A Survey on Online Judge Systems and Their Applications

Wasik, Szymon; Antczak, Maciej; Badura, Jan; Laskowski, Artur; Sternal, Tomasz

doi:10.1145/3143560

Cited by 134 publications

(118 citation statements)

References 78 publications

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“…To do this, we defined two fundamental factors in our experimental design: An objective indicator of programming expertise and a laboratory task that efficiently exhibits experts' superior performances under the general constraints of fMRI experiments. For the first factor, we adopted programmers' ratings in competitive programming contests (AtCoder), which are objectively determined by the relative positions of their actual performances among thousands of programmers 22 . We recruited top-and middle-rated programmers as well as novice controls to cover a wide range of programming expertise in our fMRI experiment (Table.1).…”

Section: Introductionmentioning

confidence: 99%

Expert programmers have fine-tuned cortical representations of source code

Ikutani

Kubo

Nishida

et al. 2020

Preprint

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Expertise enables humans to achieve outstanding performance on domain-specific tasks, and programming is no exception. Many have shown that expert programmers exhibit remarkable differences from novices in behavioral performance, knowledge structure, and selective attention. However, the underlying differences in the brain are still unclear. We here address this issue by associating the cortical representation of source code with individual programming expertise using a data-driven decoding approach. This approach enabled us to identify seven brain regions, widely distributed in the frontal, parietal, and temporal cortices, that have a tight relationship with programming expertise. In these brain regions, functional categories of source code could be decoded from brain activity and the decoding accuracies were significantly correlated with individual behavioral performances on source-code categorization. Our results suggest that programming expertise is built up on fine-tuned cortical representations specialized for the domain of programming.

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Section: Introductionmentioning

confidence: 99%

Expert programmers have fine-tuned cortical representations of source code

Ikutani

Kubo

Nishida

et al. 2020

Preprint

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show abstract

“…For example, subjective questions usually have corresponding scores and a teacher can decide the scores based on the answers. For computer programming questions, if online judge (OJ) system [22] is used, the answer results can be "pass", "error", "timeout", etc., in which "pass" indicates that the answer is correct and other results indicate that the answer is wrong. In a word, a teacher can choose the features of question answering data that are used to measure students' knowledge levels in practice.…”

Section: Student Clusteringmentioning

confidence: 99%

MOOC Video Personalized Classification Based on Cluster Analysis and Process Mining

Feng

Liu

2020

Sustainability

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In the teaching based on MOOC (Massive Open Online Courses) and flipped classroom, a teacher needs to understand the difficulty and importance of MOOC videos in real time for students at different knowledge levels. In this way, a teacher can be more focused on the different difficulties and key points contained in the videos for students in a flipped classroom. Thus, the personalized teaching can be implemented. We propose an approach of MOOC video personalized classification based on cluster analysis and process mining to help a teacher understand the difficulty and importance of MOOC videos for students at different knowledge levels. Specifically, students are first clustered based on their knowledge levels through question answering data. Then, we propose the process model of a group of students which reflects the overall video watching behavior of these students. Next, we propose to use the process mining technique to mine the process model of each student cluster by the video watching data of the involved students. Finally, we propose an approach to measure the difficulty and importance of a video based on a process model. With this approach, MOOC videos can be classified for students at different knowledge levels according to difficulty and importance. Therefore, a teacher can carry out a flipped classroom more efficiently. Experiments on a real data set show that the difficulty and importance of videos obtained by the proposed approach can reflect students’ subjective evaluation of the videos.

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“…Todos os docentes alocados a essas turmas seguiram uma mesma metodologia híbrida de ensino e aprendizagem, que mescla o ensino presencial com atividades virtuais baseadas em um juiz online. Segundo Wasik et al (2018), juízes online são plataformas web cujo propósito é compilar, executar e avaliar programas submetidos por usuários. Por meio deste, foram realizadas avaliações formativas (prática de exercícios de programação, com consulta à bibliografia, professores, tutores e colegas) e somativas (avaliação prática sem consulta) [Carvalho, Oliveira e Gadelha 2016].…”

Section: Descrição Dos Dados E Do Contexto Educacionalunclassified

Analisando a influência de atributos demográficos no desempenho de estudantes em uma disciplina de introdução à programação

Pereira¹,

Carvalho²,

Souto³

2019

Anais Do Workshop Sobre Educação Em Computação (WEI)

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O baixo desempenho de alunos em disciplinas introdutórias de programação é cenário comum em cursos de graduação de ciências exatas e de engenharia. Fatores externos ao ambiente acadêmico são tão decisivos em relação aos resultados quanto os aspectos inerentes ao processo de aprendizagem. Com base na aplicação de técnicas de mineração de dados educacionais sobre os dados de uma instituição de ensino superior brasileira, observou-se que o desempenho em uma disciplina de introdução à programação para non-majors está correlacionado a características socioeconômicas e educacionais prévias ou a um subconjunto criado a partir da combinação destes atributos. Por sua vez, identificar antecipadamente estudantes em risco de reprovação pode ajudar a reduzir a evasão em disciplinas de programação.

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A Survey on Online Judge Systems and Their Applications

Cited by 134 publications

References 78 publications

Expert programmers have fine-tuned cortical representations of source code

Expert programmers have fine-tuned cortical representations of source code

MOOC Video Personalized Classification Based on Cluster Analysis and Process Mining

Analisando a influência de atributos demográficos no desempenho de estudantes em uma disciplina de introdução à programação

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