John Stamper

See more up to date at main website:<https://dev.stamper.org>

CV: <here>

Short Bio:

John Stamper is an Associate Professor at the Human-Computer Interaction Institute at Carnegie Mellon University. He is also the Technical Director of the Pittsburgh Science of Learning Center DataShop. His primary areas of research include Educational Data Mining and Intelligent Tutoring Systems. As Technical Director, John oversees the DataShop, which is the largest open data repository of transactional educational data and set of associated visualization and analysis tools for researchers in the learning sciences. John received his PhD in Information Technology from the University of North Carolina at Charlotte, holds an MBA from the University of Cincinnati, and a BS in Systems Analysis from Miami University. Prior to returning to academia, John spent over ten years in the software industry including working with several start-ups.

Media Picture of Me:

Media Picture of John Stamper

Publications

@article{xiao2024exploring,
 title={Exploring How Multiple Levels of GPT-Generated Programming Hints Support or Disappoint Novices},
  author={Xiao, Ruiwei and Hou, Xinying and Stamper, John},
  journal={arXiv preprint arXiv:2404.02213},
  url={https://arxiv.org/pdf/2404.02213.pdf},
  year={2024}
}

@article{khosravi2023learnersourcing,
  title={Learnersourcing in the age of AI: Student, educator and machine partnerships for content creation},
  author={Khosravi, Hassan and Denny, Paul and Moore, Steven and Stamper, John},
  journal={Computers and Education: Artificial Intelligence},
  pages={100151},
  year={2023},
  publisher={Elsevier},
  url={https://arxiv.org/pdf/2306.06386.pdf}
}

@inproceedings{moore2023assessing,
  title={Assessing the quality of multiple-choice questions using gpt-4 and rule-based methods},
  author={Moore, Steven and Nguyen, Huy A and Chen, Tianying and Stamper, John},
  booktitle={European Conference on Technology Enhanced Learning},
  pages={229--245},
  year={2023},
  url ={https://dev.stamper.org/publications/ECTEL23-Moore.pdf},
  organization={Springer}
}

@inproceedings{ittsec2023King,
  title={AI/ML-Driven Content Repository Maintenance},
  author={Carney, John and Belmont, Nancy and King, James and Stamper, John and Kwon, Christine},
  booktitle={Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC)},
  year={2023},
  url ={https://dev.stamper.org/publications/ittsec2023King.pdf},
  organization={NTSA}
}

@inproceedings{ferreira2023human,
  title={A Human-Computer Interaction Perspective on Clinical Decision Support Systems: A Systematic Review of Usability, Barriers, and Recommendations for Improvement},
  author={Ferreira, Goncalo and Oliveira, Eva and Stamper, John and Coelho, Antonio and Paredes, Hugo and Rodrigues, Nuno F},
  booktitle={2023 IEEE 11th International Conference on Serious Games and Applications for Health (SeGAH)},
  year={2023},
  url ={https://dev.stamper.org/publications/SeGAH-1.pdf},
  organization={IEEE}
}

@inproceedings{oliveira2023role,
  title={The Role of Kiosks on Health Services: A Systematic Review},
  author={Oliveira, Eva and Pacheco, Paulo and Santos, Fatima and Coimbra, Joao and Stamper, John and Coelho, Antonio and Paredes, Hugo and Alves, Joana and Rodrigues, Nuno Feixa},
  booktitle={2023 IEEE 11th International Conference on Serious Games and Applications for Health (SeGAH)},
  year={2023},
  url ={https://dev.stamper.org/publications/SeGAH-2.pdf},
  organization={IEEE}
}

@inproceedings{LS2023Moore,
    url ="https://dev.stamper.org/publications/LS2023Moore.pdf",
    title={Crowdsourcing the Evaluation of Multiple-Choice Questions Using Item-Writing Flaws and Bloom's Taxonomy},
    author={Moore, Steven and Nguyen, Huy Anh and Stamper, John},
    booktitle={Proceedings of the Eighth ACM Conference on Learning@ Scale},
    year={2023}
  }

@inproceedings{moore2023ICLS,
    url ="https://dev.stamper.org/publications/MooreICLS2023.pdf",
    publisher ="International Society of the Learning Sciences",
    booktitle ="Proceedings of the 17th International Conference of the Learning Sciences",
    year ="2023",
    title ="Who Writes Tomorrow’s Learning Activities? Exploring Community College Student Participation in Learnersourcing",
    author ="Moore, Steven and Nguyen, Huy and Bier, Norman and Domadia, Tanvi and Stamper, John",
  }

@inproceedings{AIED2023Huy,
    url ="https://dev.stamper.org/publications/AIED2023Huy.pdf",
    publisher ="Springer",
    booktitle ="Proceedings of the International Conference on Artificial Intelligence in Education",
    year ="2023",
    author ="Nguyen, Huy and Hou, Xinying and Stec, Hayden and Di, Sarah and Stamper, John and McLaren, Bruce",
    title ="Examining the Learning Benefits of Different Types of Prompted Self-explanation in a Decimal Learning Game"
  }
  
  @inproceedings{AIED23-LLM-Workshop,
    url ="https://dev.stamper.org/publications/AIED23-LLM-Workshop.pdf",
    publisher ="Springer",
    booktitle ="Proceedings of the International Conference on Artificial Intelligence in Education",
    year ="2023",
    author ="Moore, Steven and Tong, Richard and Singh, Anjali and Liu, Zitao and Hu, Xiangen and Lu, Yu and Liang, Joleen and Cao, Chen and Denny, Paul and Khosravi, Hassan and Brooks, Christopher and Stamper, John",
    title ="Empowering Education with LLMs - the Next-Gen Interface and Content Generation"
  }

@InProceedings{AI4ED_stamper,
    author="Stamper, John and Gaind, Bharat and Thankachan, Karun and Nguyen, Huy and Moore, Steven",
    title="Hierarchical Concept Map Generation from Course Data",
    booktitle="AAAI 2023 Workshop on Artificial Intelligence in Education (AI4Edu)",
    year="2023",
    month="2",
    url="http://dev.stamper.org/publications/Stamper2023Hierarchical.pdf"
  }

@inproceedings{icce2023-focal,
    title={Focal: A Proposed Method of Leveraging LLMs for Automating Assessments},
    author={Meyers, Peter and Han, Peter and Grewala, Razik and Potnisa, Mitali and Stamper, John},
    booktitle={31st International Conference on Computers in Education Conference Vol II, ICCE 2023},
    pages={349--358},
    year={2023},
    organization={Asia-Pacific Society for Computers in Education},
    url={http://dev.stamper.org/publications/ICCE2023-Focal.pdf}
  }

@inproceedings{icce2023-japan,
    title={Tracking Knowledge for Learning Japanese as a 2nd Language},
    author={Okimoto, Tomoko and Johnson, Matthew and Nguyen, Huy and Moore, Steven and Eagle,  Michael and Stamper, John},
    booktitle={31st International Conference on Computers in Education Conference Vol I, ICCE 2023},
    pages={766--768},
    year={2023},
    organization={Asia-Pacific Society for Computers in Education},
    url={http://dev.stamper.org/publications/ICCE2023-Paper-Poster.pdf}
  }

@InProceedings{CROSSMMLA_LAK23,
    author="Kwon, Christine and Stamper, John and King, James and Lam, Joanie and Carney, John",
    title="Multimodal Data Support in Knowledge Objects for Real-time Knowledge Sharing",
    booktitle="Proceedings of CROSSMMLA Workshop at the 13th International Conference on Learning Analytics & Knowledge",
    year="2023",
    month="3",
    url="http://dev.stamper.org/publications/CROSSMMLA_LAK23.pdf"
  }

@InProceedings{LAK23_companion_Yiya,
    author="Kwon, Christine and Butler, Ren and Stamper, John and Ogan, Amy and Forcier, Angela and Fitzgerald, Erin and Wambuzi, Samson",
    title="Learning Analytics for Last Mile Students in Africa",
    booktitle="Companion Proceedings 13th International Conference on Learning Analytics & Knowledge",
    year="2023",
    month="3",
    pages="153--155",
    url="http://dev.stamper.org/publications/LAK23_companion_Kwon_et_al.pdf"
  }

@InProceedings{LAK23_companion_Moore,
    author="Moore, Steven and Nguyen, Huy and Stamper, John",
    title="Students’ Domain Confidence and their Participation in Optional Learnersourcing Activities",
    booktitle="Companion Proceedings 13th International Conference on Learning Analytics & Knowledge",
    year="2023",
    month="3",
    pages="147--149",
    url="http://dev.stamper.org/publications/LAK23_companion_Moore_et_al.pdf"
  }

@InProceedings{LAK23_companion_Bier,
    author="Bier, Norman and Stamper, John and Moore, Steven and Siegel, Darren and Anbar, Ariel",
    title="OLI Torus: a next-generation, open platform for adaptive courseware development, delivery, and research",
    booktitle="Companion Proceedings 13th International Conference on Learning Analytics & Knowledge",
    year="2023",
    month="3",
    pages="57--60",
    url="http://dev.stamper.org/publications/LAK23_companion_Bier_et_al.pdf"
  }

@inproceedings{2022.EDM-posters.85,
    abstract = {While Question-Answering (QA) has been a longtime area of interest for NLP and ML groups, Question Generation (QG) has been less of a focus, but has begun to generate more research in the last few years. QG specifically for education is a more narrow focus, but one that is important and holds great promise. In this work, we present a pipeline for generating and evaluating multiple-choice questions from text-based learning materials in an introductory data science course. We applied a T5 question generation model and a concept hierarchy extraction model on the text content, then ranked the generated questions based on their relevance to the resulting knowledge graph. Our evaluation with the course instructors show that the majority of high-ranked questions have acceptable quality and can be deployed in future iterations of the course. We conclude with discussions on the next steps towards refining the pipeline and promoting NLP research in educational domains.},
    address = {Durham, United Kingdom},
    url = {https://dev.stamper.org/publications/2022EDM-posters85.pdf},
    author = {Bhat, Shravya and Nguyen, Huy and Moore, Steven and Stamper, John and Sakr, Majd and Nyberg, Eric},
    booktitle = {Proceedings of the 15th International Conference on Educational Data Mining},
    doi = {10.5281/zenodo.6853085},
    editor = {Antonija Mitrovic and Nigel Bosch},
    isbn = {978-1-7336736-3-1},
    month = {July},
    pages = {701--704},
    publisher = {International Educational Data Mining Society},
    title = {Towards Automated Generation and Evaluation of Questions in Educational Domains},
    year = {2022}
   }

@inproceedings{diana2022AIEDfull,
    url ="https://www.cs.cmu.edu/~jstamper/publications/AIED2022DianaFullPaper.pdf",
    author="Diana, Nicholas
    and Stamper, John
    and Koedinger, Ken
    and Hammer, Jessica",
    editor="Rodrigo, Maria Mercedes
    and Matsuda, Noburu
    and Cristea, Alexandra I.
    and Dimitrova, Vania",
    title="Debiasing Politically Motivated Reasoning with Value-Adaptive Instruction",
    booktitle="Artificial Intelligence  in Education",
    year="2022",
    publisher="Springer International Publishing",
    address="Cham",
    pages="140--152",
    abstract="While there is a substantial appetite in the United States for improving media consumption skills, little work has focused on the biases that can make inaccurate or misleading claims feel true. This skill is particularly difficult to teach, as effective instruction requires the instructor to adapt course content to the specific beliefs of individual students, a process that is unscalable in most classrooms. Here we examine the impact of a novel method of user-centered personalized instruction that uses value-adaptivity to highlight and address user bias in the context of a civics education game. This intervention uses estimates of player and content values to predict when players may be most susceptible to biased reasoning and then intervene in those instances. We found that the intervention successfully reduced bias among high bias-regulators with practice. These results suggest that value-adaptive systems may be able to support debiasing instruction in an effective, scalable way.",
    isbn="978-3-031-11644-5"
    }
    
  @inproceedings{diana2022AIEDShort,
    url ="https://www.cs.cmu.edu/~jstamper/publications/AIED2022DianaShortPaper.pdf",
    author="Diana, Nicholas
    and Stamper, John",
    editor="Rodrigo, Maria Mercedes
    and Matsuda, Noburu
    and Cristea, Alexandra I.
    and Dimitrova, Vania",
    title="Reducing Bias in a Misinformation Classification Task with Value-Adaptive Instruction",
    booktitle="Artificial Intelligence  in Education",
    year="2022",
    publisher="Springer International Publishing",
    address="Cham",
    pages="567--572",
    abstract="Instructional technology that supports the development of media literacy skills has garnered increased attention in the wake of recent misinformation campaigns. While critical, this work often ignores the role of myside bias in the acceptance and propagation of misinformation. Here we present results from an alternative approach that uses natural language processing to model the dynamic relationship between the user and the content they are consuming. This model powers a debiasing intervention in the context of a ``fake news detection'' task. Information about the user- and content-values was used to predict when the user may be prone to myside bias. The intervention resulted in significantly better performance on the misinformation classification task. These results support the development of content-general and embedded debiasing systems that could encourage informal learning and bias reduction in real-world contexts.",
    isbn="978-3-031-11644-5"
    }
    
  @inproceedings{moore2022ICLS1,
    pages ="791--798",
    url ="https://dev.stamper.org/publications/MooreICLS2022.pdf",
    publisher ="International Society of the Learning Sciences",
    booktitle ="Proceedings of the 16th International Conference of the Learning Sciences",
    year ="2022",
    title ="Leveraging Students to Generate Skill Tags that Inform Learning Analytics",
    author ="Moore, Steven and Nguyen, Huy and Stamper, John",
  }

@inproceedings{moore2022ICLS2,
    pages ="1381--1384",
    url ="https://dev.stamper.org/publications/MooreShortICLS2022.pdf",
    publisher ="International Society of the Learning Sciences",
    booktitle ="Proceedings of the 16th International Conference of the Learning Sciences",
    year ="2022",
    title ="Participation and Success with Optional Self-Explanation for Students in Online Undergraduate Chemistry Courses",
    author ="Moore, Steven and Nguyen, Huy and Stamper, John",
  }

@InProceedings{10.1007/978-3-031-16290-9_18,
    author="Moore, Steven
    and Nguyen, Huy A.
    and Bier, Norman
    and Domadia, Tanvi
    and Stamper, John",
    editor="Hilliger, Isabel
    and Mu{\~{n}}oz-Merino, Pedro J.
    and De Laet, Tinne
    and Ortega-Arranz, Alejandro
    and Farrell, Tracie",
    title="Assessing the Quality of Student-Generated Short Answer Questions Using GPT-3",
    booktitle="Educating for a New Future: Making Sense of Technology-Enhanced Learning Adoption",
    year="2022",
    publisher="Springer International Publishing",
    address="Cham",
    pages="243--257",
    url = "https://dev.stamper.org/publications/MooreEctel2022.pdf",
    abstract="Generating short answer questions is a popular form of learnersourcing with benefits for both the students' higher-order thinking and the instructors' collection of assessment items. However, assessing the quality of the student-generated questions can involve significant efforts from instructors and domain experts. In this work, we investigate the feasibility of leveraging students to generate short answer questions with minimal scaffolding and machine learning models to evaluate the student-generated questions. We had 143 students across 7 online college-level chemistry courses participate in an activity where they were prompted to generate a short answer question regarding the content they were presently learning. Using both human and automatic evaluation methods, we investigated the linguistic and pedagogical quality of these student-generated questions. Our results showed that 32{\%} of the student-generated questions were evaluated by experts as high quality, indicating that they could be added and used in the course in their present condition. Additional expert evaluation identified that 23{\%} of the student-generated questions assessed higher cognitive processes according to Bloom's Taxonomy. We also identified the strengths and weaknesses of using a state-of-the-art language model, GPT-3, to automatically evaluate the student-generated questions. Our findings suggest that students are relatively capable of generating short answer questions that can be leveraged in their online courses. Based on the evaluation methods, recommendations for leveraging experts and automatic methods are discussed.",
    isbn="978-3-031-16290-9"
  }

@InProceedings{10.1007/978-3-031-16290-9_20,
    author="Nguyen, Huy A.
    and Bhat, Shravya
    and Moore, Steven
    and Bier, Norman
    and Stamper, John",
    editor="Hilliger, Isabel
    and Mu{\~{n}}oz-Merino, Pedro J.
    and De Laet, Tinne
    and Ortega-Arranz, Alejandro
    and Farrell, Tracie",
    title="Towards Generalized Methods for Automatic Question Generation in Educational Domains",
    booktitle="Educating for a New Future: Making Sense of Technology-Enhanced Learning Adoption",
    year="2022",
    publisher="Springer International Publishing",
    address="Cham",
    pages="272--284",
    url = "https://dev.stamper.org/publications/HuyECTEL2022.pdf",
    abstract="Students learn more from doing activities and practicing their skills on assessments, yet it can be challenging and time consuming to generate such practice opportunities. In our work, we examine how advances in natural language processing and question generation may help address this issue. In particular, we present a pipeline for generating and evaluating questions from text-based learning materials in an introductory data science course. The pipeline includes applying a text-to-text transformer (T5) question generation model and a concept hierarchy extraction model on the text content, then scoring the generated questions based on their relevance to the extracted key concepts. We further evaluated the question quality with three different approaches: information score, automated rating by a trained model (Google GPT-3) and manual review by human instructors. Our results showed that the generated questions were rated favorably by all three evaluation methods. We conclude with a discussion of the strengths and weaknesses of the generated questions and outline the next steps towards refining the pipeline and promoting natural language processing research in educational domains.",
    isbn="978-3-031-16290-9"
  }
  
  @inproceedings{10.1145/3491140.3528286,
    author = {Moore, Steven and Stamper, John and Brooks, Christopher and Denny, Paul and Khosravi, Hassan},
    title = {Learnersourcing: Student-Generated Content @ Scale},
    year = {2022},
    isbn = {9781450391580},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://dev.stamper.org/publications/LSWorkshop2022.pdf},
    doi = {10.1145/3491140.3528286},
    abstract = {The first annual workshop on Learnersourcing: Student-generated Content @ Scale is taking place at Learning @ Scale 2022. This hybrid workshop will expose attendees to the ample opportunities in the learnersourcing space, including instructors, researchers, learning engineers, and many other roles. We believe participants from a wide range of backgrounds and prior knowledge on learnersourcing can both benefit and contribute to this workshop, as learnersourcing draws on work from education, crowdsourcing, learning analytics, data mining, ML/NLP, and many more fields. Additionally, as the learnersourcing process involves many stakeholders (students, instructors, researchers, instructional designers, etc.), multiple viewpoints can help to inform what future student-generated content might be useful, new and better ways to assess the quality of the content and spark potential collaboration efforts between attendees. We ultimately want to show how everyone can make use of learnersourcing and have participants gain hands-on experience using existing tools, create their own learnersourcing activities using them or their own platforms, and take part in discussing the next challenges and opportunities in the learnersourcing space. Our hope is to attract attendees interested in scaling the generation of instructional and assessment content and those interested in the use of online learning platforms.},
    booktitle = {Proceedings of the Ninth ACM Conference on Learning @ Scale},
    pages = {259–262},
    numpages = {4},
    keywords = {student learning, multiple-choice question, assessment, student-generated content, question creation, learnersourcing},
    location = {New York City, NY, USA},
    series = {L@S '22}
  }

@inproceedings{CTESTEM2022,
    url = {https://dev.stamper.org/publications/CTESTEM2022.pdf},
    title={Developing a Continuous, Rather Than Binary, Classification for Measuring STEM Jobs},
    author={Carmichael, Ted and Stamper, John and Carney, John},
    booktitle={6th APSCE International Conference on Computational Thinking and STEM Education 2022 (CTE-STEM 2022)},
    year={2022}
  }
  
@inproceedings{moore2021examining,
  url ="https://www.cs.cmu.edu/~hn1/papers/LS2021_ExaminingEffects.pdf",
  title={Examining the Effects of Student Participation and Performance on the Quality of Learnersourcing Multiple-Choice Questions},
  author={Moore, Steven and Nguyen, Huy Anh and Stamper, John},
  booktitle={Proceedings of the Eighth ACM Conference on Learning@ Scale},
  pages={209--220},
  year={2021}
}

@inproceedings{nguyen2021exploring,
  url = "https://www.cs.cmu.edu/~hn1/papers/LAK2021_ExploringMetrics.pdf",
  title={Exploring Metrics for the Analysis of Code Submissions in an Introductory Data Science Course},
  author={Nguyen, Huy and Lim, Michelle and Moore, Steven and Nyberg, Eric and Sakr, Majd and Stamper, John},
  booktitle={LAK21: 11th International Learning Analytics and Knowledge Conference},
  pages={632--638},
  year={2021}
}

@inproceedings{moore2021human,
  url={https://dev.stamper.org/publications/ICL_Moore2021.pdf},
  title={A Human-Centered Approach to Data Driven Iterative Course Improvement},
  author={Moore, Steven and Stamper, John and Bier, Norman and Blink, Mary Jean},
  booktitle={International Conference on Remote Engineering and Virtual Instrumentation},
  pages={742--761},
  year={2021},
  organization={Springer}
}

@inproceedings{diana2020persuasion,
  url ="https://nickdiana.com/documents/CHIPlaySGC2020_preprint.pdf", 
  title={Persuasion Invasion: Reducing Bias with Value-Adaptive Instruction},
  author={Diana, Nicholas and Hammer, Jessica and Stamper, John and Koedinger, Kenneth},
  booktitle={Extended Abstracts of the 2020 Annual Symposium on Computer-Human Interaction in Play},
  pages={50--53},
  year={2020}
}

@inproceedings{diana2020towards,
  url ="http://nickdiana.com/documents/CHI2020_preprint.pdf",
  year ="2020",
  pages ="1--11",
  booktitle ="Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems",
  author ="Diana, Nicholas and Stamper, John and Koedinger, Kenneth",
  title ="Towards Value-Adaptive Instruction: A Data-Driven Method for Addressing Bias in Argument Evaluation Tasks",
}

@inproceedings{moore2020utilizing,
  title={Utilizing Crowdsourcing and Topic Modeling to Generate Knowledge Components for Math and Writing Problems},
  author={Moore, Steven and Nguyen, Huy and Stamper, John},
  booktitle={Proceedings of the 28th International Conference on Computers in Education},
  pages={31--40},
  year={2020},
  url={https://www.cs.cmu.edu/~hn1/papers/ICCE2020_UtilizingCrowdsourcing.pdf}
}

@inproceedings{nguyen2020improving,
  pages ="409--413",
  url ="http://cs.cmu.edu/~hn1/papers/AIED2020_ImprovingStudents.pdf",
  publisher ="Springer",
  booktitle ="International Conference on Artificial Intelligence in Education",
  year ="2020",
  author ="Nguyen, Huy and Guo, Yuqing and Stamper, John and McLaren, Bruce",
  title ="Improving Students’ Problem-solving Flexibility in Nonroutine Mathematics",
}

@inproceedings{nguyen2020moving,
  pages ="487--495",
  url ="http://cs.cmu.edu/~hn1/papers/EDM2020_MovingBeyond.pdf",
  author ="Nguyen, Huy and Hou, Xinying and Stamper, John and McLaren, Bruce",
  booktitle ="Proceedings of the International Conference on Educational Data Mining",
  year ="2020",
  title ="Moving beyond Test Scores: Analyzing the Effectiveness of a Digital Learning Game through Learning Analytics",
}

@inproceedings{moore2020evaluating,
  pages ="398--410",
  url ="http://cs.cmu.edu/~hn1/papers/AIED2020_EvaluatingCrowdsourcing.pdf",
  publisher ="Springer",
  booktitle ="Proceedings of the International Conference on Artificial Intelligence in Education",
  year ="2020",
  author ="Moore, Steven and Nguyen, Huy and Stamper, John",
  title ="Evaluating Crowdsourcing and Topic Modeling in Generating Knowledge Components from Explanations",
}

@inproceedings{moore2020crowdsourcing1,
  pages ="2627--2628",
  url ="http://cs.cmu.edu/~hn1/papers/ICLS2020_CrowdsourcingExplanations.pdf",
  publisher ="International Society of the Learning Sciences",
  booktitle ="Proceedings of the 15th International Conference of the Learning Sciences",
  year ="2020",
  title ="Crowdsourcing Explanations for Improving Assessment Content and Identifying Knowledge Components",
  author ="Moore, Steven and Nguyen, Huy and Stamper, John",
}

@inproceedings{moore2020crowdsourcing2,
  pages ="245--248",
  url ="http://cs.cmu.edu/~hn1/papers/LS2020_CrowdsourcingIdentification.pdf",
  organization ="ACM",
  booktitle ="Proceedings of the Seventh (2020) ACM Conference on Learning @ Scale",
  year ="2020",
  author ="Moore, Steven and Nguyen, Huy and Stamper, John",
  title ="Towards Crowdsourcing the Identification of Knowledge Component",
}

@InProceedings{AIED2019_Moore_and_Stamper,
  author="Stamper, John and Moore, Steven",
  editor="Isotani, Seiji and Mill{\'a}n, Eva and Ogan, Amy and Hastings, Peter and McLaren, Bruce and Luckin, Rose",
  title="Exploring Teachable Humans and Teachable Agents: Human Strategies Versus Agent Policies and the Basis of Expertise",
  booktitle="Artificial Intelligence in Education",
  year="2019",
  publisher="Springer International Publishing",
  address="Cham",
  pages="269--274",
  abstract="In this research, we explore how expertise is shown in both humans and AI agents. Human experts follow sets of strategies to complete domain specific tasks while AI agents follow a policy. We compare machine generated policies to human strategies in two game domains, using these examples we show how human strategies can be seen in agents. We believe this work can help lead to a better understanding of human strategies and expertise, while also leading to improved human-centered machine learning approaches. Finally, we hypothesize how a continuous improvement system of humans teaching agents who then teach humans could be created in future intelligent tutoring systems.",
  isbn="978-3-030-23207-8",
  doi="10.1007/978-3-030-23207-8_50",
  url="https://dev.stamper.org/publications/AIED2019_Moore_and_Stamper.pdf"
}

@InProceedings{AIED2019_Diana_et_al,
  author="Diana, Nicholas and Stamper, John and Koedinger, Kenneth",
  editor="Isotani, Seiji and Mill{\'a}n, Eva and Ogan, Amy and Hastings, Peter and McLaren, Bruce and Luckin, Rose",
  title="Online Assessment of Belief Biases and Their Impact on the Acceptance of Fallacious Reasoning",
  booktitle="Artificial Intelligence in Education",
  year="2019",
  publisher="Springer International Publishing",
  address="Cham",
  pages="62--66",
  abstract="Determining the impact of belief bias on everyday reasoning is critical for understanding how our beliefs can influence how we judge arguments. We examined the impact of belief bias on the user's ability to identify logical fallacies in political arguments. We found that participants had more difficulty identifying logical fallacies in arguments that aligned with their own political beliefs. Interestingly, this effect diminishes with practice. These results suggest that while belief bias is a potential barrier to correctly evaluating everyday arguments, interventions focused on activating rational engagement may mitigate its impact.",
  isbn="978-3-030-23207-8",
  doi="10.1007/978-3-030-23207-8_12",
  url="https://dev.stamper.org/publications/AIED2019_Diana_et_al.pdf"
}

@inproceedings{diana2019predicting,
  title={Predicting Bias in the Evaluation of Unlabeled Political Arguments.},
  author={Diana, Nicholas and Stamper, John C and Koedinger, Ken},
  booktitle={CogSci},
  pages={1640--1646},
  year={2019},
  url={https://dev.stamper.org/publications/CogSci2019_preprint.pdf}
}

@InProceedings{AIED2019_Wang_et_al,
  author="Wang, Yeyu and Nguyen, Huy and Harpstead, Erik and Stamper, John and McLaren, Bruce M.",
  editor="Isotani, Seiji and Mill{\'a}n, Eva and Ogan, Amy and Hastings, Peter and McLaren, Bruce and Luckin, Rose",
  title="How Does Order of Gameplay Impact Learning and Enjoyment in a Digital Learning Game?",
  booktitle="Artificial Intelligence in Education",
  year="2019",
  publisher="Springer International Publishing",
  address="Cham",
  pages="518--531",
  abstract="When students are given agency in playing and learning from a digital learning game, how do their decisions about sequence of gameplay impact learning and enjoyment? We explored this question in the context of Decimal Point, a math learning game that teaches decimals to middle-school students. Our analysis is based on students in a high-agency condition, those who can choose the order of gameplay, as well as when to stop. By clustering student mini-game sequences by edit distance -- the number of edit operations to turn one sequence into another -- we found that, among students who stopped early, those who deviated more from a canonical game sequence reported higher enjoyment than those who did not. However, there were no differences in learning gains. Our results suggest that students who can self-regulate and exercise agency will enjoy the game, but the type and number of choices may also have an impact on enjoyment factors. At the same time, more investigation into the amount and means of delivering instruction to maximize learning efficiency within the game is necessary. We conclude by discussing digital learning game design lessons to create a game that more closely aligns with students' learning needs and affective states.",
  isbn="978-3-030-23204-7",
  doi="10.1007/978-3-030-23204-7_43",
  url="https://dev.stamper.org/publications/AIED2019_Wang_et_al.pdf"
}

@inproceedings{moore2019exploring,
  title={Exploring Expertise through Visualizing Agent Policies and Human Strategies in Open-Ended Games.},
  author={Moore, Steven and Stamper, John C},
  booktitle={EDM (Workshops)},
  pages={30--37},
  year={2019},
  url={https://dev.stamper.org/publications/MooreEDMWorkshop2019.pdf}
}

@InProceedings{ITS2019_Moore_and_Stamper,

author="Moore, Steven 
and Stamper, John",

editor="Coy, Andre
and Hayashi, Yugo
and Chang, Maiga",

title="Decision Support for an Adversarial Game Environment Using Automatic Hint Generation",

booktitle="Intelligent Tutoring Systems",

year="2019",

publisher="Springer International Publishing",

address="Cham",

pages="82--88",

abstract="The Hint Factory is a method of automatic hint generation that has been used to augment hints in a number of educational systems. Although the previous implementations were done in domains with largely deterministic environments, the methods are inherently useful in stochastic environments with uncertainty. In this work, we explore the game Connect Four as a simple domain to give decision support under uncertainty. We speculate how the implementation created could be extended to other domains including simulated learning environments and advanced navigational tasks.",

isbn="978-3-030-22244-4",
  url={https://dev.stamper.org/publications/ITS2019_Moore_and_Stamper.pdf},
  award="Best Short Paper Finalist"
}

@article{zhang2019early,
  title={Early Detection of Wheel Spinning: Comparison across Tutors, Models, Features, and Operationalizations.},
  author={Zhang, Chuankai and Huang, Yanzun and Wang, Jingyu and Lu, Dongyang and Fang, Weiqi and Stamper, John and Fancsali, Stephen and Holstein, Kenneth and Aleven, Vincent},
  year={2019},
  booktitle={International Conference on Educational Data Mining},
  url={https://dev.stamper.org/publications/EDM2019WheelSpinning.pdf}
}

@inproceedings{EDM2019_Nguyen_et_al,
  title={Using Knowledge Component Modeling to Increase Domain Understanding in a Digital Learning Game},
  year={2019},
  booktitle={International Conference on Educational Data Mining},
  author={Nguyen, Huy and Wang, Yeyu and Stamper, John and McLaren, Bruce M},
  url={https://dev.stamper.org/publications/EDM2019_Nguyen_et_al.pdf}
}

@InProceedings{LAK19_companion_Stamper_et_al,

author="Stamper, John and Carvalho, Paulo and Moore, Steven and Koedinger, Kenneth",

title="Tigris: An Online Workflow Tool for Sharing Educational Data and Analytic Methods",

booktitle="Companion Proceedings 9th International Conference on Learning Analytics & Knowledge",

year="2019",

month="3",
  pages="183",

url="http://dev.stamper.org/publications/LAK19_companion_Stamper_et_al.pdf"
}

@InProceedings{LAK19_companion_Baik_et_al,

author="Baik, Jason and Stamper, John and Rangwala, Huzefa",

title="MOOC Effort Dashboard: An Interactive Web Dashboard Built in R",

booktitle="Companion Proceedings 9th International Conference on Learning Analytics & Knowledge",

year="2019",

month="3",
  pages="167",

url="http://dev.stamper.org/publications/LAK19_companion_Baik_et_al.pdf"
}

@InProceedings{LAK19_companion_Gadgil_et_al,

author="Soniya, Gadgil and Moore, Steven and Stamper, John",

title="How does Performance in an Online Primer Predict Achievement in a Future Computer Science Course?",

booktitle="Companion Proceedings 9th International Conference on Learning Analytics & Knowledge",

year="2019",

month="3",
  pages="300--306",

url="http://dev.stamper.org/publications/LAK19_companion_Gadgil_et_al.pdf"
}

@InProceedings{LAK19_Koedinger_Stamper_et_al,

author="Koedinger, Kenneth and Stamper, John and Carvalho, Paulo",

title="Sharing and Reusing Data and Analytic Methods with LearnSphere",

booktitle="Companion Proceedings 9th International Conference on Learning Analytics & Knowledge",

year="2019",

month="3",
  pages="328--331",

url="http://dev.stamper.org/publications/LAK19_Koedinger_Stamper_et_al.pdf"
}

@InProceedings{LAK19_companion_Moore_et_al,

author="Moore, Steven and Stamper, John and Soniya, Gadgil",

title="Human-Centered Data Science for Educational Technology Improvement using Crowd Workers",

booktitle="Companion Proceedings 9th International Conference on Learning Analytics & Knowledge",

year="2019",

month="3",
  pages="341--347",

url="http://dev.stamper.org/publications/LAK19_companion_Moore_et_al.pdf"
}

@InProceedings{LAK19_companion_Koedinger_et_al,

author="Koedinger, Kenneth and Stamper, John and Carvalho, Paulo",

title="LearnSphere: Learning Analytics Development and Sharing Made Simple",

booktitle="Companion Proceedings 9th International Conference on Learning Analytics & Knowledge",

year="2019",

month="3",
  pages="996",

url="http://dev.stamper.org/publications/LAK19_companion_Koedinger_et_al.pdf"
}

@InProceedings{LAK19_companion_Carmichael_et_al,

author="Carmichael, Ted and Blink, Mary Jean and Stamper, John",

title="TutorGen SCALE ® - Student Centered Adaptive Learning Engine",

booktitle="Companion Proceedings 9th International Conference on Learning Analytics & Knowledge",

year="2019",

month="3",
  pages="964--973",

url="http://www.cs.cmu.edu/~jstamper/publications/LAK19_companion_Carmichael_et_al.pdf"
}

@article{liu2019learning,
  title={Learning linkages: Integrating data streams of multiple modalities and timescales},
  author={Liu, Ran and Stamper, John and Davenport, Jodi and Crossley, Scott and McNamara, Danielle and Nzinga, Kalonji and Sherin, Bruce},
  journal={Journal of Computer Assisted Learning},
  volume={35},
  number={1},
  pages={99--109},
  year={2019},
  publisher={Wiley Online Library},
  url={https://dev.stamper.org/publications/JCAL19_Liu.pdf}
}

@article{JLA_Liu_Stamper_Davenport,
  title={A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems},
  author={Liu, Ran and Stamper, John and Davenport, Jodi},
  journal={Journal of Learning Analytics},
  volume={5},
  number={1},
  pages={41--54},
  year={2018},
  url={http://dev.stamper.org/publications/JLA_Liu_Stamper_Davenport.pdf}
}

@inproceedings{AIED2018_Diana_Stamper_Koedinger,
  title={An Instructional Factors Analysis of an Online Logical Fallacy Tutoring System},
  author={Diana, Nicholas and Stamper, John and Koedinger, Ken},
  booktitle={International Conference on Artificial Intelligence in Education},
  pages={86--97},
  year={2018},
  organization={Springer},
  url={http://dev.stamper.org/publications/AIED2018_Diana_Stamper_Koedinger.pdf}
}

@inproceedings{LAK2018_Dianaetal,
 author = {Diana, Nicholas and Eagle, Michael and Stamper, John and Grover, Shuchi and Bienkowski, Marie and Basu, Satabdi},
 title = {Data-driven Generation of Rubric Criteria from an Educational Programming Environment},
 booktitle = {Proceedings of the 8th International Conference on Learning Analytics and Knowledge},
 series = {LAK '18},
 year = {2018},
 isbn = {978-1-4503-6400-3},
 location = {Sydney, New South Wales, Australia},
 pages = {16--20},
 numpages = {5},
 url = {http://dev.stamper.org/publications/LAK2018_Dianaetal.pdf},
 doi = {10.1145/3170358.3170399},
 acmid = {3170399},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {assessment, computer science education, data-driven, programming education},
}

@inproceedings{EDM2018_Eagleetal,
  title={Predicting Individualized Learner Models across Tutor Lessons},
  author={Eagle, Michael and Corbett, Albert and Stamper, John and Mclaren, Bruce},
  booktitle={Proceedings of the 11th International Conference on Educational Data Mining},
  year={2018},
  pages={474--478},
  url={http://dev.stamper.org/publications/EDM2018_Eagleetal.pdf}
}

@inproceedings{EDM2018_TutorGenCMUWGU,
  title={Predictive Student Modeling for Interventions in Online Classes},
  author={Eagle, Michael and Carmichael, Ted and Stokes, Jessica and Blink, Mary Jean and Stamper, John and Levin, Jason},
  booktitle={Proceedings of the 11th International Conference on Educational Data Mining},
  year={2018},
  pages={619--624},
  url={http://dev.stamper.org/publications/EDM2018_TutorGenCMUWGU.pdf}
}

@inproceedings{CSEDM18_Diana_preprint,
  title={Measuring Transfer of Data-Driven Code Features Across Tasks in Alice},
  author = {Diana, Nicholas and Eagle, Michael and Stamper, John and Grover, Shuchi and Bienkowski, Marie and Basu, Satabdi},
  booktitle={Proceedings of the Educational Data Mining in Computer Science Education (CSEDM) Workshop in conjuction with the 11th International Conference on Educational Data Mining (EDM2018)},
  year={2018},
  url={http://dev.stamper.org/publications/CSEDM18_Diana_preprint.pdf}
}

@inproceedings{CSEDM18_Jiangetal,
  title={Programming Pathway Clustering Using Tree Edit Distance},
  author = {Jiang, Bo and Li, Zhixuan and Stamper, John},
  booktitle={Proceedings of the Educational Data Mining in Computer Science Education (CSEDM) Workshop in conjuction with the 11th International Conference on Educational Data Mining (EDM2018)},
  year={2018},
  url={http://dev.stamper.org/publications/CSEDM18_Jiangetal.pdf}
}

@inproceedings{ICLS2018_Diana_et_al,
  title={Peer Tutor Matching for Introductory Programming: Data-Driven Methods to Enable New Opportunities for Help},
  author = {Diana, Nicholas and Eagle, Michael and Stamper, John and Grover, Shuchi and Bienkowski, Marie and Basu, Satabdi},
  booktitle={Proceedings of the 13th International Conference of the Learning Sciences (ICLS 2018)},
  year={2018},
  pages={1377--1378},
  url={http://dev.stamper.org/publications/ICLS2018_Diana_et_al.pdf}
}

@inproceedings{carmichael2018linkage,
  author={Carmichael, Ted and Blink, Mary Jean and Stamper, John and Gieske, Elizabeth},
  title={Linkage Objects for Generalized Instruction in Coding (LOGIC)},
  booktitle={The Thirty-First International Flairs Conference},
  year={2018},
  pages={442--446},
  url={http://dev.stamper.org/publications/FLAIRS18_Carmichaeletal.pdf}
}

@inproceedings{liu2017sharing,
  title={Sharing and reusing data and analytic methods with LearnSphere},
  author={Liu, Ran and Koedinger, Kenneth and Stamper, John and Pavlik, Philip},
  booktitle={Workshop and Tutorials Chairs},
  pages={475--476},
  year={2017},
  organization={ERIC},
  url={http://dev.stamper.org/publications/EDM2017LSWrksp.pdf}
}

@article{Grover:2017:FUH:3135995.3105910,
 author = {Grover, Shuchi and Basu, Satabdi and Bienkowski, Marie and Eagle, Michael and Diana, Nicholas and Stamper, John},
 title = {A Framework for Using Hypothesis-Driven Approaches to Support Data-Driven Learning Analytics in Measuring Computational Thinking in Block-Based Programming Environments},
 journal = {ACM Trans. Comput. Educ.},
 issue_date = {August 2017},
 volume = {17},
 number = {3},
 month = aug,
 year = {2017},
 issn = {1946-6226},
 pages = {14:1--14:25},
 articleno = {14},
 numpages = {25},
 url = {http://doi.acm.org/10.1145/3105910},
 doi = {10.1145/3105910},
 acmid = {3105910},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {Blended learning analytics, K-12 computer science education, block-based programming environments, computational psychometrics, data-driven, evidence-centered design, hypothesis-driven},
}

@inproceedings{LAK2017_Dianaetal,
  title={An instructor dashboard for real-time analytics in interactive programming assignments},
  author={Diana, Nicholas and Eagle, Michael and Stamper, John and Grover, Shuchi and Bienkowski, Marie and Basu, Satabdi},
  booktitle={Proceedings of the Seventh International Learning Analytics \& Knowledge Conference},
  pages={272--279},
  year={2017},
  organization={ACM},
  url={http://dev.stamper.org/publications/LAK2017_Dianaetal.pdf}
}

@inproceedings{LAK2017_Grover_et_al,
 author = {Grover, Shuchi and Bienkowski, Marie and Basu, Satabdi and Eagle, Michael and Diana, Nicholas and Stamper, John},
 title = {A Framework for Hypothesis-driven Approaches to Support Data-driven Learning Analytics in Measuring Computational Thinking in Block-based Programming},
 booktitle = {Proceedings of the Seventh International Learning Analytics \&\#38; Knowledge Conference},
 series = {LAK '17},
 year = {2017},
 isbn = {978-1-4503-4870-6},
 location = {Vancouver, British Columbia, Canada},
 pages = {530--531},
 numpages = {2},
 url = {http://dev.stamper.org/publications/LAK2017_Grover_et_al.pdf},
 doi = {10.1145/3027385.3029440},
 acmid = {3029440},
 publisher = {ACM},
 address = {New York, NY, USA},
}

@InProceedings{AIED2017_Diana_et_al,

author="Diana, Nicholas 
and Eagle, Michael 
and Stamper, John
 and Koedinger, Kenneth",
  
editor="Andr{\'e}, Elisabeth
and Baker, Ryan
and Hu, Xiangen
and Rodrigo, Ma. Mercedes T.
and du Boulay, Benedict",

title="Teaching Informal Logical Fallacy Identification with a Cognitive Tutor",
  
booktitle="Artificial Intelligence in Education",
  
year="2017",

publisher="Springer International Publishing",
  
address="Cham",
  pages="605--608",

abstract="In this age of fake news and alternative facts, the need for a citizenry capable of critical thinking has never been greater. While teaching critical thinking skills in the classroom remains an enduring challenge, research on an ill-defined domain like critical thinking in the educational technology space is even more scarce. We propose a difficulty factors assessment (DFA) to explore two factors that may make learning to identify fallacies more difficult: type of instruction and belief bias. This study will allow us to make two key contributions. First, we will better understand the relationship between sense-making and induction when learning to identify informal fallacies. Second, we will contribute to the limited work examining the impact of belief bias on informal (rather than formal) reasoning. The results of this DFA will also be used to improve the next iteration of our fallacy tutor, which may ultimately contribute to a computational model of informal fallacies.",

isbn="978-3-319-61425-0",
  url="http://dev.stamper.org/publications/AIED2017_Diana_et_al.pdf"
}

@InProceedings{AIED2017_Eagleetal,

author="Eagle, Michael 
and Corbett, Albert
 and Stamper, John
 and McLaren, Bruce and Baker, Ryan
 and Wagner, Angela
 and MacLaren, Benjamin
 and Mitchell, Aaron",

editor="Andr{\'e}, Elisabeth
and Baker, Ryan
and Hu, Xiangen
and Rodrigo, Ma. Mercedes T.
and du Boulay, Benedict",

title="Exploring Learner Model Differences Between Students",

booktitle="Artificial Intelligence in Education",

year="2017",
publisher="Springer International Publishing",

address="Cham",

pages="494--497",

isbn="978-3-319-61425-0",
  url="http://dev.stamper.org/publications/AIED2017_Eagleetal.pdf"
}

@InProceedings{AIED2017_Diana_et_al_Data,

author="Diana, Nicholas
 and Eagle, Michael
 and Stamper, John 
and Grover, Shuchi
 and Bienkowski, Marie 
and Basu, Satabdi",

editor="Andr{\'e}, Elisabeth
and Baker, Ryan
and Hu, Xiangen
and Rodrigo, Ma. Mercedes T.
and du Boulay, Benedict",

title="Data-Driven Generation of Rubric Parameters from an Educational Programming Environment",

booktitle="Artificial Intelligence in Education",

year="2017",

publisher="Springer International Publishing",

address="Cham",

pages="490--493",

abstract="We demonstrate that, by using a small set of hand-graded students, we can automatically generate rubric parameters with a high degree of validity, and that a predictive model incorporating these rubric parameters is more accurate than a previously reported model. We present this method as one approach to addressing the often challenging problem of grading assignments in programming environments. A classic solution is creating unit-tests that the student-generated program must pass, but the rigid, structured nature of unit-tests is suboptimal for assessing more open-ended assignments. Furthermore, the creation of unit-tests requires predicting the various ways a student might correctly solve a problem -- a challenging and time-intensive process. The current study proposes an alternative, semi-automated method for generating rubric parameters using low-level data from the Alice programming environment.",

isbn="978-3-319-61425-0",
  url="http://dev.stamper.org/publications/AIED2017_Diana_et_al_Data.pdf"
}

@inproceedings{EDM2017_Dianaetal,
  title={Automatic Peer Tutor Matching: Data-Driven Methods to Enable New Opportunities for Help.},
  author={Diana, Nicholas and Eagle, Michael and Stamper, John C and Grover, Shuchi and Bienkowski, Marie A and Basu, Satabdi},
  booktitle={Proceedings of the 10th International Conference on Educational Data Mining (EDM 2017)},
  year={2017},
  pages={372--373},
  url={http://dev.stamper.org/publications/EDM2017_Dianaetal.pdf}
}

@inproceedings{EDM2017_DianaDC,
  title={Teaching Informal Logical Fallacy Identification with a Cognitive Tutor},
  author="Diana, Nicholas 
and Eagle, Michael 
and Stamper, John
 and Koedinger, Kenneth",
  
booktitle={Proceedings of the 10th International Conference on Educational Data Mining (EDM 2017)},
  year={2017},
  pages={433--435},
  url={http://dev.stamper.org/publications/EDM2017_DianaDC.pdf}
}

@inproceedings{LAKMMLA_Liu_Stamper,
  title={Multimodal Data Collection and Analysis of Collaborative Learning through an Intelligent Tutoring System},
  author={Liu, Ran and Stamper, John C},
  booktitle={MMLA-CrossLAK@ LAK},
  pages={47--52},
  year={2017},
  url={http://dev.stamper.org/publications/LAKMMLA_Liu_Stamper.pdf}
}

@inproceedings{10.1145/3027385.3029442,
  author = {Koedinger, Ken and Liu, Ran and Stamper, John and Thille, Candace and Pavlik, Phil},
  title = {Community Based Educational Data Repositories and Analysis Tools},
  year = {2017},
  isbn = {9781450348706},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3027385.3029442},
  doi = {10.1145/3027385.3029442},
  abstract = {This workshop will explore community based repositories for educational data and analytic tools that are used to connect researchers and reduce the barriers to data sharing. Leading innovators in the field, as well as attendees, will identify and report on bottlenecks that remain toward our goal of a unified repository. We will discuss these as well as possible solutions. We will present LearnSphere, an NSF funded system that supports collaborating on and sharing a wide variety of educational data, learning analytics methods, and visualizations while maintaining confidentiality. We will then have hands-on sessions in which attendees have the opportunity to apply existing learning analytics workflows to their choice of educational datasets in the repository (using a simple drag-and-drop interface), add their own learning analytics workflows (requires very basic coding experience), or both. Leaders and attendees will then jointly discuss the unique benefits as well as the limitations of these solutions. Our goal is to create building blocks to allow researchers to integrate their data and analysis methods with others, in order to advance the future of learning science.},
  booktitle = {Proceedings of the Seventh International Learning Analytics & Knowledge Conference},
  pages = {524--525},
  numpages = {2},
  keywords = {data-informed efforts, scalability, modeling, learning metrics, data-informed learning theories, data storage and sharing},
  location = {Vancouver, British Columbia, Canada},
  series = {LAK '17}
}

@inproceedings{10.1145/3027385.3029439,
  author = {Lang, Charles and Teasley, Stephanie and Stamper, John},
  title = {Building the Learning Analytics Curriculum: Workshop},
  year = {2017},
  isbn = {9781450348706},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3027385.3029439},
  doi = {10.1145/3027385.3029439},
  abstract = {Learning Analytics courses and degree programs both on-and offline have begun to proliferate over the last three years. As a result of this growth in interest from students, university administrators, researchers and instructors we believe it is a good time to review how these educational efforts are impacting the field, how synergy between instructors might be developed to greater serve the field and what kinds of best practices could be developed.},
  booktitle = {Proceedings of the Seventh International Learning Analytics & Knowledge Conference},
  pages = {520--521},
  numpages = {2},
  keywords = {learning analytics instruction, teaching, curriculum development},
  location = {Vancouver, British Columbia, Canada},
  series = {LAK '17}
}

@article{JLA2016_Stamper_Pardos,
  title={The 2010 KDD Cup Competition Dataset: Engaging the machine learning community in predictive learning analytics},
  author={Stamper, John and Pardos, Zachary A},
  journal={Journal of Learning Analytics},
  volume={3},
  number={2},
  pages={312--316},
  year={2016},
  doi = {10.18608/jla.2016.32.16},
  url={http://dev.stamper.org/publications/JLA2016_Stamper_Pardos.pdf}
}

@inproceedings{UMAP2016_Eagle_et_al,
  author = {Eagle, Michael and Corbett, Albert and Stamper, John and McLaren, Bruce M. and Baker, Ryan and Wagner, Angela and MacLaren, Benjamin and Mitchell, Aaron},
  title = {Predicting Individual Differences for Learner Modeling in Intelligent Tutors from Previous Learner Activities},
  booktitle = {Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization},
  series = {UMAP '16},
  year = {2016},
  isbn = {978-1-4503-4368-8},
  location = {Halifax, Nova Scotia, Canada},
  pages = {55--63},
  numpages = {9},
  doi = {10.1145/2930238.2930255},
  acmid = {2930255},
  publisher = {ACM},
  address = {New York, NY, USA},
  keywords = {BKT, genetics, machine learning, student modeling},
  award={Best Paper Award},
  url={http://dev.stamper.org/publications/UMAP2016_Eagle_et_al.pdf}
}

@InProceedings{ITS2016_Eagle_et_al,
  author="Eagle, Michael and Corbett, Albert and Stamper, John and McLaren, Bruce and Wagner, Angela and MacLaren, Benjamin and Mitchell, Aaron",
  editor="Micarelli, Alessandro and Stamper, John and Panourgia, Kitty",
  title="Estimating Individual Differences for Student Modeling in Intelligent Tutors from Reading and Pretest Data",
  booktitle="Intelligent Tutoring Systems",
  year="2016",
  publisher="Springer International Publishing",
  address="Cham",
  pages="133--143",
  isbn="978-3-319-39583-8",
  doi="10.1007/978-3-319-39583-8_13",
  award="Best Paper Finalist",
  url="http://dev.stamper.org/publications/ITS2016_Eagle_et_al.pdf"
}

@InProceedings{EDM2016_Liu_et_al,
  title={Beyond Log Files: Using Multi-Modal Data Streams towards Data-Driven KC Model Improvement},
  author={Liu, Ran and Davenport, Jodi and Stamper, John},
  booktitle={9th International Conference on Educational Data Mining (EDM2016)},
  pages={436--441},
  year={2016},
  url="http://dev.stamper.org/publications/EDM2016_Liu_et_al.pdf"
}

@InProceedings{EDM2016_Diana_et_al,
  title={Extracting Measures of Active Learning and Student Self-Regulated Learning Strategies from MOOC Data},
  author={Diana, Nicholas and Stamper, John and Koedinger, Ken},
  booktitle={9th International Conference on Educational Data Mining (EDM2016)},
  pages={583--584},
  year={2016},
  url="http://dev.stamper.org/publications/EDM2016_Diana_et_al.pdf"
}

@InProceedings{EDM2016_Matsuda_et_al,
  title={How quickly can wheel spinning be detected?},
  author={Matsuda, Noboru and Chandrasekaran, Sanjay and Stamper, John},
  booktitle ={9th International Conference on Educational Data Mining (EDM2016)},
  pages={607--608},
  year={2016},
  url="http://dev.stamper.org/publications/EDM2016_Matsuda_et_al.pdf"
}

@InProceedings{EDM2015A,
  title={The Future of Practical Applications of EDM at Scale},
  author={Baker, Ryan and Carney, John and Mitros, Piotr and Saxberg, Bror and Stamper, John},
  booktitle={Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015)},
  pages={11},
  year={2015},
  url="http://dev.stamper.org/publications/EDM2015A.pdf"
}

@InProceedings{EDM2015B,
  title={Ethics and privacy in EDM},
  author={Gasevic, Dragan and Martin, Taylor and Pardos, Zachary and Pechenizkiy, Mykola and Stamper, John and Zaiane, Osmar},
  booktitle={Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015)},
  pages={13},
  year={2015},
  url="http://dev.stamper.org/publications/EDM2015B.pdf"
}

@inproceedings{GIFTSym2_HPIT,
  title={Personalization, non-cognitive factors, and grain-size for measurement and analysis in intelligent tutoring systems: implications for GIFT},
  author={Fancsali, Stephen E and Ritter, Steven and Stamper, John C and Berman, Susan},
  booktitle={Proceedings of the 2nd Annual GIFT Users Symposium},
  year = {2014},
  pages={123--134},
  url="http://dev.stamper.org/publications/GIFTSym2_HPIT.pdf"
}

@InProceedings{ITS2014_Blink_et_al,
  author="Blink, Mary Jean and Stamper, John and Carmichael, Ted",
  editor="Trausan-Matu, Stefan and Boyer, Kristy Elizabeth and Crosby, Martha and Panourgia, Kitty",
  title="SCALE: Student Centered Adaptive Learning Engine",
  booktitle="Intelligent Tutoring Systems",
  year="2014",
  publisher="Springer International Publishing",
  address="Cham",
  pages="654--655",
  isbn="978-3-319-07221-0",
  url="http://dev.stamper.org/publications/ITS2014_Blink_et_al.pdf"
}

@InProceedings{ITS2014_Carmichael_et_al,
  author="Carmichael, Ted and Hadzikadic, Mirsad and Blink, Mary Jean and Stamper, John C.",
  editor="Trausan-Matu, Stefan and Boyer, Kristy Elizabeth and Crosby, Martha and Panourgia, Kitty",
  title="A Multi-level Complex Adaptive System Approach for Modeling of Schools",
  booktitle="Intelligent Tutoring Systems",
  year="2014",
  publisher="Springer International Publishing",
  address="Cham",
  pages="623--624",
  isbn="978-3-319-07221-0",
  url="http://dev.stamper.org/publications/ITS2014_Carmichael_et_al.pdf"
}

@article{Ubiquity2014_Koedinger_et_al,
  author = {Koedinger, Kenneth R. and McLaughlin, Elizabeth A. and Stamper, John C.},
  title = {Data-driven Learner Modeling to Understand and Improve Online Learning: MOOCs and Technology to Advance Learning and Learning Research (Ubiquity Symposium)},
  journal = {Ubiquity},
  issue_date = {May 2014},
  volume = {2014},
  number = {May},
  month = may,
  year = {2014},
  issn = {1530-2180},
  pages = {3:1--3:13},
  articleno = {3},
  numpages = {13},
  url = {http://doi.acm.org/10.1145/2591682},
  doi = {10.1145/2591682},
  acmid = {2591682},
  publisher = {ACM},
  address = {New York, NY, USA},
  url={http://dev.stamper.org/publications/Ubiquity2014_Koedinger_et_al.pdf}
}

@article{IJAIED2013_Stamper_et_al,
  author = {Stamper, John and Eagle, Michael and Barnes, Tiffany and Croy, Marvin},
  title = {Experimental Evaluation of Automatic Hint Generation for a Logic Tutor},
  journal = {International Journal of Artificial Intelligence in Education},
  issue_date = {January 2013},
  volume = {22},
  number = {1-2},
  month = jan,
  year = {2013},
  issn = {1560-4292},
  pages = {3--17},
  numpages = {15},
  doi = {10.3233/JAI-130029},
  acmid = {2596895},
  publisher = {IOS Press},
  address = {Amsterdam, The Netherlands, The Netherlands},
  keywords = {Data Mining, Logic Tutor, Machine Learning},
  url = {http://dev.stamper.org/publications/IJAIED2013_Stamper_et_al.pdf}
}

@article{AI_Mag_New_potentials_for_ITS_2013,
  title={New Potentials for Data-Driven Intelligent Tutoring System Development and Optimization},
  author={Koedinger, Kenneth R and Brunskill, Emma and Baker, Ryan SJd and McLaughlin, Elizabeth A and Stamper, John},
  journal={AI Magazine},
  volume={34},
  number={3},
  pages={27--41},
  year={2013},
  publisher={Citeseer},
  url={http://dev.stamper.org/publications/AI_Mag_New_potentials_for_ITS_2013.pdf}
}

@article{Topics_in_Cog_Sci_2013,
  title={LearnLab's DataShop: A Data Repository and Analytics Tool Set for Cognitive Science},
  author={Koedinger, Kenneth R and Stamper, John C and Leber, Brett and Skogsholm, Alida},
  journal={Topics in Cognitive Science},
  volume={5},
  number={3},
  pages={668--669},
  year={2013},
  publisher={Wiley Online Library},
  url={http://dev.stamper.org/publications/Topics_in_Cog_Sci_2013.pdf}
}

@InProceedings{AIED_2013_Koedinger_et_al,
  author="Koedinger, Kenneth R. and Stamper, John C. and McLaughlin, Elizabeth A. and Nixon, Tristan",
  editor="Lane, H. Chad and Yacef, Kalina and Mostow, Jack and Pavlik, Philip",
  title="Using Data-Driven Discovery of Better Student Models to Improve Student Learning",
  booktitle="Artificial Intelligence in Education",
  year="2013",
  publisher="Springer Berlin Heidelberg",
  address="Berlin, Heidelberg",
  pages="421--430",
  isbn="978-3-642-39112-5",
  url="http://dev.stamper.org/publications/AIED_2013_Koedinger_et_al.pdf"
}

@inproceedings{EDM_2013_Stamper_et_al,
  title={A comparison of model selection metrics in datashop},
  author={Stamper, John and Koedinger, Kenneth and Mclaughlin, Elizabeth},
  booktitle={In Proceedings of the 6th International Conference on Educational Data Mining (EDM 2013)},
  year={2013},
  url={http://dev.stamper.org/publications/EDM_2013_Stamper_et_al.pdf}
}

@inproceedings{EDM_2013_Johnson_et_al,
  title={An Algorithm for Reducing the Complexity of Interaction Networks},
  author={Johnson, Matthew W and Eagle, Michael and Stamper, John and Barnes, Tiffany},
  booktitle={In Proceedings of the 6thInternational Conference on Educational Data Mining (EDM 2013)},
  year={2013},
  organization={Citeseer},
  url={http://dev.stamper.org/publications/EDM_2013_Johnson_et_al.pdf}
}

@inproceedings{AIED_2013_Fancsali_et_al,
  title={Toward Hyper-Personalized Cognitive Tutors},
  author={Fancsali, Stephen E and Ritter, Steven and Stamper, John and Nixon, Tristan},
  booktitle={AIED 2013 Workshops Proceedings Volume 7},
  pages={71--79},
  year={2013},
  url={http://dev.stamper.org/publications/AIED_2013_Fancsali_et_al.pdf}
}

@inproceedings{ITICSE_2013_Spacco_et_al,
  author = {Spacco, Jaime and Fossati, Davide and Stamper, John and Rivers, Kelly},
  title = {Towards Improving Programming Habits to Create Better Computer Science Course Outcomes},
  booktitle = {Proceedings of the 18th ACM Conference on Innovation and Technology in Computer Science Education},
  series = {ITiCSE '13},
  year = {2013},
  isbn = {978-1-4503-2078-8},
  location = {Canterbury, England, UK},
  pages = {243--248},
  numpages = {6},
  doi = {10.1145/2462476.2465594},
  acmid = {2465594},
  publisher = {ACM},
  address = {New York, NY, USA},
  keywords = {computer science education, cs2, novice programmers},
  url={http://dev.stamper.org/publications/ITICSE_2013_Spacco_et_al.pdf}
}

@inproceedings{Hovemeyer:2013:CBC:2445196.2445451,
  author = {Hovemeyer, David and Hertz, Matthew and Denny, Paul and Spacco, Jaime and Papancea, Andrei and Stamper, John and Rivers, Kelly},
  title = {CloudCoder: Building a Community for Creating, Assigning, Evaluating and Sharing Programming Exercises (Abstract Only)},
  booktitle = {Proceeding of the 44th ACM Technical Symposium on Computer Science Education},
  series = {SIGCSE '13},
  year = {2013},
  isbn = {978-1-4503-1868-6},
  location = {Denver, Colorado, USA},
  pages = {742--742},
  numpages = {1},
  url = {http://doi.acm.org/10.1145/2445196.2445451},
  doi = {10.1145/2445196.2445451},
  acmid = {2445451},
  publisher = {ACM},
  address = {New York, NY, USA},
  keywords = {automated testing, cloudcoder, cs1}
}

@inproceedings{Cog_Sci_2013,
  title={Online Education: A Unique Opportunity for Cognitive Scientists to Integrate Research and},
  author={Williams, Joseph Jay and Renkl, Alexander and Koedinger, Ken},
  booktitle={Proceedings of the Annual Meeting of the Cognitive Science Society, 35 (35)},
  year={2013},
  url={http://dev.stamper.org/publications/Cog_Sci_2013.pdf}
}

@inproceedings{GLS2012Lomasetal,
  title={Internet scale experimental design and deployment for educational games using BrainPOP},
  author={Stamper, J and Lomas, Derek and Ching, Dixie and Linch, K and Ritter, S},
  booktitle={Proceedings of the 8th Games+ Learning+ Society Conference (GLS 2012)},
  pages={275--281},
  year={2012},
  url={http://dev.stamper.org/publications/GLS2012Lomasetal.pdf}
}

@inproceedings{LAK12-Panel-EDM+LAK,
  author = {Baker, Ryan S. J. d. and Duval, Erik and Stamper, John and Wiley, David and Shum, Simon Buckingham},
  title = {Educational Data Mining Meets Learning Analytics},
  booktitle = {Proceedings of the 2nd International Conference on Learning Analytics and Knowledge},
  series = {LAK '12},
  year = {2012},
  isbn = {978-1-4503-1111-3},
  location = {Vancouver, British Columbia, Canada},
  pages = {20--20},
  numpages = {1},
  doi = {10.1145/2330601.2330613},
  acmid = {2330613},
  publisher = {ACM},
  address = {New York, NY, USA},
  keywords = {data mining, education, learning analytics},
  url = {http://dev.stamper.org/publications/LAK12-Panel-EDM+LAK.pdf},
}

@inproceedings{edm2012_full_10,
  title={Automated Student Model Improvement},
  author={Koedinger, Kenneth R and McLaughlin, Elizabeth A and Stamper, John C},
  booktitle={Proceedings of the 5th International Conference on Educational Data Mining (EDM 2012)},
  year={2012},
  pages={17--24},
  award={Best Paper Award},
  url={http://dev.stamper.org/publications/edm2012_full_10.pdf}
}

@inproceedings{edm2012_stamperetal,
  title={The Rise of the Super Experiment},
  author={Stamper, John C and Lomas, Derek and Ching, Dixie and Ritter, Steve and Koedinger, Kenneth R and Steinhart, Jonathan},
  booktitle={Proceedings of the 5th International Conference on Educational Data Mining (EDM 2012)},
  year={2012},
  pages={196--200},
  url={http://dev.stamper.org/publications/edm2012_stamperetal.pdf}
}

@InProceedings{ITS2012_Lomasetal,
  author="Lomas, Derek and Stamper, John and Muller, Ryan and Patel, Kishan and Koedinger, Kenneth R.",
  editor="Cerri, Stefano A. and Clancey, William J. and Papadourakis, Giorgos and Panourgia, Kitty",
  title="The Effects of Adaptive Sequencing Algorithms on Player Engagement within an Online Game",
  booktitle="Intelligent Tutoring Systems",
  year="2012",
  publisher="Springer Berlin Heidelberg",
  address="Berlin, Heidelberg",
  pages="588--590",
  isbn="978-3-642-30950-2",
  url="http://dev.stamper.org/publications/ITS2012_Lomasetal.pdf"
}

@InProceedings{ITS2012_jinetal,
  author="Jin, Wei and Barnes, Tiffany and Stamper, John and Eagle, Michael John and Johnson, Matthew W. and Lehmann, Lorrie",
  editor="Cerri, Stefano A. and Clancey, William J. and Papadourakis, Giorgos and Panourgia, Kitty",
  title="Program Representation for Automatic Hint Generation for a Data-Driven Novice Programming Tutor",
  booktitle="Intelligent Tutoring Systems",
  year="2012",
  publisher="Springer Berlin Heidelberg",
  address="Berlin, Heidelberg",
  pages="304--309",
  isbn="978-3-642-30950-2",
  url="http://dev.stamper.org/publications/ITS2012_jinetal.pdf"
}

@InProceedings{ITS2012_ritteretal,
  author="Ritter, Steve and Nixon, Tristan and Lomas, Derek and Stamper, John and Ching, Dixie",
  editor="Cerri, Stefano A. and Clancey, William J. and Papadourakis, Giorgos and Panourgia, Kitty",
  title="Using Time Pressure to Promote Mathematical Fluency",
  booktitle="Intelligent Tutoring Systems",
  year="2012",
  publisher="Springer Berlin Heidelberg",
  address="Berlin, Heidelberg",
  pages="669--670",
  isbn="978-3-642-30950-2",
  url="http://dev.stamper.org/publications/ITS2012_ritteretal.pdf"
}

@article{IJAIED2011_Stamper_et_al,
  title={Enhancing the automatic generation of hints with expert seeding},
  author={Stamper, John and Barnes, Tiffany and Croy, Marvin},
  journal={International Journal of Artificial Intelligence in Education},
  volume={21},
  number={1-2},
  pages={153--167},
  year={2011},
  publisher={IOS Press},
  url={http://dev.stamper.org/publications/IJAIED2011_Stamper_et_al.pdf}
}

@article{EDMHandbook_Barnes_et_al,
  title={Using Markov decision processes for student problem-solving visualization and automatic hint generation},
  author={Barnes, Tiffany and Stamper, John and Croy, Marvin},
  journal={Handbook of Educational Data Mining},
  pages={467-480},
  year={2011},
  publisher={CRC Press}
}

@InProceedings{AIED2011_stamperetal,
  author="Stamper, John C. and Eagle, Michael and Barnes, Tiffany and Croy, Marvin", 
  editor="Biswas, Gautam and Bull, Susan and Kay, Judy and Mitrovic, Antonija",
  title="Experimental Evaluation of Automatic Hint Generation for a Logic Tutor",
  booktitle="Artificial Intelligence in Education",
  year="2011",
  publisher="Springer Berlin Heidelberg",
  address="Berlin, Heidelberg",
  pages="345--352",
  isbn="978-3-642-21869-9",
  award="Best Paper Finalist",
  url="http://dev.stamper.org/publications/AIED2011_stamperetal.pdf"
}

@InProceedings{AIED2011_stamper_koedinger,
  author="Stamper, John C. and Koedinger, Kenneth R.",
  editor="Biswas, Gautam and Bull, Susan and Kay, Judy and Mitrovic, Antonija",
  title="Human-Machine Student Model Discovery and Improvement Using DataShop",
  booktitle="Artificial Intelligence in Education",
  year="2011",
  publisher="Springer Berlin Heidelberg",
  address="Berlin, Heidelberg",
  pages="353--360",
  isbn="978-3-642-21869-9",
  url="http://dev.stamper.org/publications/AIED2011_stamper_koedinger.pdf"
}

@InProceedings{AIED2011_datashop.pdf,
  author="Stamper, John C. and Koedinger, Kenneth R. and Baker, Ryan S. J. d. and Skogsholm, Alida and Leber, Brett and Demi, Sandy and Yu, Shawnwen and Spencer, Duncan",
  editor="Biswas, Gautam and Bull, Susan and Kay, Judy and Mitrovic, Antonija",
  title="Managing the Educational Dataset Lifecycle with DataShop",
  booktitle="Artificial Intelligence in Education",
  year="2011",
  publisher="Springer Berlin Heidelberg",
  address="Berlin, Heidelberg",
  pages="557--559",
  isbn="978-3-642-21869-9",
  url="http://dev.stamper.org/publications/AIED2011_datashop.pdf"
}

@inproceedings{KDD2011_Jin,
  title={Towards automatic hint generation for a data-driven novice programming tutor},
  author={Jin, Wei and Lehmann, Lorrie and Johnson, Matthew and Eagle, Michael and Mostafavi, Behrooz and Barnes, Tiffany and Stamper, John},
  title={Towards Automatic Hint Generation for a DataDriven Novice Programming Tutor},
  booktitle={Workshop on Knowledge Discovery in Educational Data, 17th ACM Conference on Knowledge Discovery and Data Mining},
  year={2011},
  url={http://dev.stamper.org/publications/KDD2011_Jin.pdf}
}

@inproceedings{EDM2011Stamper,
  title={EDM and the 4th Paradigm of Scientific Discovery-Reflections on the 2010 KDD Cup Competition},
  author={Stamper, John},
  booktitle={Proceedings of the 4th International Conference on Educational Data Mining (EDM 2011)},
  pages={7},
  year={2011},
  url={http://dev.stamper.org/publications/EDM2011Stamper.pdf}
}

@inproceedings{EDM2011_koedingeretal,
  title={Avoiding problem selection thrashing with conjunctive knowledge tracing},
  author={Koedinger, Kenneth and Pavlik Jr, Philip I and Stamper, John and Nixon, Tristan and Ritter, Steven},
  booktitle={Proceedings of the 4th International Conference on Educational Data Mining (EDM 2011)},
  year={2011},
  pages={91--100},
  url="http://dev.stamper.org/publications/EDM2011_koedingeretal.pdf"
}

@InProceedings{10.1007/978-3-642-21869-9_129,
  author="Stamper, John C. and Koedinger, Kenneth R. and Baker, Ryan S. J. d. and Skogsholm, Alida and Leber, Brett and Demi, Sandy and Yu, Shawnwen and Spencer, Duncan",
  editor="Biswas, Gautam and Bull, Susan and Kay, Judy and Mitrovic, Antonija",
  title="DataShop: A Data Repository and Analysis Service for the Learning Science Community (Interactive Event)",
  booktitle="Artificial Intelligence in Education",
  year="2011",
  publisher="Springer Berlin Heidelberg",
  address="Berlin, Heidelberg",
  pages="628",
  isbn="978-3-642-21869-9",
  url="https://link.springer.com/chapter/10.1007%2F978-3-642-21869-9_129"
}

@InProceedings{ITS2010StamperBarnesCroy,
  author="Stamper, John and Barnes, Tiffany and Croy, Marvin", 
  editor="Aleven, Vincent and Kay, Judy and Mostow, Jack",
  title="Enhancing the Automatic Generation of Hints with Expert Seeding",
  booktitle="Intelligent Tutoring Systems",
  year="2010",
  publisher="Springer Berlin Heidelberg",
  address="Berlin, Heidelberg",
  pages="31--40",
  award="Best Student Paper Award",
  isbn="978-3-642-13437-1",
  url="http://dev.stamper.org/publications/ITS2010StamperBarnesCroy.pdf"
}

@article{EDMHandbook_Koedinger_et_al,
  title={A Data Repository for the EDM Community: The PSLC DataShop},
  author={Koedinger, Kenneth R and d Baker, Ryan SJ and Cunningham, Kyle and Skogsholm, Alida and Leber, Brett and Stamper, John},
  journal={Handbook of Educational Data Mining},
  pages={43},
  year={2010},
  publisher={CRC Press},
  url={http://dev.stamper.org/publications/EDMHandbook_Koedinger_et_al.pdf}
}

@InProceedings{ITS2010_Stamper_et_al,
  author="Stamper, John and Koedinger, Ken and Baker, Ryan S. J. d. and Skogsholm, Alida and Leber, Brett and Rankin, Jim and Demi, Sandy",
  editor="Aleven, Vincent and Kay, Judy and Mostow, Jack",
  title="PSLC DataShop: A Data Analysis Service for the Learning Science Community",
  booktitle="Intelligent Tutoring Systems",
  year="2010",
  publisher="Springer Berlin Heidelberg",
  address="Berlin, Heidelberg",
  pages="455--455",
  isbn="978-3-642-13437-1",
  url="http://dev.stamper.org/publications/ITS2010_Stamper_et_al.pdf"
}

@inproceedings{EDM2010KoedingerStamper,
  title={A data driven approach to the discovery of better cognitive models},
  author={Koedinger, Kenneth R and Stamper, John C},
  booktitle={Proceedings of the 3rd International Conference on Educational Data Mining (EDM 2010)},
  year={2010},
  pages={325--326},
  url="http://dev.stamper.org/publications/EDM2010KoedingerStamper.pdf"
}

@inproceedings{EDM2010Stamper,
  title={Using a Bayesian Knowledge Base for Hint Selection on Domain Specific Problems},
  author={Stamper, John and Barnes, Tiffany and Croy, Marvin},
  booktitle={Proceedings of the 3rd International Conference on Educational Data Mining (EDM 2010)},
  year={2010},
  pages={327--328},
  url={http://dev.stamper.org/publications/EDM2010Stamper.pdf}
}

@article{JETS2010Barnes_Stamper,
  ISSN = {11763647, 14364522},
  URL = {http://www.jstor.org/stable/jeductechsoci.13.1.3},
  author = {Tiffany Barnes and John Stamper},
  journal = {Journal of Educational Technology & Society},
  number = {1},
  pages = {3--12},
  publisher = {International Forum of Educational Technology & Society},
  title = {Automatic Hint Generation for Logic Proof Tutoring Using Historical Data},
  volume = {13},
  year = {2010},
  url={http://dev.stamper.org/publications/JETS2010Barnes&Stamper.pdf}
}

@inproceedings{EDM2009stamper,
  title={An unsupervised, frequency-based metric for selecting hints in an mdp-based tutor},
  author={Stamper, John and Barnes, Tiffany},
  booktitle={Proceedings of the 2nd International Conference on Educational Data Mining (EDM 2009)},
  year={2009},
  pages={180--189},
  url={http://dev.stamper.org/publications/EDM2009stamper.pdf}
}

@inproceedings{AIED2009,
  author = {Stamper, John and Barnes, Tiffany},
  title = {Utility in Hint Generation: Selection of Hints from a Corpus of Student Work},
  booktitle = {Proceedings of the 2009 Conference on Artificial Intelligence in Education: Building Learning Systems That Care: From Knowledge Representation to Affective Modelling},
  year = {2009},
  isbn = {978-1-60750-028-5},
  pages = {749--751},
  numpages = {3},
  acmid = {1659594},
  publisher = {IOS Press},
  address = {Amsterdam, The Netherlands, The Netherlands},
  keywords = {Data Mining, Logic Tutor, Machine Learning},
  url={http://dev.stamper.org/publications/AIED2009.pdf}
}
@InProceedings{ITS2008Stamper,
  author={Stamper, John and Barnes, Tiffany and Lehmann, Lorrie and Croy, Marvin},
  title={The hint factory: Automatic generation of contextualized help for existing computer aided instruction},
  booktitle={Proceedings of the 9th International Conference on Intelligent Tutoring Systems Young Researchers Track},
  pages={71--78},
  year={2008},
  url={http://dev.stamper.org/publications/ITS2008Stamper.pdf}
}

@InProceedings{ITS2008BarnesStamper,
  author="Barnes, Tiffany and Stamper, John", 
  editor="Woolf, Beverley P. and A{\"i}meur, Esma and Nkambou, Roger and Lajoie, Susanne",
  title="Toward Automatic Hint Generation for Logic Proof Tutoring Using Historical Student Data",
  booktitle="Intelligent Tutoring Systems",
  year="2008",
  publisher="Springer Berlin Heidelberg",
  address="Berlin, Heidelberg",
  pages="373--382",
  isbn="978-3-540-69132-7",
  award="Best Paper Finalist",
  url="http://dev.stamper.org/publications/ITS2008BarnesStamper.pdf"
}

@inproceedings{AAAI2008StamperJ,
  author={Stamper, John C. and Barnes, Tiffany},
  title={The Validity of Providing Automated Hints in an ITS Using a MDP},
  booktitle={Proceedings of the 23rd National Conference on Artificial Intelligence - Volume 3},
  series={AAAI'08},
  year={2008},
  isbn={978-1-57735-368-3},
  location={Chicago, Illinois},
  pages={1830--1831},
  numpages={2},
  url={http://dev.stamper.org/publications/AAAI2008StamperJ.pdf}
}

@inproceedings{AAAI2008StamperJ,
  author={Barnes, Tiffany and Stamper, John and Lehman, Lorrie and Croy, Marvin},
  title={A pilot study on logic proof tutoring using hints generated from historical student data},
  booktitle={Proceedings of the 1st International Conference on Educational Data Mining (EDM 2008)},
  pages={197--201},
  year={2008},
  url={http://dev.stamper.org/publications/EDM2008barnes-stamper-lehman.pdf}
}

@inproceedings{ComputingandPhil2008.pdf,
 author = {Croy, Marvin and Barnes, Tiffany and Stamper, John},
 title = {Towards an Intelligent Tutoring System for Propositional Proof Construction},
 booktitle = {Proceedings of the 2008 Conference on Current Issues in Computing and Philosophy},
 year = {2008},
 isbn = {978-1-58603-876-2},
 pages = {145--155},
 numpages = {11},
 url = {http://dev.stamper.org/publications/ComputingandPhil2008.pdf},
 acmid = {1566253},
 publisher = {IOS Press},
 address = {Amsterdam, The Netherlands, The Netherlands},
 keywords = {Intelligent Tutoring System, Markov Decision Processes, Proof Construction, Visualization},
}

@inproceedings{AAAI2007StamperJ,
  author = {Stamper, John C. and Barnes, Tiffany and Croy, Marvin},
  title = {Extracting Student Models for Intelligent Tutoring Systems},
  booktitle = {Proceedings of the 22nd National Conference on Artificial Intelligence - Volume 2},
  series = {AAAI'07},
  year = {2007},
  isbn = {978-1-57735-323-2},
  location = {Vancouver, British Columbia, Canada},
  pages = {1900--1901},
  numpages = {2},
  url = {http://dev.stamper.org/publications/AAAI2007StamperJ.pdf},
  acmid = {1619951},
  publisher = {AAAI Press},
}

@inproceedings{AIED2007Stamper.pdf,
 author = {Stamper, John},
 title = {Automating the Generation of Student Models for Intelligent Tutoring Systems},
 booktitle = {Proceedings of the 2007 Conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work},
 year = {2007},
 isbn = {978-1-58603-764-2},
 pages = {701--702},
 numpages = {2},
 url = {http://dev.stamper.org/publications/AIED2007Stamper.pdf},
 acmid = {1563747},
 publisher = {IOS Press},
 address = {Amsterdam, The Netherlands, The Netherlands},
 keywords = {Adaptive Learning, Educational Data-Mining, Intelligent Tutoring},
}

@inproceedings{ICBL2007StamperCarmichael.pdf,
  author={Stamper, John and Carmichael, Ted},
  title={A Complex Adaptive System Approach to Predictive Data Insertion for Missing Student Data},
  booktitle={Proceedings of the 3rd International Conference on Computer Blended Learning (ICBL 2007)},
  address={Florinopolis, Brazil},
  publisher={Kassel Press}, 
  year={2007},
  url={http://dev.stamper.org/publications/ICBL2007StamperCarmichael.pdf}
}

@inproceedings{AIED2007Stamper.pdf,
  author={Barnes, Tiffany and Stamper, John},
  year={2007},
  title={Toward the extraction of production rules for solving logic proofs}, 
  editors={Heiner, C. and Barnes, T. and Heffernan N.},
  booktitle={Proceedings of the 13th International Conference on Artificial Intelligence in Education, Educational Data Mining Workshop (AIED2007)}, 
  pages={11--20},
  url={http://dev.stamper.org/publications/AIEDEDM2007.pdf}
}

@inproceedings{ICL2006-StamperFinal,
  author={Stamper, John},
  title={Automating the Generation of Production Rules for Intelligent Tutoring Systems},
  booktitle={Proceedings of the 9th International Conference on Interactive Computer Aided Learning (ICL2006)},
  address={Villach, Austria},
  publisher={Kassel Press}, 
  year={2006},
  url={http://dev.stamper.org/publications/ICL2006-StamperFinal.pdf}
}

@inproceedings{AAAIEDM2006,
  author={Barnes, Tiffany and Stamper, John and Madhyastha, Tara},
  editors={Beck, J. and Barnes, T.},
  title={Comparative Analysis of Concept Derivation Using the Q-matrix Method and Facets},
  booktitle={Proceedings of the AAAI 21st National Conference on Artificial Intelligence Educational Data Mining Workshop (AAAI2006)},
  address={Menlo Park, CA},
  publisher={AAAI Press}, 
  year={2006},
  url={http://dev.stamper.org/publications/AAAIEDM2006.pdf}
}

@inproceedings{MASS2005,
  author={Howitt, Ivan and Stamper, John and Raja, Anita and Mappillai, V.},
  booktitle={IEEE International Conference on Mobile Adhoc and Sensor Systems Conference (MASS2005)},
  title={Predictive protocol management with contingency planning for wireless sensor networks},
  year={2005},
  pages={3 pp.-162},
  keywords={protocols;telecommunication network management;telecommunication network planning;wireless sensor networks;predictive protocol management;contingency planning;wireless sensor networks;energy utilization;radio-frequency;Wireless application protocol;Contingency management;Wireless sensor networks;Radio frequency;Interference;Energy efficiency;Routing protocols;Sensor systems and applications;Energy consumption;Computer network management},
  doi={10.1109/MAHSS.2005.1542790},
  ISSN={2155-6806},
  url={http://dev.stamper.org/publications/MASS2005.pdf}
}

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