Learning of cross‐sectional anatomy using clay models

We incorporated clay modeling into gross anatomy and neuro‐anatomy courses to help students understand cross‐sectional anatomy. By making clay models, cutting them and comparing cut surfaces to CT and MR images, students learned how cross‐sectional two‐dimensional images were created from three‐dimensional structure of human organs. Most students in a clay modeling group responded positively to this approach, and their average score on CT examination was higher than that of a group that did not use clay models. Clay modeling appears to be a useful supplement to conventional anatomy or radiologic anatomy education. It can be applied to any part of human body, and its effectiveness will be greater when a more complicated understanding of cross‐sectional anatomy is required. Anat Sci Educ 2:156–159, 2009. © 2009 American Association of Anatomists.     

Predicting student evaluations of teaching using decision tree analysis

Abstract

This study uses decision tree analysis to determine the most important variables that predict high overall teaching and course scores on a student evaluation of teaching (SET) instrument at a large public research university in the United States. Decision tree analysis is a more robust and intuitive approach for analysing and interpreting SET scores compared to more common parametric statistical approaches. Variables in this analysis included individual items on the SET instrument, self-reported student characteristics, course characteristics and instructor characteristics. The results show that items on the SET instrument that most directly address fundamental issues of teaching and learning, such as helping the student to better understand the course material, are most predictive of high overall teaching and course scores. SET items less directly related to student learning, such as those related to course grading policies, have little importance in predicting high overall teaching and course scores. Variables irrelevant to the construct, such as an instructor’s gender and race/ethnicity, were not predictive of high overall teaching and course scores. These findings provide evidence of criterion and discriminant validity, and show that high SET scores do not reflect student biases against an instructor’s gender or race/ethnicity.     

Early childhood educators’ meta-cognitive knowledge of problem-solving strategies and quality of childcare curriculum implementation

This study aims to explore the impact of early childhood educators’ meta-cognitive knowledge on the quality of their childcare curriculum implementation, and to gain insights regarding successful problem-solving strategies associated with early education and care. Early childhood educators’ implementation of general problem-solving strategies in real educational settings has not previously been studied, even though providing early education and care is a complicated ill-structured task requiring effective problem-solving strategies. The present study surveyed 166 South Korean early childhood educators regarding their meta-cognitive knowledge of five general problem-solving strategies (free production, analogy, step-by-step analysis, visualisation and combining) and childcare curriculum implementation. In general, strategic teachers provided high-quality childcare. Teachers preferred analogy most strongly among the five general problem-solving strategies, but step-by-step analysis was the most powerful predictor of quality of curriculum implementation. Among the five strategies, only three strategies (step-by-step analysis, free production and analogy) showed meaningful associations with quality of curriculum implementation.