Source: Issues in Teacher Education, Volume 20, No. 1, Spring 2011, p. 35-49.
(Reviewed by the Portal Team)
This study had two purposes:
(1) to test the hypothesis that teacher candidates who faced challenges in student teaching had lower self-ratings on teacher dispositions than their counterparts who did not face challenges in student teaching, and
(2) to develop an explanatory model to predict teacher candidates’ challenging experiences in student teaching.
This study analyzed responses to the disposition survey from 277 teacher candidates in a Multiple Subject Credential Program (MSCP) that prepares elementary school teachers at a public urban university in Southern California.
Two types of analyses were conducted to accomplish the two purposes of this study:
(1) To test the hypothesis that teacher candidates who successfully completed student teaching had significantly higher self-rating scores on dispositions than their counterparts who faced notable challenges, a Mann-Whitney U test was conducted.
(2) To accomplish the second objective, a Classification and Regression Tree (CART) technique was employed.
As the authors hypothesized, teacher candidates who successfully completed student teaching had significantly higher self-rating scores on dispositions than their counterparts who faced notable challenges.
This result lends support to the literature indicating that positive teacher dispositions predict effective, successful teaching.
This study is important as it demonstrates one method for building a model to predict student progress in a teacher education program.
Such models to predict pre-service teachers’ growth in teacher education programs are strongly sought to help teacher educators effectually guide pre-service teachers in their programs.
The findings from this study stand to advance our understanding of how dispositions relate to instructional practices and approaches.
If a model, such as ours, indicates that specific elements of dispositions predict successful teaching, then educational researchers can design focused studies to further investigate how teacher dispositions affect student learning.
In conclusion, this study applied the classification tree (CART) technique, an algorithmic model that has gained increasing prominence, to a small data set with a hope that such application would draw important implications for statistical modeling within the teacher education community.
This technique holds promise for future research on teacher dispositions, and we are hopeful that future research will produce outcomes to realize our ultimate goal that effective teaching optimizes the learning and development of all children in our nation.