La incorporación del trabajo colaborativo en cursos de programación ha sido identificada como una estrategia potencial que podría maximizar la participación de los estudiantes y tener un impacto positivo en el aprendizaje. En las fuentes consultadas no se ha encontrado un estudio dirigido a recopilar y analizar los resultados de investigación sobre este tema utilizando un método sistemático. Para tratar de llenar este vacío, se realizó una revisión sistemática de literatura con el objetivo de resumir los estudios sobre el uso del trabajo colaborativo como estrategia didáctica para la enseñanza/aprendizaje de la programación. Inicialmente, mediante la búsqueda en cuatro bases de datos de publicaciones científicas, se obtuvieron 95 estudios publicados en los últimos cinco años. Luego de un cuidadoso análisis de cada uno de ellos, se verifica que 40 coinciden con los requerimientos de la revisión, análisis que dio como resultado la síntesis de once Técnicas de Aprendizaje Colaborativo (TAC) que llevan a la práctica la estrategia, que a su vez agrupan a 19 denominaciones comunes de estrategias encontradas en los documentos, es decir, las estrategias o técnicas colaborativas asociadas a cada estudio. La revisión arrojó también una cantidad importante de aportes de la comunidad investigadora que sientan una base importante para trabajos futuros, dejando en claro que el trabajo colaborativo se consolida cada vez más como una estrategia didáctica válida y pertinente, no solo en la enseñanza/aprendizaje de la programación, sino también en otras áreas del conocimiento incluyendo las ciencias de la computación.

The incorporation of collaborative work in the educational field grows day after day, as does the research associated with this topic. One of the most recurring problems faced by teachers who want to employ this learning strategy is the good students group formation since this task can be complex both conceptually and computationally, especially if the aim is to automate it. Considering that group formation is key when developing activities in collaborative learning scenarios, this paper aims to propose a technique based on a genetic algorithm approach for achieving homogeneous groups, considering the students personality traits as grouping criteria, since, the aforementioned traits, have proven to be significantly predictive of academic performance and, in addition, to be associated with variables with a strong influence on academic success, such as intelligence and beliefs of self-efficacy. A controlled experiment was designed with 132 students, quantifying the personality traits through the Big Five InventoryBFI, forming working groups and developing a collaborative activity in the initial programming courses. The experiment results allowed validation, not only from a computational point of view evaluating the algorithm performance but also from a pedagogical point of view, confronting the results obtained by students: formation based on personality traits versus formation by the students preference. The highlight of the study is that the homogeneous groups generated by the proposed approach produced better academic results compared to those obtained by groups generated by the students' preference, at the moment of developing a collaborative activity.

La incorporación de la gamificación en cursos de Programación ha sido identificada como una estrategia potencial que podría maximizar la participación de los estudiantes y tener un impacto positivo en el aprendizaje. Hasta donde sabemos, no existe un estudio dirigido a recopilar y analizar los resultados de investigación sobre este tema utilizando un método sistemático. Para llenar este vacío, se realizó un mapeo sistemático de literatura con el objetivo de resumir los estudios sobre el uso de la gamificación como estrategia didáctica para la enseñanza/aprendizaje de la Programación. Inicialmente, mediante la búsqueda en cuatro (4) bibliotecas digitales, se obtuvieron 186 estudios. Luego, después de un cuidadoso análisis de cada uno de ellos, verificamos que sólo 78 coinciden con nuestras necesidades. Por último, hemos categorizdo las contribuciones de estos estudios para presentar una visión general de los resultados producidos por la comunidad investigadora.

In this paper, an approach based on genetic algorithms is proposed to form groups in collaborative learning scenarios, considering the students’ personality traits as a criterion for grouping. This formation is carried out in two stages: In the first, the information of the students is collected from a psychometric instrument based on the Big Five personality model; whereas, in the second, this information feeds a genetic algorithm that is in charge of performing the grouping iteratively, seeking for an optimal formation. The results presented here correspond to the functional and empirical validation of the approach. It is found that the described methodology is useful to obtain groups with the desired characteristics. The specific objective is to provide a strategy that makes it possible to subsequently assess in the context what type of approach (homogeneous, heterogeneous, or mixed) is the most appropriate to organize the groups.

In recent years, software development teaching-learning processes have taken on special importance because they are no longer exclusive of engineering fields; they are also applicable to various areas such as psychology, pedagogy, graphic design, and education. Although the literature includes some studies in this regard, none of them compiles the main problems and current practices of introductory programming courses. Therefore, this article presents a systematic review of literature on introductory programming courses in undergraduate programs. For that purpose, we searched four databases of bibliographical references of scientific publications and found 106 studies published in the last seven years. A detailed inspection determined that 46 of them met the criteria of this review. As a result, we compiled 33 software tools, 36 work strategies, and 18 methodological considerations, as well as important recommendations, experiences, practices, problems, and future trends related to introductory computer programming courses. This article is a starting point for future work that further benefits students and professors in the complex field of computer programming teaching-learning.

Considering that group formation is key when developing activities in collaborative learning scenarios, this paper aims to propose a strategy based on a genetic algorithm approach for achieving optimal collaborative learning groups, considering the students’ personality traits as grouping criteria. A controlled experiment was designed with 238 students, quantifying their personality traits through the “big five inventory” (BFI), forming working groups and developing a collaborative activity in programming and related courses. The experiment results allowed validation, not only from a computational point of view evaluating the algorithm performance but also from a pedagogical point of view, confronting the results obtained by students applying the proposed approach with those obtained through other group formation strategies. The highlight of the study is that those groups whose formation was pre-established by the teachers through the proposed strategy have generally had a better collaborative performance than the groups with traditional formation, except in the case of heterogeneous formation, at the time of developing a collaborative activity. In addition, through the experiment, it was found that not considering criteria related to personality traits before the group formation generally led to lower results.