Influential Factors That Affect Retention and Language Acquisition in Beginning ESL Adults Students

This study explored the problem of student attrition in beginning courses of an Intensive English Program (IEP) that may affect the sustainability of the IEP. The purpose of the study was to understand the perceptions of continuing students and the factors that influenced their motivation and engagement to persist studying in the IEP. Constructivism and behavioral social learning theory guided this study. The research problem addressed the need for students to remain in IEPs and achieve second language acquisition. The research questions were designed to learn what instructional approaches motivated and engaged participants to persist in successive introductory courses. A qualitative case study design, guided by interpretive epistemology, was used to collect students\u27 opinions, perceptions, and suggestions on their experiences in their first course. The target population was beginners in a second IEP course at a community college. A purposive sample of 16 participants took part in 2 focus groups, individual interviews, and open-ended surveys for data triangulation. Constant comparative analysis using open and axial coding was used to aggregate data themes for inquiry. The findings revealed that poor student engagement, lack of mentorship qualities in instructors, and little inclusion of technology have been persistent reasons for their dissatisfaction. The project, a collaborative professional development effort, was designed for IEP instructors to gain awareness on past and current research about the andragogical framework of student-centeredness which culminated with the cooperative elaboration of a set of best practices. The social impact of the study comes from benefits that sustainable IEP programs could offer to communities with large populations of immigrants and to international visitors to empower them to achieve immersion into English-speaking societies

Validation of remotely-sensed evapotranspiration and NDWI using ground measurements at Riverlands, South Africa

Quantification of the water cycle components is key to managing water resources. Remote sensing techniques and products have recently been developed for the estimation of water balance variables. The objective of this study was to test the reliability of LandSAF (Land Surface Analyses Satellite Applications Facility) evapotranspiration (ET) and SPOT-Vegetation Normalised Difference Water Index (NDWI) by comparison with ground-based measurements. Evapotranspiration (both daily and 30 min) was successfully estimated with LandSAF products in a flat area dominated by fynbos vegetation (Riverlands, Western Cape) that was representative of the satellite image pixel at 3 km resolution. Correlation coefficients were 0.85 and 0.91 and linear regressions produced R2 of 0.72 and 0.75 for 30 min and daily ET, respectively. Ground-measurements of soil water content taken with capacitance sensors at 3 depths were related to NDWI obtained from 10-daily maximum value composites of SPOT-Vegetation images at a resolution of 1 km. Multiple regression models showed that NDWI relates well to soil water content after accounting for precipitation (adjusted R2 were 0.71, 0.59 and 0.54 for 10, 40 and 80 cm soil depth, respectively). Changes in NDWI trends in different land covers were detected in 14-year time series using the breaks for additive seasonal and trend (BFAST) methodology. Appropriate usage, awareness of limitations and correct interpretation of remote sensing data can facilitate water management and planning operations.Fil: Jovanovic, Nebo. Natural Resources and Environment; SudáfricaFil: García, César Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Católica de Córdoba; ArgentinaFil: Bugan, Richard DH. Natural Resources and Environment; SudáfricaFil: Teich, Ingrid. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Departamento de Desarrollo Rural. Area de Estadística y Biometría; ArgentinaFil: Garcia Rodriguez, Carlos Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba; Argentin

Detecting and locating trending places using multimodal social network data

This paper presents a machine learning-based classifier for detecting points of interest through the combined use of images and text from social networks. This model exploits the transfer learning capabilities of the neural network architecture CLIP (Contrastive Language-Image Pre-Training) in multimodal environments using image and text. Different methodologies based on multimodal information are explored for the geolocation of the places detected. To this end, pre-trained neural network models are used for the classification of images and their associated texts. The result is a system that allows creating new synergies between images and texts in order to detect and geolocate trending places that has not been previously tagged by any other means, providing potentially relevant information for tasks such as cataloging specific types of places in a city for the tourism industry. The experiments carried out reveal that, in general, textual information is more accurate and relevant than visual cues in this multimodal setting.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research has been partially funded by project “Desarrollo de un ecosistema de datos abiertos para transformar el sector turístico” (GVA-COVID19/2021/103) funded by Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital de la Generalitat Valenciana, “A way of making Europe” European Regional Development Fund (ERDF) and MCIN/AEI/10.13039/501100011033 for supporting this work under the “CHAN-TWIN” project (grant TED2021-130890B-C21) and the HORIZON-MSCA-2021-SE-0 action number: 101086387, REMARKABLE, Rural Environmental Monitoring via ultra wide-ARea networKs And distriButed federated Learning. We also would like to thank Nvidia for their generous hardware donations that made these experiments possible

Exploiting the Relationship Between Visual and Textual Features in Social Networks for Image Classification with Zero-Shot Deep Learning

One of the main issues related to unsupervised machine learning is the cost of processing and extracting useful information from large datasets. In this work, we propose a classifier ensemble based on the transferable learning capabilities of the CLIP neural network architecture in multimodal environments (image and text) from social media. For this purpose, we used the InstaNY100K dataset and proposed a validation approach based on sampling techniques. Our experiments, based on image classification tasks according to the labels of the Places dataset, are performed by first considering only the visual part, and then adding the associated texts as support. The results obtained demonstrated that trained neural networks such as CLIP can be successfully applied to image classification with little fine-tuning, and considering the associated texts to the images can help to improve the accuracy depending on the goal. The results demonstrated what seems to be a promising research direction.This work was funded by the University of Alicante UAPOSTCOVID19-10 grant for “Collecting and publishing open data for the revival of the tourism sector post-COVID-19” project

3D-printed flexure-based finger joints for anthropomorphic hands

Flexure-based finger joints for prosthetic hands have been studied, but until now they lack stiffness and load bearing capacity. In this paper we present a design which combines large range of motion, stiffness and load bearing capacity, with an overload protection mechanism. Several planar and non-planar hinge topologies are studied to determine load capacity over the range of motion. Optimized topologies are compared, in 30 degrees deflected state, in terms of stresses by deflection and grasping forces. Additionally, support stiffnesses were computed for all hinges in the whole range of motion (45 degrees). The Hole Cross Hinge presented the best performance over the range of motion with a grasping force up to 15 N while deflected 30 degrees. A new concept, the Angle Three-Flexure Cross Hinge, provides outstanding performance for deflections from 17.5 up to 30 degrees with a 20 N maximum grasping force when fully deflected. Experimental verification of the support stiffness over the range of motion shows some additional compliances, but the stiffness trend of the printed hinge is in line with the model. The presented joints power grasping capability outperform current state flexure-base hands and are comparable to commercial non-flexure-based prosthetic hands. In the event of excessive loads, an overload protection mechanism is in place to protect the flexure- hinges