Case-Based Recommendation for Online Judges Using Learning Itineraries

López, Marco Castrillón; Jiménez-Díaz, Guillermo; Gómez-Martín, Pedro Pablo; Gómez-Martín, Marco Antonio

doi:10.1007/978-3-319-61030-6_22

Cited by 7 publications

(6 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Since there is a higher abundance of tools and frameworks for natural language processing techniques to deal with text written in English, we use first the Googletrans API 4 to translate the descriptions of the problems from Portuguese to English.…”

Section: B Preprocessing the Problem Descriptionsmentioning

confidence: 99%

“…Online Judges (OJ) are a special kind of massive open online course (MOOC) that provides a reliable, automatic, and instantaneous evaluation of an algorithm's source code sent by the learners [1], [2], [3], [4], [5]. The popularity The associate editor coordinating the review of this manuscript and approving it for publication was Utku Kose .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2023

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: B Preprocessing the Problem Descriptionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Learning itineraries are a trend in recent years (Campos & Hernández, 2020;Mor et al, 2018;Rivera & Lindín, 2019;Sánchez-Ruiz et al, 2017) and more authors are studying the benefits they offer in the learning process, both in face-to-face and in online environments. This study tries to show that, in the field of Probability, one of the branches that is most complex for students, this type of educational practices, in an online environment, can be very positive, both in the improvement of learning results compared to the traditional online class, as well as in the students' perception of their own learning and their relationship with mathematical contents.…”

Section: Introductionmentioning

confidence: 99%

Learning Itineraries to Work "Mathematic Probability" with Future Teachers in an Online Scenario with Deck.Toys Tool

Palma

Hernández

Magreñán

2022

RIED

View full text Add to dashboard Cite

The work of Probability from the Compulsory Secondary Education stage and during the Baccalaureate is not generally contextualised and is, mainly, based on the rote learning of formulas. Therefore, when students arrive at university, a significant lack of knowledge related to the calculation of probabilities is evident. Teachers have to advocate for classroom strategies that help students to achieve lasting and contextualized learning and, even more, in areas such as Probability. This paper presents the design of a didactic proposal based on the use of gamified learning itineraries utilizing Deck.Toys digital tool for the learning of Probability in a sample of students of the Primary Education Degree who worked on an online environment. For this analysis, the outcomes obtained in two tests, initial and final, are collected in an experimental group that has used the tool and compared with those of a control group that worked in a traditional way. Moreover, the data of a survey of the evaluation of the user experience was also analysed. The results gained have been very satisfactory, the tests of comparison of means in the post-test shows a significant difference of 2.704 points more in the experimental group, taking into account that both groups were homogeneous. Likewise, the results of the satisfaction survey have been positive in terms of improving the understanding of both the procedures and the concepts involved on time management since students have been able to work at their own rhythm in an efficient way.

show abstract

“…Online Judge (OJ) systems are a special kind of MOOC that provides a reliable, automatic, and instantaneous evaluation of an algorithm's source code sent by the learners [54,25,42,50,26,39,36]. The popularity of OJ systems are increasing in CS1 classes [54,20,37,11,57,49], due to their conveniences for learners (automatic code correction) and instructors (workload reduction in the students' code correction) [51,8,12,23,40,41,10].…”

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

<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

Case-Based Recommendation for Online Judges Using Learning Itineraries

Cited by 7 publications

References 12 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

Learning Itineraries to Work "Mathematic Probability" with Future Teachers in an Online Scenario with Deck.Toys Tool

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

Contact Info

Product

Resources

About