A Business Intelligence Framework for Analyzing Educational Data

Currently, universities are being forced to change the paradigms of education, where knowledge is mainly based on the experience of the teacher. This change includes the development of quality education focused on students’ learning. These factors have forced universities to look for a solution that allows them to extract data from different information systems and convert them into the knowledge necessary to make decisions that improve learning outcomes. The information systems administered by the universities store a large volume of data on the socioeconomic and academic variables of the students. In the university field, these data are generally not used to generate knowledge about their students, unlike in the business field, where the data are intensively analyzed in business intelligence to gain a competitive advantage. These success stories in the business field can be replicated by universities through an analysis of educational data. This document presents a method that combines models and techniques of data mining within an architecture of business intelligence to make decisions about variables that can influence the development of learning. In order to test the proposed method, a case study is presented, in which students are identified and classified according to the data they generate in the different information systems of a university

Analysis of Educational Data in the Current State of University Learning for the Transition to a Hybrid Education Model

Currently, the 2019 Coronavirus Disease pandemic has caused serious damage to health throughout the world. Its contagious capacity has forced the governments of the world to decree isolation and quarantine to try to control the pandemic. The consequences that it leaves in all sectors of society have been disastrous. However, technological advances have allowed people to continue their different activities to some extent while maintaining isolation. Universities have great penetration in the use of technology, but they have also been severely affected. To give continuity to education, universities have been forced to move to an educational model based on synchronous encounters, but they have maintained the methodology of a face-to-face educational model, what has caused several problems in the learning of students. This work proposes the transition to a hybrid educational model, provided that this transition is supported by data analysis to identify the new needs of students. The knowledge obtained is contrasted with the performance presented by the students in the face-to-face modality and the necessary parameters for the transition to this modality are clearly established. In addition, the guidelines and methodology of online education are considered in order to take advantage of the best of both modalities and guarantee learning

Application of a Smart City Model to a Traditional University Campus with a Big Data Architecture: A Sustainable Smart Campus

Currently, the integration of technologies such as the Internet of Things and big data seeks to cover the needs of an increasingly demanding society that consumes more resources. The massification of these technologies fosters the transformation of cities into smart cities. Smart cities improve the comfort of people in areas such as security, mobility, energy consumption and so forth. However, this transformation requires a high investment in both socioeconomic and technical resources. To make the most of the resources, it is important to make prototypes capable of simulating urban environments and for the results to set the standard for implementation in real environments. The search for an environment that represents the socioeconomic organization of a city led us to consider universities as a perfect environment for small-scale testing. The proposal integrates these technologies in a traditional university campus, mainly through the acquisition of data through the Internet of Things, the centralization of data in proprietary infrastructure and the use of big data for the management and analysis of data. The mechanisms of distributed and multilevel analysis proposed here could be a powerful starting point to find a reliable and efficient solution for the implementation of an intelligent environment based on sustainability

Hamiltonian Dynamics and the Phase Transition of the XY Model

A Hamiltonian dynamics is defined for the XY model by adding a kinetic energy term. Thermodynamical properties (total energy, magnetization, vorticity) derived from microcanonical simulations of this model are found to be in agreement with canonical Monte-Carlo results in the explored temperature region. The behavior of the magnetization and the energy as functions of the temperature are thoroughly investigated, taking into account finite size effects. By representing the spin field as a superposition of random phased waves, we derive a nonlinear dispersion relation whose solutions allow the computation of thermodynamical quantities, which agree quantitatively with those obtained in numerical experiments, up to temperatures close to the transition. At low temperatures the propagation of phonons is the dominant phenomenon, while above the phase transition the system splits into ordered domains separated by interfaces populated by topological defects. In the high temperature phase, spins rotate, and an analogy with an Ising-like system can be established, leading to a theoretical prediction of the critical temperature $T_{KT}\approx 0.855$.Comment: 10 figures, Revte

Semiclassical coherent state propagator for systems with spin

We derive the semiclassical limit of the coherent state propagator for systems with two degrees of freedom of which one degree of freedom is canonical and the other a spin. Systems in this category include those involving spin-orbit interactions and the Jaynes-Cummings model in which a single electromagnetic mode interacts with many independent two-level atoms. We construct a path integral representation for the propagator of such systems and derive its semiclassical limit. As special cases we consider separable systems, the limit of very large spins and the case of spin 1/2.Comment: 19 pages, no figure

Biomarker characterization in endometrial cancer in Europe: first survey data analysis from 69 pathological academic and hospital labs

Introduction. Endometrial carcinoma (EC) is the commonest gynecological cancer affecting women in Western populations. To predict patient risk, the 2020 edition of the World Health Organization (WHO) Classification of Tumors of the Female Genital Tract stressed the importance of integrated histo-molecular classification of the disease. This survey analysis poses attention on the most frequently used immunohistochemical and molecular markers adopted in daily categorization of ECs in European laboratories. Methods. We analyzed data collected through questionnaires administered to 40 Italian, 20 Spanish, 3 Swiss and 6 United Kingdom (UK) laboratories. We collected information regarding daily practice in EC evaluation, specifically concerning mismatch repair status (MMR) and microsatellite instability (MSI). Summary and descriptive statistical analyses were carried out to evaluate the current practice of each laboratory. Results. The results show that MMR status is mainly evaluated by using immunohistochemistry (IHC) on most EC samples. The most frequent approach for the analysis of MMR status is IHC of four proteins (PMS2, MSH6, MSH2, MLH1). MSI analysis by molecular methods is uncommon but useful as a supplemental tool in specific conditions. MLH1 promoter hypermethylation and BRAF V600 mutations analysis are performed in case of negative expression of MLH1/PMS2. Other markers (mainly p53 followed by POLE and PTEN) are investigated in particular in Spain and Switzerland in a consistent number of cases. Conclusion. Guidelines consultation and standardization of laboratory procedures are efficient means for EC prognostic risk stratification and improving the quality of care

Multicentre evaluation of the Boehringer Mannheim / Hitachi 911 Analysis System

The analytical performance and practicability of the Boehringer Mannheim (BM)/mtaci 91 analysis system have been assessed in a multicentre evaluation, which involved six laboratories from European countries. Analytes commonly used in classical clinical chemistry were tested in a core programme, which mainlyfollowed lhe ECCLS guidelines. In addition, a satellite programme covered other analytes, such as proteins, drugs and urine analytes. In total, the study comprised more than 100 000 data items collected over a three-month period. The evaluation was supported with 'Computer Aided Evaluation' (CAEv) and telecommunications. Acceptance criteria for the results were established at the beginning ofthe study. Nearly all ofthe analytes met the imprecision limits.' within-run imprecision (as CVs) was 2l/ofor enzyme and substrate assays, l%for ISE methods and 5l/o for immunoassays; between-day imprecision was 3l/ofor enzyme and substrate assays, 2o//o for ISE methods and 10% for immunoassays

The factor structure of the Forms of Self-Criticising/Attacking & Self-Reassuring Scale in thirteen distinct populations

There is considerable evidence that self-criticism plays a major role in the vulnerability to and recovery from psychopathology. Methods to measure this process, and its change over time, are therefore important for research in psychopathology and well-being. This study examined the factor structure of a widely used measure, the Forms of Self-Criticising/Attacking & Self-Reassuring Scale in thirteen nonclinical samples (N = 7510) from twelve different countries: Australia (N = 319), Canada (N = 383), Switzerland (N = 230), Israel (N = 476), Italy (N = 389), Japan (N = 264), the Netherlands (N = 360), Portugal (N = 764), Slovakia (N = 1326), Taiwan (N = 417), the United Kingdom 1 (N = 1570), the United Kingdom 2 (N = 883), and USA (N = 331). This study used more advanced analyses than prior reports: a bifactor item-response theory model, a two-tier item-response theory model, and a non-parametric item-response theory (Mokken) scale analysis. Although the original three-factor solution for the FSCRS (distinguishing between Inadequate-Self, Hated-Self, and Reassured-Self) had an acceptable fit, two-tier models, with two general factors (Self-criticism and Self-reassurance) demonstrated the best fit across all samples. This study provides preliminary evidence suggesting that this two-factor structure can be used in a range of nonclinical contexts across countries and cultures. Inadequate-Self and Hated-Self might not by distinct factors in nonclinical samples. Future work may benefit from distinguishing between self-correction versus shame-based self-criticism.Peer reviewe

Do lambs perceive regular human stroking as pleasant? Behavior and heart rate variability analyses

Stroking by humans is beneficial to the human-animal relationship and improves welfare in many species that express intraspecific allogrooming, but very few studies have looked at species like sheep that do not express such contact except around parturition. This study investigated the way lambs perceive regular human tactile contact using behavioral and physiological responses. Twenty-four lambs were reared and bucket-fed in groups of four. All were stroked daily by their familiar caregiver. At 8 weeks of age, the lambs were individually tested in their home pen but in a 1×1m open-barred pen after a 15h period of habituation to physical separation from peers while remaining in visual and auditory contact. Half of the lambs received stroking by their caregiver for 8min and half were exposed to their caregiver’s immobile presence. Heart rate and heart rate variability were recorded and analyzed by 2-min slots over the same interval based on three measures: mean heart rate value (HR), root mean square of successive differences (RMSSD) and standard deviation of all intervals measured between consecutive sinus beats (SDNN). Behavioral responses (ear postures of the lamb and time spent in contact with the familiar caregiver, on the knees of the familiar caregiver, and moving) were recorded throughout the test. Lamb HR decreased continuously while in the presence of their caregiver. Lambs being stroked showed slower HR and higher RMSSD which reflected positive emotional states compared to lambs left unstroked. All behavioral variables were highly correlated with the main component axis of the PCA analyses: the more the animals stayed in contact with their caregiver, the less they moved and the more their ears were hanging. This first component clearly differentiates lambs being stroked or not. Behavioral and physiological observations support the hypothesis that gentle physical contact with the caregiver is perceived positively by lambs