A characterisation of passive and active interactions and their influence on student's achievement using Moodle LMS Logs

During the last years, there has been much concern about learning management systems' (LMS) effectiveness when compared to traditional learning and about how to assess students' participation during the course. The tracking and monitoring capabilities of most recent LMS have made it possible to analyse every interaction in the system. The issues addressed on this study are: a) Is LMS student's interaction an indicator of academic performance?; b) Are different results in performance expected between distance and in-class LMS-supported education?; c) How can LMS interactions from logs be categorised?; d) May this categorisation detect 'learning witnesses'? To answer these questions, a set of interaction types from Moodle LMS activity record logs has been analysed during two years in online and in-class Master's degrees at the UPM. The results show partial or no evidence of influence between interaction indicators and academic performance, although the proposed categorisation may help detect learning witnesses.

Water chemistry at Hontomín-Huermeces (Burgos, Spain): insights for a pre-, intra- and post-CO2 injection geochemical monitoring.

In this study, the very first geochemical and isotopic data related to surface and spring waters and dissolved gases in the area of Hontomín-Huermeces (Burgos, Spain) are presented and discussed. Hontomín-Huermeces was selected as a pilot site for the injection of pure (>99 %) CO2. Injection and monitoring wells are planned to be drilled close to 6 oil wells completed in the 1980’s. Stratigraphical logs indicate the presence of a confined saline aquifer at the depth of about 1,500 m into which less than 100,000 tons of liquid CO2 will be injected, possibly starting in 2013. The chemical and isotopic features of the spring waters suggest the occurrence of a shallow aquifer having a Ca2+(Mg2+)-HCO3- composition, relatively low salinity (Total Dissolved Solids _800 mg/L) and a meteoric isotopic signature. Some spring waters close to the oil wells are characterized by relatively high concentrations of NO3- (up to 123 mg/L), unequivocally indicating anthropogenic contamination that adds to the main water-rock interaction processes. The latter can be referred to Ca-Mg-carbonate and, at a minor extent, Al-silicate dissolution, being the outcropping sedimentary rocks characterized by Palaeozoic to Quaternary rocks. Anomalous concentrations of Cl-, SO42-, As, B and Ba were measured in two springs discharging a few hundreds meters from the oil wells and in the Rio Ubierna, possibly indicative of mixing processes, although at very low extent, between deep and shallow aquifers. Gases dissolved in spring waters show relatively high concentrations of atmospheric species, such as N2, O2 and Ar, and isotopically negative CO2 (<-17.7 h V-PDB), likely related to a biogenic source, possibly masking any contribution related to a deep source. The geochemical and isotopic data of this study are of particular importance when a monitoring program will be established to verify whether CO2 leakages, induced by the injection of this greenhouse gas, may affect the quality of the waters of the shallow Hontomín-Huermeces hydrological circuit. In this respect, carbonate chemistry, the isotopic carbon of dissolved CO2 and TDIC (Total Dissolved Inorganic Carbon) and selected trace elements can be considered as useful parameters to trace the migration of the injected CO2 into near-surface environments.

Lagrangian tools and the assessment of the predictive capacity of geophysical data sets

We examine, with recently developed Lagrangian tools, altimeter data and numerical simulations obtained from the HYCOM model in the Gulf of Mexico. Our data correspond to the months just after the Deepwater Horizon oil spill in the year 2010. Our Lagrangian analysis provides a skeleton that allows the interpretation of transport routes over the ocean surface. The transport routes are further verified by the simultaneous study of the evolution of several drifters launched during those months in the Gulf of Mexico. We find that there exist Lagrangian structures that justify the dynamics of the drifters, although the agreement depends on the quality of the data. We discuss the impact of the Lagrangian tools on the assessment of the predictive capacity of these data sets.

Canonical Correlation Analysis for Interpreting Airborne Laser Scanning Metrics along the Lorenz Curve of Tree Size Inequality

Canonical Correlation Analysis for Interpreting Airborne Laser Scanning Metrics along the Lorenz Curve of Tree Size Inequality

Assessing the suitability of student interactions from Moodle data logs as predictors of cross-curricular competencies

In the past decades, online learning has transformed the educational landscape with the emergence of new ways to learn. This fact, together with recent changes in educational policy in Europe aiming to facilitate the incorporation of graduate students to the labor market, has provoked a shift on the delivery of instruction and on the role played by teachers and students, stressing the need for development of both basic and cross-curricular competencies. In parallel, the last years have witnessed the emergence of new educational disciplines that can take advantage of the information retrieved by technology-based online education in order to improve instruction, such as learning analytics. This study explores the applicability of learning analytics for prediction of development of two cross-curricular competencies – teamwork and commitment – based on the analysis of Moodle interaction data logs in a Master’s Degree program at Universidad a Distancia de Madrid (UDIMA) where the students were education professionals. The results from the study question the suitability of a general interaction-based approach and show no relation between online activity indicators and teamwork and commitment acquisition. The discussion of results includes multiple recommendations for further research on this topic.