The use of technology in schools is now ubiquitous but the effectiveness on the learning environment has mixed results. This paper describes the development and validation of an instrument to measure students' attitudes toward and knowledge of technology with the aim of investigating any differences based on gender after a course where the science department made use of technology as an integral part of teaching biology. In this study, conducted in one school in the state of New York, in the United
States of America the Students' Attitudes toward and Knowledge of Technology questionnaire wasadministered to nearly 700 high school science students. A principal component and principal factor analysis resulted in new scales from the validation of the instrument that demonstrated high reliabilities. There were statistically significant gender differences in all the scales of the questionnaire in favor of males.
In an attempt to engage more students in Science, Technology, Engineering and Mathematics (STEM) subjects, schools are encouraged by STEM educators and professionals to introduce students to STEM through projects which integrate skills from each of the STEM disciplines.Because little is known about the learning environment of STEM classrooms, we developed and validated a Classroom Emotional Climate (CEC) questionnaire. Initially, the questionnaire was pilot tested with six focus groups of students from three schools to obtain feedback to incorporate into a revision of the CEC. Next, the modified CEC questionnaire was administered to 698 students participating in STEM activities in 57 classes in 20 schools.Exploratory factor analysis (principal component analysis) led to reduction of the CEC to 41 items in seven dimensions: Consolidation, Collaboration, Control, Motivation, Care, Challenge and Clarity. The structure of the CEC was then further explored using confirmatory factor analysis. Internal consistency reliability, concurrent validity (ability to differentiate between classrooms), discriminant validity (scale intercorrelations) and predictive validity (associated with student attitudes) were satisfactory. Finally, Rasch analysis of data for each dimension revealed good model fit and unidimensionality of the items describing each latent variable.
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