Recomendação Automática de Problemas em Juízes Online Usando Processamento de Linguagem Natural e Análise Dirigida aos Dados

Júnior, Hermino Barbosa de Freitas; Pereira, Filipe Dwan; Oliveira, Elaine; Oliveira, David Braga Fernandes de; Carvalho, Leandro Silva Galvão de

doi:10.5753/cbie.sbie.2020.1152

Cited by 6 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Indeed, in recent works [30] and [12] about recommending problems in OJs to CS1 students and instructors, the authors explained that a recommender could be useful for students by allowing them to select appropriate problems unassisted, and for instructors by allowing them to select problems to compose lists of exercises and tests. Nonetheless, the recommender method has a limitation related to problem topics.…”

Section: Related Workmentioning

confidence: 99%

“…For students, annotation of topics would be useful because they would not have to carry out an exhaustive search for problems related to the CS1 topics in volumes of problems without any categorisation. Notice that this exhaustive search process can demotivate the student [12], [18], [30], [42]. Moreover, the ease of appropriateness for learners is vital to consider when developing a tool to facilitate learning.…”

Section: Applications and Implicationsmentioning

confidence: 99%

See 1 more Smart Citation

Toward Supporting CS1 Instructors and Learners With Fine-Grained Topic Detection in Online Judges

et al. 2023

Self Cite

View full text Add to dashboard Cite

Online judges (OJ) are a popular tool to support programming learning. However, one major issue with OJs is that problems are often put together without any associated meta-information that could, for example, be used to help classify problems. This meta-information could be extremely valuable to help users quickly find what types of problems they need most. To face this problem, several OJ administrators have recently begun manually annotating the topics of problems based on computer science-related subjects, such as dynamic programming, graphs, and data structures. Initially, these topics were used to support programming competitions and experienced learners. However, with OJs being increasingly used to support CS1 classes, such topic annotation needs to be extended to suit CS1 learners and instructors. In this work, for the first time, to the best of our knowledge, we propose and validate a predictive model that can automatically detect fine-grained topics of problems based on the CS1 syllabus. After experimenting with many shallow and deep learning models with different word representations based on cutting-edge NLP techniques, our best model is a CNN, achieving an F1-score of 88.9%. We then present how our model can be used for various applications, including (i) facilitating the search process of problems for CS1 learners and instructors and (ii) how it can be integrated into a system to recommend problems in OJs.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Applications and Implicationsmentioning

confidence: 99%

Toward Supporting CS1 Instructors and Learners With Fine-Grained Topic Detection in Online Judges

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…Indeed, in recent works [27,40] about recommending problems in OJs to CS1 students and instructors, the authors explained that a recommender could be useful for students, by allowing them to select appropriate problems unassisted, and for instructors, by allowing them to select problems to compose lists of exercises and tests. Nonetheless, the recommender method has a limitation related to problem topics.…”

Section: Related Workmentioning

confidence: 99%

“…For students, annotation of topics would be useful, so that they would not have to carry out an exhaustive search for problems related to the CS1 topics in volumes of problems, without any categorisation. Notice that this exhaustive search process can demotivate the student [55,56,27,40]. Moreover, the ease of appropriateness for learners is vital to consider, when developing a tool to facilitate learning.…”

Section: Applications and Implicationsmentioning

confidence: 99%

Towards Supporting Cs1 Instructors and Learners With Fine-Grained Topic Detection in Online Judges

Pereira¹,

Fonseca²,

Wiktor³

et al. 2022

Preprint

View full text Add to dashboard Cite

<p>Online judges (OJs) are a popular tool to support programming learning. However, one major issue with OJs is that problems are often put together without any associated meta-information that could, for example, be used to help classify problems. This meta-information could be extremely valuable to help users quickly find what types of problems they need most. To face this problem, several OJ administrators have recently begun manually annotating the topics of problems based on computer science-related subjects, such as dynamic programming, graphs, and data structures. Initially, the topics were used to support programming competitions and experienced learners. However, with OJs increasingly used to support CS1 classes, such topic annotation needs to be extended to suit CS1 learners and instructors. In this work, for the first time, to the best of our knowledge, we propose and validate a predictive model that can automatically detect fine-grained topics of problems based on the CS1 syllabus. After experimenting with many shallow and deep learning models with different word representations based on cutting-edge NLP techniques, our best model is a CNN, achieving an F1-score of 88.9%. We then present how our model can be used for various applications, including (i) facilitating the search process of problems for CS1 learners and instructors and (ii) how it can be integrated into a system to recommend problems in OJs.</p>

show abstract

“…Tais ambientes vêm sendo empregados em diversos tipos de atividades, como por exemplo em trabalhos práticos ou exames presenciais [2,4,9,12,26,30,33]. Em exames presenciais, geralmente ocorre o sorteio aleatório de um número predefinido de questões, dentre um conjunto de questões previamente selecionadas por um instrutor [17]. Refletindo um pouco sobre essa abordagem, é fácil perceber sua falha, pois o sorteio aleatório não garante equivalência no exame.…”

Section: Introductionunclassified

Uso de atributos de código para classificação da facilidade de questões de codificação

Lima¹,

Carvalho²,

Oliveira³

et al. 2021

Anais Do I Simpósio Brasileiro De Educação Em Computação (EDUCOMP 2021)

Self Cite

View full text Add to dashboard Cite

No ensino de programação, é comum o uso de Ambientes de Correção Automática de Código (ACACs). Esses apresentam uma alta diversidade de exercícios de programação que requerem que o estudante elabore um código como solução. Contudo, um obstáculo é classificar a facilidade ou dificuldade desses exercícios de modo que sejam apresentados conforme o nível de conhecimento do aluno, ou para que durante uma avaliação formulada por sorteio aleatório de questões, os alunos possam receber exercícios com nível de dificuldade semelhantes. Nesse sentido, este trabalho apresenta um método para classificar automaticamente o grau de facilidade de questões de codificação em Python, usando para isso atributos extraídos de códigos de solução. Foram analisadas 354 questões, agrupadas em 07 conjuntos segundo os tópicos abordados, que foram previamente definidos na ementa de uma disciplina de introdução à programação. Essas questões foram aplicadas em exames presenciais de turmas ministradas entre os anos de 2017 e 2019. Entre os resultados, verificou-se que a facilidade das questões pode ser estimada por meio de atributos do código de solução dos instrutores, com f1-score de 0,94 e acurácia de 0,92. Além disso, o conjunto de atributos relevantes para classificação variou conforme o tópico de programação abordado pelas questões.

show abstract

Recomendação Automática de Problemas em Juízes Online Usando Processamento de Linguagem Natural e Análise Dirigida aos Dados

Cited by 6 publications

References 8 publications

Toward Supporting CS1 Instructors and Learners With Fine-Grained Topic Detection in Online Judges

Toward Supporting CS1 Instructors and Learners With Fine-Grained Topic Detection in Online Judges

Towards Supporting Cs1 Instructors and Learners With Fine-Grained Topic Detection in Online Judges

Uso de atributos de código para classificação da facilidade de questões de codificação

Contact Info

Product

Resources

About