归属
心理学
主流
发展心理学
感觉
控制源
背景(考古学)
社会心理学
比例(比率)
感知
哲学
古生物学
物理
神学
量子力学
神经科学
生物
作者
Katy Brady,Lisa Woolfson
标识
DOI:10.1348/000709907x268570
摘要
Background Identifying the factors that influence teacher beliefs about teaching children with learning difficulties is important for the success of inclusive education. This study explores the relationship between teachers' role, self‐efficacy, attitudes towards disabled people, teaching experience and training, on teachers' attributions for children's difficulties in learning. Method One hundred and eighteen primary school teachers (44 general mainstream, 33 mainstream learning support, and 41 special education teachers) completed the short form of the Teachers' Sense of Efficacy Scale, the Interaction with Disabled Persons Scale (IDP), and a revised version of the Teacher Attribution Scale. Results Regression analysis found that teachers' role influenced stability and controllability attributions. However, for stability attributions the effect was not sustained when examined in the context of the other factors of teaching efficacy, experience, training, and attitudes towards disability. What emerged as important instead was strong feelings of sympathy towards disabled people which predicted stable attributions about learning difficulties. Experience of teaching children with additional support needs and teaching efficacy positively predicted external locus of causality attributions. Surprisingly, training was not found to have an impact on attributions. A mixed MANOVA found that mainstream teachers' controllability attributions were influenced by whether or not the child had identified learning support needs. Conclusions Teacher efficacy, experience of teaching students with support needs, attitudes towards disabled people, and teachers' role all impact on teacher attributions, but no relationship with training was found. Implications for teacher training and development, and for student achievement and student self‐perception are discussed.
科研通智能强力驱动
Strongly Powered by AbleSci AI