Collaborative Data Analysis in the Science Classroom - This course includes 4 required lessons, plus an optional lesson for those who wish additional experience. All videos are shown in full-screen HD. Please remember to click the back button in your browser window to return from the videos to the course website.
Science classrooms should provide hands-on activities wherein students investigate phenomena, test ideas, make observations, analyze data, and draw conclusions. More often than not, students spend most of their time on data collection at the expense of analysis and interpretation of the data. In other cases, there is time for analysis, but due to the amount of scaffolding that needs to take place, it is topical and teacher-led rather than in-depth and student-centered. The New Generation Science Standards (NGSS) require more in-depth data analysis so that students can develop skills to analyze and interpret data and communicate their findings.
In an effort to more fully engage learners in the science and engineering practices required by the NGSS, we have developed a pedagogy entitled Computer-Supported Collaborative Science (CSCS). The incorporation of cloud-based collaborative documents as a tool for elucidating, resolving and even animating “live” data can both expedite the data collection process and enhance analyses. The “collaborative” aspect means that students are working together to produce findings. For example, rather than having a single small group of students plot 5 data points for an experiment, 10 groups in a classroom (or even 50 groups across periods) can pool their data for a much larger sample size. Using the CSCS model, charts and graphs are readily available to instantly illustrate patterns and trends in the data. In this mini-course, you will learn how to employ CSCS techniques to engage learners in the science and engineering practices mandated by the Next Generation ScienceStandards. You will learn how your students can pool their data, perform statistical analyses, and interpret trends in large-group data.
Computer Supported Collaborative Science (CSCS) is a pedagogy for STEM classrooms that engages learners in the science and engineering practices of the Next Generation Science Standards. There are three major features of CSCS: Collaborative (Pooled) Data Analysis, Contnuous Formative Assessment (CFA), and Collaborative Resource Development. This course focuses on Collaborative (Pooled) Data Analysis. Continuous Formative Assessment and Collaborative Document Development are the subjects of future courses.
Objectives for this course -
Norman HerrNorman Herr is a professor of science and computer education at California State University, Northridge (CSUN). He earned his Ph.D. from the University of California, Los Angeles, his master of science degree from the University of California, Davis, and his bachelor of science degree from the University of California, Irvine. Dr. Herr has taught 30 science and technology related courses at UCDavis, Glendale College, Maranatha High School, and CSUN. Dr. Herr is a member of the Computer Supported Collaborative Science (CSCS) Team at CSUN where he develops curriculum for use in middle school, high school, and college science courses. Dr. Herr is an avid backpacker, mountain climber, mountain biker, skier and nature photographer.
Kevin G. Bugg is a graduate of California State University, Northridge, where he earned a bachelor of science degree in biology. He works as a research assistant with the Computer Supported Collaborative Science (CSCS) Team at CSUN. Kevin has been instrumental in promoting the CSCS learning/teaching model by providing support for professors at CSUN as well as technical and curricular support for secondary education teachers in their home classrooms.