A Recommender System for Programming Online Judges Using Fuzzy Information Modeling

Toledo, Raciel Yera; Mota, Yailé Caballero; Martínez, Luis

doi:10.3390/informatics5020017

Cited by 22 publications

(19 citation statements)

References 34 publications

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“…Indeed, so far few research focused in the recommendation of tasks in OJs: we mention the traditional collaborative filtering method with a new similarity measure adapted to the case (Toledo and Mota, 2014), and an approach based on fuzzy logic (Yera Toledo et al, 2018). Caro and Jimenez considered user-based and similarity-based approaches In (Caro-Martinez and Jimenez-Diaz, 2017).…”

Section: Recommender Systems In Ojsmentioning

confidence: 99%

“…However, choosing the right task is becoming a complex problem, and an example of an information overloading scenario, as observed in Yera Toledo et al (2018): an unexperienced user has to choose from thousands programming tasks, many of which are probably beyond his current abilities. For example, University of Valladolid Online Judge has more than 200k users and 2k tasks, whilst SPOJ accounts approximately 600k users and 6k (public) tasks; in Fig.…”

Section: Introductionmentioning

confidence: 99%

“…There are several popular OJ platform, we cite the already mentioned University of Valladolid Online Judge https://uva.onlinejudge. org, Sphere Online Judge (SPOJ) https://www.spoj.com/, CodeChef https:// www.codechef.com/, and Peking University Online Judge http://poj.org.Yera andToledo (Yera Toledo et al, 2018) present a brief survey on OJs, whilst more information on tools and techniques for automatic evaluation of solutions submitted to OJs can be found in(Ala-Mutka, 2005;Caiza and Del Alamo, 2013).…”

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confidence: 99%

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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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Section: Recommender Systems In Ojsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Recommending Tasks in Online Judges using Autoencoder Neural Networks

Fantozzi¹,

Laura

2020

IOI

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“…Educational big data refers to data generated by individuals in the entire process of educational activities, through the integration of which, existing educational or teaching problems could be diagnosed from the massive, complex and diverse sources, so as to evaluate the teaching and forecast development trends, explore educational or teaching models, investigate the implicit correlation between learning behavior data and the learning effect, and achieve targeted personalized education [8]. Thereby, it plays a crucial role in improving the quality of education management [9], promoting personalized management of students [10], and assisting scientific decision-making in universities [11].…”

Section: Introductionmentioning

confidence: 99%

On Learning Path Planning Algorithm Based on Collaborative Analysis of Learning Behavior

Shou

et al. 2020

IEEE Access

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Studying learning path planning can help find useful implicit learning behavior patterns from learners' online learning behavior data, which is conducive to helping beginners or learners with low participation to reasonably arrange the learning sequence of online knowledge points. This paper proposes a learning path planning algorithm based on collaborative analysis of learning behavior through collaborative data analysis of online learning behaviors. The algorithm, based on the learner's online learning behavior data set, first establishes the concept interaction degree model of knowledge points and the directed learning path network, and proposes a local structure similarity measurement method between the knowledge nodes of the directed learning path network. Second, based on the learner's Kullback-Leibler divergence (KLD) matrix, a learning behavior similarity calculation method on the basis of eigenvector matrix similarity is proposed, which is used to perform cluster analysis on learners with similar learning behaviors and to analyze the personalized optimal learning path of each kind of learners. Finally, the clustering algorithm and the evaluation index of the directed complex network have both verified the advantages of the algorithm. This paper employs the online behavior data set and online test data set obtained from the e-learning platform to conduct an empirical analysis of the learning path planning algorithm proposed hereof. The results show that the learner's learning effect has been improved, verifying the validity and reliability of the algorithm. INDEX TERMS local structural similarity on the knowledge node of the directed learning path network, similarity measure on learning behavior, concept interaction degree of knowledge points, individualized optimal learning path

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“…However, as observed in [17], the large number of tasks available to users is a typical example of information overloading scenario: an unexperienced user has to choose from thousands programming tasks, many of which are probably too difficult for him. Just to provide some examples, University of Valladolid Online Judge has more than 200k users and 2k tasks, whilst SPOJ accounts approximately 600k users and 6k (public) tasks.…”

Section: Introductionmentioning

confidence: 99%

Recommending Tasks in Online Judges

Audrito

Mascio

Fantozzi

et al. 2019

Advances in Intelligent Systems and Computing

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Online Judges are e-learning tools used to improve the programming skills, tipically for programming contests such as International Olympiads in Informatics and ACM International Collegiate Programming Contest. In this context, due to the nowadays broad list of programming tasks available in Online Judges, it is crucial to help the learner by recommending a challenging but not unsolvable task. So far, in the literature, few authors focused on Recommender Systems (RSs) for Online Judges; in this paper we discuss some peculiarities of this problem, that prevent the use of standard RSs, and address a first building brick: the assessment of (relative) tasks hardness. We also present the results of a preliminary experimental evaluation of our approach, that proved to be effective against the available dataset, consisting in all the submissions made in the Italian National Online Judge, used to train students for the Italian Olympiads in Informatics.

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A Recommender System for Programming Online Judges Using Fuzzy Information Modeling

Cited by 22 publications

References 34 publications

Recommending Tasks in Online Judges using Autoencoder Neural Networks

Recommending Tasks in Online Judges using Autoencoder Neural Networks

On Learning Path Planning Algorithm Based on Collaborative Analysis of Learning Behavior

Recommending Tasks in Online Judges

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