A three-stage DEA model to evaluate learning-teaching technical efficiency: Key performance indicators and contextual variables

This study evaluates the technical efficiency of the learning-teaching process in higher education using a three-stage procedure that offers advances in comparison to previous studies and improves the quality of the results. First, it utilizes a multiple stage Data Envelopment Analysis (DEA) with contextual variables. Second, the levels of super efficiency are calculated in order to prioritize the efficiency units. And finally, through sensitivity analysis, the contribution of each key performance indicator (KPI) is established with respect to the efficiency levels without omission of variables. The analytical data was collected from a survey completed by 633 tourism students during the 2011/12, 2012/13 and 2013/14 academic course years. The results suggest that level of satisfaction with the course, diversity of materials and satisfaction with the teacher were the most important factors affecting teaching performance. Furthermore, the effect of the contextual variables was found to be significant.

SARASOM: a supervised architecture based on the recurrent associative SOM

We present and evaluate a novel supervised recurrent neural network architecture, the SARASOM, based on the associative self-organizing map. The performance of the SARASOM is evaluated and compared with the Elman network as well as with a hidden Markov model (HMM) in a number of prediction tasks using sequences of letters, including some experiments with a reduced lexicon of 15 words. The results were very encouraging with the SARASOM learning better and performing with better accuracy than both the Elman network and the HMM.

An exercises editor for an e-learning environment

One of the most important tenets of e-learning is that it bridges work and learning. A great e-learning experience brings learning into the work environment. This is a key point, the capacity to construct a work environment when the student can develop proper tasks to complete the learning process. This paper describes a work environment based on the development of two tools, an exercises editor and an exercises viewer. Both tools are able to manage color images where, because of the implementation of basic steganographic techniques, it is possible to add information, exercises, questions, and so on. The exercises editor allows to decide which information must be visible or remain hidden to the user, when the image is loaded in the exercises viewer. Therefore, it is possible to hide the solutions of the proposed tasks; this is very useful to complete a self-evaluation learning process. These tools constitute a learning architecture with the final objective that learners can apply and practice new concepts or skills.

Entornos virtuales: predicción de la satisfacción en contexto universitario

El presente estudio persigue un doble objetivo: evaluar el grado de satisfacción de los estudiantes con la formación recibida en un entorno virtual y, analizar su capacidad predictiva sobre la satisfacción. Se ha utilizado la versión española del cuestionario Distance Education Learning Environments Survey (Sp-DELES). Los resultados ponen de manifiesto el significativo nivel de satisfacción de los estudiantes con la experiencia y revelan las variables más importantes a la hora de explicar la varianza en satisfacción.

Functional outcomes after stabilization with Dynesys in patients with spinal degenerative diseases

Objective: To know the impact of the Dynesys system on the functional outcomes in patients with spinal degenerative diseases. Summary of background data: Dynesys system has been proposed as an alternative to vertebral fusion for several spinal degenerative diseases. The fact that it has been used in people with different diagnosis criteria using different tools to measure clinical outcomes makes very difficult unifying the results available nowadays. Methods: The data base of Medlars Online International Literature (MEDLINE) via PubMed©, EMBASE©, and the Cochrane Library Plus were reviewed in search of all the studies published until November 2012 in which an operation with Dynesys in patients with spinal degenerative diseases and an evaluation of the results by an analysis of functional outcomes had taken place. No limits were used to article type, date of publication or language. Results: A total of 134 articles were found, 26 of which fulfilled the inclusion criteria after being assessed by two reviewers. All of them were case series, except for a multicenter randomized clinical trial (RCT) and a prospective case-control study. The selected articles made a total of 1507 cases. The most frequent diagnosis were lumbar spinal canal stenosis (LSCS), degenerative disc disease (DDD), degenerative spondylolisthesis (DS) and lumbar degenerative scoliosis (LDS). In cases of lumbar spinal canal stenosis Dynesys was associated to surgical decompression. Several tools to measure the functional disability and general health status were found. Oswestry Disability Index (ODI), the ODI Korean version (K-Odi), Prolo, Sf-36, Sf-12, Roland-Morris disability questionnaire (RMDQ), and the pain Visual Analogue Scale (VAS) were the most used. They showed positive results in all cases series reviewed. In most studies the ODI decreased about 25% (e.g. from a score of 85% to 60%). Better results when dynamic fusion was combined with nerve root decompression were found. Functional outcomes and leg pain scores with Dynesys were statistically non-inferior to posterolateral spinal fusion using autogenous bone. When Dynesys and decompression was compared with posterior interbody lumbar fixation (PLIF) and decompression, differences in ODI and VAS were not statistically significant. Conclusions: In patients with spinal degenerative diseases due to degenerative disc disorders, spinal canal stenosis and degenerative spondylolisthesis, surgery with Dynesys and decompression improves functional outcomes, decreases disability, and reduces back and leg pain. More studies are needed to conclude that dynamic stabilization is better than posterolateral and posterior interbody lumbar fusion. Studies comparing Dynesys with decompression against decompression alone should be done in order to isolate the effect of the dynamic stabilization.

GPGPU implementation of growing neural gas: application to 3D scene reconstruction

Self-organising neural models have the ability to provide a good representation of the input space. In particular the Growing Neural Gas (GNG) is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation. However, this type of learning is time-consuming, especially for high-dimensional input data. Since real applications often work under time constraints, it is necessary to adapt the learning process in order to complete it in a predefined time. This paper proposes a Graphics Processing Unit (GPU) parallel implementation of the GNG with Compute Unified Device Architecture (CUDA). In contrast to existing algorithms, the proposed GPU implementation allows the acceleration of the learning process keeping a good quality of representation. Comparative experiments using iterative, parallel and hybrid implementations are carried out to demonstrate the effectiveness of CUDA implementation. The results show that GNG learning with the proposed implementation achieves a speed-up of 6× compared with the single-threaded CPU implementation. GPU implementation has also been applied to a real application with time constraints: acceleration of 3D scene reconstruction for egomotion, in order to validate the proposal.

μ-MAR: Multiplane 3D Marker based Registration for depth-sensing cameras

Many applications including object reconstruction, robot guidance, and. scene mapping require the registration of multiple views from a scene to generate a complete geometric and appearance model of it. In real situations, transformations between views are unknown and it is necessary to apply expert inference to estimate them. In the last few years, the emergence of low-cost depth-sensing cameras has strengthened the research on this topic, motivating a plethora of new applications. Although they have enough resolution and accuracy for many applications, some situations may not be solved with general state-of-the-art registration methods due to the signal-to-noise ratio (SNR) and the resolution of the data provided. The problem of working with low SNR data, in general terms, may appear in any 3D system, then it is necessary to propose novel solutions in this aspect. In this paper, we propose a method, μ-MAR, able to both coarse and fine register sets of 3D points provided by low-cost depth-sensing cameras, despite it is not restricted to these sensors, into a common coordinate system. The method is able to overcome the noisy data problem by means of using a model-based solution of multiplane registration. Specifically, it iteratively registers 3D markers composed by multiple planes extracted from points of multiple views of the scene. As the markers and the object of interest are static in the scenario, the transformations obtained for the markers are applied to the object in order to reconstruct it. Experiments have been performed using synthetic and real data. The synthetic data allows a qualitative and quantitative evaluation by means of visual inspection and Hausdorff distance respectively. The real data experiments show the performance of the proposal using data acquired by a Primesense Carmine RGB-D sensor. The method has been compared to several state-of-the-art methods. The results show the good performance of the μ-MAR to register objects with high accuracy in presence of noisy data outperforming the existing methods.

Detección precoz de dificultades en el aprendizaje. Herramienta para la predicción del rendimiento de los estudiantes

Inspirados por las estrategias de detección precoz aplicadas en medicina, proponemos el diseño y construcción de un sistema de predicción que permita detectar los problemas de aprendizaje de los estudiantes de forma temprana. Partimos de un sistema gamificado para el aprendizaje de Lógica Computacional, del que se recolectan masivamente datos de uso y, sobre todo, resultados de aprendizaje de los estudiantes en la resolución de problemas. Todos estos datos se analizan utilizando técnicas de Machine Learning que ofrecen, como resultado, una predicción del rendimiento de cada alumno. La información se presenta semanalmente en forma de un gráfico de progresión, de fácil interpretación pero con información muy valiosa. El sistema resultante tiene un alto grado de automatización, es progresivo, ofrece resultados desde el principio del curso con predicciones cada vez más precisas, utiliza resultados de aprendizaje y no solo datos de uso, permite evaluar y hacer predicciones sobre las competencias y habilidades adquiridas y contribuye a una evaluación realmente formativa. En definitiva, permite a los profesores guiar a los estudiantes en una mejora de su rendimiento desde etapas muy tempranas, pudiendo reconducir a tiempo los posibles fracasos y motivando a los estudiantes.