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方善郁 청주대학교 교육문제연구소 1997 교육과학연구 Vol.11 No.1
The purpose of this study is to critically inquire into alternative approach in intelligence research and to suggest new perspective to the nature and development of intelligence. Virtually every history of human intelligence research places the formal beginning at the turn of the 20th century, with the work of Spearman and Binet. Their psychometric approach to identify individual differences in intelligence was clearly the first successful and the most influential one. But, in the 1960s and 1970s, many investigators became disillusioned with the psychometric approach, because psychometric model of intelligence has not been particularly successful in generating successful intervention programs for training intelligent functioning. Beginning in the mid-1960s, researchers explored a long series of topics whose organizing feature was a concern with the actual, on-line cognitive activity of the individual. This information-processing framework provided the impetus for the development of sophistigated experimental tasks and techniques to investigate various aspects of human cognition. The theoretical complementarity of psychometric and cognitive approach to human cognitive activity, in particular as represented by information processing and measurement of intelligence, was soon noted and employed. This evolutionary view was predicted a successful integration of correlationally based structural theories and experimentally bated processing theories in the future. But it is persuasive enough to argue such an integration is unlikely if not possible, because those approaches have fundamental misconceptions about the nature and development of human intelligence. Most of the misconceptions due to the missing contextual constructs such as development, meaningfulness, goals, motivation, social change, social structure, and culture. In recent, the systems theories attempt indifferent wags to integrate cognition and context. But they are unclear, because they have taken the notion of intelligence too far, and have included within their scope abilities or attributes that go beyond intelligence. Conclusionally, recognizing and refuting not only the critically unexamined core of conventional thinking about human intelligence but also the obstacles to that critical examination that stem from social structure of social science research, are major step toward improved theory and practice.
방선욱 청주대학교 학술연구소 2012 淸大學術論集 Vol.19 No.-
The purpose of this study is to examine the general tendencies and relationship between self-efficacy and self-regulated learning of college students. For this study, C University located in Chongju city was selected and total 292 available data was analyzed. Main variables verified were gender and major which was classified by liberal arts, social science, natural science, and music and sports. Two kinds of questionnaires were employed and each item was designed with 5-point Likert scales, which include self-regulated learning inventory for university students by Chung(2005) and general self-efficacy inventory by Kim(1997). The former is composed of three sub factors as motivation regulation, cognition regulation, and behavior regulation. The latter is composed of three sub factors such as self-confidence, self-regulation efficacy, and task difficulty preference. The results were as follows; First, there were significant differences(p<.01) between male and female students in general self-efficacy, especially in subsets of self-confidence and self-regulated efficacy. Also there was significant differences(p<.05) among majors in general self-efficacy, especially in subsets of self-confidence. Second, there was neither significant differences between male and female students nor among majors in self-regulated learning strategies. Third, there were significant correlation(p<.001) between subsets of general self-efficacy and self-regulated learning except two case. Those were first, the correlation between confidence and behavior modification, second, the correlation between task difficulty preference and motivation modification. Fourth, the result showed that 38% of self-regulated learning were explained by the subsets of self-efficacy and 22% of self-efficacy were explained by the subsets of self-regulated learning. In addition, the most significant predictor on self-regulated learning was self-regulated efficacy and the most significant predictor on self-efficacy was cognition regulation.