973 resultados para Literacy programs.
Resumo:
Four studies report on outcomes for long-term unemployed individuals who attend occupational skills/personal development training courses in Australia. Levels of distress, depression, guilt, anger, helplessness, positive and negative affect, life satisfaction and self esteem were used as measures of well-being. Employment value, employment expectations and employment commitment were used as measures of work attitude. Social support, financial strain, and use of community resources were used as measures of life situation. Other variables investigated were causal attribution, unemployment blame, levels of coping, self efficacy, the personality variable of neuroticism, the psycho-social climate of the training course, and changes to occupational status. Training courses were (a) government funded occupational skills-based programs which included some components of personal development training, and (b) a specially developed course which focused exclusively on improving well-being, and which utilised the cognitive-behavioural therapy (CBT) approach. Data for all studies were collected longitudinally by having subjects complete questionnaires pre-course, post-course, and (for 3 of the 4 studies) at 3 months follow-up, in order to investigate long-term effects. One of the studies utilised the case-study methodology and was designed to be illustrative and assist in interpreting the quantitative data from the other 3 evaluations. The outcomes for participants were contrasted with control subjects who met the same sel~tion criteria for training. Results confirmed earlier findings that the experiences of unemployment were negative. Immediate effects of the courses were to improve well-being. Improvements were greater for those who attended courses with higher levels of personal development input, and the best results were obtained from the specially developed CBT program. Participants who had lower levels of well-being at the beginning of the courses did better as a result of training than those who were already functioning at higher levels. Course participants gained only marginal advantages over control subjects in relation to improving their occupational status. Many of the short term well-being gains made as a result of attending the courses were still evident at 3 months follow-up. Best results were achieved for the specially designed CBT program. Results were discussed in the context of prevailing theories of Ynemployment (Fryer, 1986,1988; Jahoda, 1981, 1982; Warr, 1987a, 1987b).
Resumo:
The present paper motivates the study of mind change complexity for learning minimal models of length-bounded logic programs. It establishes ordinal mind change complexity bounds for learnability of these classes both from positive facts and from positive and negative facts. Building on Angluin’s notion of finite thickness and Wright’s work on finite elasticity, Shinohara defined the property of bounded finite thickness to give a sufficient condition for learnability of indexed families of computable languages from positive data. This paper shows that an effective version of Shinohara’s notion of bounded finite thickness gives sufficient conditions for learnability with ordinal mind change bound, both in the context of learnability from positive data and for learnability from complete (both positive and negative) data. Let Omega be a notation for the first limit ordinal. Then, it is shown that if a language defining framework yields a uniformly decidable family of languages and has effective bounded finite thickness, then for each natural number m >0, the class of languages defined by formal systems of length <= m: • is identifiable in the limit from positive data with a mind change bound of Omega (power)m; • is identifiable in the limit from both positive and negative data with an ordinal mind change bound of Omega × m. The above sufficient conditions are employed to give an ordinal mind change bound for learnability of minimal models of various classes of length-bounded Prolog programs, including Shapiro’s linear programs, Arimura and Shinohara’s depth-bounded linearly covering programs, and Krishna Rao’s depth-bounded linearly moded programs. It is also noted that the bound for learning from positive data is tight for the example classes considered.