Computer Supported Collaborative Science (CSCS) is a teaching pedagogy that uses collaborative web-based resources to engage all learners in the collection, analysis, and interpretation of whole-class data sets, and is useful for helping secondary and college students learn to think like scientists and engineers. This chapter presents the justification for utilizing whole-class data analysis as an important aspect of the CSCS pedagogy and demonstrates how it aligns with the Next Generation Science Standards (NGSS). The chapter achieves this end in several ways. First, it reviews rationale outlined in the NGSS science and engineering practices for adapting 21st century technologies to teach students 21st century science inquiry skills. Second, it provides a brief overview of the basis for our pedagogical perspective for engaging learners in pooled data analysis and presents five principles of CSCS instruction. Third, we offer several real-world and research-based excerpts as illustrative examples indicating the value and merit of utilizing CSCS whole-class data analysis. Fourth, we postulate recommendations for improving the ways science, as well as other subject matter content areas, will need to be taught as the U.S. grapples with the role-out of new Common Core State Standards (CCSS) and NGSS. Taken together, these components of CSCS whole-class data analysis help constitute a pedagogical model for teaching that functionally shifts the focus of science teaching from cookbook data collection to pooled data analysis, resulting in deeper understanding.

Continuous Formative Assessment (CFA) is a strategy that employs free and accessible collaborative cloud-based technologies to collect, stream, and archive evidence of student knowledge, reasoning, and understanding during STEM lessons, so that instructors and students can make evidence-based decisions for adjusting lessons to optimize learning. Writing samples, diagrams, equations, drawings, photos, and movies are collected from all students and archived in cloud-based databases so that instructors can assess student understanding during instruction, and monitor learning gains over time. This chapter introduces and explains CFA techniques and provides preliminary research pertaining to the effectiveness of CFA instructional strategies in promoting student accountability, metacognition, and engagement in STEM courses, and suggests avenues for future research.

Continuous Formative Assessment (CFA) is a strategy that employs free and accessible collaborative cloud-based technologies to collect, stream, and archive evidence of student knowledge, reasoning, and understanding during STEM lessons, so that instructors and students can make evidence-based decisions for adjusting lessons to optimize learning. Writing samples, diagrams, equations, drawings, photos, and movies are collected from all students and archived in cloud-based databases so that instructors can assess student understanding during instruction, and monitor learning gains over time. This chapter introduces and explains CFA techniques and provides preliminary research pertaining to the effectiveness of CFA instructional strategies in promoting student accountability, metacognition, and engagement in STEM courses, and suggests avenues for future research.

Computer Supported Collaborative Science (CSCS) is a teaching pedagogy that uses collaborative web-based resources to engage all learners in the collection, analysis, and interpretation of whole-class data sets, and is useful for helping secondary and college students learn to think like scientists and engineers. This chapter presents the justification for utilizing whole-class data analysis as an important aspect of the CSCS pedagogy and demonstrates how it aligns with the Next Generation Science Standards (NGSS). The chapter achieves this end in several ways. First, it reviews rationale outlined in the NGSS science and engineering practices for adapting 21st century technologies to teach students 21st century science inquiry skills. Second, it provides a brief overview of the basis for our pedagogical perspective for engaging learners in pooled data analysis and presents five principles of CSCS instruction. Third, we offer several real-world and research-based excerpts as illustrative examples indicating the value and merit of utilizing CSCS whole-class data analysis. Fourth, we postulate recommendations for improving the ways science, as well as other subject matter content areas, will need to be taught as the U.S. grapples with the role-out of new Common Core State Standards (CCSS) and NGSS. Taken together, these components of CSCS whole-class data analysis help constitute a pedagogical model for teaching that functionally shifts the focus of science teaching from cookbook data collection to pooled data analysis, resulting in deeper understanding.