Tele-education and competencies assessment to Brazilian's auxiliary nurse

This paper presents Brazilian's experience with the organization of methods and strategies for the assessment of competencies for technical level of nursing workers. The evaluative process proposed includes the creation of a learning-oriented and distance-based virtual assessment environment. The proposed methodology for professional competencies assessment adopted a critical-emancipatory perspective. A tele-education environment was deployed, involving software development - a virtual man - and an assessment cybertutor. Learning modules for the cybertutor were developed and videos of clinical simulations, structured around assessment in cognitive, behavioral, and simulation areas. The evaluation modules considered aspects of competencies in know-know, know-how and know-act professional ethics. Also the variability of practices of nursing - hospitals and primary health care units - was considered. This instrument showed as an important strategy for the optimization of assessment procedures that are widely used across Brazil and it is a powerful tool for incorporation into the continuing professional education.

ITE Education News, November/December 2003

Newsletter produced by the Iowa Information Technology Enterprise

ITE Education News, May/June 2004

Newsletter produced by the Iowa Information Technology Enterprise

ITE Education News, September/October 2004

Newsletter produced by the Iowa Information Technology Enterprise

Update & Career Technical Education, 2000

Departmental report produced by the Department of Education.

ITE Education News, July/August 2004

Newsletter produced by the Iowa Information Technology Enterprise

ITE Education News, November/December 2004

Newsletter produced by the Iowa Information Technology Enterprise

ITE Education News, March/April 2005

Newsletter produced by the Iowa Information Technology Enterprise

Special Investigation of Certain Grants Administered by The Department of Curriculum and Instruction, College of Education, Iowa State University of Science and Technology, For the Period September 1, 1999 through November 30, 2003

State University Audit Report - Special Investigation

Special Investigation of Certain Grants Administered by The Department of Curriculum and Instruction, College of Education, Iowa State University of Science and Technology, For the Period September 1, 1999 through November 30, 2003

State University Audit Report - Special Investigation

Education, Equity and Distributive Beliefs: Evidences from the Chilean Case

During the year 2011, Chile has been scenario of several student's demonstrations claiming for more equity in the access to the higher education. The high support to the protests by the side of the general population (nearly 89% of approval in public opinion polls) seems to suggest the existence of a large consensus about the weaknesses of the Chilean educative model, a model that would challenge the traditional ideals of meritocracy and social mobility that are at the core of the educational systems in modern societies. In this context, a question that remains open is to what extent these claims are mostly based on consensual equality ideals, or whether they are influenced by individual socio-economic determinants vis-à-vis rational motives. Using data of the social inequality module International Social Survey Program (ISSP) of 2009, this research analyzes perceptions and beliefs about education and the distributive system as well as the influence of income and educational variables, through a structural equation modeling framework. Preliminary results indicate the presence of socioeconomic cleavages in relation to the fairness of the educational system, questioning the assumption about a normative consensus.

Cross recurrence quantification for cover song identification

There is growing evidence that nonlinear time series analysis techniques can be used to successfully characterize, classify, or process signals derived from realworld dynamics even though these are not necessarily deterministic and stationary. In the present study we proceed in this direction by addressing an important problem our modern society is facing, the automatic classification of digital information. In particular, we address the automatic identification of cover songs, i.e. alternative renditions of a previously recorded musical piece. For this purpose we here propose a recurrence quantification analysis measure that allows tracking potentially curved and disrupted traces in cross recurrence plots. We apply this measure to cross recurrence plots constructed from the state space representation of musical descriptor time series extracted from the raw audio signal. We show that our method identifies cover songs with a higher accuracy as compared to previously published techniques. Beyond the particular application proposed here, we discuss how our approach can be useful for the characterization of a variety of signals from different scientific disciplines. We study coupled Rössler dynamics with stochastically modulated mean frequencies as one concrete example to illustrate this point.

Predictability of music descriptor time series and its application to cover song detection

Intuitively, music has both predictable and unpredictable components. In this work we assess this qualitative statement in a quantitative way using common time series models fitted to state-of-the-art music descriptors. These descriptors cover different musical facets and are extracted from a large collection of real audio recordings comprising a variety of musical genres. Our findings show that music descriptor time series exhibit a certain predictability not only for short time intervals, but also for mid-term and relatively long intervals. This fact is observed independently of the descriptor, musical facet and time series model we consider. Moreover, we show that our findings are not only of theoretical relevance but can also have practical impact. To this end we demonstrate that music predictability at relatively long time intervals can be exploited in a real-world application, namely the automatic identification of cover songs (i.e. different renditions or versions of the same musical piece). Importantly, this prediction strategy yields a parameter-free approach for cover song identification that is substantially faster, allows for reduced computational storage and still maintains highly competitive accuracies when compared to state-of-the-art systems.