524815}, DOI={10.1145/3502718.3524815}, abstractNote={Research has shown that high self-assessment of ability, sense of belonging, and professional role confidence are crucial for students' persistence in computing. As grades in introductory computer science courses tend to be lower than other courses, it is essential to provide students with contextualized feedback about their performance in these courses. Giving students unambiguous and con- textualized feedback is especially important during COVID when many classes have moved online and instructors and students have fewer opportunities to interact. In this study, we investigate the effect of a lightweight, scalable intervention where students received personalized, contextualized feedback from their instructors after two major assignments during the semester. After each intervention, we collected survey data to assess students' self-assessment of computing ability, sense of belonging, intentions to persist in computing, professional role confidence, and the likelihood of stating intention to pursue a major in computer science. To analyze the effectiveness of our intervention, we conducted linear regression and mediation analysis on student survey responses. Our results have shown that providing students with personalized feedback can significantly improve their self-assessment of computing ability, which will significantly improve their intentions to persist in computing. Furthermore, our results have demonstrated that our intervention can significantly improve students' sense of belonging, professional role confidence, and the likelihood of stating an intention to pursue a major in computer science.}, journal={PROCEEDINGS OF THE 27TH ACM CONFERENCE ON INNOVATION AND TECHNOLOGY IN COMPUTER SCIENCE EDUCATION, ITICSE 2022, VOL 1}, publisher={ACM}, author={Akram, Bita and Fisk, Susan and Yoder, Spencer and Hunt, Cynthia and Price, Thomas and Battestilli, Lina and Barnes, Tiffany}, year={2022}, pages={526–532} }
 @inproceedings{lytle_cateté_dong_boulden_akram_houchins_barnes_wiebe_2019, place={Chengdu, Sichuan, China}, title={CEO: A Triangulated Evaluation of a Modeling-Based CT-Infused CS Activity for Non-CS Middle Grade Students}, ISBN={9781450362597}, url={http://dx.doi.org/10.1145/3300115.3309527}, DOI={10.1145/3300115.3309527}, abstractNote={With the increased demand for introducing computational thinking (CT) in K-12 classrooms, educational researchers are developing integrated lesson plans that can teach CT fundamentals in non-computing specific classrooms. Although these lessons reach more students through the core curriculum, proper evaluation methods are needed to ensure the quality of the design and integration. As part of a research practice partnership, we work to infuse research-backed curricula into science courses. We find a three-pronged approach of evaluation can help us make better decisions on how to improve experimental curricula for active classrooms. This CEO model uses three data sources (student code traces, exit ticket responses, and field observations) as a triangulated approach that can be used to identify programming behavior among novice developers, preferred task ordering for the assignment, and scaffolding recommendations to teachers. This approach allows us to evaluate the practical implementations of our initiative and create a focused approach for designing more effective lessons.}, booktitle={Proceedings of the ACM Conference on Global Computing Education  - CompEd '19}, publisher={ACM Press}, author={Lytle, Nicholas and Cateté, Veronica and Dong, Yihuan and Boulden, Danielle and Akram, Bita and Houchins, Jennifer and Barnes, Tiffany and Wiebe, Eric}, year={2019}, pages={58–64} }
 @article{catete_lytle_dong_boulden_akram_houchins_barnes_wiebe_lester_mott_et al._2018, title={Infusing Computational Thinking into Middle Grade Science Classrooms: Lessons Learned}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85056713650&partnerID=MN8TOARS}, DOI={10.1145/3265757.3265778}, abstractNote={There is a growing need to present all students with an opportunity to learn computer science and computational thinking (CT) skills during their primary and secondary education. Traditionally, these opportunities are available outside of the core curriculum as stand-alone courses often taken by those with preparatory privilege. Researchers have identified the need to integrate CT into core classes to provide equitable access to these critical skills. We have worked in a research-practice partnership with two magnet middle schools focused on digital sciences to develop and implement computational thinking into life sciences classes. In this report, we present initial lessons learned while conducting our design-based implementation research on integrating computational thinking into middle school science classes. These case studies suggest that several factors including teacher engagement, teacher attitudes, student prior experience with CS/CT, and curriculum design can all impact student engagement in integrated science-CT lessons.}, journal={WIPSCE'18: PROCEEDINGS OF THE 13TH WORKSHOP IN PRIMARY AND SECONDARY COMPUTING EDUCATION}, publisher={ACM Press}, author={Catete, Veronica and Lytle, Nicholas and Dong, Yihuan and Boulden, Danielle and Akram, Bita and Houchins, Jennifer and Barnes, Tiffany and Wiebe, Eric and Lester, James and Mott, Bradford and et al.}, year={2018}, pages={109–114} }