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The concept of reform according to which the restructuring of the Bulgarian higher education system considered as a dynamic open system and as a product of the country's social development, is based on the discussions, relative to the restructuring of the educational system, that took place between 1978 and 1987. The changes in the concept of educational models, those of higher education included, not only reflect the results of social development but embody a new position arising from the requirements of scientific and technological revolution. The latter have transformed education from a result into a cause and have thus conditioned the rate of social and economic development. The author thus attempts to relate the evolution of the concept through its different stages. He points out some specific problems and decisions which characterize the complexity and the outlooks of the decision.  相似文献   
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Utilizing a correlational research design, we sought to examine the relationship between the technological pedagogical content knowledge (TPACK) of in-service teachers and student achievement measured with each individual teacher's Value-Added Model (VAM) score. The TPACK survey results and a teacher's VAM score were also examined, separately, with respect to teacher attributes. Upon examination of VAM scores and teacher attributes, the subject area, the percentage of economically disadvantaged students at the teacher's school, and the grade level of the teacher were all significantly related to VAM scores. In addition, the TPACK survey results overall and its constructs were examined with teacher attributes. The significant relationships were gender, subject area, the number of certifications, highest degree held by the teacher, years teaching, and teaching in/out of area. The analysis showed no significant relationship between a teacher's VAM score and the TPACK survey overall or its individual constructs.  相似文献   
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Probabilistic topic models are unsupervised generative models which model document content as a two-step generation process, that is, documents are observed as mixtures of latent concepts or topics, while topics are probability distributions over vocabulary words. Recently, a significant research effort has been invested into transferring the probabilistic topic modeling concept from monolingual to multilingual settings. Novel topic models have been designed to work with parallel and comparable texts. We define multilingual probabilistic topic modeling (MuPTM) and present the first full overview of the current research, methodology, advantages and limitations in MuPTM. As a representative example, we choose a natural extension of the omnipresent LDA model to multilingual settings called bilingual LDA (BiLDA). We provide a thorough overview of this representative multilingual model from its high-level modeling assumptions down to its mathematical foundations. We demonstrate how to use the data representation by means of output sets of (i) per-topic word distributions and (ii) per-document topic distributions coming from a multilingual probabilistic topic model in various real-life cross-lingual tasks involving different languages, without any external language pair dependent translation resource: (1) cross-lingual event-centered news clustering, (2) cross-lingual document classification, (3) cross-lingual semantic similarity, and (4) cross-lingual information retrieval. We also briefly review several other applications present in the relevant literature, and introduce and illustrate two related modeling concepts: topic smoothing and topic pruning. In summary, this article encompasses the current research in multilingual probabilistic topic modeling. By presenting a series of potential applications, we reveal the importance of the language-independent and language pair independent data representations by means of MuPTM. We provide clear directions for future research in the field by providing a systematic overview of how to link and transfer aspect knowledge across corpora written in different languages via the shared space of latent cross-lingual topics, that is, how to effectively employ learned per-topic word distributions and per-document topic distributions of any multilingual probabilistic topic model in various cross-lingual applications.  相似文献   
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The aim of the present study was to develop and cross-validate anthropometrical prediction equations for segmental lean tissue mass (SLM). One hundred and seventeen young healthy Caucasians (67 men and 50 women; mean age: 31.9 ± 10.0 years; Body Mass Index: 24.3 ± 3.2 kg · m(-2)) were included. Body mass (BM), stretch stature (SS), 14 circumferences (CC), 13 skinfolds (SF) and 4 bone breadths (BB) were used as anthropometric measurements. Segmental lean mass of both arms, trunk and both legs were measured by dual energy X-ray absorptiometry as the criterion method. Three prediction equations for SLM were developed as follows: arms = 40.394(BM) + 169.836(CCarm-tensed) + 399.162(CCwrist) - 85.414(SFtriceps) - 39.790(SFbiceps) - 7289.190, where Adj.R (2) = 0.97, P < 0.001, and standard error of estimate (SEE) = 355 g;trunk = 181.530(BM) + 155.037(SS) + 534.818(CCneck) + 175.638(CCchest) - 88.359(SFchest) - 147.232(SFsupraspinale) - 46522.165, where Adj.R(2) = 0.97, P < 0.001, and SEE = 1077g; and legs = 55.838(BM) + 88.356(SS) + 235.579(CCmid-thigh) + 278.595(CCcalf) + 288.984(CCankle) - 84.954(SFfront-thigh) - 53.009(SFmedial calf) - 28522.241, where Adj.R (2) = 0.96, P < 0.001, and SEE = 724 g. Cross-validation statistics showed no significant differences (P < 0.05) between observed and predicted SLM. Root mean squared errors were smallest for arms (362 g), followed by legs (820 g) and trunk (1477 g). These new prediction equations allow an accurate estimation of segmental lean mass in groups of young adults, but estimation errors of 8 to 14% can occur in certain individuals.  相似文献   
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We present a user-centred, task-oriented, comparative evaluation of two within-document retrieval tools. ProfileSkim computes a relevance profile for a document with respect to a query, and presents the profile as an interactive bar graph. FindSkim provides similar functionality to the web browser “Find” command. A novel simulated work task was devised, where participants are asked to identify (index) relevant pages of an electronic book, given topics from the existing book index. The original book index provides the ground truth, against which the indexing results of the participants can be compared. We confirmed a major hypothesis, namely ProfileSkim proved significantly more efficient than Find-Skim, as measured by time for task. The study indicates that ProfileSkim was as least as effective as FindSkim in identifying relevant pages, as measured by traditional information retrieval measures, and there is some evidence that ProfileSkim is a precision-enhancing tool. Based on qualitative data from questionnaires, we also provide strong evidence to support our conjecture that the participants would be more satisfied when using ProfileSkim than FindSkim. The experimental study confirmed the potential of relevance profiling for improving within-document retrieval. Relevance profiling should prove highly beneficial for users trying to identify relevant information within long documents.  相似文献   
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