A recommendation approach for programming online judges supported by data preprocessing techniques

Toledo, Raciel Yera; Martínez, Luis

doi:10.1007/s10489-016-0892-x

Cited by 57 publications

(56 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The current contribution presents a fuzzy-based approach for suggesting problems to be solved by the users in this scenario, assuming that the objective of the users in this kind of applications is to solve as many problems as they can, to improve the knowledge acquisition process. These users' goal in POJ has been previously documented by several authors [13,22,23], pointing out that this success implies a stronger degree of achievement and satisfaction [23], increasing then the learners' efforts and performance in knowledge acquisition according to a basic pedagogical rule ("learners' effort will increase if they are more satisfied") [22].…”

Section: Programming Online Judgesmentioning

confidence: 84%

“…Recently, Yera and Martínez [13] have completed a further detailed representation of the user profiles, by taking into account in addition to solved/not solved problems, the amount of previous failed attempts which occur before the final effective or ineffective problem solution. The experimental study developed in this work, proves that the development of advanced user similarity functions that consider this additional information leads to an improvement in the recommendation performance.…”

Section: Recommender Systems In Pojs Scenariosmentioning

confidence: 99%

“…In the specific case of e-learning, there are also specific application scenarios such as the recommendation in learning objects repositories [9], in learning management systems [10], or in the students' course recommendation [11]. However, in contrast to its wide use in other e-learning context, there have been few research efforts focused on the development of recommendation tools for POJs [12,13]. Regarding the e-learning systems research area, it is important to remark that the problem we are facing in the current research is different from the task related to users' guiding through the learning context, which is usually handled with very traditional approaches such as Intelligent Tutoring Systems or ontologies-supported approaches [14,15].…”

mentioning

confidence: 99%

See 2 more Smart Citations

A Recommender System for Programming Online Judges Using Fuzzy Information Modeling

2018

Self Cite

View full text Add to dashboard Cite

Programming online judges (POJs) are an emerging application scenario in e-learning recommendation areas. Specifically, they are e-learning tools usually used in programming practices for the automatic evaluation of source code developed by students when they are solving programming problems. Usually, they contain a large collection of such problems, to be solved by students at their own personalized pace. The more problems in the POJ the harder the selection of the right problem to solve according to previous users performance, causing information overload and a widespread discouragement. This paper presents a recommendation framework to mitigate this issue by suggesting problems to solve in programming online judges, through the use of fuzzy tools which manage the uncertainty related to this scenario. The evaluation of the proposal uses real data obtained from a programming online judge, and shows that the new approach improves previous recommendation strategies which do not consider uncertainty management in the programming online judge scenarios. Specifically, the best results were obtained for short recommendation lists.

show abstract

Section: Programming Online Judgesmentioning

confidence: 84%

Section: Recommender Systems In Pojs Scenariosmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

A Recommender System for Programming Online Judges Using Fuzzy Information Modeling

2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…Future works will be focused on: 1) exploring the behavior of the proposal in the group recommendation scenario [37]; 2) incorporating the time dimension into the proposal, and 3) exploring its effect in the recommendation performance at more specific recommendation domains, such as an e-learning scenario [38].…”

Section: Discussionmentioning

confidence: 99%

Exploring Fuzzy Rating Regularities for Managing Natural Noise in Collaborative Recommendation

Toledo¹,

Barranco²,

Alzahrani³

et al. 2019

IJCIS

Self Cite

View full text Add to dashboard Cite

Recommender systems have played a relevant role in e-commerce for supporting online users to obtain suggestions about products that best fit their preferences and needs in overloaded search spaces. In such a context, several authors have proposed methods focused on removing the users' inconsistencies when they rate items, so-called natural noise, improving in this way the recommendation performance. The current paper explores the use of rating regularities for managing the natural noise in collaborative filtering recommendation, having as key feature the use of fuzzy techniques for coping with the uncertainty associated to such scenarios. Specifically, such regularities are used for representing common rating patterns and thus detect noisy ratings when they tend to contradict such patterns. An experimental study is developed for showing the performance of the proposal, as well as analyzing its behavior in contrast to previous natural noise management procedures.

show abstract

“…In recent years, different support systems and recommendation systems have been proposed for online judge programming environment [14,15]. The support system includes problem difficulty assessment [16,17], problem suggestion [18,19] and other services. Most of the proposed support systems are based on collaborative filtering [14,16,17] and are limited to classroom data.…”

Section: Students' Understanding Of Learning Materials Difficultiesmentioning

confidence: 99%

SCFH: A Student Analysis Model to Identify Students’ Programming Levels in Online Judge Systems

et al. 2020

View full text Add to dashboard Cite

Computer basic teaching is an essential basic learning content in higher education teaching. In order to encourage students and enable them to practice and improve their programming ability, the online judge system has been introduced into the programming course for compiling, executing and evaluating the algorithm source code submitted by students. The asymmetry of students’ programming level is an important issue when teachers guide the programming of online judge system. We used the exploratory factor analysis method to identify the potential variable structure from the log data submitted by the students of the online judge system, and evaluate the programming level of the students to predict the “at risk” learners. We proposed a student participation model, SCFH, based on this variable structure. Using the log data of the students in the C language course and their final exam results, we trained a deep neural network based on SCFH to divide the students into three different grades, namely “risky”, “intermediate” and “advanced”. To verify the validity of the model, we used the prediction model to classify students in another C++ language programming course. The results show that the submission log data model SCFH can be used to predict the programming ability of students, and the validity of these results can be tested by examination results.

show abstract

A recommendation approach for programming online judges supported by data preprocessing techniques

Cited by 57 publications

References 49 publications

A Recommender System for Programming Online Judges Using Fuzzy Information Modeling

A Recommender System for Programming Online Judges Using Fuzzy Information Modeling

Exploring Fuzzy Rating Regularities for Managing Natural Noise in Collaborative Recommendation

SCFH: A Student Analysis Model to Identify Students’ Programming Levels in Online Judge Systems

Contact Info

Product

Resources

About