Factors Facilitating Successful Educational Innovation with ICT in Schools

[EN]This paper proposes an approach to the concept of ICT-based innovation in schools, from the perspective of the factors that facilitate the innovative use of ICT. To this end, a scale has been developed and validated to identify these factors through the participation of 195 teachers from 16 schools considered by the Basque educational authorities as innovators in ICT. The results obtained attest to the validity and reliability of both the scale and the 5 key factors that influence innovation in technology-based teaching. This factor-based structure enables a holistic view of ICT innovation in schools across 3 key areas: school context, teachers and the education authorities.

Ancient DNA from Hunter-Gatherer and Farmer Groups from Northern Spain Supports a Random Dispersion Model for the Neolithic Expansion into Europe

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Irakasleen jarrerak eta usteak ingelesaren sarrera goiztiarrarekiko

LABURPENA: Ingelesaren sarrera goiztiarra ohikoa bihurtu da Euskal Autonomia Erkidegoko ikastetxeetan. Jendeak, oro har, bere eraginkortasuna defendatzen du, baina bere hedapen neurrigarriak kontrako jarrerak ere sustatu ditu. Zurrunbiloaren erdian dago haurra, ingurutik helarazten zaizkion mezuen bitartez hizkuntzekiko bere ikuspegia osatuz doalarik. Bere jarreretan eragiten duten aldagai anitzen artean irakasleek zeresan berezia dute. Hori dela eta, zenbait irakaslek ingelesaren sarrera goiztiarrarekiko duten ikuspuntua aztertzea du helburu ikerketak. Emaitzei erreparatuz, nola tutoreek hala ingeleseko irakasleek jarrera baikorra agertu duten arren, ingelesari eskaini beharko litzaiokeen denborak bereizketa argia azalerazi du. Halaber, irakasleen uste eta iritziak, zenbaitetan, ikasleen adinaren garrantzian oinarritutako sineskerietatik haratago doazela plazaratu da.

MW, Medical Watches

My project is a business plan about the set up of a company and the development of a new and innovative product aimed for the elders. I decide do this project when I discover that one of the more important needs that have the elders is to remember the medicines that they have to take. I thought that a good way could be through a smart watch. My watch have an only function, is a cheap device, easy to use, easy to understand and easy to set up, because the elders usually do not know to use complex electronics devices. There are other similar smart watches and other devices but do not have the necessary characteristics to be a good reminder for elders. My watch is centred to improve the life of the elders, but my product could also be useful for ill people who have to take many medicines during the day. After realizing this business plan, I have proved that my company is viable in the environment and profitable in the market.

Towards Application of One-Class Classification Methods to Medical Data

In the problem of one-class classification (OCC) one of the classes, the target class, has to be distinguished from all other possible objects, considered as nontargets. In many biomedical problems this situation arises, for example, in diagnosis, image based tumor recognition or analysis of electrocardiogram data. In this paper an approach to OCC based on a typicality test is experimentally compared with reference state-of-the-art OCC techniques-Gaussian, mixture of Gaussians, naive Parzen, Parzen, and support vector data description-using biomedical data sets. We evaluate the ability of the procedures using twelve experimental data sets with not necessarily continuous data. As there are few benchmark data sets for one-class classification, all data sets considered in the evaluation have multiple classes. Each class in turn is considered as the target class and the units in the other classes are considered as new units to be classified. The results of the comparison show the good performance of the typicality approach, which is available for high dimensional data; it is worth mentioning that it can be used for any kind of data (continuous, discrete, or nominal), whereas state-of-the-art approaches application is not straightforward when nominal variables are present.

Anisotropic expansion and SNIa: An open issue

We review the appropriateness of using SNIa observations to detect potential signatures of anisotropic expansion in the Universe. We focus on Union2 and SNLS3 SNIa datasets and use the hemispherical comparison method to detect possible anisotropic features. Unlike some previous works where nondiagonal elements of the covariance matrix were neglected, we use the full covariance matrix of the SNIa data, thus obtaining more realistic and not underestimated errors. As a matter of fact, the significance of previously claimed detections of a preferred direction in the Union2 dataset completely disappears once we include the effects of using the full covariance matrix. Moreover, we also find that such apreferred direction is aligned with the orthogonal direction of the SDSS observational plane and this suggests a clear indication that the SDSS subsample of the Union2 dataset introduces a significant bias, making the detected preferred direction unphysical. We thus find that current SNIa surveys are inappropriate to test anisotropic features due to their highly non-homogeneous angular distribution in the sky. In addition, after removal of the highest in homogeneous sub-samples, the number of SNIa is too low. Finally, we take advantage of the particular distribution of SNLS SNIa sub- sample in the SNLS3 data set, in which the observations were taken along four different directions. We fit each direction independently and find consistent results at the 1 sigma level. Although the likelihoods peak at relatively different values of Omega(m), the low number of data along each direction gives rise to large errors so that the likelihoods are sufficiently broad as to overlap within 1 sigma. (C) 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons. org/licenses/by/4.0/).