2 research outputs found
Examining How Middle School Science Teachers Implement a Multimedia-enriched Problem-based Learning Environment
This study examined how a group of ten middle school teachers implemented a technology enriched problem-based learning (PBL) environment. The goal was to understand their motivation, document their implementation techniques, and identify factors that teachers considered important in using technology-based PBL tools in their teaching. The analysis identified four factors that provided the impetus for teachers to consider the adoption of technology-based PBL instruction. These factors are (1) the PBL program addresses the teachers’ curricular needs and implementing it has campus administrative and technical support, (2) the method is aligned with teachers’ pedagogical beliefs, (3) the PBL program offers a new way of teaching and promotes the development of higher-order thinking skills, and (4) the PBL program challenges students in a captivating manner and supports the learning needs of all students. Teachers’ implementation techniques with over 1,000 sixth graders were documented in detail with regard to: 1) the teacher’s roles, 2) the student’s role, and 3) the classroom interactions during the implementation of the PBL program. In addition, a detailed description of contrasting narratives of two pairs of teachers is provided, illustrating the range of implementation techniques that can occur using the same PBL program to allow for individualized instruction to meet different students’ needs. The goal of providing detailed implementation practices is to address the lack of “how to” in PBL implementation in K-12 classrooms as indicated in the literature and offer insights and ideas to those interested in adopting and implementing PBL. Findings are discussed within the theoretical framework and implications are provided
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Systematic digital inequities : evidence from the School Technology and Readiness chart
The primary purpose of this study was to identify and quantify the relationship between school and student characteristics and the campus technology readiness score as reported on the School Technology and Readiness (STaR) 2013 report issued by the Texas Education Agency. The secondary purpose was to identify those student and school characteristics that are statistically significant in predicting STaR composite scores as an indicator of technology integration.
This study contributes to research on the digital equity and inequity by exploring the differences between K-12 schools in Texas. The unit of research was the schools themselves, thus changing the research focus from individuals and households to institutionalized, public, educational campuses. Secondly, the study used quantitative measures of technology readiness submitted by approximately 224,243 (StarChart, 2015) Texas teachers and aggregated to 6,091 schools.
To address the research questions, quantitative methods were applied. Research questions and hypotheses were developed and tested to investigate whether a significant relationship existed between the dependent variable, the campus technology readiness score, and school characteristics and student characteristics. There were five independent variables for school characteristics and six independent variables for student characteristics. A parsimonious model was developed that identified the factors already evaluated independently, which were statistically significant in explaining the variation in the STaR composite score of technology readiness.
Data analysis of 6,091 schools indicated that technology integration in Texas schools was statistically unequal based on student and school characteristics. Of the 11 factors tested, 10 were statistically significant, indicating that the differences were due to the evaluated factors rather than chance. Of the factors tested with ANOVA methodology, schools with Title 1 status had the highest R-squared (.024). Of the factors tested with Pearson product-moment correlation, schools educating higher percentages of economically disadvantaged students had the most influential Pearson r (-0.234). Using step-wise modeling, seven factors were included in the parsimonious model. The factors that contributed most to variation in technology readiness were percentage of economically disadvantaged students and the percentage of African American and Hispanic students.
This research presents statistical evidence that technology integration practices vary between K-12 campuses in Texas and that there are systemic digital inequities. The research is a call to action to address digital inequity in Texas schools.Curriculum and Instructio