This paper presents a programming exercise recommender system based on the Student’s Multiple Abilities and Skills (SMAS) model, which is developed from Item Response Theory and Elo System Classification, for estimation of multiple student’s abilities. This model assumes that programming exercises have many ways to be solved (paths) and each path requires different abilities from the student. To evaluate the recommender system, an experiment was conducted in a class of Algorithms and Data Structures I. For this study case, the recommender was connected to an Online Judge system that had a programming problem base. The results show that the proposed recommender has the ability to indicate relevant problems according to the student’s abilities.

This article presents a multi-skills estimation model for students using Online Judge systems. It is understood that there is not only one way to solve programming problems; and, for each solution form, a skill set is needed for the solution to be successful. The proposed model is based on performance expectations and integrates the Elo model, to estimate student’s abilities and problems, to the Multidimensional Item Response Theory model, which estimates the probability of success for each solution path. To validate the proposed model, a case study was carried out with students from the computing area, who solved problems on the beecrowd Online Judge platform. The proposed model was applied to the generated database. According to these results, it is observed that, in cases where the students got the solution right, more than 60% of the paths chosen by students are in accordance with paths indicated by the proposed model.

This Research to Practice Full Paper presents a proposed model to estimate the multiple skills of students in massive online environments that provide programming exercises, whose assessment methods occur automatically without human intervention. The proposed model is based on the M-ERS model and incorporates, from the TrueSkill model, the uncertainty regarding the student's skills. To validate the model, a database from the URI Online Judge platform was used and the M-ERS and TriMElo models were applied to compare the performance and behavior of the two models. The empirical results show that the proposed model updates student's skills more smoothly, according to the correctness or error of the exercise, according to the uncertainty of the skills.

This Full Paper in the category Research-to-Practice presents a proposed model for assessing source codes through static analysis, an applied experiment and results. The presence of computation is constantly growing in the contemporaneous world, and in that way, the demand for professionals capable to develop and maintain software is also in constant growth. The present work aims to present and discuss the results obtained by applying the proposed model based in TFIDF to a dataset. Results showed that by enabling the comparison of different skills present on each source code, the proposed model offers an asset for teachers to identify potential weaknesses on a student's set of computer programming skills, and therefore be able to work on solutions for their educational development.

This Research to Practice Work in Progress paper presents a proposal for source code assessment through static analysis. The presence of computation is constantly growing in the contemporaneous world, and in that way, the demand for professionals capable to develop and maintain software is also in constant growth. The present work aims to develop a model where teachers can identify potentially weakness on student's set of skills for programming, and therefore be able to work on solutions for their educational development. Preliminary results show that it is possible to identify prominent skills used to solve a given problem, but also that it is possible to compensate the lack of those skills with others.

This complete article, from the research to practice category, presents an experiment carried out combining two models used to evaluate student skills, ELO Multidimensional and M-ERS. The objective of this experiment is to estimate and map the history of their multiple skills, in that way it was carried out incorporating the characteristic of the Multidimensional ELO - to track the history of multiple skills, and M-ERS - to estimating multiple skills that can be compensatory. To validate the experiment, we used a database composed of user submissions from an Online Judge platform from Brazil. Through the experiment results obtained, we concluded that for online programming problems platforms, the combination of both models proved to be satisfactory, through it was possible to map and observe the evolution of student's multiple skills.