There is empirical evidence concerning the effectiveness and benefits of game-based learning (GBL). Our mainly interest is to present a tool that can be used to complement teaching software engineering in a motivating and didactic way. This paper studies the use of a GBL tool called SimulES-W (Simulation in Software Engineering), to teach Software Engineering in an undergraduate engineering program. SimulES-W has three characteristics: it is based on real software cases, it can be customized during the learning process, and it is a collaborative game. These characteristics are important because they help us understand and propose a new learning scenario, and to research with this the learning processes in their environments According to it, the first characteristic of SimulES-W makes it a motivating and engaging game, which brings up cases, which usually are only present in real software projects. Thanks to the second characteristic, the educators can use SimuelES-W to customize the education material, and tune the game for specific software engineering courses. The third characteristic is related to the proposed game as activity that involves group discussions and decision-making. This paper presents SimulES-W a digital version of SimulES and reports the results of an evaluation from a pedagogical perspective, where game adequacy for teaching a subject and positive potential impact in student's academic performance are investigated.
El aprendizaje activo consiste en cualquier método de instrucción que comprometa a los estudiantes en su proceso de aprendizaje a través de actividades o discusiones realizadas principalmente durante las clases. En este artículo se reportaron los resultados de la comparación de dos estrategias de aprendizaje activo para la enseñanza del marco de trabajo ágil Scrum en el contexto de un curso introductorio de Ingeniería de Software. La comparación se hizo a través de un cuasiexperimento en el que los participantes se dividieron en dos grupos. En un grupo se utilizó la estrategia de lectura activa sobre conceptos básicos de Scrum, mientras que en el otro grupo se utilizó un juego; dos técnicas usadas en el aprendizaje activo. Los resultados dieron indicios a nivel de población que hay diferencias significativas en cuanto a los conceptos aprendidos por los integrantes de los grupos y confirmaron el valor que tiene utilizar estrategias de aprendizaje activo para enseñar Scrum. En particular, los resultados aportaron evidencia empírica que indica que utilizar diversas estrategias de aprendizaje activo facilita la retención y apropiación de conceptos relacionados con Scrum, y los resultados constituyen un punto de referencia para los docentes sobre la efectividad de estos dos métodos de aprendizaje activo en la enseñanza de este marco de trabajo ágil.
Requirements engineering is a systematic and disciplined approach for the specification and management of software requirements; one of its objectives is to transform the requirements of the stakeholders into formal spec-ifications in order to analyze and implement a system. These requirements are usually expressed and articulated in natural language, this due to the universality and facility that natural language presents for communicating them. To facilitate the transformation processes and to improve the quality of the resulting requirements, several authors have proposed templates for writing requirements in structured natural language. However, these templates do not allow writing certain functional requirements, non-functional requirements and constraints, and they do not adapt correctly to certain types of systems such as self-adaptive, product line-based and embedded systems. This paper (i) presents evidence of the weaknesses of the template recommended by the IREB® (International Requirements Engineering Institute), and (ii) lays the foundations, through a new template, for facilitating the work of the re-quirements engineers and therefore improving the quality of the products specified with the new template. This new template was built and evaluated through two active research cycles. In each cycle we identified the problems specifying the requirements of the corresponding industrial case with the corresponding base-line template, pro-pose some improvements to address these problems and analyze the results of using the new template to specify the requirements of each case. Thus, the resulting template was able to correctly write all requirements of both industrial cases. Despite the promising results of this new template, it is still preliminary work regarding its cov-erage and the quality level of the requirements that can be written with it.
This paper presents a method for predicting the evaluation results of learners interacting with a context-aware microlearning system. We use ASUM-DM to guide different data analytics tasks, including applying a genetic algorithm that selects the prediction’s highest weight features. Then, we apply Machine Learning models like Random Forest, Gradient Boosting Tree, Decision Tree, SVM, and Neural Networks to train data and evaluate the context’s effects, either success or failure of the learner’s evaluation. We are interested in finding the model of significant context-influence to the learner’s evaluation results. The Random Forest model provided an accuracy of 94%, which was calculated with the cross-validation technique. Thus, it is possible to conclude that the model can accurately predict the evaluation result and relate it to the learner context. The model result is a useful insight for sending notifications to the learners to improve the learning process. We want to provide recommendations about learner behavior and context and adapt the microlearning content in the future.
El diseño urbano-arquitectónico participativo busca potenciar las capacidades de la población, promover la cooperación y fortalecer los canales de comunicación entre los beneficiarios para llevar adelante procesos de diseño del espacio público, cuyos resultados se constituyan en herramienta de gestión para la comunidad. Este artículo presenta el caso de dos intervenciones en las ciudades de Saquisilí y Quito en Ecuador, lideradas por un equipo de estudiantes y docentes de las universidades Tecnológica Equinoccial y Central, en las cuales, a partir de un análisis diagnóstico se desarrollaron estrategias de transformación del espacio público involucrando a la comunidad. Los dos proyectos fueron ejecutados durante tres años y el impacto social generado fue significativo tanto para la comunidad como para los estudiantes que enfrentaron problemas sociales tangibles y llegaron a soluciones usando el mínimo de recursos materiales. ABSTRACTParticipatory urban-architectural design seeks to enhance population capacities, to promote cooperation and to strengthen the communication channels among beneficiaries. With the design process of public spaces is possible to obtain results that will become tools for community development. This paper presents the cases of two interventions in the cities of Saquisilí and Quito in Equator, which were led by a team of students and teachers from the Universidad Tecnológica Equinoccial and Universidad Central. The interventions began with diagnostic analysis, after that, transformation of public space strategies were used to involve the community. The two projects were implemented during three years, it generated significant social impacts for both: the community and for students, who faced tangible social problems and reached solutions using the minimum of material resource.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
334 Leonard St
Brooklyn, NY 11211
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.