Tempos de Transição em Estados de Corretude e Erro como Indicadores de Desempenho em Juízes Online

Santos, Ingrid Lima dos; Oliveira, David Braga Fernandes de; Carvalho, Leandro Silva Galvão de; Pereira, Filipe Dwan; Oliveira, Elaine

doi:10.5753/cbie.sbie.2020.1283

Cited by 5 publications

(4 citation statements)

References 8 publications

(22 reference statements)

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“…Despite the benefits of OJs for programming learning, recent studies [54,37,20,14,41] report that OJ systems need to be further improved to serve students, instructors, and administrators better. In this sense, one of the biggest issues of these systems is that the available problems are not organised based on meta-information that facilitates the users' searching process [56,25,40,54,41].…”

Section: Introductionmentioning

confidence: 99%

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

Pereira¹,

Fonseca²,

Wiktor³

et al. 2022

Preprint

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<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>

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Section: Introductionmentioning

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

show abstract

“…Programming Online Judges (POJs) are automatic code correction environments that are typically used by students to improve their programming skills and/or train for programming competitions [41,46,26,40,47]. The adoption of these environments by instructors and institutions has increased in the last few years in introductory computing (so-called 'CS1') classes [41,36].…”

Section: Introductionmentioning

confidence: 99%

A Recommender System Based on Effort: Towards Minimising Negative Affects and Maximising Achievement in CS1 Learning

Pereira

Hermino²,

Rodriguez³

et al. 2021

Intelligent Tutoring Systems

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Programming online judges (POJs) are autograders that have been increasingly used in introductory programming courses (also known as CS1) since these systems provide instantaneous and accurate feedback for learners' codes solutions and reduce instructors' workload in evaluating the assignments. Nonetheless, learners typically struggle to find problems in POJs that are adequate for their programming skills. A potential reason is that POJs present problems with varied categories and difficulty levels, which may cause a cognitive overload, due to the large amount of information (and choice) presented to the student. Thus, students can often feel less capable, which may result in undesirable affective states, such as frustration and demotivation, decreasing their performance and potentially leading to increasing dropout rates. Recently, new research emerged on systems to recommend problems in POJs; however, the data collection for these approaches was not fine-grained; importantly, they did not take into consideration the students' previous effort and achievement. Thus, this study proposes for the first time a prescriptive analytics solution for students' programming behaviour by constructing and evaluating an automatic recommender module based on students' effort, to personalise the problems presented to the learner in POJs. The aim is to improve the learners achievement, whilst minimising negative affective states in CS1 courses. Results in a within-subject double-blind controlled experiment showed that our method significantly improved positive affective states, whilst minimising the negatives ones. Moreover, our recommender significantly increased students' achievement (correct solutions) and reduced dropout and failure in problem-solving.

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“…Juízes on-line são sistemas de correc ¸ão automática de códigos-fonte comumente empregados como ferramentas de apoio pedagógico em disciplinas de programac ¸ão [Galvão et al 2016, Santos et al 2020, Pereira et al 2020b, Costa et al 2021. Eles proveem feedbacks instantâneos e automáticos aos estudantes durante suas tentativas de solucionar os exercícios de programac ¸ão cadastrados por seus professores [Fonseca et al 2019, Pereira et al 2020c, Freitas Júnior et al 2020.…”

Section: Introduc ¸ãOunclassified

Um Método de Detecção de Plágio para Sistemas Juiz On-line baseado no Comportamento dos Alunos

Oliveira

Filho

Oliveira

et al. 2021

Anais Do XXXII Simpósio Brasileiro De Informática Na Educação (SBIE 2021)

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A prática do plágio é um problema grave e crescente no meio acadêmico, que interfere diretamente na qualidade do ensino. Esta pesquisa se contextualiza no problema da detecção de plágio entre códigos-fonte nas disciplinas de introdução de programação. Nessas disciplinas, os códigos desenvolvidos pelos alunos tendem a ser simples e pequenos, dificultando o processo de detecção de plágio por parte dos métodos tradicionais baseados em similaridade de código. Para contornar essa dificuldade, neste trabalho é proposto um método de detecção de plágio baseado em evidências extraídas de registros de logs de sistemas juízes online, onde as evidências estão relacionadas com o comportamento dos alunos durante suas tentativas de resolução dos exercícios de programação. Como resultado, o método proposto alcançou 0.83 na medida-F durante o processo de detecção de plágio.

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Tempos de Transição em Estados de Corretude e Erro como Indicadores de Desempenho em Juízes Online

Cited by 5 publications

References 8 publications

Towards 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

A Recommender System Based on Effort: Towards Minimising Negative Affects and Maximising Achievement in CS1 Learning

Um Método de Detecção de Plágio para Sistemas Juiz On-line baseado no Comportamento dos Alunos

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