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How teachers relate to high-achieving students
Citation
Ee, J. (1999). How teachers relate to high-achieving students. In M. Waas (Ed.), Enhancing learning: Challenge of integrating thinking and information technology into the curriculum: Proceedings of the 12th Annual Conference of the Educational Research Association (pp. 213-223). Educational Research Association (Singapore).
Author
Ee, Jessie
Abstract
Motivation and strategy are important variables in students’ learning. Similarly, teachers’ goal orientations and the way they teach students also impact on students’ achievement. Past research have shown that the pursuit of task goal orientations are likely to enhance the usage of self-regulated learning strategies. However, what are the goal orientations, knowledge and usage of self-regulated learning of high-achievers and to what extend are their goals, self-regulated learning and achievement influenced by teachers’ classroom goal orientations and strategy-based instruction? This study examines high achievers’ goal orientations, knowledge and usage of strategies and the relationship among their teachers’ classroom goal orientations and strategy-based instruction and their goal orientations, self-regulated learning and achievement. Participants are 566 high-achieving students and their 38 Primary 6 teachers in 34 Singapore schools. Each student responded to The Personal Goals Scale adapted from Nicholls, Patashnick and Nolen (1985) and the Self-Regulated Learning Strategies Scale of Youlden and Chan (1994) whilst each teacher responded to the Teachers’ Classroom Practices developed by the researcher. Students’ achievement was obtained from their standardized Primary Six Leaving School Examination (PSLE). PRELIS and LISREL7 were used to form one-factor congeneric analysis to overcome the problem associated with large numbers of indicator variables as well as for a more realistic representation of the data. The pattern of influence of the teacher and student variables on achievement was examined using multi-level modeling (Mln) to cater for the hierarchical structure of the data. The results and implications of the study will be discussed.
Date Issued
December 1999
Description
This paper was published in the 1999 Proceedings of the ERA Annual Conference held at Plaza Parkroyal Hotel, Singapore from 23-25 November 1998