references.bib

@Article{Abbas2024,
  author       = {Abbas, Muhammad and Jam, Farooq Ahmed and Khan, Tariq Iqbal},
  date         = {2024-02},
  journaltitle = {International Journal of Educational Technology in Higher Education},
  title        = {Is It Harmful or Helpful? {{Examining}} the Causes and Consequences of Generative {{AI}} Usage among University Students},
  doi          = {10.1186/s41239-024-00444-7},
  issn         = {2365-9440},
  langid       = {english},
  number       = {1},
  pages        = {10},
  urldate      = {2024-08-16},
  volume       = {21},
  abstract     = {Abstract              While the discussion on generative artificial intelligence, such as ChatGPT, is making waves in academia and the popular press, there is a need for more insight into the use of ChatGPT among students and the potential harmful or beneficial consequences associated with its usage. Using samples from two studies, the current research examined the causes and consequences of ChatGPT usage among university students. Study 1 developed and validated an eight-item scale to measure ChatGPT usage by conducting a survey among university students (N\,=\,165). Study 2 used a three-wave time-lagged design to collect data from university students (N\,=\,494) to further validate the scale and test the study's hypotheses. Study 2 also examined the effects of academic workload, academic time pressure, sensitivity to rewards, and sensitivity to quality on ChatGPT usage. Study 2 further examined the  effects of ChatGPT usage on students' levels of procrastination, memory loss, and academic performance. Study 1 provided evidence for the validity and reliability of the ChatGPT usage scale. Furthermore, study 2 revealed that when students faced higher academic workload and time pressure, they were more likely to use ChatGPT. In contrast,~students who were sensitive to rewards were less likely to use ChatGPT. Not surprisingly, use of ChatGPT was likely to develop tendencies for procrastination and memory loss and dampen the students' academic performance. Finally, academic workload, time pressure, and sensitivity to rewards had indirect effects on students' outcomes through ChatGPT usage.},
  groups       = {PTAS Project},
  shorttitle   = {Is It Harmful or Helpful?},
}

@InCollection{Ajzen1985,
  author     = {Ajzen, Icek},
  booktitle  = {Action {{Control}}},
  date       = {1985},
  title      = {From {{Intentions}} to {{Actions}}: {{A Theory}} of {{Planned Behavior}}},
  doi        = {10.1007/978-3-642-69746-3_2},
  editor     = {Kuhl, Julius and Beckmann, J{\"u}rgen},
  isbn       = {978-3-642-69748-7 978-3-642-69746-3},
  location   = {Berlin, Heidelberg},
  pages      = {11--39},
  publisher  = {Springer Berlin Heidelberg},
  urldate    = {2024-11-15},
  groups     = {PTAS Project},
  langid     = {english},
  shorttitle = {From {{Intentions}} to {{Actions}}},
}

@Book{Anderson2001,
  author    = {Anderson, Lorin and Krathwohl, David and Airasian, Peter and Cruikshank, Kathleen and Mayer, Richard and Pintrich, Paul and Raths, James and Wittrock, Merlin},
  date      = {2001},
  title     = {A {{Taxonomy}} for {{Learning}}, {{Teaching}}, and {{Assessing}}: {{A Revision}} of {{Bloom}}'s {{Taxonomy}} of {{Educational Objectives}}, {{Complete Edition}}},
  edition   = {1},
  isbn      = {978-0-321-08405-7},
  publisher = {Pearson},
  groups    = {PTAS Project},
}

@Book{Anderson2009,
  date       = {2009},
  title      = {A Taxonomy for Learning, Teaching, and Assessing: A Revision of {{Bloom}}'s Taxonomy of Educational Objectives},
  edition    = {Abridged ed., [Nachdr.]},
  editor     = {Anderson, Lorin W.},
  isbn       = {978-0-8013-1903-7 978-0-321-08405-7},
  langid     = {english},
  location   = {New York Munich},
  publisher  = {Longman},
  groups     = {PTAS Project},
  shorttitle = {A Taxonomy for Learning, Teaching, and Assessing},
}

@Article{Bagozzi1992,
  author       = {Bagozzi, Richard P. and Davis, Fred D. and Warshaw, Paul R.},
  date         = {1992-07},
  journaltitle = {Human Relations},
  title        = {Development and {{Test}} of a {{Theory}} of {{Technological Learning}} and {{Usage}}},
  doi          = {10.1177/001872679204500702},
  issn         = {0018-7267, 1741-282X},
  langid       = {english},
  number       = {7},
  pages        = {659--686},
  urldate      = {2024-11-15},
  volume       = {45},
  abstract     = {Beliefs, attitudes, and intentions are important factors in the adoption of computer technologies. While contemporary representations have focused on explaining the act of using computers, the role of learning to use the computer needs to be better understood within the overall adoption process. Inadequate learning can curtail the adoption and use of a potentially productive system. We introduce a new theoretical model, the theory of trying, in which computer learning is conceptualized as a goal determined by three attitude components: attitude toward success, attitude toward failure, and attitude toward the process of goal pursuit. Intentions to try and actual trying are the theoretical mechanisms linking these goal-directed attitudes to goal attainment. An empirical study is conducted to ascertain the construct validity and utility of the new theory within the context of the adoption of a word processing package. Specifically, we examine convergent validity, internal consistency reliability, stability, discriminant validity, criterion related validity, predictive validity, and nomological validity in a longitudinal field study of 107 users of the program. The new theory is compared to two models: the theory of reasoned action from the field of social psychology and the technology acceptance model, recently introduced in the management literature. Overall, the findings stress the importance of scrutinizing the goals of decision makers and their psychological reactions to these goals in the prediction of the adoption of computers.},
  copyright    = {https://journals.sagepub.com/page/policies/text-and-data-mining-license},
  groups       = {PTAS Project},
}

@Article{Bandura1977,
  author       = {Bandura, Albert},
  date         = {1977},
  journaltitle = {Psychological Review},
  title        = {Self-Efficacy: {{Toward}} a Unifying Theory of Behavioral Change.},
  doi          = {10.1037/0033-295X.84.2.191},
  issn         = {1939-1471, 0033-295X},
  langid       = {english},
  number       = {2},
  pages        = {191--215},
  urldate      = {2024-07-12},
  volume       = {84},
  groups       = {PTAS Project},
  shorttitle   = {Self-Efficacy},
}

@Article{Bates2020,
  author       = {Bates, Tony and Cobo, Crist{\'o}bal and Mari{\~n}o, Olga and Wheeler, Steve},
  date         = {2020-12},
  journaltitle = {International Journal of Educational Technology in Higher Education},
  title        = {Can Artificial Intelligence Transform Higher Education?},
  doi          = {10.1186/s41239-020-00218-x},
  issn         = {2365-9440},
  langid       = {english},
  number       = {1},
  pages        = {42, s41239-020-00218-x},
  urldate      = {2024-08-16},
  volume       = {17},
  groups       = {PTAS Project},
}

@Misc{Becker2022,
  author      = {Becker, Brett A. and Denny, Paul and {Finnie-Ansley}, James and {Luxton-Reilly}, Andrew and Prather, James and Santos, Eddie Antonio},
  date        = {2022-12},
  title       = {Programming {{Is Hard}} -- {{Or}} at {{Least It Used}} to {{Be}}: {{Educational Opportunities And Challenges}} of {{AI Code Generation}}},
  doi         = {10.48550/arXiv.2212.01020},
  eprint      = {2212.01020},
  eprintclass = {cs},
  eprinttype  = {arXiv},
  urldate     = {2024-06-03},
  abstract    = {The introductory programming sequence has been the focus of much research in computing education. The recent advent of several viable and freely-available AI-driven code generation tools present several immediate opportunities and challenges in this domain. In this position paper we argue that the community needs to act quickly in deciding what possible opportunities can and should be leveraged and how, while also working on how to overcome or otherwise mitigate the possible challenges. Assuming that the effectiveness and proliferation of these tools will continue to progress rapidly, without quick, deliberate, and concerted efforts, educators will lose advantage in helping shape what opportunities come to be, and what challenges will endure. With this paper we aim to seed this discussion within the computing education community.},
  groups      = {PTAS Project},
  keywords    = {Computers and Society},
  number      = {arXiv:2212.01020},
  publisher   = {arXiv},
  shorttitle  = {Programming {{Is Hard}} -- {{Or}} at {{Least It Used}} to {{Be}}},
}

@InProceedings{Bender2021,
  author     = {Bender, Emily M. and Gebru, Timnit and {McMillan-Major}, Angelina and Shmitchell, Shmargaret},
  booktitle  = {Proceedings of the 2021 {{ACM Conference}} on {{Fairness}}, {{Accountability}}, and {{Transparency}}},
  date       = {2021-03},
  title      = {On the {{Dangers}} of {{Stochastic Parrots}}: {{Can Language Models Be Too Big}}? 🦜},
  doi        = {10.1145/3442188.3445922},
  isbn       = {978-1-4503-8309-7},
  location   = {Virtual Event Canada},
  pages      = {610--623},
  publisher  = {ACM},
  urldate    = {2024-08-16},
  groups     = {PTAS Project},
  langid     = {english},
  shorttitle = {On the {{Dangers}} of {{Stochastic Parrots}}},
}

@Article{Bergdahl2023,
  author       = {Bergdahl, Jenna and Latikka, Rita and Celuch, Magdalena and Savolainen, Iina and Soares Mantere, Eerik and Savela, Nina and Oksanen, Atte},
  date         = {2023-08},
  journaltitle = {Telematics and Informatics},
  title        = {Self-Determination and Attitudes toward Artificial Intelligence: {{Cross-national}} and Longitudinal Perspectives},
  doi          = {10.1016/j.tele.2023.102013},
  issn         = {0736-5853},
  langid       = {english},
  pages        = {102013},
  urldate      = {2024-09-15},
  volume       = {82},
  groups       = {PTAS Project},
  shorttitle   = {Self-Determination and Attitudes toward Artificial Intelligence},
}

@Book{Biggs2014,
  author     = {Biggs, John B. and Collis, Kevin F.},
  date       = {2014},
  title      = {Evaluating the Quality of Learning: The {{SOLO}} Taxonomy (Structure of the Observed Learning Outcome)},
  isbn       = {978-1-4832-7331-0},
  langid     = {english},
  location   = {New York London Toronto Sydney; San Francisco},
  publisher  = {Academic Press},
  series     = {Educational Psychology Series},
  abstract   = {Evaluating the Quality of Learning},
  groups     = {PTAS Project},
  shorttitle = {Evaluating the Quality of Learning},
}

@Book{Biggs2022,
  author    = {Biggs, John B. and Tang, Catherine So-kum and Kennedy, Gregor},
  date      = {2022},
  title     = {Teaching for Quality Learning at University},
  edition   = {Fifth edition},
  isbn      = {978-0-335-25082-0},
  langid    = {english},
  location  = {Maidenhead},
  publisher = {Open University Press, McGraw Hill},
  groups    = {PTAS Project},
}

@Book{Bloom1956,
  author     = {Bloom, Benjamin S. and Engelhart, Max D. and Furst, E. J. and Hill, W. H. and Krathwohl, David R.},
  date       = {1956},
  title      = {Taxonomy of Educational Objectives: The Classification of Educational Goals. {{Handbook}} 1, {{Cognitive}} Domain},
  langid     = {english},
  location   = {New York},
  publisher  = {David McKay Company},
  abstract   = {This volume classifies learning behaviors and provides concrete measures for identifying different levels of learning. The cognitive domain consists of 6 levels: knowledge, comprehension, application, analysis, synthesis and evaluation. Each level is associated with specific learning behaviors and descriptive verbs for use in writing instructional objectives},
  annotation = {OCLC: 86054529},
  groups     = {PTAS Project},
  shorttitle = {Taxonomy of Educational Objectives},
}

@Article{Bodin2012,
  author       = {Bodin, Madelen},
  date         = {2012-04},
  journaltitle = {Physical Review Physics Education Research},
  title        = {Mapping University Students' Epistemic Framing of Computational Physics Using Network Analysis},
  doi          = {10.1103/PhysRevSTPER.8.010115},
  number       = {1},
  pages        = {010115},
  volume       = {8},
  abstract     = {Solving physics problem in university physics education using a computational approach requires knowledge and skills in several domains, for example, physics, mathematics, programming, and modeling. These competences are in turn related to students' beliefs about the domains as well as about learning. These knowledge and beliefs components are referred to here as epistemic elements, which together represent the students' epistemic framing of the situation. The purpose of this study was to investigate university physics students' epistemic framing when solving and visualizing a physics problem using a particle-spring model system. Students' epistemic framings are analyzed before and after the task using a network analysis approach on interview transcripts, producing visual representations as epistemic networks. The results show that students change their epistemic framing from a modeling task, with expectancies about learning programming, to a physics task, in which they are challenged to use physics principles and conservation laws in order to troubleshoot and understand their simulations. This implies that the task, even though it is not introducing any new physics, helps the students to develop a more coherent view of the importance of using physics principles in problem solving. The network analysis method used in this study is shown to give intelligible representations of the students' epistemic framing and is proposed as a useful method of analysis of textual data.},
  groups       = {PTAS Project},
}

@Article{Boguslawski2024,
  author       = {Boguslawski, Samuel and Deer, Rowan and Dawson, Mark G.},
  date         = {2024-07},
  journaltitle = {Information and Learning Sciences},
  title        = {Programming Education and Learner Motivation in the Age of Generative {{AI}}: Student and Educator Perspectives},
  doi          = {10.1108/ILS-10-2023-0163},
  issn         = {2398-5348, 2398-5348},
  langid       = {english},
  urldate      = {2024-07-25},
  abstract     = {Purpose                Programming education is being rapidly transformed by generative AI tools and educators must determine how best to support students in this context. This study aims to explore the experiences of programming educators and students to inform future education provision.                                            Design/methodology/approach                Twelve students and six members of faculty in a small technology-focused university were interviewed. Thematic analysis of the interview data was combined with data collected from a survey of 44 students at the same university. Self-determination theory was applied as an analytical framework.                                            Findings                Three themes were identified -- bespoke learning, affect and support -- that significantly impact motivation and learning outcomes in programming education. It was also found that students are already making extensive use of large language models (LLMs). LLMs can significantly improve learner autonomy and sense of competence by improving the options for bespoke learning; fostering emotions that are conducive to engendering and maintaining motivation; and inhibiting the negative affective states that discourage learning. However, current LLMs cannot adequately provide or replace social support, which is still a key factor in learner motivation.                                            Research limitations/implications                Integrating the use of LLMs into curricula can improve learning motivation and outcomes. It can also free educators from certain tasks, leaving them with more time and capacity to focus their attention on developing social learning opportunities to further enhance learner motivation.                                            Originality/value                To the best of the authors' knowledge, this is the first attempt to explore the relationship between motivation and LLM use in programming education.},
  copyright    = {https://www.emerald.com/insight/site-policies},
  groups       = {PTAS Project},
  shorttitle   = {Programming Education and Learner Motivation in the Age of Generative {{AI}}},
}

@Article{Bond2024,
  author       = {Bond, Melissa and Khosravi, Hassan and De Laat, Maarten and Bergdahl, Nina and Negrea, Violeta and Oxley, Emily and Pham, Phuong and Chong, Sin Wang and Siemens, George},
  date         = {2024-01},
  journaltitle = {International Journal of Educational Technology in Higher Education},
  title        = {A Meta Systematic Review of Artificial Intelligence in Higher Education: A Call for Increased Ethics, Collaboration, and Rigour},
  doi          = {10.1186/s41239-023-00436-z},
  issn         = {2365-9440},
  langid       = {english},
  number       = {1},
  pages        = {4},
  urldate      = {2024-08-23},
  volume       = {21},
  abstract     = {Abstract              Although the field of Artificial Intelligence in Education (AIEd) has a substantial history as a research domain, never before has the rapid evolution of AI applications in education sparked such prominent public discourse. Given the already rapidly growing AIEd literature base in higher education, now is the time to ensure that the field has a solid research and conceptual grounding. This review of reviews is the first comprehensive meta review to explore the scope and nature of AIEd in higher education (AIHEd) research, by synthesising secondary research (e.g., systematic reviews), indexed in the Web of Science, Scopus, ERIC, EBSCOHost, IEEE Xplore, ScienceDirect and ACM Digital Library, or captured through snowballing in OpenAlex, ResearchGate and Google Scholar. Reviews were included if they synthesised applications of AI solely in formal higher or continuing education, were published in English between 2018 and July 2023, were journal articles or full conference papers, and if they had a method section 66 publications were included for data extraction and synthesis in EPPI Reviewer, which were predominantly systematic reviews (66.7\%), published by authors from North America (27.3\%), conducted in teams (89.4\%) in mostly domestic-only collaborations (71.2\%). Findings show that these reviews mostly focused on AIHEd generally (47.0\%) or Profiling and Prediction (28.8\%) as thematic foci, however key findings indicated a predominance of the use of Adaptive Systems and Personalisation in higher education. Research gaps identified suggest a need for greater ethical, methodological, and contextual considerations within future research, alongside interdisciplinary approaches to AIHEd application. Suggestions are provided to guide future primary and secondary research.},
  groups       = {PTAS Project},
  shorttitle   = {A Meta Systematic Review of Artificial Intelligence in Higher Education},
}

@Book{Brookfield1995,
  author    = {Brookfield, Stephen},
  date      = {1995},
  title     = {Becoming a Critically Reflective Teacher},
  edition   = {1. ed},
  isbn      = {978-0-7879-0131-8},
  langid    = {english},
  location  = {San Francisco},
  publisher = {Jossey-Bass},
  series    = {The {{Jossey-Bass}} Higher and Adult Education Series},
  groups    = {PTAS Project},
}

@Book{Brookfield2017,
  author    = {Brookfield, Stephen D.},
  date      = {2017},
  title     = {Becoming a Critically Reflective Teacher},
  edition   = {Second Edition},
  isbn      = {978-1-119-04970-8 978-1-119-05065-0},
  langid    = {english},
  location  = {Somerset},
  publisher = {John Wiley \& Sons, Incorporated},
  groups    = {PTAS Project},
}

@Article{Caballero2018,
  author       = {Caballero, Marcos D and Merner, Laura},
  date         = {2018-12},
  journaltitle = {Physical Review Physics Education Research},
  title        = {Prevalence and Nature of Computational Instruction in Undergraduate Physics Programs across the {{United States}}},
  doi          = {10.1103/PhysRevPhysEducRes.14.020129},
  pages        = {020129},
  volume       = {14},
  abstract     = {A national survey of physics faculty was conducted to investigate the prevalence and nature of computational instruction in physics courses across the United States. 1246 faculty from 357 unique institutions responded to the survey. The results suggest that more faculty have some form of computational teaching experience than a decade ago, but it appears that this experience does not necessarily translate to computational instruction in undergraduate students' formal course work. Further, we find that formal programs in computational physics are absent from most departments. A majority of faculty do report using computation on homework and in projects, but few report using computation with interactive engagement methods in the classroom or on exams. Specific factors that underlie these results are the subject of future work, but we do find that there is a variation on the reported experience with computation and the highest degree that students can earn at the surveyed institutions.},
  groups       = {PTAS Project},
}

@Article{Carmi2024,
  author       = {Carmi, Golan},
  date         = {2024-06},
  journaltitle = {Heliyon},
  title        = {E-{{Learning}} Using Zoom: {{A}} Study of Students' Attitude and Learning Effectiveness in Higher Education},
  doi          = {10.1016/j.heliyon.2024.e30229},
  issn         = {2405-8440},
  langid       = {english},
  number       = {11},
  urldate      = {2024-06-06},
  volume       = {10},
  groups       = {PTAS Project},
  publisher    = {Elsevier},
  shorttitle   = {E-{{Learning}} Using Zoom},
}

@Article{Chonacky2008,
  author       = {Chonacky, Norman and Winch, David},
  date         = {2008-04},
  journaltitle = {American Journal of Physics},
  title        = {Integrating Computation into the Undergraduate Curriculum: {{A}} Vision and Guidelines for Future Developments},
  doi          = {10.1119/1.2837811},
  issn         = {0002-9505, 1943-2909},
  langid       = {english},
  number       = {4},
  pages        = {327--333},
  urldate      = {2024-11-20},
  volume       = {76},
  abstract     = {There is substantial evidence of a need to make computation an integral part of the undergraduate physics curriculum. This need is consistent with data from surveys in both the academy and the workplace, and has been reinforced by two years of exploratory efforts by a group of physics faculty for whom computation is a special interest. We have examined past and current efforts at reform and a variety of strategic, organizational, and institutional issues involved in any attempt to broadly transform existing practice. We propose a set of guidelines for development based on this past work and discuss our vision of computationally integrated physics.},
  groups       = {PTAS Project},
  shorttitle   = {Integrating Computation into the Undergraduate Curriculum},
}

@Article{Crompton2023,
  author       = {Crompton, Helen and Burke, Diane},
  date         = {2023-04},
  journaltitle = {International Journal of Educational Technology in Higher Education},
  title        = {Artificial Intelligence in Higher Education: The State of the Field},
  doi          = {10.1186/s41239-023-00392-8},
  issn         = {2365-9440},
  langid       = {english},
  number       = {1},
  pages        = {22},
  urldate      = {2024-08-16},
  volume       = {20},
  abstract     = {Abstract              This systematic review provides unique findings with an up-to-date examination of artificial intelligence (AI) in higher education (HE) from 2016 to 2022. Using PRISMA principles and protocol, 138 articles were identified for a full examination. Using a priori, and grounded coding, the data from the 138 articles were extracted, analyzed, and coded. The findings of this study show that in 2021 and 2022, publications rose nearly two to three times the number of previous years. With this rapid rise in the number of AIEd HE publications, new trends have emerged. The findings show that research was conducted in six of the seven continents of the world. The trend has shifted from the US to China leading in the number of publications. Another new trend is in the researcher affiliation as prior studies showed a lack of researchers from departments of education. This has now changed to be the most dominant department. Undergraduate students were the most studied students at 72\%. Similar to the findings of other studies, language learning was the most common subject domain. This included writing, reading, and vocabulary acquisition. In examination of who the AIEd was intended for 72\% of the studies focused on students, 17\% instructors, and 11\% managers. In answering the overarching question of how AIEd was used in HE, grounded coding was used. Five usage codes emerged from the data: (1) Assessment/Evaluation, (2) Predicting, (3) AI Assistant, (4) Intelligent Tutoring System (ITS), and (5) Managing Student Learning. This systematic review revealed gaps in the literature to be used as a springboard for future researchers, including new tools, such as Chat GPT.},
  groups       = {PTAS Project},
  shorttitle   = {Artificial Intelligence in Higher Education},
}

@InProceedings{Crow2018,
  author     = {Crow, Tyne and {Luxton-Reilly}, Andrew and Wuensche, Burkhard},
  booktitle  = {Proceedings of the 20th {{Australasian Computing Education Conference}}},
  date       = {2018-01},
  title      = {Intelligent Tutoring Systems for Programming Education: A Systematic Review},
  doi        = {10.1145/3160489.3160492},
  isbn       = {978-1-4503-6340-2},
  location   = {Brisbane Queensland Australia},
  pages      = {53--62},
  publisher  = {ACM},
  urldate    = {2024-08-16},
  groups     = {PTAS Project},
  langid     = {english},
  shorttitle = {Intelligent Tutoring Systems for Programming Education},
}

@Misc{Cui2024,
  author     = {Cui, Zheyuan and Demirer, Mert and Jaffe, Sonia and Musolff, Leon and Peng, Sida and Salz, Tobias},
  date       = {2024},
  title      = {The {{Effects}} of {{Generative AI}} on {{High Skilled Work}}: {{Evidence}} from {{Three Field Experiments}} with {{Software Developers}}},
  doi        = {10.2139/ssrn.4945566},
  urldate    = {2024-10-10},
  groups     = {PTAS Project},
  shorttitle = {The {{Effects}} of {{Generative AI}} on {{High Skilled Work}}},
}

@Article{Davis1989,
  author       = {Davis, Fred D.},
  date         = {1989-09},
  journaltitle = {MIS Quarterly},
  title        = {Perceived {{Usefulness}}, {{Perceived Ease}} of {{Use}}, and {{User Acceptance}} of {{Information Technology}}},
  doi          = {10.2307/249008},
  eprint       = {249008},
  eprinttype   = {jstor},
  issn         = {0276-7783},
  number       = {3},
  pages        = {319},
  urldate      = {2024-11-15},
  volume       = {13},
  groups       = {PTAS Project},
}

@Book{Dewey1916,
  author    = {Dewey, John},
  date      = {1916},
  title     = {Democracy and {{Education}}},
  location  = {New York},
  publisher = {Macmillan},
  groups    = {PTAS Project},
}

@Article{Dillon1996,
  author       = {Dillon, Andrew and Morris, Michael G.},
  date         = {1996},
  journaltitle = {Annual Review of Information Science and Technology},
  title        = {User {{Acceptance}} of {{Information Technology}}: {{Theories}} and {{Models}}},
  volume       = {31},
  groups       = {PTAS Project},
}

@Article{Dolmans2016,
  author       = {Dolmans, Diana H. J. M. and Loyens, Sofie M. M. and Marcq, H{\'e}l{\`e}ne and Gijbels, David},
  date         = {2016-12},
  journaltitle = {Advances in Health Sciences Education},
  title        = {Deep and Surface Learning in Problem-Based Learning: A Review of the Literature},
  doi          = {10.1007/s10459-015-9645-6},
  issn         = {1382-4996, 1573-1677},
  langid       = {english},
  number       = {5},
  pages        = {1087--1112},
  urldate      = {2024-08-16},
  volume       = {21},
  groups       = {PTAS Project},
  shorttitle   = {Deep and Surface Learning in Problem-Based Learning},
}

@InCollection{Eccles1983,
  author    = {Eccles, Jacquelynne S.},
  booktitle = {Achievement and Achievement Motives: {{Psychological}} and Sociological Approaches},
  date      = {1983},
  title     = {Expectancies, Values, and Academic Behaviors},
  editor    = {Spence, Janet T.},
  location  = {San Francisco, CA},
  pages     = {75--146},
  publisher = {W. H. Freeman},
  groups    = {PTAS Project},
}

@InCollection{English2016,
  author    = {English, Andrea},
  booktitle = {Dewey in Our {{Time}}},
  date      = {2016-09},
  title     = {The 'in-between' of Learning: ({{Re}})Valuing the Process of Learning},
  isbn      = {978-1-78277-170-8},
  location  = {London},
  pages     = {128--143},
  publisher = {UCL Institute of Education Press},
  urldate   = {2024-06-24},
  groups    = {PTAS Project},
}

@Article{Entwistle1979,
  author       = {Entwistle, Noel and Hanley, Maureen and Hounsell, Dai},
  date         = {1979-07},
  journaltitle = {Higher Education},
  title        = {Identifying Distinctive Approaches to Studying},
  doi          = {10.1007/BF01680525},
  issn         = {0018-1560, 1573-174X},
  langid       = {english},
  number       = {4},
  pages        = {365--380},
  urldate      = {2024-07-12},
  volume       = {8},
  copyright    = {http://www.springer.com/tdm},
  groups       = {PTAS Project},
}

@InCollection{Entwistle2005,
  author    = {Entwistle, Noel},
  booktitle = {The {{Experience}} of {{Learning}}},
  date      = {2005},
  title     = {Constrasting {{Perspectives}} on {{Learning}}},
  edition   = {3},
  location  = {Edinburgh},
  pages     = {3--22},
  publisher = {{University of Edinburgh, Centre for Teaching, Learning and Assessment}},
  groups    = {PTAS Project},
}

@InProceedings{FinnieAnsley2022,
  author     = {{Finnie-Ansley}, James and Denny, Paul and Becker, Brett A. and {Luxton-Reilly}, Andrew and Prather, James},
  booktitle  = {Proceedings of the 24th {{Australasian Computing Education Conference}}},
  date       = {2022-02},
  title      = {The {{Robots Are Coming}}: {{Exploring}} the {{Implications}} of {{OpenAI Codex}} on {{Introductory Programming}}},
  doi        = {10.1145/3511861.3511863},
  isbn       = {978-1-4503-9643-1},
  location   = {Virtual Event Australia},
  pages      = {10--19},
  publisher  = {ACM},
  urldate    = {2024-09-13},
  groups     = {PTAS Project},
  langid     = {english},
  shorttitle = {The {{Robots Are Coming}}},
}

@Misc{Fu2024,
  author      = {Fu, Yujia and Liang, Peng and Tahir, Amjed and Li, Zengyang and Shahin, Mojtaba and Yu, Jiaxin and Chen, Jinfu},
  date        = {2024-04},
  title       = {Security {{Weaknesses}} of {{Copilot Generated Code}} in {{GitHub}}},
  doi         = {10.48550/arxiv.2310.02059},
  eprint      = {2310.02059},
  eprintclass = {cs},
  eprinttype  = {arXiv},
  urldate     = {2024-11-20},
  abstract    = {Modern code generation tools, utilizing AI models like Large Language Models (LLMs), have gained popularity for producing functional code. However, their usage presents security challenges, often resulting in insecure code merging into the code base. Evaluating the quality of generated code, especially its security, is crucial. While prior research explored various aspects of code generation, the focus on security has been limited, mostly examining code produced in controlled environments rather than real-world scenarios. To address this gap, we conducted an empirical study, analyzing code snippets generated by GitHub Copilot from GitHub projects. Our analysis identified 452 snippets generated by Copilot, revealing a high likelihood of security issues, with 32.8\% of Python and 24.5\% of JavaScript snippets affected. These issues span 38 different Common Weakness Enumeration (CWE) categories, including significant ones like CWE-330: Use of Insufficiently Random Values, CWE-78: OS Command Injection, and CWE-94: Improper Control of Generation of Code. Notably, eight CWEs are among the 2023 CWE Top-25, highlighting their severity. Our findings confirm that developers should be careful when adding code generated by Copilot and should also run appropriate security checks as they accept the suggested code. It also shows that practitioners should cultivate corresponding security awareness and skills.},
  groups      = {PTAS Project},
  keywords    = {Computer Science - Cryptography and Security,Computer Science - Software Engineering},
  number      = {arXiv:2310.02059},
  publisher   = {arXiv},
}

@Book{Giddens2003,
  author     = {Giddens, Anthony},
  date       = {2003},
  title      = {Modernity and Self-Identity: Self and Society in the Late Modern Age},
  isbn       = {978-0-8047-1944-5 978-0-8047-1943-8},
  langid     = {english},
  location   = {Stanford, Calif},
  publisher  = {Stanford Univ. Press},
  groups     = {PTAS Project},
  shorttitle = {Modernity and Self-Identity},
}

@Article{Girdharry2024,
  author       = {Girdharry, Kristi and Khachatryan, Davit},
  date         = {2024},
  journaltitle = {Double Helix},
  title        = {Meaningful {{Writing}} in the {{Age}} of {{Generative Artificial Intelligence}}},
  doi          = {10.37514/DBH-J.2023.11.1.04},
  volume       = {11},
  groups       = {PTAS Project},
}

@Book{Grundy2013,
  author     = {Grundy, Shirley},
  date       = {2013-10},
  title      = {Curriculum: {{Product Or Praxis}}?},
  doi        = {10.4324/9780203058848},
  edition    = {0},
  isbn       = {978-1-136-61266-4},
  langid     = {english},
  publisher  = {Routledge},
  urldate    = {2024-12-01},
  groups     = {PTAS Project},
  shorttitle = {Curriculum},
}

@Article{Hanauer2012,
  author       = {Hanauer, D. I. and Frederick, J. and Fotinakes, B. and Strobel, S. A.},
  date         = {2012-12},
  journaltitle = {CBE---Life Sciences Education},
  title        = {Linguistic {{Analysis}} of {{Project Ownership}} for {{Undergraduate Research Experiences}}},
  doi          = {10.1187/cbe.12-04-0043},
  editor       = {Sevian, Hannah},
  issn         = {1931-7913},
  langid       = {english},
  number       = {4},
  pages        = {378--385},
  urldate      = {2024-07-12},
  volume       = {11},
  abstract     = {We used computational linguistic and content analyses to explore the concept of project ownership for undergraduate research. We used linguistic analysis of student interview data to develop a quantitative methodology for assessing project ownership and applied this method to measure degrees of project ownership expressed by students in relation to different types of educational research experiences. The results of the study suggest that the design of a research experience significantly influences the degree of project ownership expressed by students when they describe those experiences. The analysis identified both positive and negative aspects of project ownership and provided a working definition for how a student experiences his or her research opportunity. These elements suggest several features that could be incorporated into an undergraduate research experience to foster a student's sense of project ownership.},
  groups       = {PTAS Project},
}

@Article{Hanauer2014,
  author       = {Hanauer, David I. and Dolan, Erin L.},
  date         = {2014-03},
  journaltitle = {CBE---Life Sciences Education},
  title        = {The {{Project Ownership Survey}}: {{Measuring Differences}} in {{Scientific Inquiry Experiences}}},
  doi          = {10.1187/cbe.13-06-0123},
  editor       = {Smith, Michelle},
  issn         = {1931-7913},
  langid       = {english},
  number       = {1},
  pages        = {149--158},
  urldate      = {2024-07-12},
  volume       = {13},
  abstract     = {A growing body of research documents the positive outcomes of research experiences for undergraduates, including increased persistence in science. Study of undergraduate lab learning experiences has demonstrated that the design of the experience influences the extent to which students report ownership of the project and that project ownership is one of the psychosocial factors involved in student retention in the sciences. To date, methods for measuring project ownership have not been suitable for the collection of larger data sets. The current study aims to rectify this by developing, presenting, and evaluating a new instrument for measuring project ownership. Eighteen scaled items were generated based on prior research and theory related to project ownership and combined with 30 items shown to measure respondents' emotions about an experience, resulting in the Project Ownership survey (POS). The POS was analyzed to determine its dimensionality, reliability, and validity. The POS had a coefficient alpha of 0.92 and thus has high internal consistency. Known-groups validity was analyzed through the ability of the instrument to differentiate between students who studied in traditional versus research-based laboratory courses. The POS scales as differentiated between the groups and findings paralleled previous results in relation to the characteristics of project ownership.},
  groups       = {PTAS Project},
  shorttitle   = {The {{Project Ownership Survey}}},
}

@Article{Harter1978,
  author       = {Harter, Susan},
  date         = {1978},
  journaltitle = {Human Development},
  title        = {Effectance {{Motivation Reconsidered Toward}} a {{Developmental Model}}},
  doi          = {10.1159/000271574},
  issn         = {1423-0054, 0018-716X},
  langid       = {english},
  number       = {1},
  pages        = {34--64},
  urldate      = {2024-07-12},
  volume       = {21},
  groups       = {PTAS Project},
}

@InProceedings{Isomottonen2020,
  author    = {Isomottonen, Ville and Lakanen, Antti-Jussi and Nieminen, Paavo},
  booktitle = {2020 {{IEEE Frontiers}} in {{Education Conference}} ({{FIE}})},
  date      = {2020-10},
  title     = {Exploring {{Creativity Expectation}} in {{CS1 Students}}' {{View}} of {{Programming}}},
  doi       = {10.1109/FIE44824.2020.9274134},
  isbn      = {978-1-72818-961-1},
  location  = {Uppsala, Sweden},
  pages     = {1--8},
  publisher = {IEEE},
  urldate   = {2024-08-19},
  copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
  groups    = {PTAS Project},
}

@InProceedings{Jenkins2001,
  author    = {Jenkins, Tony},
  booktitle = {Proceedings of the 6th Annual Conference on {{Innovation}} and Technology in Computer Science Education},
  date      = {2001-06},
  title     = {The Motivation of Students of Programming},
  doi       = {10.1145/377435.377472},
  isbn      = {978-1-58113-330-1},
  location  = {Canterbury United Kingdom},
  pages     = {53--56},
  publisher = {ACM},
  urldate   = {2024-11-20},
  groups    = {PTAS Project},
  langid    = {english},
}

@Misc{Kaplan2020,
  author      = {Kaplan, Jared and McCandlish, Sam and Henighan, Tom and Brown, Tom B. and Chess, Benjamin and Child, Rewon and Gray, Scott and Radford, Alec and Wu, Jeffrey and Amodei, Dario},
  date        = {2020-01},
  title       = {Scaling {{Laws}} for {{Neural Language Models}}},
  doi         = {10.48550/arxiv.2001.08361},
  eprint      = {2001.08361},
  eprintclass = {cs},
  eprinttype  = {arXiv},
  urldate     = {2024-11-20},
  abstract    = {We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern the dependence of overfitting on model/dataset size and the dependence of training speed on model size. These relationships allow us to determine the optimal allocation of a fixed compute budget. Larger models are significantly more sample-efficient, such that optimally compute-efficient training involves training very large models on a relatively modest amount of data and stopping significantly before convergence.},
  groups      = {PTAS Project},
  keywords    = {Computer Science - Machine Learning,Statistics - Machine Learning},
  number      = {arXiv:2001.08361},
  publisher   = {arXiv},
}

@InProceedings{Kazemitabaar2023,
  author    = {Kazemitabaar, Majeed and Chow, Justin and Ma, Carl Ka To and Ericson, Barbara J. and Weintrop, David and Grossman, Tovi},
  booktitle = {Proceedings of the 2023 {{CHI Conference}} on {{Human Factors}} in {{Computing Systems}}},
  date      = {2023-04},
  title     = {Studying the Effect of {{AI Code Generators}} on {{Supporting Novice Learners}} in {{Introductory Programming}}},
  doi       = {10.1145/3544548.3580919},
  isbn      = {978-1-4503-9421-5},
  location  = {Hamburg Germany},
  pages     = {1--23},
  publisher = {ACM},
  urldate   = {2024-08-23},
  groups    = {PTAS Project},
  langid    = {english},
}

@Article{Kelly2023,
  author       = {Kelly, Sage and Kaye, Sherrie-Anne and {Oviedo-Trespalacios}, Oscar},
  date         = {2023-02},
  journaltitle = {Telematics and Informatics},
  title        = {What Factors Contribute to the Acceptance of Artificial Intelligence? {{A}} Systematic Review},
  doi          = {10.1016/j.tele.2022.101925},
  issn         = {0736-5853},
  langid       = {english},
  pages        = {101925},
  urldate      = {2024-09-15},
  volume       = {77},
  groups       = {PTAS Project},
  shorttitle   = {What Factors Contribute to the Acceptance of Artificial Intelligence?},
}

@InProceedings{Kiesler2020,
  author    = {Kiesler, Natalie},
  booktitle = {Koli {{Calling}} '20: {{Proceedings}} of the 20th {{Koli Calling International Conference}} on {{Computing Education Research}}},
  date      = {2020-11},
  title     = {On {{Programming Competence}} and Its {{Classification}}},
  doi       = {10.1145/3428029.3428030},
  isbn      = {978-1-4503-8921-1},
  location  = {Koli Finland},
  pages     = {1--10},
  publisher = {ACM},
  urldate   = {2024-11-19},
  groups    = {PTAS Project},
  langid    = {english},
}

@InCollection{Klimmt2006,
  author    = {Klimmt, Christoph and Hartmann, Tilo},
  booktitle = {Playing Video Games: {{Motives}}, Responses, and Consequences},
  date      = {2006},
  title     = {Effectance, {{Self-Efficacy}}, and the {{Motivation}} to {{Play Video Games}}},
  pages     = {133--145},
  publisher = {Lawrence Erlbaum Associates Publishers},
  groups    = {PTAS Project},
}

@Book{Kolb1984,
  author     = {Kolb, David A.},
  date       = {1984},
  title      = {Experimental Learning: Experience as the Source of Learning and Development},
  isbn       = {978-0-13-295261-3},
  langid     = {english},
  location   = {Englewood Cliffs, N.J},
  publisher  = {Prentice-Hall},
  groups     = {PTAS Project},
  shorttitle = {Experimental Learning},
}

@Misc{Krupp2023,
  author      = {Krupp, Lars and Steinert, Steffen and {Kiefer-Emmanouilidis}, Maximilian and Avila, Karina E. and Lukowicz, Paul and Kuhn, Jochen and K{\"u}chemann, Stefan and Karolus, Jakob},
  date        = {2023-08},
  title       = {Unreflected {{Acceptance}} -- {{Investigating}} the {{Negative Consequences}} of {{ChatGPT-Assisted Problem Solving}} in {{Physics Education}}},
  doi         = {10.48550/arxiv.2309.03087},
  eprint      = {2309.03087},
  eprintclass = {physics},
  eprinttype  = {arXiv},
  urldate     = {2024-08-16},
  abstract    = {Large language models (LLMs) have recently gained popularity. However, the impact of their general availability through ChatGPT on sensitive areas of everyday life, such as education, remains unclear. Nevertheless, the societal impact on established educational methods is already being experienced by both students and educators. Our work focuses on higher physics education and examines problem solving strategies. In a study, students with a background in physics were assigned to solve physics exercises, with one group having access to an internet search engine (N=12) and the other group being allowed to use ChatGPT (N=27). We evaluated their performance, strategies, and interaction with the provided tools. Our results showed that nearly half of the solutions provided with the support of ChatGPT were mistakenly assumed to be correct by the students, indicating that they overly trusted ChatGPT even in their field of expertise. Likewise, in 42\% of cases, students used copy \& paste to query ChatGPT -- an approach only used in 4\% of search engine queries -- highlighting the stark differences in interaction behavior between the groups and indicating limited reflection when using ChatGPT. In our work, we demonstrated a need to (1) guide students on how to interact with LLMs and (2) create awareness of potential shortcomings for users.},
  groups      = {PTAS Project},
  keywords    = {Computer Science - Artificial Intelligence,Computer Science - Human-Computer Interaction,Physics - Physics Education},
  number      = {arXiv:2309.03087},
  publisher   = {arXiv},
}

@Article{Landau2007,
  author       = {Landau, Rubin H},
  date         = {2007-07},
  journaltitle = {Computer Physics Communications},
  title        = {Computational {{Physics Education}}; Why, What and How},
  doi          = {10.1016/j.cpc.2007.02.040},
  number       = {1-2},
  pages        = {191--194},
  volume       = {177},
  abstract     = {Progress in developing, implementing, publishing, and refining a coherent set of education materials in computational physics education will be described. These materials form the binding for a four-year undergraduate degree program leading to a Bachelor's degree in Computational Physics. Also described will be the status of the conversion of these materials into electronic formats that may be used for online education and as electronic textbooks. The online materials are to be part of a proposed national repository of university-offered, undergraduate courses and modules in computational science gathered from various pioneering programs throughout the country.},
  groups       = {PTAS Project},
}

@PhdThesis{Larsen2024,
  author      = {Larsen, Gretchen},
  date        = {2024-05},
  institution = {The University of Nebraska},
  title       = {Supporting the {{Creative Side}} of {{Creative Coding}}: {{Helping Students Wield Code}} in the {{Pursuit}} of {{Their Own Dreams}}},
  location    = {Lincoln, Nebraska},
  urldate     = {2024-08-19},
  groups      = {PTAS Project},
}

@InProceedings{Lau2023,
  author     = {Lau, Sam and Guo, Philip},
  booktitle  = {Proceedings of the 2023 {{ACM Conference}} on {{International Computing Education Research V}}.1},
  date       = {2023-08},
  title      = {From "{{Ban It Till We Understand It}}" to "{{Resistance}} Is {{Futile}}": {{How University Programming Instructors Plan}} to {{Adapt}} as {{More Students Use AI Code Generation}} and {{Explanation Tools}} Such as {{ChatGPT}} and {{GitHub Copilot}}},
  doi        = {10.1145/3568813.3600138},
  isbn       = {978-1-4503-9976-0},
  location   = {Chicago IL USA},
  pages      = {106--121},
  publisher  = {ACM},
  urldate    = {2024-08-16},
  groups     = {PTAS Project},
  langid     = {english},
  shorttitle = {From "{{Ban It Till We Understand It}}" to "{{Resistance}} Is {{Futile}}"},
}

@Book{Lave1991,
  author     = {Lave, Jean and Wenger, Etienne},
  date       = {1991-09},
  title      = {Situated {{Learning}}: {{Legitimate Peripheral Participation}}},
  doi        = {10.1017/CBO9780511815355},
  edition    = {1},
  isbn       = {978-0-521-41308-4 978-0-521-42374-8 978-0-511-81535-5},
  publisher  = {Cambridge University Press},
  urldate    = {2024-12-01},
  copyright  = {https://www.cambridge.org/core/terms},
  groups     = {PTAS Project},
  shorttitle = {Situated {{Learning}}},
}

@Article{Lindell2014,
  author       = {Lindell, Rikard},
  date         = {2014-03},
  journaltitle = {Personal and Ubiquitous Computing},
  title        = {Crafting Interaction: {{The}} Epistemology of Modern Programming},
  doi          = {10.1007/s00779-013-0687-6},
  issn         = {1617-4909, 1617-4917},
  langid       = {english},
  number       = {3},
  pages        = {613--624},
  urldate      = {2024-08-19},
  volume       = {18},
  copyright    = {http://www.springer.com/tdm},
  groups       = {PTAS Project},
  shorttitle   = {Crafting Interaction},
}

@Article{Lister2006,
  author       = {Lister, Raymond and Simon, Beth and Thompson, Errol and Whalley, Jacqueline L. and Prasad, Christine},
  date         = {2006-09},
  journaltitle = {ACM SIGCSE Bulletin},
  title        = {Not Seeing the Forest for the Trees: Novice Programmers and the {{SOLO}} Taxonomy},
  doi          = {10.1145/1140123.1140157},
  issn         = {0097-8418},
  langid       = {english},
  number       = {3},
  pages        = {118--122},
  urldate      = {2024-11-19},
  volume       = {38},
  abstract     = {This paper reports on the authors use of the SOLO taxonomy to describe differences in the way students and educators solve small code reading exercises. SOLO is a general educational taxonomy, and has not previously been applied to the study of how novice programmers manifest their understanding of code. Data was collected in the form of written and think-aloud responses from students (novices) and educators (experts), using exam questions. During analysis, the responses were mapped to the different levels of the SOLO taxonomy. From think-aloud responses, the authors found that educators tended to manifest a SOLO relational response on small reading problems, whereas students tended to manifest a multistructural response. These results are consistent with the literature on the psychology of programming, but the work in this paper extends on these findings by analyzing the design of exam questions.},
  groups       = {PTAS Project},
  shorttitle   = {Not Seeing the Forest for the Trees},
}

@InProceedings{Lister2009,
  author    = {Lister, Raymond and Fidge, Colin and Teague, Donna},
  booktitle = {Proceedings of the 14th Annual {{ACM SIGCSE}} Conference on {{Innovation}} and Technology in Computer Science Education},
  date      = {2009-07},
  title     = {Further Evidence of a Relationship between Explaining, Tracing and Writing Skills in Introductory Programming},
  doi       = {10.1145/1562877.1562930},
  isbn      = {978-1-60558-381-5},
  location  = {Paris France},
  pages     = {161--165},
  publisher = {ACM},
  urldate   = {2024-11-19},
  groups    = {PTAS Project},
  langid    = {english},
}

@InProceedings{Lopez2008,
  author    = {Lopez, Mike and Whalley, Jacqueline and Robbins, Phil and Lister, Raymond},
  booktitle = {Proceedings of the {{Fourth}} International {{Workshop}} on {{Computing Education Research}}},
  date      = {2008-09},
  title     = {Relationships between Reading, Tracing and Writing Skills in Introductory Programming},
  doi       = {10.1145/1404520.1404531},
  isbn      = {978-1-60558-216-0},
  location  = {Sydney Australia},
  pages     = {101--112},
  publisher = {ACM},
  urldate   = {2024-09-06},
  groups    = {PTAS Project},
  langid    = {english},
}

@InProceedings{Mahon2024,
  author    = {Mahon, Joyce and Mac Namee, Brian and Becker, Brett A.},
  booktitle = {Proceedings of the 2024 on {{Innovation}} and {{Technology}} in {{Computer Science Education V}}. 1},
  date      = {2024-07},
  title     = {Guidelines for the {{Evolving Role}} of {{Generative AI}} in {{Introductory Programming Based}} on {{Emerging Practice}}},
  doi       = {10.1145/3649217.3653602},
  isbn      = {9798400706004},
  location  = {Milan Italy},
  pages     = {10--16},
  publisher = {ACM},
  urldate   = {2024-08-16},
  groups    = {PTAS Project},
  langid    = {english},
}

@Article{Maloney2010,
  author       = {Maloney, John and Resnick, Mitchel and Rusk, Natalie and Silverman, Brian and Eastmond, Evelyn},
  date         = {2010-11},
  journaltitle = {ACM Transactions on Computing Education},
  title        = {The {{Scratch Programming Language}} and {{Environment}}},
  doi          = {10.1145/1868358.1868363},
  issn         = {1946-6226},
  langid       = {english},
  number       = {4},
  pages        = {1--15},
  urldate      = {2024-11-19},
  volume       = {10},
  abstract     = {Scratch is a visual programming environment that allows users (primarily ages 8 to 16) to learn computer programming while working on personally meaningful projects such as animated stories and games. A key design goal of Scratch is to support self-directed learning through tinkering and collaboration with peers. This article explores how the Scratch programming language and environment support this goal.},
  groups       = {PTAS Project},
}

@Article{Martin2016,
  author       = {Martin, Richard F},
  date         = {2016},
  journaltitle = {Journal of Physics: Conference Series},
  title        = {Undergraduate Computational Physics Education: Uneven History and Promising Future},
  doi          = {10.1088/1742-6596/759/1/012005},
  pages        = {012005},
  series       = {{{XXVII IUPAP Conference}} on {{Computational Physics}} ({{CCP2015}})},
  volume       = {759},
  groups       = {PTAS Project},
}

@Article{McGregor2020,
  author       = {McGregor, Sue L. T.},
  date         = {2020-12},
  journaltitle = {Northeast Journal of Complex Systems},
  title        = {Emerging from the {{Deep}}: {{Complexity}}, {{Emergent Pedagogy}} and {{Deep Learning}}},
  doi          = {10.22191/nejcs/vol2/iss1/2},
  issn         = {2577-8439},
  number       = {1},
  urldate      = {2024-07-12},
  volume       = {2},
  groups       = {PTAS Project},
  shorttitle   = {Emerging from the {{Deep}}},
}

@InProceedings{Mitchell2019,
  author      = {Mitchell, Margaret and Wu, Simone and Zaldivar, Andrew and Barnes, Parker and Vasserman, Lucy and Hutchinson, Ben and Spitzer, Elena and Raji, Inioluwa Deborah and Gebru, Timnit},
  booktitle   = {Proceedings of the {{Conference}} on {{Fairness}}, {{Accountability}}, and {{Transparency}}},
  date        = {2019-01},
  title       = {Model {{Cards}} for {{Model Reporting}}},
  doi         = {10.1145/3287560.3287596},
  eprint      = {1810.03993},
  eprintclass = {cs},
  eprinttype  = {arXiv},
  pages       = {220--229},
  urldate     = {2024-06-06},
  abstract    = {Trained machine learning models are increasingly used to perform high-impact tasks in areas such as law enforcement, medicine, education, and employment. In order to clarify the intended use cases of machine learning models and minimize their usage in contexts for which they are not well suited, we recommend that released models be accompanied by documentation detailing their performance characteristics. In this paper, we propose a framework that we call model cards, to encourage such transparent model reporting. Model cards are short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions, such as across different cultural, demographic, or phenotypic groups (e.g., race, geographic location, sex, Fitzpatrick skin type) and intersectional groups (e.g., age and race, or sex and Fitzpatrick skin type) that are relevant to the intended application domains. Model cards also disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information. While we focus primarily on human-centered machine learning models in the application fields of computer vision and natural language processing, this framework can be used to document any trained machine learning model. To solidify the concept, we provide cards for two supervised models: One trained to detect smiling faces in images, and one trained to detect toxic comments in text. We propose model cards as a step towards the responsible democratization of machine learning and related AI technology, increasing transparency into how well AI technology works. We hope this work encourages those releasing trained machine learning models to accompany model releases with similar detailed evaluation numbers and other relevant documentation.},
  groups      = {PTAS Project},
  keywords    = {jupiter},
}

@Article{Murdoch2020,
  author       = {Murdoch, Diana and English, Andrea R. and Hintz, Allison and Tyson, Kersti},
  date         = {2020-10},
  journaltitle = {Educational Theory},
  title        = {{\emph{Feeling }}{{{\emph{Heard}}}} : {{Inclusive Education}}, {{Transformative Learning}}, and {{Productive Struggle}}},
  doi          = {10.1111/edth.12449},
  issn         = {0013-2004, 1741-5446},
  langid       = {english},
  number       = {5},
  pages        = {653--679},
  urldate      = {2024-07-12},
  volume       = {70},
  abstract     = {Abstract                                            Developments in international inclusive education policy, including in prominent UN documents, often refer to the aim of a                quality                education                for all                . Yet, it remains unclear: What exactly is meant by quality education? And, under what conditions are quality educational experiences possible for all learners? In this essay, Diana Murdoch, Andrea English, Allison Hintz, and Kersti Tyson bring together research on inclusive education with philosophy of transformative learning, in particular John Dewey and phenomenology, to further the discussion on these two questions. The authors argue that teacher--learner relationships, of a particular kind, are necessary for fostering environments wherein all learners have access to quality educational experiences associated with                productive struggle                as an indispensable aspect of transformative learning processes. They define such relationships as ``educational relationships that support students to                feel heard                .'' In developing their argument, the authors first analyze the concept of productive struggle, an aspect of learning increasingly recognized in research and policy as an indicator of quality education. Second, they discuss three necessary, though not sufficient, conditions for the teacher to cultivate educational relationships that support students to feel heard. Third, they draw out connections between environments that support feeling heard and those that support productive struggle, and they discuss teachers' challenges and risk-taking in creating such environments. The authors close with a discussion of implications for international policy, practice, and research.},
  groups       = {PTAS Project},
  shorttitle   = {{\emph{Feeling }}{{{\emph{Heard}}}}},
}

@InProceedings{Nelson2017,
  author     = {Nelson, Greg L. and Xie, Benjamin and Ko, Amy J.},
  booktitle  = {Proceedings of the 2017 {{ACM Conference}} on {{International Computing Education Research}}},
  date       = {2017-08},
  title      = {Comprehension {{First}}: {{Evaluating}} a {{Novel Pedagogy}} and {{Tutoring System}} for {{Program Tracing}} in {{CS1}}},
  doi        = {10.1145/3105726.3106178},
  isbn       = {978-1-4503-4968-0},
  location   = {Tacoma Washington USA},
  pages      = {2--11},
  publisher  = {ACM},
  urldate    = {2024-09-15},
  groups     = {PTAS Project},
  langid     = {english},
  shorttitle = {Comprehension {{First}}},
}

@Article{Olitsky2007,
  author       = {Olitsky, Stacy},
  date         = {2007-02},
  journaltitle = {Cultural Studies of Science Education},
  title        = {Structure, Agency, and the Development of Students' Identities as Learners},
  doi          = {10.1007/s11422-006-9033-x},
  issn         = {1871-1502, 1871-1510},
  langid       = {english},
  number       = {4},
  pages        = {745--766},
  urldate      = {2024-11-29},
  volume       = {1},
  copyright    = {http://www.springer.com/tdm},
  groups       = {PTAS Project},
}

@InProceedings{Pan2024,
  author     = {Pan, Wei Hung and Chok, Ming Jie and Wong, Jonathan Leong Shan and Shin, Yung Xin and Poon, Yeong Shian and Yang, Zhou and Chong, Chun Yong and Lo, David and Lim, Mei Kuan},
  booktitle  = {Proceedings of the 46th {{International Conference}} on {{Software Engineering}}: {{Software Engineering Education}} and {{Training}}},
  date       = {2024-04},
  title      = {Assessing {{AI Detectors}} in {{Identifying AI-Generated Code}}: {{Implications}} for {{Education}}},
  doi        = {10.1145/3639474.3640068},
  isbn       = {9798400704987},
  location   = {Lisbon Portugal},
  pages      = {1--11},
  publisher  = {ACM},
  urldate    = {2024-09-15},
  groups     = {PTAS Project},
  langid     = {english},
  shorttitle = {Assessing {{AI Detectors}} in {{Identifying AI-Generated Code}}},
}

@Article{Parlett1972,
  author = {Parlett, Malcolm and Hamilton, David},
  date   = {1972-01},
  title  = {Evaluation as {{Illumination}}: {{A New Approach}} to the {{Study}} of {{Innovatory Programs}}},
  groups = {PTAS Project},
}

@InProceedings{Perry2023,
  author      = {Perry, Neil and Srivastava, Megha and Kumar, Deepak and Boneh, Dan},
  booktitle   = {Proceedings of the 2023 {{ACM SIGSAC Conference}} on {{Computer}} and {{Communications Security}}},
  date        = {2023-11},
  title       = {Do {{Users Write More Insecure Code}} with {{AI Assistants}}?},
  doi         = {10.1145/3576915.3623157},
  eprint      = {2211.03622},
  eprintclass = {cs},
  eprinttype  = {arXiv},
  pages       = {2785--2799},
  urldate     = {2024-09-23},
  abstract    = {We conduct the first large-scale user study examining how users interact with an AI Code assistant to solve a variety of security related tasks across different programming languages. Overall, we find that participants who had access to an AI assistant based on OpenAI's codex-davinci-002 model wrote significantly less secure code than those without access. Additionally, participants with access to an AI assistant were more likely to believe they wrote secure code than those without access to the AI assistant. Furthermore, we find that participants who trusted the AI less and engaged more with the language and format of their prompts (e.g. re-phrasing, adjusting temperature) provided code with fewer security vulnerabilities. Finally, in order to better inform the design of future AI-based Code assistants, we provide an in-depth analysis of participants' language and interaction behavior, as well as release our user interface as an instrument to conduct similar studies in the future.},
  groups      = {PTAS Project},
  keywords    = {Computer Science - Cryptography and Security},
}

@Article{Robins2003,
  author       = {Robins, Anthony and Rountree, Janet and Rountree, Nathan},
  date         = {2003-06},
  journaltitle = {Computer Science Education},
  title        = {Learning and {{Teaching Programming}}: {{A Review}} and {{Discussion}}},
  doi          = {10.1076/csed.13.2.137.14200},
  issn         = {0899-3408, 1744-5175},
  langid       = {english},
  number       = {2},
  pages        = {137--172},
  urldate      = {2024-09-15},
  volume       = {13},
  groups       = {PTAS Project},
  shorttitle   = {Learning and {{Teaching Programming}}},
}

@Book{Rogers1969,
  author    = {Rogers, Carl},
  date      = {1969},
  title     = {Freedom to {{Learn}}},
  location  = {Colombus, Ohio},
  publisher = {Merrill},
  groups    = {PTAS Project},
}

@InProceedings{Sanders2012,
  author    = {Sanders, Kate and Boustedt, Jonas and Eckerdal, Anna and McCartney, Robert and Mostr{\"o}m, Jan Erik and Thomas, Lynda and Zander, Carol},
  booktitle = {Proceedings of the Ninth Annual International Conference on {{International}} Computing Education Research},
  date      = {2012-09},
  title     = {Threshold Concepts and Threshold Skills in Computing},
  doi       = {10.1145/2361276.2361283},
  isbn      = {978-1-4503-1604-0},
  location  = {Auckland New Zealand},
  pages     = {23--30},
  publisher = {ACM},
  urldate   = {2024-09-06},
  groups    = {PTAS Project},
  langid    = {english},
}

@InProceedings{Selby2015,
  author     = {Selby, Cynthia C.},
  booktitle  = {Proceedings of the {{Workshop}} in {{Primary}} and {{Secondary Computing Education}}},
  date       = {2015-11},
  title      = {Relationships: Computational Thinking, Pedagogy of Programming, and {{Bloom}}'s {{Taxonomy}}},
  doi        = {10.1145/2818314.2818315},
  isbn       = {978-1-4503-3753-3},
  location   = {London United Kingdom},
  pages      = {80--87},
  publisher  = {ACM},
  urldate    = {2024-11-19},
  groups     = {PTAS Project},
  langid     = {english},
  shorttitle = {Relationships},
}

@Article{Serhan2020,
  author       = {Serhan, Derar},
  date         = {2020},
  journaltitle = {International Journal of Technology in Education and Science},
  title        = {Transitioning from {{Face-to-Face}} to {{Remote Learning}}: {{Students}}' {{Attitudes}} and {{Perceptions}} of {{Using Zoom}} during {{COVID-19 Pandemic}}},
  langid       = {english},
  number       = {4},
  pages        = {335--342},
  urldate      = {2024-06-06},
  volume       = {4},
  abstract     = {Due to the COVID-19 pandemic and during the middle of the spring 2020 semester, many universities were forced to move from face-to-face (FTF) in-classroom to remote instruction. Many institutions used Zoom as their delivery platform. The purpose of this study was to investigate students' attitudes towards the use of Zoom in remote learning, and their perceptions of its effects on their learning and engagement in comparison to FTF learning. Thirty-one university students participated in this study. Data were collected using a 5-point Likert-type survey. The results indicated that students had a negative attitude toward the use of Zoom and perceived it as having a negative effect on their learning experience and their motivation to learn. Students listed flexibility as a main advantage to using Zoom for learning.},
  annotation   = {ERIC Number: EJ1271211},
  groups       = {PTAS Project},
  keywords     = {Computers and Society,learning},
  shorttitle   = {Transitioning from {{Face-to-Face}} to {{Remote Learning}}},
}

@InProceedings{Sheard2008,
  author    = {Sheard, Judy and Carbone, Angela and Lister, Raymond and Simon, Beth and Thompson, Errol and Whalley, Jacqueline L.},
  booktitle = {Proceedings of the 13th Annual Conference on {{Innovation}} and Technology in Computer Science Education},
  date      = {2008-06},
  title     = {Going {{SOLO}} to Assess Novice Programmers},
  doi       = {10.1145/1384271.1384328},
  isbn      = {978-1-60558-078-4},
  location  = {Madrid Spain},
  pages     = {209--213},
  publisher = {ACM},
  urldate   = {2024-11-19},
  groups    = {PTAS Project},
  langid    = {english},
}

@InProceedings{Sheard2024,
  author    = {Sheard, Judy and Denny, Paul and Hellas, Arto and Leinonen, Juho and Malmi, Lauri and {Simon}},
  booktitle = {Proceedings of the 55th {{ACM Technical Symposium}} on {{Computer Science Education V}}. 1},
  date      = {2024-03},
  title     = {Instructor {{Perceptions}} of {{AI Code Generation Tools}} - {{A Multi-Institutional Interview Study}}},
  doi       = {10.1145/3626252.3630880},
  isbn      = {9798400704239},
  location  = {Portland OR USA},
  pages     = {1223--1229},
  publisher = {ACM},
  urldate   = {2024-08-16},
  groups    = {PTAS Project},
  langid    = {english},
}

@Book{Spence1983,
  date       = {1983},
  title      = {Achievement and Achievement Motives: Psychological and Sociological Approaches},
  editor     = {Spence, Janet T.},
  isbn       = {978-0-7167-1396-8 978-0-7167-1397-5},
  langid     = {english},
  location   = {San Francisco},
  publisher  = {W.H. Freeman},
  series     = {A Series of Books in Psychology},
  groups     = {PTAS Project},
  shorttitle = {Achievement and Achievement Motives},
}

@Article{Stanley2015,
  author    = {Stanley, Jacob T. and {Dounas-Frazer}, Dimitri R. and Kiepura, Laura and Lewandowski, H. J.},
  date      = {2015},
  title     = {Investigating Student Ownership of Projects in an Upper-Division Physics Lab Course},
  doi       = {10.48550/ARXIV.1507.03947},
  urldate   = {2024-07-12},
  abstract  = {In undergraduate research experiences, student development of an identity as a scientist is coupled to their sense of ownership of their research projects. As a a first step towards studying similar connections in physics laboratory courses, we investigate student ownership of projects in a lasers-based upper-division course. Students spent the final seven weeks of the semester working in groups on final projects of their choosing. Using data from the Project Ownership Survey and weekly student reflections, we investigate student ownership as it relates to students' personal agency, self-efficacy, peer interactions, and complex affective responses to challenges and successes. We present evidence of students' project ownership in an upper-division physics lab. Additionally, we find that there is a complex relationship between student affect and their sense of ownership.},
  copyright = {arXiv.org perpetual, non-exclusive license},
  groups    = {PTAS Project},
  keywords  = {FOS: Physical sciences,Physics Education (physics.ed-ph)},
}

@Article{Stein2024,
  author       = {Stein, Jan-Philipp and Messingschlager, Tanja and Gnambs, Timo and Hutmacher, Fabian and Appel, Markus},
  date         = {2024-02},
  journaltitle = {Scientific Reports},
  title        = {Attitudes towards {{AI}}: Measurement and Associations with Personality},
  doi          = {10.1038/s41598-024-53335-2},
  issn         = {2045-2322},
  langid       = {english},
  number       = {1},
  pages        = {2909},
  urldate      = {2024-09-15},
  volume       = {14},
  abstract     = {Abstract                              Artificial intelligence (AI) has become an integral part of many contemporary technologies, such as social media platforms, smart devices, and global logistics systems. At the same time, research on the public acceptance of AI shows that many people feel quite apprehensive about the potential of such technologies---an observation that has been connected to both demographic and sociocultural user variables (e.g., age, previous media exposure). Yet, due to divergent and often ad-hoc measurements of AI-related attitudes, the current body of evidence remains inconclusive. Likewise, it is still unclear if attitudes towards AI are also affected by users' personality traits. In response to these research gaps, we offer a two-fold contribution. First, we present a novel, psychologically informed questionnaire (ATTARI-12) that captures attitudes towards AI as a single construct, independent of specific contexts or applications. Having observed good reliability and validity for our new measure across two studies (                N                1                \,=\,490;                N                2                \,=\,150), we examine several personality traits---the Big Five, the Dark Triad, and conspiracy mentality---as potential predictors of AI-related attitudes in a third study (                N                3                \,=\,298). We find that agreeableness and younger age predict a more positive view towards artificially intelligent technology, whereas the susceptibility to conspiracy beliefs connects to a more negative attitude. Our findings are discussed considering potential limitations and future directions for research and practice.},
  groups       = {PTAS Project},
  shorttitle   = {Attitudes towards {{AI}}},
}

@Misc{Steinert2023,
  author      = {Steinert, Steffen and Avila, Karina E. and Ruzika, Stefan and Kuhn, Jochen and K{\"u}chemann, Stefan},
  date        = {2023-11},
  title       = {Harnessing {{Large Language Models}} to {{Enhance Self-Regulated Learning}} via {{Formative Feedback}}},
  doi         = {10.48550/arxiv.2311.13984},
  eprint      = {2311.13984},
  eprintclass = {physics},
  eprinttype  = {arXiv},
  urldate     = {2024-08-16},
  abstract    = {Effectively supporting students in mastering all facets of self-regulated learning is a central aim of teachers and educational researchers. Prior research could demonstrate that formative feedback is an effective way to support students during self-regulated learning (SRL). However, for formative feedback to be effective, it needs to be tailored to the learners, requiring information about their learning progress. In this work, we introduce LEAP, a novel platform that utilizes advanced large language models (LLMs), such as ChatGPT, to provide formative feedback to students. LEAP empowers teachers with the ability to effectively pre-prompt and assign tasks to the LLM, thereby stimulating students' cognitive and metacognitive processes and promoting self-regulated learning. We demonstrate that a systematic prompt design based on theoretical principles can provide a wide range of types of scaffolds to students, including sense-making, elaboration, self-explanation, partial task-solution scaffolds, as well as metacognitive and motivational scaffolds. In this way, we emphasize the critical importance of synchronizing educational technological advances with empirical research and theoretical frameworks.},
  groups      = {PTAS Project},
  keywords    = {Physics - Physics Education},
  number      = {arXiv:2311.13984},
  publisher   = {arXiv},
}

@InProceedings{Styve2024,
  author    = {Styve, Arne and Virkki, Outi T. and Naeem, Usman},
  booktitle = {2024 {{IEEE Global Engineering Education Conference}} ({{EDUCON}})},
  date      = {2024-05},
  title     = {Developing {{Critical Thinking Practices Interwoven}} with {{Generative AI Usage}} in an {{Introductory Programming Course}}},
  doi       = {10.1109/EDUCON60312.2024.10578746},
  isbn      = {9798350394023},
  location  = {Kos Island, Greece},
  pages     = {01--08},
  publisher = {IEEE},
  urldate   = {2024-08-16},
  copyright = {https://doi.org/10.15223/policy-029},
  groups    = {PTAS Project},
}

@Article{Sun2024,
  author       = {Sun, Dan and Boudouaia, Azzeddine and Zhu, Chengcong and Li, Yan},
  date         = {2024-02},
  journaltitle = {International Journal of Educational Technology in Higher Education},
  title        = {Would {{ChatGPT-facilitated}} Programming Mode Impact College Students' Programming Behaviors, Performances, and Perceptions? {{An}} Empirical Study},
  doi          = {10.1186/s41239-024-00446-5},
  issn         = {2365-9440},
  langid       = {english},
  number       = {1},
  pages        = {14},
  urldate      = {2024-07-12},
  volume       = {21},
  abstract     = {Abstract              ChatGPT, an AI-based chatbot with automatic code generation abilities, has shown its promise in improving the quality of programming education by providing learners with opportunities to better understand the principles of programming. However, limited empirical studies have explored the impact of ChatGPT on learners' programming processes. This study employed a quasi-experimental design to explore the possible impact of ChatGPT-facilitated programming mode on college students' programming behaviors, performances, and perceptions. 82 college students were randomly divided into two classes. One class employed ChatGPT-facilitated programming (CFP) practice and the other class utilized self-directed programming (SDP) mode. Mixed methods were utilized to collect multidimensional data. Data analysis uncovered some intriguing results. Firstly, students in the CFP mode had more frequent behaviors of debugging and receiving error messages, as well as pasting console messages on the website and reading feedback. At the same time, students in the CFP mode had more frequent behaviors of copying and pasting codes from ChatGPT and debugging, as well as pasting codes to ChatGPT and reading feedback from ChatGPT. Secondly, CFP practice would improve college students' programming performance, while the results indicated that there was no statistically significant difference between the students in CFP mode and the SDP mode. Thirdly, student interviews revealed three highly concerned themes from students' user experience about ChatGPT: the services offered by ChatGPT, the stages of ChatGPT usage, and experience with ChatGPT. Finally, college students' perceptions toward ChatGPT significantly changed after CFP practice, including its perceived usefulness, perceived ease of use, and intention to use. Based on these findings, the study proposes implications for future instructional design and the development of AI-powered tools like ChatGPT.},
  groups       = {PTAS Project},
  shorttitle   = {Would {{ChatGPT-facilitated}} Programming Mode Impact College Students' Programming Behaviors, Performances, and Perceptions?},
}

@Article{Templeton2024,
  author       = {Templeton, Adly and Conerly, Conerly and {et. al.}},
  date         = {2024-05},
  journaltitle = {Anthropic},
  title        = {Scaling {{Monosemanticity}}: {{Extracting Interpretable Features}} from {{Claude}} 3 {{Sonnet}}},
  urldate      = {2024-06-03},
  volume       = {Transformer Circuits Thread},
  groups       = {PTAS Project},
  keywords     = {Computers and Society},
  shorttitle   = {Mapping the {{Mind}} of {{LLMS}}},
}

@Article{Thiry2011,
  author       = {Thiry, Heather and Laursen, Sandra L. and Hunter, Anne-Barrie},
  date         = {2011-07},
  journaltitle = {The Journal of Higher Education},
  title        = {What {{Experiences Help Students Become Scientists}}? {{A Comparative Study}} of {{Research}} and Other {{Sources}} of {{Personal}} and {{Professional Gains}} for {{STEM Undergraduates}}},
  doi          = {10.1080/00221546.2011.11777209},
  issn         = {0022-1546, 1538-4640},
  langid       = {english},
  number       = {4},
  pages        = {357--388},
  urldate      = {2024-07-12},
  volume       = {82},
  groups       = {PTAS Project},
  shorttitle   = {What {{Experiences Help Students Become Scientists}}?},
}

@Misc{Tian2024,
  author      = {Tian, Minyang and Gao, Luyu and Zhang, Shizhuo Dylan and Chen, Xinan and Fan, Cunwei and Guo, Xuefei and Haas, Roland and Ji, Pan and Krongchon, Kittithat and Li, Yao and Liu, Shengyan and Luo, Di and Ma, Yutao and Tong, Hao and Trinh, Kha and Tian, Chenyu and Wang, Zihan and Wu, Bohao and Xiong, Yanyu and Yin, Shengzhu and Zhu, Minhui and Lieret, Kilian and Lu, Yanxin and Liu, Genglin and Du, Yufeng and Tao, Tianhua and Press, Ofir and Callan, Jamie and Huerta, Eliu and Peng, Hao},
  date        = {2024-07},
  title       = {{{SciCode}}: {{A Research Coding Benchmark Curated}} by {{Scientists}}},
  doi         = {10.48550/arxiv.2407.13168},
  eprint      = {2407.13168},
  eprintclass = {cs},
  eprinttype  = {arXiv},
  urldate     = {2024-10-09},
  abstract    = {Since language models (LMs) now outperform average humans on many challenging tasks, it has become increasingly difficult to develop challenging, high-quality, and realistic evaluations. We address this issue by examining LMs' capabilities to generate code for solving real scientific research problems. Incorporating input from scientists and AI researchers in 16 diverse natural science sub-fields, including mathematics, physics, chemistry, biology, and materials science, we created a scientist-curated coding benchmark, SciCode. The problems in SciCode naturally factorize into multiple subproblems, each involving knowledge recall, reasoning, and code synthesis. In total, SciCode contains 338 subproblems decomposed from 80 challenging main problems. It offers optional descriptions specifying useful scientific background information and scientist-annotated gold-standard solutions and test cases for evaluation. Claude3.5-Sonnet, the best-performing model among those tested, can solve only 4.6\% of the problems in the most realistic setting. We believe that SciCode demonstrates both contemporary LMs' progress towards becoming helpful scientific assistants and sheds light on the development and evaluation of scientific AI in the future.},
  groups      = {PTAS Project},
  keywords    = {Computer Science - Artificial Intelligence,Computer Science - Computation and Language},
  number      = {arXiv:2407.13168},
  publisher   = {arXiv},
  shorttitle  = {{{SciCode}}},
}

@Misc{Ulfsnes2024,
  author      = {Ulfsnes, Rasmus and Moe, Nils Brede and Stray, Viktoria and Skarpen, Marianne},
  date        = {2024-02},
  title       = {Transforming {{Software Development}} with {{Generative AI}}: {{Empirical Insights}} on {{Collaboration}} and {{Workflow}}},
  doi         = {10.48550/arxiv.2405.01543},
  eprint      = {2405.01543},
  eprintclass = {cs},
  eprinttype  = {arXiv},
  urldate     = {2024-10-10},
  abstract    = {Generative AI (GenAI) has fundamentally changed how knowledge workers, such as software developers, solve tasks and collaborate to build software products. Introducing innovative tools like ChatGPT and Copilot has created new opportunities to assist and augment software developers across various problems. We conducted an empirical study involving interviews with 13 data scientists, managers, developers, designers, and frontend developers to investigate the usage of GenAI. Our study reveals that ChatGPT signifies a paradigm shift in the workflow of software developers. The technology empowers developers by enabling them to work more efficiently, speed up the learning process, and increase motivation by reducing tedious and repetitive tasks. Moreover, our results indicate a change in teamwork collaboration due to software engineers using GenAI for help instead of asking co-workers which impacts the learning loop in agile teams.},
  groups      = {PTAS Project},
  keywords    = {Computer Science - Software Engineering},
  number      = {arXiv:2405.01543},
  publisher   = {arXiv},
  shorttitle  = {Transforming {{Software Development}} with {{Generative AI}}},
}

@InProceedings{Venables2009,
  author    = {Venables, Anne and Tan, Grace and Lister, Raymond},
  booktitle = {Proceedings of the Fifth International Workshop on {{Computing}} Education Research Workshop},
  date      = {2009-08},
  title     = {A Closer Look at Tracing, Explaining and Code Writing Skills in the Novice Programmer},
  doi       = {10.1145/1584322.1584336},
  isbn      = {978-1-60558-615-1},
  location  = {Berkeley CA USA},
  pages     = {117--128},
  publisher = {ACM},
  urldate   = {2024-11-19},
  groups    = {PTAS Project},
  langid    = {english},
}

@Article{Venkatesh2003,
  author       = {Venkatesh, Viswanath and Morris, Michael G. and David, Gordon B. and David, Fred D.},
  date         = {2003-09},
  journaltitle = {MIS Quarterly},
  title        = {User {{Acceptance}} of {{Information Technology}}: {{Toward}} a {{Unified View}}},
  pages        = {425--478},
  volume       = {27},
  groups       = {PTAS Project},
}

@Article{Wenger2000,
  author       = {Wenger, Etienne},
  date         = {2000-05},
  journaltitle = {Organization},
  title        = {Communities of {{Practice}} and {{Social Learning Systems}}},
  doi          = {10.1177/135050840072002},
  issn         = {1350-5084, 1461-7323},
  langid       = {english},
  number       = {2},
  pages        = {225--246},
  urldate      = {2024-11-29},
  volume       = {7},
  abstract     = {This essay argues that the success of organizations depends on their ability to design themselves as social learning systems and also to participate in broader learning systems such as an industry, a region, or a consortium. It explores the structure of these social learning systems. It proposes a social definition of learning and distinguishes between three `modes of belonging' by which we participate in social learning systems. Then it uses this framework to look at three constitutive elements of these systems: communities of practice, boundary processes among these communities, and identities as shaped by our participation in these systems.},
  copyright    = {https://journals.sagepub.com/page/policies/text-and-data-mining-license},
  groups       = {PTAS Project},
}

@Article{Werse2023,
  author       = {Werse, Nicholas R},
  date         = {2023},
  journaltitle = {Double Helix},
  title        = {What {{Will Be Lost}}? {{Critical Reflections}} on {{ChatGPT}}, {{Artificial Intelligence}}, and the {{Value}} of {{Writing Instruction}}},
  doi          = {10.37514/DBH-J.2023.11.1.07},
  volume       = {11},
  groups       = {PTAS Project},
}

@InProceedings{Whalley2006,
  author    = {Whalley, Jacqueline L. and Lister, Raymond and Thompson, Errol and Clear, Tony and Robbins, Phil and Kumar, P. K. Ajith and Prasad, Christine},
  booktitle = {Eights {{Australasian Computing Education Conference}} ({{ACE2006}})},
  date      = {2006-01},
  title     = {An {{Australasian}} Study of Reading and Comprehension Skills in Novice Programmers, Using the Bloom and {{SOLO}} Taxonomies},
  location  = {Hobart, Tasmania, Australia},
  publisher = {Confrences in Research in Practice in Information Technology},
  volume    = {52},
  groups    = {PTAS Project},
}

@InProceedings{Wheeler2023,
  author    = {Wheeler, Sav and Scherr, Rachel E.},
  booktitle = {2023 {{Physics Education Research Conference Proceedings}}},
  date      = {2023-10},
  title     = {{{ChatGPT}} Reflects Student Misconceptions in Physics},
  isbn      = {978-1-931024-39-6},
  pages     = {386--390},
  urldate   = {2024-06-06},
  abstract  = {With many students turning to machine learning models such as ChatGPT for homework help, diagnosing the accuracy of the information these technologies purport across different fields is more important than ever. In this study, we find that{\dots}},
  groups    = {PTAS Project},
  issn      = {2377-2379},
  keywords  = {skill acquisition,theory of instruction},
  langid    = {english},
}

@Article{White1959,
  author       = {White, Robert W.},
  date         = {1959-09},
  journaltitle = {Psychological Review},
  title        = {Motivation Reconsidered: {{The}} Concept of Competence.},
  doi          = {10.1037/h0040934},
  issn         = {1939-1471, 0033-295X},
  langid       = {english},
  number       = {5},
  pages        = {297--333},
  urldate      = {2024-07-12},
  volume       = {66},
  groups       = {PTAS Project},
  shorttitle   = {Motivation Reconsidered},
}

@Article{White1971,
  author       = {White, Robert},
  date         = {1971-09},
  journaltitle = {American Journal of Occupational Therapy},
  title        = {The Urge towards Competence},
  number       = {6},
  pages        = {271--80},
  volume       = {25},
  groups       = {PTAS Project},
}

@MastersThesis{Wiley2009,
  author      = {Wiley, Justin},
  date        = {2009},
  institution = {University of Hawai'i at Manoa},
  title       = {Student Ownership of Learning: {{An}} Analysis},
  groups      = {PTAS Project},
}

@Article{Williamson2023,
  author       = {Williamson, Ben},
  date         = {2023-07},
  journaltitle = {International Journal of Artificial Intelligence in Education},
  title        = {The {{Social}} Life of {{AI}} in {{Education}}},
  doi          = {10.1007/s40593-023-00342-5},
  issn         = {1560-4292, 1560-4306},
  langid       = {english},
  urldate      = {2024-09-16},
  groups       = {PTAS Project},
}

@Article{Williamson2024a,
  author       = {Williamson, Ben and Macgilchrist, Felicitas and Potter, John},
  date         = {2024-07},
  journaltitle = {Learning, Media and Technology},
  title        = {Against Contextlessness in {{Learning}}, {{Media}} and {{Technology}}},
  doi          = {10.1080/17439884.2024.2374266},
  issn         = {1743-9884, 1743-9892},
  langid       = {english},
  number       = {3},
  pages        = {335--338},
  urldate      = {2024-09-16},
  volume       = {49},
  groups       = {PTAS Project},
}

@Article{Wing2006,
  author       = {Wing, Jeannette M.},
  date         = {2006-03},
  journaltitle = {Communications of the ACM},
  title        = {Computational Thinking},
  doi          = {10.1145/1118178.1118215},
  issn         = {0001-0782, 1557-7317},
  langid       = {english},
  number       = {3},
  pages        = {33--35},
  urldate      = {2024-11-19},
  volume       = {49},
  abstract     = {It represents a universally applicable attitude and skill set everyone, not just computer scientists, would be eager to learn and use.},
  groups       = {PTAS Project},
}

@Article{Wingstroem2024,
  author       = {Wingstr{\"o}m, Roosa and Hautala, Johanna and Lundman, Riina},
  date         = {2024-04},
  journaltitle = {Creativity Research Journal},
  title        = {Redefining {{Creativity}} in the {{Era}} of {{AI}}? {{Perspectives}} of {{Computer Scientists}} and {{New Media Artists}}},
  doi          = {10.1080/10400419.2022.2107850},
  issn         = {1040-0419, 1532-6934},
  langid       = {english},
  number       = {2},
  pages        = {177--193},
  urldate      = {2024-08-19},
  volume       = {36},
  groups       = {PTAS Project},
  shorttitle   = {Redefining {{Creativity}} in the {{Era}} of {{AI}}?},
}

@Article{Wolsink2007,
  author       = {Wolsink, Maarten},
  date         = {2007-08},
  journaltitle = {Renewable and Sustainable Energy Reviews},
  title        = {Wind Power Implementation: {{The}} Nature of Public Attitudes: {{Equity}} and Fairness Instead of `Backyard Motives'},
  doi          = {10.1016/j.rser.2005.10.005},
  issn         = {1364-0321},
  langid       = {english},
  number       = {6},
  pages        = {1188--1207},
  urldate      = {2024-09-15},
  volume       = {11},
  copyright    = {https://www.elsevier.com/tdm/userlicense/1.0/},
  groups       = {PTAS Project},
  shorttitle   = {Wind Power Implementation},
}

@Article{Wolsink2018,
  author       = {Wolsink, Maarten},
  date         = {2018-12},
  journaltitle = {Energy Research \& Social Science},
  title        = {Social Acceptance Revisited: Gaps, Questionable Trends, and an Auspicious Perspective},
  doi          = {10.1016/j.erss.2018.07.034},
  issn         = {2214-6296},
  langid       = {english},
  pages        = {287--295},
  urldate      = {2024-09-15},
  volume       = {46},
  groups       = {PTAS Project},
  shorttitle   = {Social Acceptance Revisited},
}

@Misc{Xiao2023,
  author      = {Xiao, Ping and Chen, Yuanyuan and Bao, Weining},
  date        = {2023-05},
  title       = {Waiting, {{Banning}}, and {{Embracing}}: {{An Empirical Analysis}} of {{Adapting Policies}} for {{Generative AI}} in {{Higher Education}}},
  doi         = {10.48550/arXiv.2305.18617},
  eprint      = {2305.18617},
  eprintclass = {cs},
  eprinttype  = {arXiv},
  urldate     = {2024-06-03},
  abstract    = {Generative AI tools such as ChatGPT have recently gained significant attention in higher education. This study aims to understand how universities establish policies regarding the use of AI tools and explore the factors that influence their decisions. Our study examines ChatGPT policies implemented at universities around the world, including their existence, content, and issuance dates. Specifically, we analyzed the top 500 universities according to the 2022 QS World University Rankings. Our findings indicate that there is significant variation in university policies. Less than one-third of the universities included in the study had implemented ChatGPT policies. Of the universities with ChatGPT policies, approximately 67 percent embraced ChatGPT in teaching and learning, more than twice the number of universities that banned it. The majority of the universities that ban the use of ChatGPT in assessments allow individual instructors to deviate from this restrictive policy. Our empirical analysis identifies several factors that are significantly and positively correlated with a university's likelihood of having a ChatGPT policy, including the university's academic reputation score, being in an English-speaking country, and the general public attitudes toward ChatGPT. In addition, we found that a university's likelihood of having a ban policy is positively associated with faculty student ratio, citations, and the English-speaking country dummy, while negatively associated with the number of peer universities within the same country that have banned ChatGPT. We discuss the challenges faced by universities based our empirical findings.},
  groups      = {PTAS Project},
  keywords    = {policy},
  number      = {arXiv:2305.18617},
  publisher   = {arXiv},
  shorttitle  = {Waiting, {{Banning}}, and {{Embracing}}},
}

@Article{Xie2019,
  author       = {Xie, Benjamin and Loksa, Dastyni and Nelson, Greg L. and Davidson, Matthew J. and Dong, Dongsheng and Kwik, Harrison and Tan, Alex Hui and Hwa, Leanne and Li, Min and Ko, Amy J.},
  date         = {2019-07},
  journaltitle = {Computer Science Education},
  title        = {A Theory of Instruction for Introductory Programming Skills},
  doi          = {10.1080/08993408.2019.1565235},
  issn         = {0899-3408},
  number       = {2-3},
  pages        = {205--253},
  urldate      = {2024-06-05},
  volume       = {29},
  abstract     = {Background and Context: Current introductory instruction fails to identify, structure, and sequence the many skills involved in programming. Objective: We proposed a theory which identifies four distinct skills that novices learn incrementally. These skills are tracing, writing syntax, comprehending templates (reusable abstractions of programming knowledge), and writing code with templates. We theorized that explicit instruction of these skills decreases cognitive demand. Method: We conducted an exploratory mixed-methods study and compared students' exercise completion rates, error rates, ability to explain code, and engagement when learning to program. We compared material that reflects this theory to more traditional material that does not distinguish between skills. Findings: Teaching skills incrementally resulted in improved completion rate on practice exercises, and decreased error rate and improved understanding of the post-test. Implications: By structuring programming skills such that they can be taught explicitly and incrementally, we can inform instructional design and improve future research on understanding how novice programmers develop understanding.},
  groups       = {PTAS Project},
  keywords     = {skill acquisition,theory of instruction},
  publisher    = {Routledge},
}

@Article{Yeadon2023,
  author       = {Yeadon, Will and Inyang, Oto-Obong and Mizouri, Arin and Peach, Alex and Testrow, Craig P},
  date         = {2023-05},
  journaltitle = {Physics Education},
  title        = {The Death of the Short-Form Physics Essay in the Coming {{AI}} Revolution},
  doi          = {10.1088/1361-6552/acc5cf},
  issn         = {0031-9120, 1361-6552},
  number       = {3},
  pages        = {035027},
  urldate      = {2024-07-12},
  volume       = {58},
  abstract     = {Abstract                              The latest AI language modules can produce original, high quality full short-form (300-word) Physics essays within seconds. These technologies such as ChatGPT and davinci-003 are freely available to anyone with an internet connection. In this work, we present evidence of AI generated short-form essays achieving First-Class grades on an essay writing assessment from an accredited, current university Physics module. The assessment requires students answer five open-ended questions with a short, 300-word essay each. Fifty AI answers were generated to create ten submissions that were independently marked by five separate markers. The AI generated submissions achieved an average mark of                                                                                                              71                    {\textpm}                    2                    \%                                                                    , in strong agreement with the current module average of                                                                                                              71                    {\textpm}                    5                    \%                                                                    . A typical AI submission would therefore most-likely be awarded a First Class, the highest classification available at UK universities. Plagiarism detection software returned a plagiarism score between                                                                                                              2                    {\textpm}                    1                                                                    \% (Grammarly) and                                                                                                              7                    {\textpm}                    2                                                                    \% (TurnitIn). We argue that these results indicate that current natural language processing AI represent a significant threat to the fidelity of short-form essays as an assessment method in Physics courses.},
  groups       = {PTAS Project},
}

@Article{Young2023,
  author       = {Young, Jamaal Rashad and Bevan, Danielle and Sanders, Miriam},
  date         = {2023-11},
  journaltitle = {International Journal of Education in Mathematics, Science and Technology},
  title        = {How {{Productive}} Is the {{Productive Struggle}}? {{Lessons Learned}} from a {{Scoping Review}}},
  doi          = {10.46328/ijemst.3364},
  issn         = {2147-611X},
  number       = {2},
  pages        = {470--495},
  urldate      = {2024-08-23},
  volume       = {12},
  abstract     = {Since its first notable appearance in the Second Handbook of Research on Mathematics Teaching and Learning, the phrase productive struggle has garnered support from a few, yet very influential, educational researchers. However, since the phrase was popularized by Hiroko Warshauer in 2011, examinations of the productive struggle have grown substantially within mathematics education. In this article, we present a systematic review of the literature on the productive struggle to quantify the breadth and depth of existing work in the area.~ Our analysis indicates that many studies: (1) provide a direct implementation of the productive struggle; (2) primarily involve teachers and not students; (3) present primarily qualitative or non-empirical research approaches; and (4) specify the NCTM content area focus less often across studies. Thus, we conclude that there are opportunities for rigorous research designs reporting on observable trends in the implementation of the productive struggle, explicitly targeting underexamined NCTM content and process standards, and focusing on how the productive struggle can support equity in mathematics. We believe that these opportunities should be investigated to provide a broader evidence base for developing meaningful experiences in mathematics for school-aged children that capitalize on the interconnectedness of attribution, meta-cognition, and controlled frustration in mathematics learning.},
  copyright    = {https://creativecommons.org/licenses/by-nc/4.0},
  groups       = {PTAS Project},
  shorttitle   = {How {{Productive}} Is the {{Productive Struggle}}?},
}

@Article{ZawackiRichter2019,
  author       = {{Zawacki-Richter}, Olaf and Mar{\'i}n, Victoria I. and Bond, Melissa and Gouverneur, Franziska},
  date         = {2019-12},
  journaltitle = {International Journal of Educational Technology in Higher Education},
  title        = {Systematic Review of Research on Artificial Intelligence Applications in Higher Education -- Where Are the Educators?},
  doi          = {10.1186/s41239-019-0171-0},
  issn         = {2365-9440},
  langid       = {english},
  number       = {1},
  pages        = {39},
  urldate      = {2024-07-12},
  volume       = {16},
  abstract     = {Abstract              According to various international reports, Artificial Intelligence in Education (AIEd) is one of the currently emerging fields in educational technology. Whilst it has been around for about 30\,years, it is still unclear for educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningfully on teaching and learning in higher education. This paper seeks to provide an overview of research on AI applications in higher education through a systematic review. Out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis, according to explicit inclusion and exclusion criteria. The descriptive results show that most of the disciplines involved in AIEd papers come from Computer Science and STEM, and that quantitative methods were the most frequently used in empirical studies. The synthesis of results presents four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. The conclusions reflect on the almost lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education.},
  groups       = {PTAS Project},
}

@Article{ZawackiRichter2024,
  author       = {{Zawacki-Richter}, Olaf and Bai, John Y. H. and Lee, Kyungmee and Slagter Van Tryon, Patricia J. and Prinsloo, Paul},
  date         = {2024-05},
  journaltitle = {International Journal of Educational Technology in Higher Education},
  title        = {New Advances in Artificial Intelligence Applications in Higher Education?},
  doi          = {10.1186/s41239-024-00464-3},
  issn         = {2365-9440},
  langid       = {english},
  number       = {1},
  pages        = {32, s41239-024-00464-3},
  urldate      = {2024-08-16},
  volume       = {21},
  groups       = {PTAS Project},
}

@Article{Wang2025,
  author       = {Wang, Jin and Fan, Wenxiang},
  date         = {2025-05},
  journaltitle = {Humanities and Social Sciences Communications},
  title        = {The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: insights from a meta-analysis},
  doi          = {10.1057/s41599-025-04787-y},
  issn         = {2662-9992},
  number       = {1},
  volume       = {12},
  groups       = {Education, PTAS Project},
  publisher    = {Springer Science and Business Media LLC},
}

@Book{Fengchun2023,
  author    = {Fengchun, Miao and Holmes, Wayne},
  date      = {2023},
  title     = {Guidance for generative AI in education and research},
  doi       = {10.54675/ewzm9535},
  isbn      = {9789231006128},
  publisher = {UNESCO},
  groups    = {PTAS Project},
}

@InProceedings{Becker2023,
  author     = {Becker, Brett A. and Denny, Paul and Finnie-Ansley, James and Luxton-Reilly, Andrew and Prather, James and Santos, Eddie Antonio},
  booktitle  = {Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1},
  date       = {2023-03},
  title      = {Programming Is Hard - Or at Least It Used to Be: Educational Opportunities and Challenges of AI Code Generation},
  doi        = {10.1145/3545945.3569759},
  pages      = {500--506},
  publisher  = {ACM},
  series     = {SIGCSE 2023},
  collection = {SIGCSE 2023},
  groups     = {PTAS Project},
}

@Online{Williamson2023a,
  author  = {Williamson, Ben},
  date    = {2023-11-03},
  title   = {The power of edtech investors in education},
  url     = {https://codeactsineducation.wordpress.com/2023/11/03/the-power-of-edtech-investors-in-education/},
  urldate = {2024-01-21},
  groups  = {PTAS Project},
}