出版社：Eurasian Society of Educational Research Association
摘要：Schools and teacher induction programs around the world routinely assess teaching best practice to inform accreditation, tenure/promotion, and professional development decisions. Routine assessment is also necessary to ensure that teachers entering the profession get the assistance they need to develop and succeed. We introduce the Item-Level Assessment of Teaching practice (I-LAST) as a flexible framework-based approach for quantitative evaluation of teaching best practice in the induction stages. We based the I-LAST on a novel framework for teaching best practice, and used Fuller’s scale as a framework for understanding the potential of the I-LAST in providing longitudinal measures for growth. Using the context of a year-long teacher induction program in the Midwestern United States, we collected data through an online survey from 46 teaching supervisors who were asked to evaluate their interns. We used the Rasch partial credit model as a criterion for construct validity, and measured dimensionality and reliability from both Rasch and classical frameworks. The I-LAST was found to be a unidimensional, valid, and reliable measure for teaching best practice. It demonstrated the ability to provide reliable scores for specific sub-dimensions of best practice, including those which manifest at various stages along Fuller’s scale. Potential uses of the I-LAST to advance understanding of the role of teacher induction programs in fostering productive growth in new teachers is discussed.
