Make Learning Fun with Programming Contests

Garcı́a-Mateos, Ginés; Fernández-Alemán, José Luis

doi:10.1007/978-3-642-03270-7_17

Cited by 22 publications

(8 citation statements)

References 6 publications

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“…The importance and the effectiveness of programming contests in learning programming and, more generally, computer science has been observed and emphasized greatly in the literature: we mention the works of Dagienė (Dagienė, 2010) and Garcia-Mateos and Fernandez-Aleman (Garcia-Mateos and Fernandez-Aleman, 2009). …”

Section: Related Workmentioning

confidence: 99%

oii-web: an Interactive Online Programming Contest Training System

Luigi¹,

Farina²,

Laura³

et al. 2016

IOI

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Abstract. In this paper we report our experience, related to the online training for the Italian and International Olympiads in Informatics. We developed an interactive online system, based on the Contest management System (CMS), the grading system used in several major programming contests including the International Olympiads in Informatics (IOI), and used it in three distinct context: training students for the Italian Olympiads in Informatics (OII), training teachers in order to be able to assist students for the OII, and training the Italian team for the IOI. The system, that is freely available, proved to be a game changer for the whole Italian Olympiads in informatics ecosystem: in one year, we almost doubled the participation to OII, from 13k to 21k secondary school students.Being developed in CMS (http://cms-dev.github.io/), the system is highly available to extensions supporting, for instance, the production of feedback on problems solutions submitted by trainees. It is also freely available, with the idea of allowing for support to alternative necessities and developments.Keywords: programming contest, Olympiads in informatics, programming training, problem recommendation model. -mail: {gabr.farina,williamdiluigi,veluca93}@gmail.com {laura,nanni,marte}@dis.uniroma1.it Abstract. In this paper we report our experience, related to the online training for the Italian andInternational Olympiads in Informatics. We developed an interactive online system, based on the Contest management System (CMS), the grading system used in several major programming contests including the International Olympiads in Informatics (IOI), and used it in three distinct context: training students for the Italian Olympiads in Informatics (OII), training teachers in order to be able to assist students for the OII, and training the Italian team for the IOI. The system, that is freely available, proved to be a game changer for the whole Italian Olympiads in informatics ecosystem: in one year, we almost doubled the participation to OII, from 13k to 21k secondary school students. Being developed in CMS (http://cms-dev.github.io/), the system is highly available to extensions supporting, for instance, the production of feedback on problems solutions submitted by trainees. It is also freely available, with the idea of allowing for support to alternative necessities and developments.

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Section: Related Workmentioning

confidence: 99%

oii-web: an Interactive Online Programming Contest Training System

Luigi¹,

Farina²,

Laura³

et al. 2016

IOI

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“…implantado con éxito como un sistema de aprendizaje y evaluación on-line en asignaturas de titulaciones de Informática (12,13,14).…”

Section: Enfermería Globalunclassified

Experiencia piloto con la herramienta mooshak en un grupo de alumnos de enfermería: cuestionario de satisfacción

2010

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El objetivo de este trabajo es evaluar nuevos recursos educativos que ayuden a los docentes y a los alumnos a conseguir los objetivos que se plantean en el nuevo Espacio Europeo de Educación Superior (EEES). Este estudio presenta los resultados de un cuestionario de retroalimentación sobre una experiencia piloto que propone un entorno web en el proceso de aprendizaje de la materia Enfermería Médico-Quirúrgica (EMQ) de una titulación universitaria de enfermería. Una herramienta web de evaluación automática llamada Mooshak fue utilizada por los 54 alumnos que participaron en la experiencia de forma voluntaria, logrando 5 de ellos resolver la totalidad de los problemas propuestos. Un total de 16 (28.57%) alumnos respondieron a una encuesta (on-line), 15 de los cuales indicaron que la experiencia había sido satisfactoria o muy satisfactoria, obteniendo la actividad online una calificación media de 7.96 sobre 10. En base a esta experiencia, podemos afirmar que hay evidencias de que el uso del recurso on-line Mooshak ha tenido una incidencia positiva en la formación de los alumnos de EMQ.

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“…Programming Contests (PCs) are competitions in which participants are faced a set of tasks that require writing computer programs. Recent literature have emphasized the importance and the effectiveness of PCs in the process of learning computer programming (Audrito et al, 2012;Astrachan, 2004;Blumenstein et al, 2008;Dagienė, 2010;Garcia-Mateos and Fernandez-Aleman, 2009;Wang et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Recommending Tasks in Online Judges using Autoencoder Neural Networks

Fantozzi¹,

Laura

2020

IOI

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Programming contests such as International Olympiads in Informatics (IOI) and ACM International Collegiate Programming Contest (ICPC) are becoming increasingly popular in recent years. To train for these contests, there are several Online Judges available, in which users can test their skills against a usually large set of programming tasks. In the literature, so far few papers have addressed the problem of recommending tasks in online judges. Most notably, as opposed with traditional Recommender Systems, since the learners improve their skills as they solve more problems, there is an intrinsic dynamic dimension that has to be considered: when recommending movies or books, it is likely that the preferences of the users are more or less stable, whilst in recommending tasks this does not hold true. In order to help the learners, it is crucial to recommend them tasks that are challenging but not unsolvable compared with their current set of skills. In this paper we present a Recommender System (RS) for Online Judges based on an Autoencoder (Artificial) Neural Network (ANN). We also discuss the results of an experimental evaluation of our approach in both the scenarios in which we consider, or not, the intrinsic dynamic dimension of the problem. The ANNs are trained with the dataset of all the submissions in the Italian National Online Judge, used to train students for the Italian Olympiads in Informatics.

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Make Learning Fun with Programming Contests

Cited by 22 publications

References 6 publications

oii-web: an Interactive Online Programming Contest Training System

oii-web: an Interactive Online Programming Contest Training System

Experiencia piloto con la herramienta mooshak en un grupo de alumnos de enfermería: cuestionario de satisfacción

Recommending Tasks in Online Judges using Autoencoder Neural Networks

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