Pros and cons gamification and gaming in classroom

The aim of the current work is to assess the challenges that gamification in education are facing nowadays. Benefits and disadvantages of using gamification in classroom are both discussed to offer a clearer view on the impact of using gamification within learning process. Exploratory study cases are provided to investigate the relation between motivation and engagement of the students and gamification in training. Following this idea, a survey was conducted to assess how students behavior and motivation is affected by introducing a single, specific gamification element during a semester learning process. To stimulate competition among students, a ranking type plugin was introduced within the university learning management system used for extramural education. The results prove that motivation decreases by comparison to the previous semester.Comment: 7 pages, 3 figure

IMPROVING CONTENT MARKETING PROCESSES WITH THE APPROACHES BY ARTIFICIAL INTELLIGENCE

Content marketing is today’s one of the most remarkable approaches in the context of marketing processes of companies. Value of this kind of marketing has improved in time, thanks to the latest developments regarding to computer and communication technologies. Nowadays, especially social media based platforms have a great importance on enabling companies to design multimedia oriented, interactive content. But on the other hand, there is still something more to do for improved content marketing approaches. In this context, objective of this study is to focus on intelligent content marketing, which can be done by using artificial intelligence. Artificial Intelligence is today’s one of the most remarkable research fields and it can be used easily as multidisciplinary. So, this study has aimed to discuss about its potential on improving content marketing. In detail, the study has enabled readers’ to improve their awareness about the intersection point of content marketing and artificial intelligence. Furthermore, the authors have introduced some example models of intelligent content marketing, which can be achieved by using current Web technologies and artificial intelligence techniques

Estimación de áreas quemadas en incendios forestales utilizando redes neurales artificiales

Introduction: This article is the product of the research “Developing an Artificial Neural Network Based Model for Estimating Burned Areas in Forest Fires”, developed at Karadeniz Technical University in the year 2020. Problem: Forest Fires are an issue that greatly affect human life and the ecological order, leaving long-term issues. It should be estimated because it is not known when, where and how much the fire will be in the area. Objective: The objective of the research is to use artificial neural networks to estimate the burned areas in forest fires. Methodology: A feed-forward backpropagation neural network model was used for estimating the burned areas. Results: We performed a performance evaluation over the proposed model by considering Regression values, Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE). The results show that the model is efficient in terms of its estimation of burnt areas. Conclusions: The proposed artificial neural network model has low error rate and high estimation accuracy. It is more effective than traditional methods for estimating burned areas in forests. Originality: To the best of our knowledge, this is the first time that this real, unique data has been used for building and testing the model’s estimations and the improvements that have been made in producing results faster and more accurately than with traditional methods. Limitations: Since there are regional differences over different forest areas, effective criteria need to be analysed regarding the target regions.  Introducción: Este artículo es el producto de la investigación "Desarrollo de un modelo basado en redes neuronales artificiales para estimar áreas quemadas en incendios forestales", desarrollado en la Universidad Técnica de Karadeniz en el año 2020. Problema: los incendios forestales son un problema que afecta en gran medida la vida humana y el orden ecológico, dejando problemas a largo plazo. Debe estimarse porque no se sabe cuándo, dónde y cuánto será el incendio en el área. Objetivo: El objetivo de la investigación es utilizar redes neuronales artificiales para estimar las áreas quemadas en incendios forestales. Metodología: Se usó un modelo de red neuronal de propagación hacia atrás para estimar las áreas quemadas. Resultados: Realizamos una evaluación de desempeño sobre el modelo propuesto considerando los valores de regresión, el error de porcentaje absoluto medio (MAPE) y el error de cuadrado medio (MSE). Los resultados muestran que el modelo es eficiente en términos de su estimación de áreas quemadas. Conclusiones: El modelo de red neuronal artificial propuesto tiene una baja tasa de error y una alta precisión de estimación. Es más efectivo que los métodos tradicionales para estimar áreas quemadas en los bosques. Originalidad: según nuestro conocimiento, esta es la primera vez que esta información real y única se ha utilizado para construir y probar las estimaciones del modelo y las mejoras que se han realizado para producir resultados más rápido y con mayor precisión que con los métodos tradicionales. Limitaciones: Dado que existen diferencias regionales sobre las diferentes áreas forestales, es necesario analizar criterios efectivos con respecto a las regiones objetivo