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This study presents the main advances made in recent years by an innovative teaching group at the University of Almeria, who are attempting to incorporate alternative spaces for teaching--learning interaction. This group is developing a new line of study to obtain the main vectors for creating an accessible 3D virtual campus on the Second Life platform, recognized as a multi-user virtual environment (MUVE). The goal of this work was to measure the satisfaction and perception of a group of university students. To do this, a co-design approach based on student opinions by relating methodological procedures with an adaptation of the Kano model usually used for product design has been followed. The results found by a panel of postgraduate students in the second stage of the educational project, suggest that the synchronous distance teaching proposal developed can effectively facilitate conciliation of family, work and personal life with online teaching--learning. In the case of undergraduate students with class attendance, the use of the 3D virtual campus is perceived as something more complementary.

This article shows how a group of language teachers use Twitter as a tool for continuous professional development through the #MFLtwitterati hashtag. Based on data collected through a survey (n = 116) and interviews (n = 11), it describes how this collective of teachers use the hashtag and evaluates the impact of their Twitter network on their teaching practices. The results show that most users try the suggestions and ideas that they find on this network, which have a positive impact on their teaching. Finally, the article assesses whether the hashtag users can be described as a community of practice.

The widespread growth in availability and use of smartphones and tablets has facilitated an unprecedented avalanche of new software applications with language learning and teaching capabilities. However, little has been published in terms of effective design and evaluation of language learning apps. This article reviews current research about the potential of apps for language learning and presents a taxonomy of available apps and their use for language learning. The article also presents a framework consisting of four categories for evaluating language learning apps (technology, pedagogy, user experience, and language learning) and a set of criteria within the categories. Finally, the article proposes areas for further research.

Sugarcane is a crucial crop for sugar and bioenergy production. Saccharose content and total weight are the two main key commercial traits that compose sugarcane's yield. These traits are under complex genetic control and their response patterns are influenced by the genotype-by-environment (G×E) interaction. An efficient breeding of sugarcane demands an accurate assessment of the genotype stability through multi-environment trials (METs), where genotypes are tested/evaluated across different environments. However, phenotyping all genotype-in-environment combinations is often impractical due to cost and limited availability of propagation-materials. This study introduces the sparse testing designs as a viable alternative, leveraging genomic information to predict unobserved combinations through genomic prediction models. This approach was applied to a dataset comprising 186 genotypes across six environments (6×186=1,116 phenotypes). Our study employed three predictive models, including environment, genotype, and genomic markers as main effects, as well as the G×E to predict saccharose accumulation (SA) and tons of cane per hectare (TCH). Calibration sets sizes varying between 72 (6.5%) to 186 (16.7%) of the total number of phenotypes were composed to predict the remaining 930 (83.3%). Additionally, we explored the optimal number of common genotypes across environments for G×E pattern prediction. Results demonstrate that maximum accuracy for SA (ρ = 0.611) and for TCH (ρ=0.341) was achieved using in training sets few (3) to no common (0) genotype across environments maximizing the number of different genotypes that were tested only once. Significantly, we show that reducing phenotypic records for model calibration has minimal impact on predictive ability, with sets of 12 non-overlapped genotypes per environment (72=12×6) being the most convenient cost-benefit combination. [ABSTRACT FROM AUTHOR]

Three raster-based (RB) and one point cloud-based (PCB) algorithms were tested to segment individual Aleppo pine trees and extract their tree height (H) and crown diameter (CD) using two types of point clouds generated from two different techniques: (1) Low-Density (≈1.5 points/m2) Airborne Laser Scanning (LD-ALS) and (2) photogrammetry based on high-resolution unmanned aerial vehicle (UAV) images. Through intensive experiments, it was concluded that the tested RB algorithms performed best in the case of UAV point clouds (F1-score > 80.57%, H Pearson's r > 0.97, and CD Pearson´s r > 0.73), while the PCB algorithm yielded the best results when working with LD-ALS point clouds (F1-score = 89.51%, H Pearson´s r = 0.94, and CD Pearson´s r = 0.57). The best set of algorithm parameters was applied to all plots, i.e., it was not optimized for each plot, in order to develop an automatic pipeline for mapping large areas of Mediterranean forests. In this case, tree detection and height estimation showed good results for both UAV and LD-ALS (F1-score > 85% and >76%, and H Pearson´s r > 0.96 and >0.93, respectively). However, very poor results were found when estimating crown diameter (CD Pearson´s r around 0.20 for both approaches). [ABSTRACT FROM AUTHOR]

Studies into the use of Twitter for language learning have mostly been small-scale evaluations undertaken by teachers researching the effectiveness of their own initiatives to use it with their students. To date, there has not been a large quantitative study of how language learners use Twitter autonomously. This paper reports on a large-scale study (n=370) of language learners who use Twitter. It provides a participant profile, their practices, and beliefs about how helpful Twitter is as a tool for language learning. The results provide the first profile of the autonomous user of Twitter as a language learning tool, show very positive attitudes towards the use of Twitter, and provide evidence that learners learn new vocabulary and culturally-relevant information about the areas where the target language is spoken. Many learners engage in production of target language output and make the most of the opportunities Twitter presents to be exposed to target language input and interaction with native speakers, making Twitter a useful tool for their autonomous language learning development.

This paper presents both the pedagogical design principles and the challenges faced during the development of a mobile app, Chinese Characters First Steps, to support recognising, learning and practising writing Chinese characters at beginners' level, including the difficult balance between pedagogy and technical affordances. We also present the results of a research study which collected quantitative data from over 130 users of the app as well as qualitative data from interviews with four users. The results offer a profile of language learning app users and very positive attitudes towards learning with apps. They also reveal users' reasons for learning Chinese, patterns of learning, expectations of the app, and evaluation of the different features for their own personal learning. Finally, the paper concludes that the chosen design principles for the app were appropriate for its purpose.