Gamificacion in education and active methodologies at Higher education

In recent years, there has been an increasing interest in applying Gamification in Education, which can be defined as the application of game design elements to learning activities. Its purpose is to motivate students by creating an engaging learning experience that can keep students focused on the learning task and its application in the classroom, is still in its emergent stages. Gamification is a great challenge for education, particularly in Higher Education Institutions (HEI) in such a traditional context, as is the case with courses like Management and Administration Business, Finance and Accounting, Marketing and Market Research, Chemistry, Accounting and Administration and Business Communication. This paper presents a study, applied in the 2016/2017 and 2017/2018 academic years, in which the teaching method focuses on a blended learning approach, through the implementation of a flipped classroom model and also through the introduction of online gamification activities such Kahoot! application. Kahoot is a game-based learning platform, used as educational technology that can easily be used for initial, formative and summative assessment of students’ knowledge using individual or collaborative team work mode, adding vitality, student engagement, and also meta-cognitive supports to higher education classrooms with limited instructor or student training required. The participants, in the study, were about 3 000 students of 17 different subjects from the aforementioned courses, of the Malaga University and Polytechnic of Porto. The results of this study suggest that this model improves student learning and are of relevance to researchers, educators and game-based learning designers.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Collaborative E-learning Methodologies: an Experience of Active Knowledge in ICT Classrooms

In the present study we highlight a specific environment that makes use of collaborative technological tools, like wikis and forums within an e-learning platform. Both of these approaches convey a lot of responsibility from the teacher to the students and the hoping, as backed up by the literature, is to promote deeper learning and reasoning skills at a higher level. The general goal of this paper is to contribute for the theoretical discussion on how active and collaborative experiences in ICT classrooms play a role on the construction of knowledge in HEIs. Based on the pointed outlines, we intend to: (1) understand how collaborative e-learning environments get students actively involved in the learning process;(2) perspective the role of collaborative tools at the level of group work and (3) find out how students assess their performance within a working group. Data was collected through questionnaires available on the e-learning platform Moodle. Descriptive statistical techniques were used to analyze quantitative data. Within the research questions proposed, the study, points towards some understanding of how a collaborative learning environment seems to get students actively involved in the learning process mainly if the tasks to be perform have an empirical component. The study also has shown that students seem to identify themselves with the need to be involved in simulations of their future professional activity, as well as with the need to regulate their own learning and to promote discussion not only between peers but also with the teacher

Optimal Fully Electric Vehicle load balancing with an ADMM algorithm in Smartgrids

In this paper we present a system architecture and a suitable control methodology for the load balancing of Fully Electric Vehicles at Charging Station (CS). Within the proposed architecture, control methodologies allow to adapt Distributed Energy Resources (DER) generation profiles and active loads to ensure economic benefits to each actor. The key aspect is the organization in two levels of control: at local level a Load Area Controller (LAC) optimally calculates the FEVs charging sessions, while at higher level a Macro Load Area Aggregator (MLAA) provides DER with energy production profiles, and LACs with energy withdrawal profiles. Proposed control methodologies involve the solution of a Walrasian market equilibrium and the design of a distributed algorithm.Comment: This paper has been accepted for the 21st Mediterranean Conference on Control and Automation, therefore it is subjected to IEEE Copyrights. See IEEE copyright notice at http://www.ieee.org/documents/ieeecopyrightform.pd

A Survey of the Trends in Facial and Expression Recognition Databases and Methods

Automated facial identification and facial expression recognition have been topics of active research over the past few decades. Facial and expression recognition find applications in human-computer interfaces, subject tracking, real-time security surveillance systems and social networking. Several holistic and geometric methods have been developed to identify faces and expressions using public and local facial image databases. In this work we present the evolution in facial image data sets and the methodologies for facial identification and recognition of expressions such as anger, sadness, happiness, disgust, fear and surprise. We observe that most of the earlier methods for facial and expression recognition aimed at improving the recognition rates for facial feature-based methods using static images. However, the recent methodologies have shifted focus towards robust implementation of facial/expression recognition from large image databases that vary with space (gathered from the internet) and time (video recordings). The evolution trends in databases and methodologies for facial and expression recognition can be useful for assessing the next-generation topics that may have applications in security systems or personal identification systems that involve "Quantitative face" assessments.Comment: 16 pages, 4 figures, 3 tables, International Journal of Computer Science and Engineering Survey, October, 201

Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks

How can we reuse existing knowledge, in the form of available datasets, when solving a new and apparently unrelated target task from a set of unlabeled data? In this work we make a first contribution to answer this question in the context of image classification. We frame this quest as an active learning problem and use zero-shot classifiers to guide the learning process by linking the new task to the existing classifiers. By revisiting the dual formulation of adaptive SVM, we reveal two basic conditions to choose greedily only the most relevant samples to be annotated. On this basis we propose an effective active learning algorithm which learns the best possible target classification model with minimum human labeling effort. Extensive experiments on two challenging datasets show the value of our approach compared to the state-of-the-art active learning methodologies, as well as its potential to reuse past datasets with minimal effort for future tasks

Reporting guidelines, review of methodological standards, and challenges toward harmonization in bone marrow adiposity research. Report of the Methodologies Working Group of the International Bone Marrow Adiposity Society

The interest in bone marrow adiposity (BMA) has increased over the last decade due to its association with, and potential role, in a range of diseases (osteoporosis, diabetes, anorexia, cancer) as well as treatments (corticosteroid, radiation, chemotherapy, thiazolidinediones). However, to advance the field of BMA research, standardization of methods is desirable to increase comparability of study outcomes and foster collaboration. Therefore, at the 2017 annual BMA meeting, the International Bone Marrow Adiposity Society (BMAS) founded a working group to evaluate methodologies in BMA research. All BMAS members could volunteer to participate. The working group members, who are all active preclinical or clinical BMA researchers, searched the literature for articles investigating BMA and discussed the results during personal and telephone conferences. According to the consensus opinion, both based on the review of the literature and on expert opinion, we describe existing methodologies and discuss the challenges and future directions for (1) histomorphometry of bone marrow adipocytes, (2

Modelling Dependency Structures Produced by the Introduction of a Flipped Classroom

Teaching processes have been changing in the lasts few decades from a traditional lecture-example-homework format to more active strategies to engage the students in the learning process. One of the most popular methodologies is the flipped classroom, where traditional structure of the course is turned over by moving out of the classroom, most basic knowledge acquisition. However, due to the workload involved in this kind of methodology, an objective analysis of the results should be carried out to assess whether the lecturer’s workload is worth the effort or not. In this paper, we compare the results obtained from two different methodologies: traditional lecturing and flipped classroom methodology, in terms of some performance indicators and an attitudinal survey, in an introductory statistics course for engineering students. Finally, we analysed the changes in the relationships among variables of interest when the traditional teaching was moved to a flipped classroom by using Bayesian networks