Publications

@inproceedings{Mamun2026Prompted,
  title = {Prompted Debugging as a Scaffold for Novice Programmers: Evidence from Lab-Scale Deployment},
  author = {Sabbir Hosen Mamun and Priya S. Nair and Lena M. Schreiber},
  year = {2026},
  booktitle = {ACM Technical Symposium on Computer Science Education},
  pages = {214-221},
  address = {St. Louis, USA},
  publisher = {ACM},
  series = {SIGCSE TS '26},
  abstract = {Debugging support for beginners often alternates between too little guidance and complete solution disclosure. This study evaluates prompted debugging, a scaffold that asks learners to explain symptoms, localise faults, and predict fixes before receiving automated hints. Across 14 lab sections and 386 submissions, prompted debugging reduced repeated syntax-fix cycles by 27% and increased successful self-repair on semantic bugs. Interaction logs suggest the strongest gains came from prompts that asked students to compare expected and observed program state before editing code.},
}

S. H. Mamun, P. S. Nair, L. M. Schreiber, "Prompted Debugging as a Scaffold for Novice Programmers: Evidence from Lab-Scale Deployment," in ACM Technical Symposium on Computer Science Education, 2026, pp. 214-221.

Sabbir Hosen Mamun, Priya S. Nair, and Lena M. Schreiber. 2026. Prompted Debugging as a Scaffold for Novice Programmers: Evidence from Lab-Scale Deployment. In ACM Technical Symposium on Computer Science Education (St. Louis, USA). ACM, 214-221.

Mamun, S. H., Nair, P. S., & Schreiber, L. M. (2026). Prompted Debugging as a Scaffold for Novice Programmers: Evidence from Lab-Scale Deployment. In ACM Technical Symposium on Computer Science Education (pp. 214-221). ACM.

@misc{Mamun2026Rubric,
  title = {Rubric Drift in Large Programming Courses: Measuring and Mitigating Inconsistency Across Grading Teams},
  author = {Sabbir Hosen Mamun and Helena R. Novak and Daniel A. Fernández},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2601.09231}},
  note = {Preprint},
  abstract = {Large programming courses rely on teams of teaching assistants to apply shared rubrics, but grading expectations can drift as edge cases accumulate. We introduce a rubric drift metric based on disagreement trajectories over time and evaluate it on 18,742 graded submissions from three offerings of a second-year software engineering course. Calibration checkpoints reduced late-semester disagreement by 19%, while exemplar-based norming was most effective for design-quality criteria that lacked executable tests.},
}

S. H. Mamun, H. R. Novak, D. A. Fernández, "Rubric Drift in Large Programming Courses: Measuring and Mitigating Inconsistency Across Grading Teams," 2026.

Sabbir Hosen Mamun, Helena R. Novak, and Daniel A. Fernández. 2026. Rubric Drift in Large Programming Courses: Measuring and Mitigating Inconsistency Across Grading Teams. Preprint. https://arxiv.org/abs/2601.09231.

Mamun, S. H., Novak, H. R., & Fernández, D. A. (2026). Rubric Drift in Large Programming Courses: Measuring and Mitigating Inconsistency Across Grading Teams. Preprint. https://arxiv.org/abs/2601.09231

@article{Mamun2025Mining,
  title = {Mining Student Specifications from Test Suites to Diagnose Misconceptions in Software Design Assignments},
  author = {Sabbir Hosen Mamun and Marcus E. Hoffmann and Yuki Tanaka},
  year = {2025},
  journal = {ACM Transactions on Software Engineering and Methodology},
  volume = {34},
  number = {6},
  pages = {1-34},
  publisher = {ACM},
  abstract = {Student-written tests reveal how learners understand a required software design, but instructors rarely have time to inspect those tests manually. This article presents a specification-mining pipeline that clusters assertions, extracts behavioural expectations, and compares them against instructor reference models. In a dataset of 2,914 project repositories, mined specifications identified missing boundary cases and incorrect object responsibilities earlier than implementation-only analysis.},
}

S. H. Mamun, M. E. Hoffmann, Y. Tanaka, "Mining Student Specifications from Test Suites to Diagnose Misconceptions in Software Design Assignments," ACM Transactions on Software Engineering and Methodology, vol. 34, no. 6, pp. 1-34, 2025.

Sabbir Hosen Mamun, Marcus E. Hoffmann, and Yuki Tanaka. 2025. Mining Student Specifications from Test Suites to Diagnose Misconceptions in Software Design Assignments. ACM Transactions on Software Engineering and Methodology 34, 6, 1-34.

Mamun, S. H., Hoffmann, M. E., & Tanaka, Y. (2025). Mining Student Specifications from Test Suites to Diagnose Misconceptions in Software Design Assignments. ACM Transactions on Software Engineering and Methodology, 34(6), 1-34.

@inproceedings{Mamun2025Calibrating,
  title = {Calibrating Peer Review in Project-Based CS Courses with Lightweight Reviewer Training},
  author = {Sabbir Hosen Mamun and Beatriz M. Cardoso and Frederik A. Jensen},
  year = {2025},
  booktitle = {Koli Calling International Conference on Computing Education Research},
  pages = {77-88},
  address = {Koli, Finland},
  publisher = {ACM},
  abstract = {Peer review can scale feedback in project-based courses, but students need support to distinguish actionable critique from surface-level comments. We compare three reviewer-training formats: annotated examples, contrastive comment ranking, and short synchronous calibration. Contrastive ranking produced the largest improvement in review specificity, while synchronous calibration best reduced severity mismatch between student and instructor judgements.},
}

S. H. Mamun, B. M. Cardoso, F. A. Jensen, "Calibrating Peer Review in Project-Based CS Courses with Lightweight Reviewer Training," in Koli Calling International Conference on Computing Education Research, 2025, pp. 77-88.

Sabbir Hosen Mamun, Beatriz M. Cardoso, and Frederik A. Jensen. 2025. Calibrating Peer Review in Project-Based CS Courses with Lightweight Reviewer Training. In Koli Calling International Conference on Computing Education Research (Koli, Finland). ACM, 77-88.

Mamun, S. H., Cardoso, B. M., & Jensen, F. A. (2025). Calibrating Peer Review in Project-Based CS Courses with Lightweight Reviewer Training. In Koli Calling International Conference on Computing Education Research (pp. 77-88). ACM.

@inproceedings{Mamun2025Where,
  title = {Where Automated Feedback Should Stop: Boundary Cases for LLM Support in Programming Assignments},
  author = {Sabbir Hosen Mamun and Yuki Tanaka},
  year = {2025},
  booktitle = {Learning Analytics and Knowledge Workshop on AI-Supported Feedback},
  pages = {31-38},
  address = {Dublin, Ireland},
  publisher = {SoLAR},
  abstract = {Generative feedback systems can support learners, but they can also overstep by solving the task, masking uncertainty, or bypassing intended struggle. This workshop paper analyses 420 feedback interactions from an introductory programming course and proposes a boundary taxonomy for feedback granularity. The taxonomy distinguishes conceptual nudges, diagnostic explanations, implementation hints, and solution-equivalent disclosure.},
}

S. H. Mamun, Y. Tanaka, "Where Automated Feedback Should Stop: Boundary Cases for LLM Support in Programming Assignments," in Learning Analytics and Knowledge Workshop on AI-Supported Feedback, 2025, pp. 31-38.

Sabbir Hosen Mamun, and Yuki Tanaka. 2025. Where Automated Feedback Should Stop: Boundary Cases for LLM Support in Programming Assignments. In Learning Analytics and Knowledge Workshop on AI-Supported Feedback (Dublin, Ireland). SoLAR, 31-38.

Mamun, S. H., & Tanaka, Y. (2025). Where Automated Feedback Should Stop: Boundary Cases for LLM Support in Programming Assignments. In Learning Analytics and Knowledge Workshop on AI-Supported Feedback (pp. 31-38). SoLAR.

@misc{Mamun2025LLMs,
  title = {LLMs as On-Demand Teaching Assistants: Evidence from a Semester-Long Deployment Across Three CS Courses},
  author = {Sabbir Hosen Mamun and Yuki Tanaka and Helena R. Novak},
  year = {2025},
  howpublished = {\url{https://arxiv.org/abs/2502.12345}},
  note = {Preprint},
  abstract = {We report findings from a semester-long deployment of a retrieval-augmented LLM teaching assistant across three undergraduate CS courses (n = 412 students). The assistant answered 87% of student queries without instructor intervention, maintained factual accuracy above 92% on a held-out evaluation set constructed by course staff, and reduced after-hours instructor workload by an estimated 4.2 hours per week. Students reported high satisfaction (μ = 4.1 / 5) but expressed concern about over-reliance and reduced contact with instructors. We identify three failure modes — hallucinated API documentation, context collapse on multi-file debugging questions, and inconsistent enforcement of course-specific constraints — and discuss mitigation strategies. Implications for instructor workload, academic integrity policy, and pedagogical design are discussed.},
}

S. H. Mamun, Y. Tanaka, H. R. Novak, "LLMs as On-Demand Teaching Assistants: Evidence from a Semester-Long Deployment Across Three CS Courses," 2025.

Sabbir Hosen Mamun, Yuki Tanaka, and Helena R. Novak. 2025. LLMs as On-Demand Teaching Assistants: Evidence from a Semester-Long Deployment Across Three CS Courses. Preprint. https://arxiv.org/abs/2502.12345.

Mamun, S. H., Tanaka, Y., & Novak, H. R. (2025). LLMs as On-Demand Teaching Assistants: Evidence from a Semester-Long Deployment Across Three CS Courses. Preprint. https://arxiv.org/abs/2502.12345

@inproceedings{Mamun2024NotebookBased,
  title = {Notebook-Based Labs for Data Structures: Reducing Setup Friction Without Hiding Computational Cost},
  author = {Sabbir Hosen Mamun and Thorsten K. Wiegand and Priya S. Nair},
  year = {2024},
  booktitle = {ACM Conference on Innovation and Technology in Computer Science Education},
  pages = {319-326},
  address = {Milan, Italy},
  publisher = {ACM},
  series = {ITiCSE '24},
  abstract = {Interactive notebooks can make data-structures labs easier to start, but they risk obscuring runtime, memory, and modularity concerns. We describe a notebook lab design that exposes traces, heap diagrams, and timing probes alongside conventional implementation tasks. In two course offerings, students completed setup faster and produced more accurate complexity explanations, while final project performance remained comparable to the traditional IDE-based cohort.},
}

S. H. Mamun, T. K. Wiegand, P. S. Nair, "Notebook-Based Labs for Data Structures: Reducing Setup Friction Without Hiding Computational Cost," in ACM Conference on Innovation and Technology in Computer Science Education, 2024, pp. 319-326.

Sabbir Hosen Mamun, Thorsten K. Wiegand, and Priya S. Nair. 2024. Notebook-Based Labs for Data Structures: Reducing Setup Friction Without Hiding Computational Cost. In ACM Conference on Innovation and Technology in Computer Science Education (Milan, Italy). ACM, 319-326.

Mamun, S. H., Wiegand, T. K., & Nair, P. S. (2024). Notebook-Based Labs for Data Structures: Reducing Setup Friction Without Hiding Computational Cost. In ACM Conference on Innovation and Technology in Computer Science Education (pp. 319-326). ACM.

@inproceedings{Mamun2024ExplainCode,
  title = {ExplainCode: Towards Explainable Automated Feedback for Introductory Programming},
  author = {Sabbir Hosen Mamun and Priya S. Nair and Marcus E. Hoffmann and Beatriz M. Cardoso},
  year = {2024},
  booktitle = {ACM/IEEE International Conference on Software Engineering},
  pages = {1432–1444},
  address = {Lisbon, Portugal},
  publisher = {ACM},
  series = {ICSE '24},
  doi = {10.1145/3597503.3608128},
  abstract = {Automated feedback tools for introductory programming typically report what is wrong but not why, limiting their educational value. ExplainCode combines lightweight program analysis with a retrieval-augmented language model to generate student-facing explanations grounded in common misconception taxonomies. In a randomised controlled study (n = 96), learners who received explanatory feedback achieved significantly higher scores on the subsequent assignment (M = 72.4 vs. 61.3, t(94) = 3.12, p < 0.01) and rated the feedback as more actionable (4.2 vs. 2.8 on a 5-point scale). Qualitative coding of student responses reveals that explanation quality — not mere correctness — is the strongest predictor of follow-through.},
}

S. H. Mamun et al., "ExplainCode: Towards Explainable Automated Feedback for Introductory Programming," in ACM/IEEE International Conference on Software Engineering, 2024, pp. 1432–1444. doi: 10.1145/3597503.3608128.

Sabbir Hosen Mamun, Priya S. Nair, Marcus E. Hoffmann, and Beatriz M. Cardoso. 2024. ExplainCode: Towards Explainable Automated Feedback for Introductory Programming. In ACM/IEEE International Conference on Software Engineering (Lisbon, Portugal). ACM, 1432–1444. https://doi.org/10.1145/3597503.3608128

Mamun, S. H., Nair, P. S., Hoffmann, M. E., & Cardoso, B. M. (2024). ExplainCode: Towards Explainable Automated Feedback for Introductory Programming. In ACM/IEEE International Conference on Software Engineering (pp. 1432–1444). ACM. https://doi.org/10.1145/3597503.3608128

@inproceedings{Mamun2024Towards,
  title = {Towards Static-Analysis-Guided Grading of Student Code: Tool Design and Early Evaluation},
  author = {Sabbir Hosen Mamun and Thorsten K. Wiegand},
  year = {2024},
  booktitle = {Workshop on Software Engineering Education and Training},
  pages = {12–18},
  address = {Lisbon, Portugal},
  publisher = {ACM},
  doi = {10.1145/3643796.3648437},
  abstract = {Automated grading of student programming submissions typically relies on test-suite pass rates alone, missing structural and stylistic concerns that human graders would flag. We present a prototype that integrates static analysis metrics — cyclomatic complexity, coupling between objects, and naming-convention adherence — into a weighted rubric framework. A formative study with 48 student submissions shows the tool agrees with expert grader rankings at Spearman ρ = 0.81, with the largest disagreements occurring on submissions that pass all tests yet contain deeply nested control flow.},
}

S. H. Mamun, T. K. Wiegand, "Towards Static-Analysis-Guided Grading of Student Code: Tool Design and Early Evaluation," in Workshop on Software Engineering Education and Training, 2024, pp. 12–18. doi: 10.1145/3643796.3648437.

Sabbir Hosen Mamun, and Thorsten K. Wiegand. 2024. Towards Static-Analysis-Guided Grading of Student Code: Tool Design and Early Evaluation. In Workshop on Software Engineering Education and Training (Lisbon, Portugal). ACM, 12–18. https://doi.org/10.1145/3643796.3648437

Mamun, S. H., & Wiegand, T. K. (2024). Towards Static-Analysis-Guided Grading of Student Code: Tool Design and Early Evaluation. In Workshop on Software Engineering Education and Training (pp. 12–18). ACM. https://doi.org/10.1145/3643796.3648437

@article{Mamun2024Instructor,
  title = {Instructor Dashboards for Early Intervention in Programming Courses: A Field Evaluation},
  author = {Sabbir Hosen Mamun and Helena R. Novak and Lena M. Schreiber},
  year = {2024},
  journal = {International Journal of Artificial Intelligence in Education},
  volume = {34},
  number = {3},
  pages = {410-438},
  publisher = {Springer},
  abstract = {Early-warning dashboards promise timely instructor intervention, but many systems surface too many signals without indicating which actions are appropriate. We evaluate a dashboard that groups programming-course signals into progress, persistence, and misconception indicators, each linked to suggested instructor responses. Field deployment across 611 students showed faster identification of inactive groups and more targeted office-hour outreach, with instructors reporting lower triage effort.},
}

S. H. Mamun, H. R. Novak, L. M. Schreiber, "Instructor Dashboards for Early Intervention in Programming Courses: A Field Evaluation," International Journal of Artificial Intelligence in Education, vol. 34, no. 3, pp. 410-438, 2024.

Sabbir Hosen Mamun, Helena R. Novak, and Lena M. Schreiber. 2024. Instructor Dashboards for Early Intervention in Programming Courses: A Field Evaluation. International Journal of Artificial Intelligence in Education 34, 3, 410-438.

Mamun, S. H., Novak, H. R., & Schreiber, L. M. (2024). Instructor Dashboards for Early Intervention in Programming Courses: A Field Evaluation. International Journal of Artificial Intelligence in Education, 34(3), 410-438.

@inproceedings{Mamun2023Using,
  title = {Using Mutation Testing to Teach Test Adequacy in Undergraduate Software Engineering},
  author = {Sabbir Hosen Mamun and Daniel A. Fernández and Beatriz M. Cardoso},
  year = {2023},
  booktitle = {ACM Global Computing Education Conference},
  pages = {142-151},
  address = {Hyderabad, India},
  publisher = {ACM},
  abstract = {Students often interpret passing tests as evidence that a program is thoroughly validated. We introduce a sequence of mutation-testing labs that expose weak assertions, untested branches, and overfitted fixtures. Compared with a coverage-only baseline, mutation-based feedback led to stronger boundary-value tests and better explanations of why line coverage alone is insufficient.},
}

S. H. Mamun, D. A. Fernández, B. M. Cardoso, "Using Mutation Testing to Teach Test Adequacy in Undergraduate Software Engineering," in ACM Global Computing Education Conference, 2023, pp. 142-151.

Sabbir Hosen Mamun, Daniel A. Fernández, and Beatriz M. Cardoso. 2023. Using Mutation Testing to Teach Test Adequacy in Undergraduate Software Engineering. In ACM Global Computing Education Conference (Hyderabad, India). ACM, 142-151.

Mamun, S. H., Fernández, D. A., & Cardoso, B. M. (2023). Using Mutation Testing to Teach Test Adequacy in Undergraduate Software Engineering. In ACM Global Computing Education Conference (pp. 142-151). ACM.

@incollection{Mamun2023Formative,
  title = {Formative Assessment Strategies for Programming Courses},
  author = {Sabbir Hosen Mamun and Helena R. Novak},
  year = {2023},
  booktitle = {Handbook of Research on Computer Science Education},
  publisher = {Springer},
  pages = {281–308},
  series = {Springer Handbooks in Education},
  isbn = {978-3-031-29414-0},
  editor = {Sandra L. Morris and Frederik A. Jensen},
  doi = {10.1007/978-3-031-29414-0_12},
  abstract = {Formative assessment — evaluation intended to guide ongoing learning rather than certify outcomes — is particularly powerful in programming education, where learners benefit from rapid, specific feedback on executable artefacts. This chapter surveys strategies ranging from automated test-suite feedback and peer code review to reflective journals and office-hour protocols. We synthesise empirical evidence for each approach, discuss implementation considerations for large enrolments, and offer a practical framework for selecting and combining strategies based on course context, instructor capacity, and student experience level.},
}

S. H. Mamun, H. R. Novak, "Formative Assessment Strategies for Programming Courses," in Handbook of Research on Computer Science Education, Springer, 2023. doi: 10.1007/978-3-031-29414-0_12.

Sabbir Hosen Mamun, and Helena R. Novak. 2023. Formative Assessment Strategies for Programming Courses. In Handbook of Research on Computer Science Education, Sandra L. Morris and Frederik A. Jensen (Eds.). Springer, 281–308. https://doi.org/10.1007/978-3-031-29414-0_12

Mamun, S. H., & Novak, H. R. (2023). Formative Assessment Strategies for Programming Courses. In Sandra L. Morris & Frederik A. Jensen (Eds.), Handbook of Research on Computer Science Education (pp. 281–308). Springer. https://doi.org/10.1007/978-3-031-29414-0_12

@inproceedings{Mamun2023Teaching,
  title = {Teaching Systems Programming Through Incremental Complexity: A Two-Semester Evaluation},
  author = {Sabbir Hosen Mamun and Beatriz M. Cardoso},
  year = {2023},
  booktitle = {ACM Technical Symposium on Computer Science Education},
  pages = {450–456},
  address = {Toronto, Canada},
  publisher = {ACM},
  series = {SIGCSE TS '23},
  doi = {10.1145/3545945.3569781},
  abstract = {Introductory systems courses frequently overwhelm students by introducing pointers, memory management, and concurrency simultaneously. We describe a course redesign that introduces each concept in isolation using a sequence of small, self-contained projects. Evaluation across two semesters (n = 184) shows a 31% reduction in assignment failure rates and improved retention of pointer semantics on post-course assessments (Cohen's d = 0.74). A replication package including course materials and anonymised grade data is publicly available.},
}

S. H. Mamun, B. M. Cardoso, "Teaching Systems Programming Through Incremental Complexity: A Two-Semester Evaluation," in ACM Technical Symposium on Computer Science Education, 2023, pp. 450–456. doi: 10.1145/3545945.3569781.

Sabbir Hosen Mamun, and Beatriz M. Cardoso. 2023. Teaching Systems Programming Through Incremental Complexity: A Two-Semester Evaluation. In ACM Technical Symposium on Computer Science Education (Toronto, Canada). ACM, 450–456. https://doi.org/10.1145/3545945.3569781

Mamun, S. H., & Cardoso, B. M. (2023). Teaching Systems Programming Through Incremental Complexity: A Two-Semester Evaluation. In ACM Technical Symposium on Computer Science Education (pp. 450–456). ACM. https://doi.org/10.1145/3545945.3569781

@article{Mamun2022Lightweight,
  title = {Lightweight Feedback Loops in Undergraduate Software Projects: A Controlled Study Across Four Cohorts},
  author = {Sabbir Hosen Mamun and Helena R. Novak and Joris T. Vandenberghe},
  year = {2022},
  journal = {IEEE Transactions on Education},
  volume = {65},
  number = {4},
  pages = {88–102},
  publisher = {IEEE},
  doi = {10.1109/TE.2022.3168402},
  abstract = {Frequent, low-overhead feedback during undergraduate software projects improves both product quality and student confidence. This paper examines three lightweight feedback practices — weekly check-ins, automated diff reviews, and peer walkthroughs — applied across four cohorts totalling 312 students. We find statistically significant improvements in on-time delivery (p < 0.01) and self-reported understanding of version control workflows (Cohen's d = 0.61). Peer walkthroughs produced the largest effect on code quality metrics, while automated diff reviews had the strongest correlation with final-examination performance.},
}

S. H. Mamun, H. R. Novak, J. T. Vandenberghe, "Lightweight Feedback Loops in Undergraduate Software Projects: A Controlled Study Across Four Cohorts," IEEE Transactions on Education, vol. 65, no. 4, pp. 88–102, 2022. doi: 10.1109/TE.2022.3168402.

Sabbir Hosen Mamun, Helena R. Novak, and Joris T. Vandenberghe. 2022. Lightweight Feedback Loops in Undergraduate Software Projects: A Controlled Study Across Four Cohorts. IEEE Transactions on Education 65, 4, 88–102. https://doi.org/10.1109/TE.2022.3168402

Mamun, S. H., Novak, H. R., & Vandenberghe, J. T. (2022). Lightweight Feedback Loops in Undergraduate Software Projects: A Controlled Study Across Four Cohorts. IEEE Transactions on Education, 65(4), 88–102. https://doi.org/10.1109/TE.2022.3168402

@inproceedings{Mamun2022Equitable,
  title = {Equitable Autograding Policies for Programming Assignments: Late Work, Resubmission, and Feedback Timing},
  author = {Sabbir Hosen Mamun and Helena R. Novak},
  year = {2022},
  booktitle = {Educational Data Mining Workshop on Computer Science Education},
  pages = {55-62},
  address = {Durham, UK},
  abstract = {Autograders shape student work patterns through policy choices as much as through test results. This paper compares late-penalty, resubmission-limit, and feedback-delay policies across archival traces from four programming courses. Flexible resubmission policies improved eventual correctness without increasing final-hour submission spikes, while delayed feedback disproportionately affected students with less prior programming experience.},
}

S. H. Mamun, H. R. Novak, "Equitable Autograding Policies for Programming Assignments: Late Work, Resubmission, and Feedback Timing," in Educational Data Mining Workshop on Computer Science Education, 2022, pp. 55-62.

Sabbir Hosen Mamun, and Helena R. Novak. 2022. Equitable Autograding Policies for Programming Assignments: Late Work, Resubmission, and Feedback Timing. In Educational Data Mining Workshop on Computer Science Education (Durham, UK), 55-62.

Mamun, S. H., & Novak, H. R. (2022). Equitable Autograding Policies for Programming Assignments: Late Work, Resubmission, and Feedback Timing. In Educational Data Mining Workshop on Computer Science Education (pp. 55-62).

@article{Mamun2021Integrating,
  title = {Integrating Continuous Integration Practices into Software Engineering Curricula: A Multi-Institution Study},
  author = {Sabbir Hosen Mamun and Joris T. Vandenberghe and Daniel A. Fernández},
  year = {2021},
  journal = {Journal of Systems and Software},
  volume = {182},
  pages = {111043},
  publisher = {Elsevier},
  doi = {10.1016/j.jss.2021.111043},
  abstract = {Continuous integration (CI) practices are standard in industry but poorly represented in undergraduate curricula. This paper reports a multi-institution study spanning six universities, 18 course sections, and 742 students, evaluating the impact of embedding CI tools — automated build, lint, and test pipelines — into a second-year software engineering course. Groups using CI submitted more frequently, had lower defect rates on final deliverables (–23%), and self-reported higher confidence in collaborative development workflows. Regression analysis shows that early adoption of CI in the first two weeks of a project is the strongest predictor of on-time delivery (β = 0.41, p < 0.001). We provide a full replication package including course materials and anonymised grade and commit data.},
}

S. H. Mamun, J. T. Vandenberghe, D. A. Fernández, "Integrating Continuous Integration Practices into Software Engineering Curricula: A Multi-Institution Study," Journal of Systems and Software, vol. 182, pp. 111043, 2021. doi: 10.1016/j.jss.2021.111043.

Sabbir Hosen Mamun, Joris T. Vandenberghe, and Daniel A. Fernández. 2021. Integrating Continuous Integration Practices into Software Engineering Curricula: A Multi-Institution Study. Journal of Systems and Software 182, 111043. https://doi.org/10.1016/j.jss.2021.111043

Mamun, S. H., Vandenberghe, J. T., & Fernández, D. A. (2021). Integrating Continuous Integration Practices into Software Engineering Curricula: A Multi-Institution Study. Journal of Systems and Software, 182, 111043. https://doi.org/10.1016/j.jss.2021.111043

@inproceedings{Mamun2021A,
  title = {A Misconception Taxonomy for State, References, and Mutation in Introductory Python},
  author = {Sabbir Hosen Mamun and Joris T. Vandenberghe},
  year = {2021},
  booktitle = {ACM International Computing Education Research Conference},
  pages = {201-214},
  address = {Virtual Event},
  publisher = {ACM},
  abstract = {Misconceptions about state and mutation are difficult to diagnose because incorrect programs can fail for many surface-level reasons. We derive a taxonomy from think-aloud sessions, exam responses, and 8,300 submitted Python programs. The resulting categories distinguish aliasing errors, stale mental models of loop variables, and incorrect assumptions about object identity, providing a basis for targeted automated feedback.},
}

S. H. Mamun, J. T. Vandenberghe, "A Misconception Taxonomy for State, References, and Mutation in Introductory Python," in ACM International Computing Education Research Conference, 2021, pp. 201-214.

Sabbir Hosen Mamun, and Joris T. Vandenberghe. 2021. A Misconception Taxonomy for State, References, and Mutation in Introductory Python. In ACM International Computing Education Research Conference (Virtual Event). ACM, 201-214.

Mamun, S. H., & Vandenberghe, J. T. (2021). A Misconception Taxonomy for State, References, and Mutation in Introductory Python. In ACM International Computing Education Research Conference (pp. 201-214). ACM.

@inproceedings{Mamun2019From,
  title = {From Compiler Errors to Learning Opportunities: Rewriting Diagnostics for First-Semester Programmers},
  author = {Sabbir Hosen Mamun and Sandra L. Morris},
  year = {2019},
  booktitle = {IEEE Frontiers in Education Conference},
  pages = {1-8},
  address = {Covington, USA},
  publisher = {IEEE},
  abstract = {Compiler diagnostics are written for programmers who already understand the language model. This paper reports a redesign of common C compiler messages for first-semester learners, replacing terse error labels with explanations, examples, and next-step checks. In a controlled lab study, rewritten diagnostics reduced help requests for syntax errors and improved students' ability to describe the cause of failed compilation.},
}

S. H. Mamun, S. L. Morris, "From Compiler Errors to Learning Opportunities: Rewriting Diagnostics for First-Semester Programmers," in IEEE Frontiers in Education Conference, 2019, pp. 1-8.

Sabbir Hosen Mamun, and Sandra L. Morris. 2019. From Compiler Errors to Learning Opportunities: Rewriting Diagnostics for First-Semester Programmers. In IEEE Frontiers in Education Conference (Covington, USA). IEEE, 1-8.

Mamun, S. H., & Morris, S. L. (2019). From Compiler Errors to Learning Opportunities: Rewriting Diagnostics for First-Semester Programmers. In IEEE Frontiers in Education Conference (pp. 1-8). IEEE.