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.
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.
Understanding learners’ behavior is the key to the success of any learning process. The more we know about them, the more likely we can personalize learning experiences and provide successful feedback. This paper presents a feedback model implemented in a ubiquitous microlearning environment based on contextual and behavioral information and evaluation results. The model uses SECA rules where the Scenario (S) represents the ubiquitous context variables reflecting the learner behavior during the learning process. The Event (E) identifies the probability that a learner fails or passes its evaluation. Condition (C) evaluates the results of the events. Moreover, Action (A) provides feedback to the learner. The proposal is developed through a controlled experiment whereby a microlearning environment can collect data from a ubiquitous context. The feedback model applies an analytics process to find the best context and behavior variables through different classification models. Those models predict whether a learner could fail, determine evaluation results’ causes, and provide feedback. The Random Forest was the model with the best performance. Thus, 94% accuracy, a 97% Recall, a 93% Precision, an F1 score of 95%, and a Jaccard of 91%. Hence, each scenario is defined from a branch of every tree obtained from the Random Forest model personalizing feedback actions applying clustering techniques. Finally, we presented an exemplified set of feedback rules, providing automatic recommendations and improving learner experiences. Thus, the experiment allows analyzing the learner behavior in a ubiquitous microlearning context from a feedback perspective.
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
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.