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Apparently I didn't use the latest version of the pass/fail article :s

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@ -160,18 +160,7 @@
publisher = {{University of Canterbury}},
url = {https://ir.canterbury.ac.nz/handle/10092/14446},
urldate = {2022-07-06},
abstract = {Detection of similar programs is a highly studied problem. Detecting similar code is
an important strategy for detecting badly modularized code, finding vulnerabilities
due to error prone copy-paste programming methodologies, and detecting academic
dishonesty in online code assignment submissions following the copy-paste-adapt-it
pattern. The latter is the impetus for this work.
A novel system is presented that is specifically adapted to programs that may
be small, and similar by virtue of being written to solve the same problem. The
system is also adapted toward specific expected behaviors of plagiarists, making
use of algorithms custom built to both recognize these behaviors while satisfying
hierarchical properties. A defining and novel property of the proposed method is
the categorical information it provides. A hierarchy of categories with an implication
relationship are leveraged in the production of descriptive, rank-able results.},
abstract = {Detection of similar programs is a highly studied problem. Detecting similar code is an important strategy for detecting badly modularized code, finding vulnerabilities due to error prone copy-paste programming methodologies, and detecting academic dishonesty in online code assignment submissions following the copy-paste-adapt-it pattern. The latter is the impetus for this work. A novel system is presented that is specifically adapted to programs that may be small, and similar by virtue of being written to solve the same problem. The system is also adapted toward specific expected behaviors of plagiarists, making use of algorithms custom built to both recognize these behaviors while satisfying hierarchical properties. A defining and novel property of the proposed method is the categorical information it provides. A hierarchy of categories with an implication relationship are leveraged in the production of descriptive, rank-able results.},
copyright = {All Right Reserved},
langid = {english},
annotation = {Accepted: 2017-10-03T02:53:23Z},
@ -582,7 +571,7 @@
@inproceedings{caizaProgrammingAssignmentsAutomatic2013,
title = {Programming Assignments Automatic Grading: Review of Tools and Implementations},
shorttitle = {Programming Assignments Automatic Grading},
booktitle = {7th {{International Technology}}, {{Education}} and {{Development Conference}} ({{INTED2013}}) | 7th {{International Technology}}, {{Education}} and {{Development Conference}} ({{INTED2013}}) | 04/03/2013 - 06/03/2013 | {{Valencia}}, {{Spain}}},
booktitle = {7th {{International Technology}}, {{Education}} and {{Development Conference}} ({{INTED2013}}) {\textbar} 7th {{International Technology}}, {{Education}} and {{Development Conference}} ({{INTED2013}}) {\textbar} 04/03/2013 - 06/03/2013 {\textbar} {{Valencia}}, {{Spain}}},
author = {Caiza, Julio C. and del {\'A}lamo Ramiro, Jos{\'e} Mar{\'i}a},
year = {2013},
pages = {5691--5700},
@ -792,11 +781,7 @@
publisher = {{EUROSIS-ETI}},
url = {http://repositorium.sdum.uminho.pt/},
urldate = {2021-09-16},
abstract = {Although the educational level of the Portuguese population has improved in the last decades, the statistics keep Portugal at Europe's tail end due to its high student failure rates. In particular, lack of success in the core classes of Mathematics and the Portuguese language is extremely serious. On the other hand, the fields of Business Intelligence (BI)/Data Mining (DM), which aim at extracting high-level knowledge from raw data, offer interesting automated tools that can aid the
education domain. The present work intends to approach student achievement in secondary education using BI/DM techniques. Recent real-world data (e.g. student grades, demographic, social and school related
features) was collected by using school reports and questionnaires. The two core classes (i.e. Mathematics and Portuguese) were modeled under binary/five-level classification and regression tasks. Also, four DM models (i.e. Decision Trees, Random Forest, Neural Networks
and Support Vector Machines) and three input
selections (e.g. with and without previous grades) were tested. The results show that a good predictive accuracy can be achieved, provided that the first and/or second school period grades are available. Although student achievement is highly influenced by past evaluations, an explanatory analysis has shown that there are also other relevant features (e.g. number of absences, parent's job and education, alcohol consumption). As a direct outcome of this research, more efficient student prediction tools can be be developed, improving the quality of education and enhancing school resource management.},
abstract = {Although the educational level of the Portuguese population has improved in the last decades, the statistics keep Portugal at Europe's tail end due to its high student failure rates. In particular, lack of success in the core classes of Mathematics and the Portuguese language is extremely serious. On the other hand, the fields of Business Intelligence (BI)/Data Mining (DM), which aim at extracting high-level knowledge from raw data, offer interesting automated tools that can aid the education domain. The present work intends to approach student achievement in secondary education using BI/DM techniques. Recent real-world data (e.g. student grades, demographic, social and school related features) was collected by using school reports and questionnaires. The two core classes (i.e. Mathematics and Portuguese) were modeled under binary/five-level classification and regression tasks. Also, four DM models (i.e. Decision Trees, Random Forest, Neural Networks and Support Vector Machines) and three input selections (e.g. with and without previous grades) were tested. The results show that a good predictive accuracy can be achieved, provided that the first and/or second school period grades are available. Although student achievement is highly influenced by past evaluations, an explanatory analysis has shown that there are also other relevant features (e.g. number of absences, parent's job and education, alcohol consumption). As a direct outcome of this research, more efficient student prediction tools can be be developed, improving the quality of education and enhancing school resource management.},
copyright = {openAccess},
isbn = {978-90-77381-39-7},
langid = {english},
@ -1117,6 +1102,26 @@
file = {/home/charlotte/sync/Zotero/storage/US6RPQBH/Farrell et al. - 2002 - Assertions and Protocol for the OASIS Security Ass.pdf}
}
@inproceedings{feldmanAnsweringAmRight2019,
title = {Towards Answering ``{{Am I}} on the Right Track?'' Automatically Using Program Synthesis},
shorttitle = {Towards Answering ``{{Am I}} on the Right Track?},
booktitle = {Proceedings of the 2019 {{ACM SIGPLAN Symposium}} on {{SPLASH-E}}},
author = {Feldman, Molly Q and Wang, Yiting and Byrd, William E. and Guimbreti{\`e}re, Fran{\c c}ois and Andersen, Erik},
year = {2019},
month = oct,
series = {{{SPLASH-E}} 2019},
pages = {13--24},
publisher = {{Association for Computing Machinery}},
address = {{New York, NY, USA}},
doi = {10.1145/3358711.3361626},
url = {https://dl.acm.org/doi/10.1145/3358711.3361626},
urldate = {2024-01-22},
abstract = {Students learning to program often need help completing assignments and understanding why their code does not work as they expect it to. One common place where they seek such help is at teaching assistant office hours. We found that teaching assistants in introductory programming (CS1) courses frequently answer some variant of the question ``Am I on the Right Track?''. The goal of this work is to develop an automated tool that provides similar feedback for students in real-time from within an IDE as they are writing their program. Existing automated tools lack the generality that we seek, often assuming a single approach to a problem, using hand-coded error models, or applying sample fixes from other students. In this paper, we explore the use of program synthesis to provide less constrained automated answers to ``Am I on the Right Track'' (AIORT) questions. We describe an observational study of TA-student interactions that supports targeting AIORT questions, as well as the development of and design considerations behind a prototype integrated development environment (IDE). The IDE uses an existing program synthesis engine to determine if a student is on the right track and we present pilot user studies of its use.},
isbn = {978-1-4503-6989-3},
keywords = {Computer science education,program synthesis,user interfaces},
file = {/home/charlotte/sync/Zotero/storage/7QWDMXTP/feldman2019.pdf.pdf;/home/charlotte/sync/Zotero/storage/AZFIKMYP/Feldman et al. - 2019 - Towards answering “Am I on the right track” autom.pdf}
}
@article{fergusonInconsistentMaximumLikelihood1982,
title = {An {{Inconsistent Maximum Likelihood Estimate}}},
author = {Ferguson, Thomas S.},
@ -1189,7 +1194,7 @@
year = {2006},
journal = {Information Technologies at School},
pages = {553--563},
publisher = {{2nd International Conference on Informatics in Secondary Schools: Evolution {\ldots}}}
publisher = {{2nd International Conference on Informatics in Secondary Schools: Evolution {\dots}}}
}
@article{forisekSuitabilityProgrammingTasks2006,
@ -3742,6 +3747,23 @@
file = {/home/charlotte/sync/Zotero/storage/PTM64G3B/Watson and Li - 2014 - Failure rates in introductory programming revisite.pdf}
}
@article{werthPredictingStudentPerformance1986,
title = {Predicting Student Performance in a Beginning Computer Science Class},
author = {Werth, Laurie Honour},
year = {1986},
month = feb,
journal = {ACM SIGCSE Bulletin},
volume = {18},
number = {1},
pages = {138--143},
issn = {0097-8418},
doi = {10.1145/953055.5701},
url = {https://dl.acm.org/doi/10.1145/953055.5701},
urldate = {2024-01-22},
abstract = {This study investigated the relationship between the student's grade in a beginning computer science course and their sex, age, high school and college academic performance, number of mathematics courses, and work experience. Standard measures of cognitive development, cognitive style, and personality factors were also given to 58 students in three sections of the beginning Pascal programming class. Significant relationships were found between the letter grade and the students' college grades, the number of hours worked and the number of high school mathematics classes. Both the Group Embedded Figures Test (GEFT) and the measure of Piagetian intellectual development stages were also significantly correlated with grade in the course. There was no relationship between grade and the personality type, as measured by the Myers-Briggs Type Indicator (MBTI); however, an interesting and distinctive personality profile was evident.},
file = {/home/charlotte/sync/Zotero/storage/4FDEYD73/werth1986.pdf.pdf;/home/charlotte/sync/Zotero/storage/VYZFFEPK/Werth - 1986 - Predicting student performance in a beginning comp.pdf}
}
@misc{wickhamGgplot2CreateElegant2023,
title = {Ggplot2: {{Create Elegant Data Visualisations Using}} the {{Grammar}} of {{Graphics}}},
shorttitle = {Ggplot2},