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dc.contributor.author
Moresi, Marco
dc.contributor.author
Gómez, Marcos Javier
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dc.contributor.author
Benotti, Luciana
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dc.date.available
2022-02-22T17:38:52Z
dc.date.issued
2021-06
dc.identifier.citation
Moresi, Marco; Gómez, Marcos Javier; Benotti, Luciana; Predicting Students' Difficulties from a Piece of Code; Institute of Electrical and Electronics Engineers; IEEE Transactions on Learning Technologies; 14; 3; 6-2021; 386-399
dc.identifier.issn
1939-1382
dc.identifier.uri
http://hdl.handle.net/11336/152508
dc.description.abstract
Based on hundreds of thousands of hours of data about how students learn in massive open online courses, educational machine learning promises to help students who are learning to code. However, in most classrooms, students and assignments do not have enough historical data for feeding these data hungry algorithms. Previous work on predicting dropout is data hungry and, moreover, requires the code to be syntactically correct. As we deal with beginners' code in a text-based language our models are trained on noisy student text; almost 40% of the code in our datasets contains parsing errors. In this article, we compare two machine learning models that predict whether students need help regardless of whether their code compiles or not. That is, we compare two methods for automatically predicting whether students will be able to solve a programming exercise on their own. The first model is a heavily feature-engineered approach that implements pedagogical theories of the relation between student interaction patterns and the probability of dropout; it requires a rich history of student interaction. The second method is based on a short program (that may contain errors) written by a student, together with a few hundred attempts by their classmates on the same exercise. This second method uses natural language processing techniques; it is based on the intuition that beginners' code may be closer to a natural language than to a formal one. It is inspired by previous work on predicting people's fluency when learning a second natural language.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Institute of Electrical and Electronics Engineers
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dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
AND PREDICTION
dc.subject
COMPUTER SCIENCE EDUCATION
dc.subject
INTERACTIVE ENVIRONMENTS
dc.subject
MACHINE LEARNING
dc.subject
MODELING
dc.subject.classification
Otras Ciencias de la Computación e Información
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dc.subject.classification
Ciencias de la Computación e Información
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
Predicting Students' Difficulties from a Piece of Code
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-02-09T14:17:27Z
dc.identifier.eissn
2372-0050
dc.journal.volume
14
dc.journal.number
3
dc.journal.pagination
386-399
dc.journal.pais
Estados Unidos
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dc.journal.ciudad
Washington D. C.
dc.description.fil
Fil: Moresi, Marco. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina
dc.description.fil
Fil: Gómez, Marcos Javier. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina
dc.description.fil
Fil: Benotti, Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina
dc.journal.title
IEEE Transactions on Learning Technologies
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/9466389
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1109/TLT.2021.3092998
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