3 resultados para Illinois Early Learning Council

em Universidad de Alicante


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The present study examined the predictive effects of gender, intellectual ability, self-concept, motivation, learning strategies, popularity and parent involvement on academic achievement. Hiearchical regression analysis were performed with six steps in which each variable was included, among a sample of 1398 high school students (mean age = 12.5; standard deviation = .67) of eight education centers from the province of Alicante (Spain). The results revealed significant predictive effects of all of the variables, explaining 59.1% of the total variance.

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In the chemical textile domain experts have to analyse chemical components and substances that might be harmful for their usage in clothing and textiles. Part of this analysis is performed searching opinions and reports people have expressed concerning these products in the Social Web. However, this type of information on the Internet is not as frequent for this domain as for others, so its detection and classification is difficult and time-consuming. Consequently, problems associated to the use of chemical substances in textiles may not be detected early enough, and could lead to health problems, such as allergies or burns. In this paper, we propose a framework able to detect, retrieve, and classify subjective sentences related to the chemical textile domain, that could be integrated into a wider health surveillance system. We also describe the creation of several datasets with opinions from this domain, the experiments performed using machine learning techniques and different lexical resources such as WordNet, and the evaluation focusing on the sentiment classification, and complaint detection (i.e., negativity). Despite the challenges involved in this domain, our approach obtains promising results with an F-score of 65% for polarity classification and 82% for complaint detection.

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The present study examined the predictive effects of intellectual ability, self-concept, goal orientations, learning strategies, popularity and parent involvement on academic achievement. Hierarchical regression analysis and path analysis were performed among a sample of 1398 high school students (mean age = 12.5; SD =.67) from eight education centers from the province of Alicante (Spain). Cognitive and non-cognitive variables were measured using validated questionnaires, whereas academic achievement was assessed using end-of-term grades obtained by students in nine subjects. The results revealed significant predictive effects of all of the variables. The model proposed had a satisfactory fit, and all of the hypothesized relationships were significant. These findings support the importance of including non-cognitive variables along with cognitive variables when predicting a model of academic achievement.