Publications

Preprints

Gold, G., Asher, M. W., & Carvalho, P. F. (2025). Well-calibrated intuitions, flawed judgments: Low post-instruction self-efficacy steers students away from efficient learning. OSF Preprints. https://osf.io/preprints/osf/6bq4d_v1
```
@unpublished{gold_asher_carvalho_2025,
  title = {Well-calibrated intuitions, flawed judgments: Low post-instruction self-efficacy steers students away from efficient learning},
  url = {https://osf.io/preprints/osf/6bq4d_v1},
  publisher = {OSF Preprints},
  author = {Gold, Gillian and Asher, Michael W and Carvalho, Paulo F},
  year = {2025},
  month = jun
}
```
Hridi, A. P., Hoq, M., Gao, Z., Lynch, C., Sahay, R., Hosseinalipour, S., & Akram, B. (2025). Privacy-Preserving Distributed Link Predictions Among Peers in Online Classrooms Using Federated Learning. https://arxiv.org/abs/2504.10456
```
@unpublished{hridi2025privacypreservingdistributedlinkpredictions,
  title = {Privacy-Preserving Distributed Link Predictions Among Peers in Online Classrooms Using Federated Learning},
  author = {Hridi, Anurata Prabha and Hoq, Muntasir and Gao, Zhikai and Lynch, Collin and Sahay, Rajeev and Hosseinalipour, Seyyedali and Akram, Bita},
  year = {2025},
  eprint = {2504.10456},
  archiveprefix = {arXiv},
  primaryclass = {cs.SI},
  url = {https://arxiv.org/abs/2504.10456}
}
```
Duan, Z., Fernandez, N., Narayanan, A. B. L., Hassany, M., de Alencar, R. S., Brusilovsky, P., Akram, B., & Lan, A. (2025). Automated Knowledge Component Generation and Knowledge Tracing for Coding Problems. https://arxiv.org/abs/2502.18632
```
@unpublished{duan2025automatedknowledgecomponentgeneration,
  title = {Automated Knowledge Component Generation and Knowledge Tracing for Coding Problems},
  author = {Duan, Zhangqi and Fernandez, Nigel and Narayanan, Arun Balajiee Lekshmi and Hassany, Mohammad and de Alencar, Rafaella Sampaio and Brusilovsky, Peter and Akram, Bita and Lan, Andrew},
  year = {2025},
  eprint = {2502.18632},
  archiveprefix = {arXiv},
  primaryclass = {cs.AI},
  url = {https://arxiv.org/abs/2502.18632}
}
```
Ebrahimi, M., Sahay, R., Hosseinalipour, S., & Akram, B. (2025). The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions. https://arxiv.org/abs/2501.11714
```
@unpublished{ebrahimi2025transitioncentralizedmachinelearning,
  title = {The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions},
  author = {Ebrahimi, Maryam and Sahay, Rajeev and Hosseinalipour, Seyyedali and Akram, Bita},
  year = {2025},
  eprint = {2501.11714},
  archiveprefix = {arXiv},
  primaryclass = {cs.CY},
  url = {https://arxiv.org/abs/2501.11714}
}
```
Wei, Y., Carvalho, P. F., & Stamper, J. (2024). Uncovering Name-Based Biases in Large Language Models Through Simulated Trust Game. https://arxiv.org/abs/2404.14682
```
@unpublished{wei2024uncoveringnamebasedbiaseslarge,
  title = {Uncovering Name-Based Biases in Large Language Models Through Simulated Trust Game},
  author = {Wei, Yumou and Carvalho, Paulo F. and Stamper, John},
  year = {2024},
  eprint = {2404.14682},
  archiveprefix = {arXiv},
  primaryclass = {cs.CY},
  url = {https://arxiv.org/abs/2404.14682}
}
```

Refereed journal articles

Asher, M. W., Sana, F., Koedinger, K. R., & Carvalho, P. F. (2025). Practice with feedback versus lecture: Consequences for learning, efficiency, and motivation. Journal of Applied Research in Memory and Cognition.
```
@article{asher2025practice,
  title = {Practice with feedback versus lecture: Consequences for learning, efficiency, and motivation.},
  author = {Asher, Michael W and Sana, Faria and Koedinger, Kenneth R and Carvalho, Paulo F},
  journal = {Journal of Applied Research in Memory and Cognition},
  year = {2025},
  publisher = {Educational Publishing Foundation}
}
```
Hridi, A. P., Sahay, R., Hosseinalipour, S., & Akram, B. (2024). Revolutionizing AI-Assisted Education with Federated Learning: A Pathway to Distributed, Privacy-Preserving, and Debiased Learning Ecosystems. Proceedings of the AAAI Symposium Series, 3(1), 297–303. https://ojs.aaai.org/index.php/AAAI-SS/article/view/31217
```
@article{Hridi_Sahay_Hosseinalipour_Akram_2024,
  title = {Revolutionizing AI-Assisted Education with Federated Learning: A Pathway to Distributed, Privacy-Preserving, and Debiased Learning Ecosystems},
  volume = {3},
  url = {https://ojs.aaai.org/index.php/AAAI-SS/article/view/31217},
  doi = {10.1609/aaaiss.v3i1.31217},
  number = {1},
  journal = {Proceedings of the AAAI Symposium Series},
  author = {Hridi, Anurata Prabha and Sahay, Rajeev and Hosseinalipour, Seyyedali and Akram, Bita},
  year = {2024},
  month = may,
  pages = {297--303}
}
```
Hoq, M., Brusilovsky, P., Akram, B., & others. (2024). Explaining explainability: Early performance prediction with student programming pattern profiling. Journal of Educational Data Mining, 16(2), 115–148.
```
@article{hoq2024explaining,
  title = {Explaining explainability: Early performance prediction with student programming pattern profiling},
  author = {Hoq, Muntasir and Brusilovsky, Peter and Akram, Bita and others},
  journal = {Journal of Educational Data Mining},
  volume = {16},
  number = {2},
  pages = {115--148},
  year = {2024}
}
```
Brusilovsky, P. (2024). Intelligent Technologies for Personalized Practice Systems. Information and Technology in Education and Learning, 4(1), Inv–p001.
```
@article{brusilovsky2024intelligent,
  title = {Intelligent Technologies for Personalized Practice Systems},
  author = {Brusilovsky, Peter},
  journal = {Information and Technology in Education and Learning},
  volume = {4},
  number = {1},
  pages = {Inv--p001},
  year = {2024},
  publisher = {Japan Society for Educational Technology \& Japanese Society for Information~…}
}
```
Carvalho, P. F., McLaughlin, E. A., & Koedinger, K. R. (2022). Varied practice testing is associated with better learning outcomes in self-regulated online learning. Journal of Educational Psychology, 114(8), 1723–1742.
```
@article{carvalho-etal-2022-practice-testing,
  title = {Varied practice testing is associated with better learning outcomes in self-regulated online learning},
  author = {Carvalho, Paulo F. and McLaughlin, Elizabeth A. and Koedinger, Kenneth R.},
  year = {2022},
  journal = {Journal of Educational Psychology},
  volume = {114},
  number = {8},
  pages = {1723--1742},
  doi = {10.1037/edu0000754}
}
```
Brusilovsky, P., Malmi, L., Hosseini, R., Guerra, J., Sirkiä, T., & Pollari-Malmi, K. (2018). An integrated practice system for learning programming in Python: design and evaluation [Journal Article]. Research and Practice in Technology Enhanced Learning, 13(18), 18.1–18.40. https://doi.org/10.1186/s41039-018-0085-9
```
@article{RN5231,
  author = {Brusilovsky, Peter and Malmi, Lauri and Hosseini, Roya and Guerra, Julio and Sirkiä, Teemu and Pollari-Malmi, Kerttu},
  title = {An integrated practice system for learning programming in Python: design and evaluation},
  journal = {Research and Practice in Technology Enhanced Learning},
  volume = {13},
  number = {18},
  pages = {18.1-18.40},
  doi = {10.1186/s41039-018-0085-9},
  url = {https://doi.org/10.1186/s41039-018-0085-9},
  year = {2018},
  type = {Journal Article}
}
```
Brusilovsky, P., Somyurek, S., Guerra, J., Hosseini, R., Zadorozhny, V., & Durlach, P. (2016). Open Social Student Modeling for Personalized Learning [Journal Article]. IEEE Transactions on Emerging Topics in Computing, 4(3), 450–461. http://doi.ieeecomputersociety.org/10.1109/TETC.2015.2501243
```
@article{RN4852,
  author = {Brusilovsky, Peter and Somyurek, Sibel and Guerra, Julio and Hosseini, Roya and Zadorozhny, Vladimir and Durlach, Paula},
  title = {Open Social Student Modeling for Personalized Learning},
  journal = {IEEE Transactions on Emerging Topics in Computing},
  volume = {4},
  number = {3},
  pages = {450-461},
  doi = {10.1109/TETC.2015.2501243},
  url = {http://doi.ieeecomputersociety.org/10.1109/TETC.2015.2501243},
  year = {2016},
  type = {Journal Article}
}
```
Akram, B., Min, W., Wiebe, E., Mott, B., Boyer, K. E., & Lester, J. Improving Stealth Assessment in Game-Based Learning with LSTM-Based Analytics. International Conference on Educational Data Mining. https://par.nsf.gov/biblio/10100664
```
@article{osti_10100664,
  title = {Improving Stealth Assessment in Game-Based Learning with LSTM-Based Analytics},
  url = {https://par.nsf.gov/biblio/10100664},
  journal = {International Conference on Educational Data Mining},
  author = {Akram, Bita and Min, W. and Wiebe, E. and Mott, B. and Boyer, K. E. and Lester, J.}
}
```

Refereed conference proceedings

Lekshmi-Narayanan, A.-B., Asher, M. W., Brusilovsky, P., & Carvalho, P. (2025). Can Motivated Students Do More Activities?
```
@inproceedings{lekshmi2025can,
  title = {Can Motivated Students Do More Activities?},
  author = {Lekshmi-Narayanan, Arun-Balajiee and Asher, Michael W and Brusilovsky, Peter and Carvalho, Paulo},
  year = {2025},
  organization = {9th Educational Data Mining in Computer Science Education (CSEDM) Workshop~…}
}
```
Sampaio de Alencar, R., Demirtas, M. A., Saha, A. S., Shi, Y., & Brusilovsky, P. (2025). Integrating Expert Knowledge With Automated Knowledge Component Extraction for Student Modeling. Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization, 307–312.
```
@inproceedings{sampaio2025integrating,
  title = {Integrating Expert Knowledge With Automated Knowledge Component Extraction for Student Modeling},
  author = {Sampaio de Alencar, Rafaella and Demirtas, Mehmet Arif and Saha, Adittya Soukarjya and Shi, Yang and Brusilovsky, Peter},
  booktitle = {Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization},
  pages = {307--312},
  year = {2025}
}
```
Poh, A., Hridi, A., Barria-Pineda, J., Brusilovsky, P., & Akram, B. (2025). Example Explorers and Persistent Finishers: Exploring Student Practice Behaviors in a Python Practice System.
```
@inproceedings{poh2025example,
  title = {Example Explorers and Persistent Finishers: Exploring Student Practice Behaviors in a Python Practice System},
  author = {Poh, Allison and Hridi, Anurata and Barria-Pineda, Jordan and Brusilovsky, Peter and Akram, Bita},
  year = {2025},
  organization = {Proceedings of 9th Educational Data Mining in Computer Science Education~…}
}
```
Heickal, H., & Lan, A. (2025). Learning Code-Edit Embeddings to Model Student Debugging Behavior. International Conference on Artificial Intelligence in Education, 91–98.
```
@inproceedings{heickal2025learning,
  title = {Learning Code-Edit Embeddings to Model Student Debugging Behavior},
  author = {Heickal, Hasnain and Lan, Andrew},
  booktitle = {International Conference on Artificial Intelligence in Education},
  pages = {91--98},
  year = {2025},
  organization = {Springer}
}
```
Hoq, M., Patil, A., Akhuseyinoglu, K., Brusilovsky, P., & Akram, B. (2025). An automated approach to recommending relevant worked examples for programming problems. Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 1, 527–533.
```
@inproceedings{hoq2025automated,
  title = {An automated approach to recommending relevant worked examples for programming problems},
  author = {Hoq, Muntasir and Patil, Atharva and Akhuseyinoglu, Kamil and Brusilovsky, Peter and Akram, Bita},
  booktitle = {Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 1},
  pages = {527--533},
  year = {2025}
}
```
Gold, G., Asher, M. W., & Carvalho, P. F. (2025). To Honor or Dishonor Student Choices? The Impact of Self-Regulation on Instructional Methods and Learning Outcomes. Proceedings of the Annual Meeting of the Cognitive Science Society, 47.
```
@inproceedings{gold2025honor,
  title = {To Honor or Dishonor Student Choices? The Impact of Self-Regulation on Instructional Methods and Learning Outcomes},
  author = {Gold, Gillian and Asher, Michael W and Carvalho, Paulo F},
  booktitle = {Proceedings of the Annual Meeting of the Cognitive Science Society},
  volume = {47},
  year = {2025}
}
```
Barria-Pineda, J., Sonmez Unal, D., Akhuseyinoglu, K., Brusilovsky, P., & Walker, E. (2025). Using Self-regulated Learning Theory to Inform the Design of Educational Recommender Systems for Introductory Programming. International Conference on Artificial Intelligence in Education, 276–284.
```
@inproceedings{barria2025using,
  title = {Using Self-regulated Learning Theory to Inform the Design of Educational Recommender Systems for Introductory Programming},
  author = {Barria-Pineda, Jordan and Sonmez Unal, Deniz and Akhuseyinoglu, Kamil and Brusilovsky, Peter and Walker, Erin},
  booktitle = {International Conference on Artificial Intelligence in Education},
  pages = {276--284},
  year = {2025},
  organization = {Springer}
}
```
Duan, Z., Fernandez, N., Hicks, A., & Lan, A. (2025). Test case-informed knowledge tracing for open-ended coding tasks. Proceedings of the 15th International Learning Analytics and Knowledge Conference, 238–248.
```
@inproceedings{duan2025test,
  title = {Test case-informed knowledge tracing for open-ended coding tasks},
  author = {Duan, Zhangqi and Fernandez, Nigel and Hicks, Alexander and Lan, Andrew},
  booktitle = {Proceedings of the 15th International Learning Analytics and Knowledge Conference},
  pages = {238--248},
  year = {2025}
}
```
Akhuseyinoglu, K., Klašnja-Milicevic, A., & Brusilovsky, P. (2024). The impact of connecting worked examples and completion problems for introductory programming practice. European Conference on Technology Enhanced Learning, 3–18.
```
@inproceedings{akhuseyinoglu2024impact,
  title = {The impact of connecting worked examples and completion problems for introductory programming practice},
  author = {Akhuseyinoglu, Kamil and Kla{\v{s}}nja-Milicevic, Aleksandra and Brusilovsky, Peter},
  booktitle = {European Conference on Technology Enhanced Learning},
  pages = {3--18},
  year = {2024},
  organization = {Springer}
}
```

Hoq, M., Shi, Y., Leinonen, J., Babalola, D., Lynch, C., Price, T., & Akram, B. (2024). Detecting ChatGPT-Generated Code Submissions in a CS1 Course Using Machine Learning Models. Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1, 526–532. https://doi.org/10.1145/3626252.3630826

@inproceedings{10.1145/3626252.3630826,
  author = {Hoq, Muntasir and Shi, Yang and Leinonen, Juho and Babalola, Damilola and Lynch, Collin and Price, Thomas and Akram, Bita},
  title = {Detecting ChatGPT-Generated Code Submissions in a CS1 Course Using Machine Learning Models},
  year = {2024},
  isbn = {9798400704239},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3626252.3630826},
  doi = {10.1145/3626252.3630826},
  booktitle = {Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1},
  pages = {526–532},
  numpages = {7},
  keywords = {artificial intelligence, chatgpt, cheat detection, cs1, introductory programming course, large language model, plagiarism detection},
  location = {Portland, OR, USA},
  series = {SIGCSE 2024}
}

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.

Hoq, M., Brusilovsky, P., & Akram, B. (2023, July). Analysis of an Explainable Student Performance Prediction Model in an Introductory Programming Course. Paper Presented at the International Conference on Educational Data Mining (EDM).
```
@inproceedings{hoq-etal-2023-explainable-model,
  title = {Analysis of an Explainable Student Performance Prediction Model in an Introductory Programming Course},
  author = {Hoq, Muntasir and Brusilovsky, Peter and Akram, Bita},
  year = {2023},
  month = jul,
  booktitle = {Paper presented at the International Conference on Educational Data Mining (EDM)},
  address = {Bengaluru, India}
}
```

Hoq, M., Chilla, S. R., Ahmadi Ranjbar, M., Brusilovsky, P., & Akram, B. (2023). SANN: Programming Code Representation Using Attention Neural Network with Optimized Subtree Extraction. Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 783–792. https://doi.org/10.1145/3583780.3615047

@inproceedings{10.1145/3583780.3615047,
  author = {Hoq, Muntasir and Chilla, Sushanth Reddy and Ahmadi Ranjbar, Melika and Brusilovsky, Peter and Akram, Bita},
  title = {SANN: Programming Code Representation Using Attention Neural Network with Optimized Subtree Extraction},
  year = {2023},
  isbn = {9798400701245},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3583780.3615047},
  doi = {10.1145/3583780.3615047},
  booktitle = {Proceedings of the 32nd ACM International Conference on Information and Knowledge Management},
  pages = {783–792},
  numpages = {10},
  keywords = {algorithm detection, code representation, program analysis, program correctness prediction, static analysis},
  location = {Birmingham, United Kingdom},
  series = {CIKM '23}
}

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.

Akram, B., & Magooda, A. (2023). Analysis of Students’ Problem-Solving Behavior when Using Copilot for Open-Ended Programming Projects. Proceedings of the 2023 ACM Conference on International Computing Education Research - Volume 2, 32. https://doi.org/10.1145/3568812.3603487
```
@inproceedings{10.1145/3568812.3603487,
  author = {Akram, Bita and Magooda, Ahmed},
  title = {Analysis of Students’ Problem-Solving Behavior when Using Copilot for Open-Ended Programming Projects},
  year = {2023},
  isbn = {9781450399753},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3568812.3603487},
  doi = {10.1145/3568812.3603487},
  booktitle = {Proceedings of the 2023 ACM Conference on International Computing Education Research - Volume 2},
  pages = {32},
  numpages = {1},
  keywords = {CS1, Copilot, generative AI in CS education, introductory programming classrooms},
  location = {Chicago, IL, USA},
  series = {ICER '23}
}
```
Barria-Pineda, J., Akhuseyinoglu, K., & Brusilovsky, P. (2023). Adaptive Navigational Support and Explainable Recommendations in a Personalized Programming Practice System [Conference Proceedings]. 34th ACM Conference on Hypertext and Social Media, 1–9. https://dl.acm.org/doi/10.1145/3603163.3609054
```
@inproceedings{RN5964,
  author = {Barria-Pineda, Jordan and Akhuseyinoglu, Kamil and Brusilovsky, Peter},
  title = {Adaptive Navigational Support and Explainable Recommendations in a Personalized Programming Practice System},
  booktitle = {34th ACM Conference on Hypertext and Social Media},
  publisher = {ACM},
  pages = {1-9},
  doi = {10.1145/3603163.3609054},
  url = {https://dl.acm.org/doi/10.1145/3603163.3609054},
  year = {2023},
  type = {Conference Proceedings}
}
```

Liu, N., Wang, Z., Baraniuk, R., & Lan, A. (2022). Open-ended Knowledge Tracing for Computer Science Education. In Y. Goldberg, Z. Kozareva, & Y. Zhang (Eds.), Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (pp. 3849–3862). Association for Computational Linguistics. https://aclanthology.org/2022.emnlp-main.254

@inproceedings{liu-etal-2022-open,
  title = {Open-ended Knowledge Tracing for Computer Science Education},
  author = {Liu, Naiming and Wang, Zichao and Baraniuk, Richard and Lan, Andrew},
  editor = {Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue},
  booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
  month = dec,
  year = {2022},
  address = {Abu Dhabi, United Arab Emirates},
  publisher = {Association for Computational Linguistics},
  url = {https://aclanthology.org/2022.emnlp-main.254},
  doi = {10.18653/v1/2022.emnlp-main.254},
  pages = {3849--3862}
}

In educational applications, knowledge tracing refers to the problem of estimating students’ time-varying concept/skill mastery level from their past responses to questions and predicting their future performance.One key limitation of most existing knowledge tracing methods is that they treat student responses to questions as binary-valued, i.e., whether they are correct or incorrect. Response correctness analysis/prediction is straightforward, but it ignores important information regarding mastery, especially for open-ended questions.In contrast, exact student responses can provide much more information.In this paper, we conduct the first exploration int open-ended knowledge tracing (OKT) by studying the new task of predicting students’ exact open-ended responses to questions.Our work is grounded in the domain of computer science education with programming questions. We develop an initial solution to the OKT problem, a student knowledge-guided code generation approach, that combines program synthesis methods using language models with student knowledge tracing methods. We also conduct a series of quantitative and qualitative experiments on a real-world student code dataset to validate and demonstrate the promise of OKT.

Barria-Pineda, J., Akhuseyinoglu, K., Želem-Ćelap, S., Brusilovsky, P., Klasnja Milicevic, A., & Ivanovic, M. (2021). Explainable Recommendations in a Personalized Programming Practice System [Conference Proceedings]. In I. Roll, D. McNamara, S. Sosnovsky, R. Luckin, & V. Dimitrova (Eds.), 22nd International Conference on Artificial Intelligence in Education, AIED 2021 (Vol. 12748, pp. 64–76). Springer. https://doi.org/10.1007/978-3-030-78292-4_6
```
@inproceedings{RN5686,
  author = {Barria-Pineda, Jordan and Akhuseyinoglu, Kamil and Želem-Ćelap, Stefan and Brusilovsky, Peter and Klasnja Milicevic, Aleksandra and Ivanovic, Mirjana},
  title = {Explainable Recommendations in a Personalized Programming Practice System},
  booktitle = {22nd International Conference on Artificial Intelligence in Education, AIED 2021},
  editor = {Roll, Ido and McNamara, Danielle and Sosnovsky, Sergey and Luckin, Rose and Dimitrova, Vania},
  series = {Lecture Notes in Computer Science},
  address = {Cham},
  publisher = {Springer},
  volume = {12748},
  pages = {64-76},
  url = {https://doi.org/10.1007/978-3-030-78292-4_6},
  year = {2021},
  type = {Conference Proceedings}
}
```

Thaker, K., Carvalho, P., & Koedinger, K. (2019). Comprehension Factor Analysis: Modeling student’s reading behaviour: Accounting for reading practice in predicting students’ learning in MOOCs. Proceedings of the 9th International Conference on Learning Analytics & Knowledge, 111–115. https://doi.org/10.1145/3303772.3303817

@inproceedings{thaker-etal-2019-cfa,
  author = {Thaker, Khushboo and Carvalho, Paulo and Koedinger, Kenneth},
  title = {Comprehension Factor Analysis: Modeling student's reading behaviour: Accounting for reading practice in predicting students' learning in MOOCs},
  year = {2019},
  isbn = {9781450362566},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3303772.3303817},
  doi = {10.1145/3303772.3303817},
  booktitle = {Proceedings of the 9th International Conference on Learning Analytics \& Knowledge},
  pages = {111–115},
  numpages = {5},
  keywords = {Education Data Mining, MOOCs, Reading Behaviour, Student modeling},
  location = {Tempe, AZ, USA},
  series = {LAK19}
}

Massive Open Online Courses (MOOCs) often incorporate lecture-based learning along with lecture notes, textbooks, and videos to students. Moreover, MOOCs also incorporate practice activities and quizzes. Student learning in MOOCs can be tracked and improved using state-of-the-art student modeling. Currently, this means employing conventional student models that are constructed around Intelligent Tutoring Systems (ITS). Traditional ITS systems only utilize students performance interactions (quiz, problem-solving or practice activities). Therefore, text interactions are entirely ignored while modeling students performance in MOOCs using these cognitive models. In this work, we propose a Comprehension Factor Analysis model (CFM) for online courses, which integrates student reading interactions in student models to track and predict learning outcomes. Our model evaluation shows that CFM outperforms state-of-the-art models in predicting students’ performance in a MOOC. These models can help better student-wise adaptation in the context of MOOCs.

Chounta, I.-A., & Carvalho, P. F. (2019). Square it up! How to model step duration when predicting student performance. Proceedings of the 9th International Conference on Learning Analytics & Knowledge, 330–334. https://doi.org/10.1145/3303772.3303827

@inproceedings{chounta-carvalho-2019-square-it-up,
  author = {Chounta, Irene-Angelica and Carvalho, Paulo F.},
  title = {Square it up! How to model step duration when predicting student performance},
  year = {2019},
  isbn = {9781450362566},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3303772.3303827},
  doi = {10.1145/3303772.3303827},
  booktitle = {Proceedings of the 9th International Conference on Learning Analytics \& Knowledge},
  pages = {330–334},
  numpages = {5},
  keywords = {step duration, intelligent tutoring systems, Student Modeling},
  location = {Tempe, AZ, USA},
  series = {LAK19}
}

In this paper, we explore how we can model students’ response times to predict student performance in Intelligent Tutoring Systems. Related research suggests that response time can provide information with respect to correctness. However, time is not consistently used when modeling students’ performance. Here, we build on previous work that indicated that the relationship between response time and student performance is non-linear. Based on this concept, we compare three models: a standard Additive Factors Analysis Model (AFM), an AFM model enhanced with a linear step duration parameter and an AFM model enhanced with a quadratic, step duration parameter. The results of this comparison show that the AFM model that is enhanced with the quadratic step duration parameter outperforms the other models over four different datasets and for most of the metrics we used to evaluate the models in cross validation and prediction.

Barria-Pineda, J., Akhuseyinoglu, K., & Brusilovsky, P. (2019). Explaining Need-based Educational Recommendations Using Interactive Open Learner Models [Conference Proceedings]. International Workshop on Transparent Personalization Methods Based on Heterogeneous Personal Data, ExHUM at the 27th ACM Conference On User Modelling, Adaptation And Personalization, UMAP ’19, 273–277. https://dl.acm.org/citation.cfm?id=3323463
```
@inproceedings{RN5309,
  author = {Barria-Pineda, Jordan and Akhuseyinoglu, Kamil and Brusilovsky, Peter},
  title = {Explaining Need-based Educational Recommendations Using Interactive Open Learner Models},
  booktitle = {International Workshop on Transparent Personalization Methods based on Heterogeneous Personal Data, ExHUM at the 27th ACM Conference On User Modelling, Adaptation And Personalization, UMAP '19},
  pages = {273–277},
  doi = {10.1145/3314183.3323463},
  url = {https://dl.acm.org/citation.cfm?id=3323463},
  year = {2019},
  type = {Conference Proceedings}
}
```
Carvalho, P. F., Gao, M., Motz, B. A., & Koedinger, K. R. (2018, July). Analyzing the Relative Learning Benefits of Completing Required Activities and Optional Readings in Online Courses. Paper Presented at the International Conference on Educational Data Mining (EDM).
```
@inproceedings{carvalho-etal-2018-learning-benefits,
  title = {Analyzing the Relative Learning Benefits of Completing Required Activities and Optional Readings in Online Courses},
  author = {Carvalho, Paulo F. and Gao, Min and Motz, Benjamin A. and Koedinger, Kenneth R.},
  year = {2018},
  month = jul,
  booktitle = {Paper presented at the International Conference on Educational Data Mining (EDM)},
  address = {Raleigh, NC}
}
```
Barria-Pineda, J., Guerra-Hollstein, J., & Brusilovsky, P. (2018). A Fine-Grained Open Learner Model for an Introductory Programming Course [Conference Proceedings]. 26th Conference on User Modeling, Adaptation and Personalization, UMAP ’18, 53–61.
```
@inproceedings{RN5126,
  author = {Barria-Pineda, Jordan and Guerra-Hollstein, Julio and Brusilovsky, Peter},
  title = {A Fine-Grained Open Learner Model for an Introductory Programming Course},
  booktitle = {26th Conference on User Modeling, Adaptation and Personalization, UMAP '18},
  publisher = {ACM},
  pages = {53-61},
  year = {2018},
  type = {Conference Proceedings}
}
```
Carvalho, P. F., McLaughlin, E. A., & Koedinger, K. R. (2017). Is there an explicit learning bias? Students beliefs, behaviors and learning outcomes. Proceedings of the Annual Meeting of the Cognitive Science Society, 39, 204–209. escholarship.org/uc/item/00w8g6df#author
```
@inproceedings{carvalho-etal-2017-learning-bias,
  title = {Is there an explicit learning bias? Students beliefs, behaviors and learning outcomes},
  author = {Carvalho, Paulo F. and McLaughlin, Elizabeth A. and Koedinger, Kenneth R.},
  booktitle = {Proceedings of the Annual Meeting of the Cognitive Science Society},
  volume = {39},
  url = {escholarship.org/uc/item/00w8g6df#author},
  year = {2017},
  pages = {204--209}
}
```
Hosseini, R., Brusilovsky, P., Yudelson, M., & Hellas, A. (2017). Stereotype Modeling for Problem-Solving Performance Predictions in MOOCs and Traditional Courses [Conference Proceedings]. Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, 76–84. https://www.researchgate.net/publication/316650044_Stereotype_Modeling_for_Problem-Solving_Performance_Predictions_in_MOOCs_and_Traditional_Courses
```
@inproceedings{RN4947,
  author = {Hosseini, Roya and Brusilovsky, Peter and Yudelson, Michael and Hellas, Arto},
  title = {Stereotype Modeling for Problem-Solving Performance Predictions in MOOCs and Traditional Courses},
  booktitle = {Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization},
  publisher = {ACM},
  pages = {76-84},
  isbn = {978-1-4503-4635-1},
  doi = {10.1145/3079628.3079672},
  url = {https://www.researchgate.net/publication/316650044_Stereotype_Modeling_for_Problem-Solving_Performance_Predictions_in_MOOCs_and_Traditional_Courses},
  year = {2017},
  type = {Conference Proceedings}
}
```
Guerra Hollstein, J., Barria Pineda, J., Schunn, C., Bull, S., & Brusilovsky, P. (2017). Fine-Grained Open Learner Models: Complexity Versus Support [Conference Proceedings]. 25th Conference on User Modeling, Adaptation and Personalization, 41–49. http://dx.doi.org/10.1145/3079628.3079682
```
@inproceedings{RN4945,
  author = {Guerra Hollstein, Julio and Barria Pineda, Jordan and Schunn, Christian and Bull, Susan and Brusilovsky, Peter},
  title = {Fine-Grained Open Learner Models: Complexity Versus Support},
  booktitle = {25th Conference on User Modeling, Adaptation and Personalization},
  publisher = {ACM},
  pages = {41-49},
  isbn = {978-1-4503-4635-1},
  doi = {10.1145/3079628.3079682},
  url = {http://dx.doi.org/10.1145/3079628.3079682},
  year = {2017},
  type = {Conference Proceedings}
}
```
Yudelson, M., Hosseini, R., Vihavainen, A., & Brusilovsky, P. (2014). Investigating Automated Student Modeling in a Java MOOC [Conference Proceedings]. In J. Stamper, Z. Pardos, M. Mavrikis, & B. M. McLaren (Eds.), the 7th International Conference on Educational Data Mining (EDM 2014) (pp. 261–264). http://educationaldatamining.org/EDM2014/uploads/procs2014/short%20papers/261_EDM-2014-Short.pdf
```
@inproceedings{RN4394,
  author = {Yudelson, Michael and Hosseini, Roya and Vihavainen, Arto and Brusilovsky, Peter},
  title = {Investigating Automated Student Modeling in a Java MOOC},
  booktitle = {the 7th International Conference on Educational Data Mining (EDM 2014)},
  editor = {Stamper, John and Pardos, Zachary and Mavrikis, Manolis and McLaren, Bruce M.},
  pages = {261-264},
  url = {http://educationaldatamining.org/EDM2014/uploads/procs2014/short%20papers/261_EDM-2014-Short.pdf},
  year = {2014},
  type = {Conference Proceedings}
}
```
Loboda, T., Guerra, J., Hosseini, R., & Brusilovsky, P. (2014). Mastery Grids: An Open Source Social Educational Progress Visualization [Conference Proceedings]. In S. de Freitas, C. Rensing, P. J. Muñoz Merino, & T. Ley (Eds.), 9th European Conference on Technology Enhanced Learning (EC-TEL 2014) (Vol. 8719, pp. 235–248). http://link.springer.com/chapter/10.1007%2F978-3-319-11200-8_18
```
@inproceedings{RN4397,
  author = {Loboda, Tomasz and Guerra, Julio and Hosseini, Roya and Brusilovsky, Peter},
  title = {Mastery Grids: An Open Source Social Educational Progress Visualization},
  booktitle = {9th European Conference on Technology Enhanced Learning (EC-TEL 2014)},
  editor = {de Freitas, Sara and Rensing, Christoph and Muñoz Merino, Pedro J. and Ley, Tobias},
  series = {Lecture Notes in Computer Science},
  volume = {8719},
  pages = {235-248},
  url = {http://link.springer.com/chapter/10.1007%2F978-3-319-11200-8_18},
  year = {2014},
  type = {Conference Proceedings}
}
```