Conference:
ECER 2008
Format:
Symposium Paper
Session Information
09 SES 03B, Relationship in Science Performance (Part 3)
Symposium continued from 09 SES 2B to be continued in 09 SES 4B
Time:
2008-09-10
14:00-15:30
Room:
AK2 137
Chair:
Wilfried Bos
Contribution
Student Motivation and self-beliefs have been a significant part of PISA since its beginning. Not only does the relation to student achievement make these factors so important, they also represent essential targets of schools or school systems respectively.
How much young people are interested in different subjects, how deeply involved they are and which opinions they have regarding school and learning often determines future educational and occupational careers. High student motivation, self-beliefs and positive expectations are therefore a desirable output of schools. On the other side they represent conditions for learning in school – they build the context in which learning takes place.
This analysis focuses on motivational factors, self-beliefs and expectations in science, as scientific literacy was the main domain of PISA 2006 and therefore a broad variety of context information is provided.
Another crucial aspect is that first results of PISA 2006 showed that student motivation in science in most European countries is rather low, similar to science self-beliefs and value beliefs. Also the percentage of students expecting a science related career is rather low in most European countries.
Austria is the most affected country concerning motivational factors, but also students in quite a lot of other European countries show poor motivation in science: they do not know what they should do with what they are learning in science at school and if they ever will enter upon a science related career.
The analyses show in an international context with focus on European countries (a) specific patterns concerning student motivation, self-beliefs and future expectations in science, (b) the relationships between these context factors, (c) the influence on student performance in science and d) gender issues in regard to these context factors. Methods for data analysis are correlative statistics and regression analysis.
Results of this study will provide valuable information on how motivational factors, self-beliefs and expectations in science influence each other as well as student achievement in science. Identifying and comparing patterns among European countries may show a way in to improve motivational and belief-factors along with future expectations.
References
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