Vocational Higher Secondary Education in Kerala: A Status Study of Students, Pass Outs and Training Components

This study analyses the socio-economic backgrounds and entrepreneurial profiles of the students and pass outs of the Vocational Higher Secondary Education in Kerala and the academic achievements of the Vocational Higher Secondary students and pass outs in Kerala in terms of their performance in the examinations. The study also analyses the quality and availability of the various training and support facilities of the Vocational Higher Secondary Schools in Kerala, nature and rate of employment and higher studies among the pass outs of the Vocational Higher Secondary Education in Kerala and the awareness of students, pass outs, teachers and principals regarding the goals and objectives, mode of implementation, apprenticeship training and higher study and employment opportunities of the programme of the Vocational Higher Secondary Education in Kerala.

Recruitment And Training Of Managers In The Public Sector Concerns In Kerala

Public undertakings have been assigned a significant role to play in the systematic socio-economic development of India. My interest in the subject was kindled while I was doing my Masters Diploma in Public Administration at the Indian Institute of Public Administration, New Delhi during 1960-61. It was further strengthened by my teaching of the subject in different courses offered by me at the School of Management Studies and in several programmes organised by various voluntary and training organisations like the Institute of Management in Government, Trivandrum, Centre for Management Development, Trivandrum, etc. The several years in which I served as a member of the faculty in the School of Management Studies, University of Cochin,gave me the opportunity to come into close contact with different public sector concerns and their managers at various levels. This rich opportunity gave me a better insight into the problems faced by these concerns. The present study is a result of the interest so developed.

Alignment Model and Training Technique in SMT from English to Malayalam

This paper investigates certain methods of training adopted in the Statistical Machine Translator (SMT) from English to Malayalam. In English Malayalam SMT, the word to word translation is determined by training the parallel corpus. Our primary goal is to improve the alignment model by reducing the number of possible alignments of all sentence pairs present in the bilingual corpus. Incorporating morphological information into the parallel corpus with the help of the parts of speech tagger has brought around better training results with improved accuracy

Evaluation of the agricultural vocational training programmes conducted by the Krishi Vigyan Kendras (Farm Science Centres) in Indian Punjab

Krishin Vigyan Kendras-KVKs (Farm Science Centres) have been established by the Indian Council of Agricultural Research in 569 districts. The trust areas of KVKs are refinement and demonstration of technologies, and training of farmers and extension functionaries. Imparting vocational trainings in agriculture and allied fields for the rural youth is one of its mandates. The study was undertaken to do a formative and summative (outcome and impact) evaluation of the beekeeping and mushroom growing vocational training programmes in the Indian state of Punjab. One-group pre and post evaluation design was employed for conducting a formative and outcome evaluation. The knowledge tests were administered to 35 beekeeping and 25 mushroom cultivation trainees, before and after the training programmes organized in 2004. The trainees significantly gained in knowledge. A separate sample of 640 trainees, trained prior to 2004, was selected for finding the adoption status. Out of 640, a sample of 200 was selected by proportionate sampling technique out of three categories, namely: non-adopters, discontinued-adopters and continued-adopters for evaluating the long-term impact of these training programmes. Ex-post-facto one-shot case study design was applied for this impact analysis. The vocational training programmes have resulted in continued-adoption of beekeeping and mushroom cultivation enterprises by 20% and 51% trained farmers, respectively. Age and trainee occupation had significant influence on the adoption decision of beekeeping vocation, whereas education and family income significantly affected the adoption decision of mushroom cultivation. The continued adopters of beekeeping and mushroom growing had increased their family income by 49% and 24%, respectively. These training programmes are augmenting the dwindling farm income of the farmers in Indian Punjab.

Enlarging a training set for genomic selction by imputation of un-genotyped animals in populations of varying genetic architecture

Background: The most common application of imputation is to infer genotypes of a high-density panel of markers on animals that are genotyped for a low-density panel. However, the increase in accuracy of genomic predictions resulting from an increase in the number of markers tends to reach a plateau beyond a certain density. Another application of imputation is to increase the size of the training set with un-genotyped animals. This strategy can be particularly successful when a set of closely related individuals are genotyped. ----- Methods: Imputation on completely un-genotyped dams was performed using known genotypes from the sire of each dam, one offspring and the offspring’s sire. Two methods were applied based on either allele or haplotype frequencies to infer genotypes at ambiguous loci. Results of these methods and of two available software packages were compared. Quality of imputation under different population structures was assessed. The impact of using imputed dams to enlarge training sets on the accuracy of genomic predictions was evaluated for different populations, heritabilities and sizes of training sets. ----- Results: Imputation accuracy ranged from 0.52 to 0.93 depending on the population structure and the method used. The method that used allele frequencies performed better than the method based on haplotype frequencies. Accuracy of imputation was higher for populations with higher levels of linkage disequilibrium and with larger proportions of markers with more extreme allele frequencies. Inclusion of imputed dams in the training set increased the accuracy of genomic predictions. Gains in accuracy ranged from close to zero to 37.14%, depending on the simulated scenario. Generally, the larger the accuracy already obtained with the genotyped training set, the lower the increase in accuracy achieved by adding imputed dams. ----- Conclusions: Whenever a reference population resembling the family configuration considered here is available, imputation can be used to achieve an extra increase in accuracy of genomic predictions by enlarging the training set with completely un-genotyped dams. This strategy was shown to be particularly useful for populations with lower levels of linkage disequilibrium, for genomic selection on traits with low heritability, and for species or breeds for which the size of the reference population is limited.

Förderung des kritischen Denkens thailändischer Deutschlernender durch Training des kritischen Lesens im Deutschunterricht

Die vorliegende Dissertation untersucht das Leseverhalten thailändischer Deutschlernender mit dem Ziel, ihre Fähigkeit zum kritischen Lesen unter Anwendung des MURDER-Schemas im fremdsprachlichen Deutschunterricht zu fördern. Neben der Lesefertigkeit soll aufgrund der bestehenden Zusammenhänge zusätzlich das kritische Denken der Lernenden gefördert werden.

Rotation Invariant Object Recognition from One Training Example

Local descriptors are increasingly used for the task of object recognition because of their perceived robustness with respect to occlusions and to global geometrical deformations. Such a descriptor--based on a set of oriented Gaussian derivative filters-- is used in our recognition system. We report here an evaluation of several techniques for orientation estimation to achieve rotation invariance of the descriptor. We also describe feature selection based on a single training image. Virtual images are generated by rotating and rescaling the image and robust features are selected. The results confirm robust performance in cluttered scenes, in the presence of partial occlusions, and when the object is embedded in different backgrounds.

Support Vector Machines: Training and Applications

The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT&T Bell Labs. This new learning algorithm can be seen as an alternative training technique for Polynomial, Radial Basis Function and Multi-Layer Perceptron classifiers. An interesting property of this approach is that it is an approximate implementation of the Structural Risk Minimization (SRM) induction principle. The derivation of Support Vector Machines, its relationship with SRM, and its geometrical insight, are discussed in this paper. Training a SVM is equivalent to solve a quadratic programming problem with linear and box constraints in a number of variables equal to the number of data points. When the number of data points exceeds few thousands the problem is very challenging, because the quadratic form is completely dense, so the memory needed to store the problem grows with the square of the number of data points. Therefore, training problems arising in some real applications with large data sets are impossible to load into memory, and cannot be solved using standard non-linear constrained optimization algorithms. We present a decomposition algorithm that can be used to train SVM's over large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of, and also establish the stopping criteria for the algorithm. We present previous approaches, as well as results and important details of our implementation of the algorithm using a second-order variant of the Reduced Gradient Method as the solver of the sub-problems. As an application of SVM's, we present preliminary results we obtained applying SVM to the problem of detecting frontal human faces in real images.

Validating Co-Training Models for Web Image Classification

Co-training is a semi-supervised learning method that is designed to take advantage of the redundancy that is present when the object to be identified has multiple descriptions. Co-training is known to work well when the multiple descriptions are conditional independent given the class of the object. The presence of multiple descriptions of objects in the form of text, images, audio and video in multimedia applications appears to provide redundancy in the form that may be suitable for co-training. In this paper, we investigate the suitability of utilizing text and image data from the Web for co-training. We perform measurements to find indications of conditional independence in the texts and images obtained from the Web. Our measurements suggest that conditional independence is likely to be present in the data. Our experiments, within a relevance feedback framework to test whether a method that exploits the conditional independence outperforms methods that do not, also indicate that better performance can indeed be obtained by designing algorithms that exploit this form of the redundancy when it is present.

Implementation of training programs in self-regulated learning strategies in Moodle format : Results of a experience in higher education

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How to improve the formal analytic reasoning and scientific motivation in the intellectual training of medical students

A review article of the The New England Journal of Medicine refers that almost a century ago, Abraham Flexner, a research scholar at the Carnegie Foundation for the Advancement of Teaching, undertook an assessment of medical education in 155 medical schools in operation in the United States and Canada. Flexner’s report emphasized the nonscientific approach of American medical schools to preparation for the profession, which contrasted with the university-based system of medical education in Germany. At the core of Flexner’s view was the notion that formal analytic reasoning, the kind of thinking integral to the natural sciences, should hold pride of place in the intellectual training of physicians. This idea was pioneered at Harvard University, the University of Michigan, and the University of Pennsylvania in the 1880s, but was most fully expressed in the educational program at Johns Hopkins University, which Flexner regarded as the ideal for medical education. (...)

Attention training in Attention Deficit Hyperactivity Disorder

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Training and pre-decisional bias in a multiattribute decision task. 'Formaci??n y sesgo pre-decisional en una tarea de decisi??n multiatributiva'

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Inputs for the incorporation of the UNESCO guidelines on ICT competency standards for teachers : the training of teachers of mathematics in Central America

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The Challenges of didactics on scholar inclusion: specialized teachers perceptions

This study is a section of the GP-FOREESP - Formation of Human Resources and Learning in Special Education Group’s agenda. This Group is engaged in the development of researches with the intent to contribute on the process to universalize the access to school as well as on the improvement of education system that is currently available to the target population of Special Education. Nowadays, the inclusion process subject has been prioritized by that research group, as they consider that, along with other reasons, the efforts to establish an inclusive education system would be the unique alternative to solve the problem regarding the access to school, which is currently limited, and also to improve the quality on special education, since the level presented in the country is low. Guided by such premise, the present work is a supplementary project developed within the group extent to generate knowledge on school inclusion matter