COURSE-1 Collaborative Data Analysis in the Science Classroom

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.

YouTube Video

NMC Academy - The New Media Academy (formerly the HP Catalyst Academy) is a fresh approach to accelerate professional learning among STEMx (science, technology, engineering, math and all the other 21st century high-tech disciplines) educators, providing personalized and powerful mini-courses that inspire and transform teaching practices. This exciting development comes from the HP Catalyst Initiative, which focuses on improving the quality of STEMx education — a major priority around the world. The demand for highly qualified STEMx teachers is increasing, yet traditional models of professional development do not adequately meet the demand. The HP Catalyst Academy’s free, online mini-courses are specifically designed for STEMx teachers and faculty serving students in grades 6 through 16 (secondary and undergraduate tertiary)

How to Enroll in this HP Catalyst MiniCourse
(1) First you need to sign up for HP Catalyst Academy
(2) You will then receive an email.  Click the link in the email to enter HP Catalyst.
(3) You can now enroll in an HP Catalyst Academy Class.  Select "Everyone in the Pool"
(4) Once you enroll in the class you will receive a new email saying that you are now enrolled in the class and can begin. 

Objectives for this course - 
  • Stage 1 -   Learn how to engage learners in the analysis of pooled data.  Learn how to copy and adapt existing CSCS lessons to meet your own curricular needs. 
  • Stage 2 -  Learn how to use CSCS techniques to engage leaners in the science & engineering practices specified in Dimension 1 of the Next Generation Science Standards
  • Stage 3 Learn how to collect, analyze, and interpret data from an entire science class in real-time.  
  • Stage 4  Learn how to transform a traditional science investigation into a CSCS activity in which students analyze pooled data. Create a CSCS lesson from scratch. 
  • Stage 5 Learn how use PC-based applications to analyze data

About the instructors

Norman Herr
Norman 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
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.