Integrating Technology, Curriculum, and Online Resources: A Multilevel Model Study of Impacts on Science Teachers and Students

This scale-up study investigated the impact of a teacher technology tool (Curriculum Customization Service, CCS), curriculum, and online resources on earth science teachers’ attitudes, beliefs, and practices and on students’ achievement and engagement with science learning. Participants included 73 teachers and over 2,000 ninth-grade students within five public school districts in the western U.S. To assess the impact on teachers, changes between pre- and postsurveys were examined. Results suggest that the CCS tool appeared to significantly increase both teachers’ awareness of other earth science teachers’ practices and teachers’ frequency of using interactive resources in their lesson planning and classroom teaching. A standard multiple regression model was developed. In addition to “District,” “Training condition”(whether or not teachers received CCS training) appeared to predict teachers’ attitudes, beliefs, and practices. Teachers who received CCS training tended to have lower postsurvey scores than their peers who had no CCS training. Overall, usage of the CCS tool tended to be low, and there were differences among school districts. To assess the impact on students, changes were examined between pre- and postsurveys of (1) knowledge assessment and (2) students’ engagement with science learning. Students showed pre- to postsurvey improvements in knowledge assessment, with small to medium effect sizes. A nesting effect (students clustered within teachers) in the Earth’s Dynamic Geosphere (EDG) knowledge assessment was identified and addressed by fitting a two-level hierarchical linear model (HLM). In addition, significant school district differences existed for student post-knowledge assessment scores. On the student engagement questionnaire, students tended to be neutral or to slightly disagree that science learning was important in terms of using science in daily life, stimulating their thinking, discovering science concepts, and satisfying their own curiosity. Students did not appear to change their self-reported engagement level after the intervention. Additionally, three multiple regression models were developed. Factors from the district, teacher, and student levels were identified to predict student post-knowledge assessments and their engagement with science learning. The results provide information to both the research community and practitioners

Automating Open Educational Resources Assessments: A Machine LearningGeneralization Study

Assessing the quality of online educational resources in a cost effective manner is a critical issue for educational digital libraries. This study reports on the approach for extending the Open Educational Resource Assessments (OPERA) algorithm from assessing vetted to peer-produced content. This article reports details of changes to the algorithm, comparisons between human raters and the algorithm, and the extent the algorithm can automate the review process