--- title: "Data-Driven Instruction: Collecting The Right Data" description: The first in a series on data-driven instruction, this post identifies the types of data you should begin collecting in your class. image: https://blog.theexpertta.com/hubfs/Blog%20Design/businessman%20hand%20working%20with%20modern%20technology%20and%20digital%20layer%20effect%20as%20business%20strategy%20concept.jpeg --- ![](https://blog.theexpertta.com/hubfs/Blog%20Design/businessman%20hand%20working%20with%20modern%20technology%20and%20digital%20layer%20effect%20as%20business%20strategy%20concept.jpeg) With the multitude of ways for teachers to gather and analyze information about their classes, and the wealth of information available to instructors via the internet, becoming a data-driven instructor can be an overwhelming thought. There are many questions to consider when you are first getting started - “What does data-driven instruction mean?” “How should I collect data?” “What information is most important to me?” This post is the first in our series on data-driven education, and through the posts to follow we hope to shed some light on this important topic and cover the key aspects relevant to those teaching Introductory Physics. In this post we will focus specifically on the type of data you should be collecting in your class. First, let’s begin with a general definition of data-driven instruction, and how this philosophy and practice plays out in the classroom. Essentially, instruction that is data-focused builds off of a [continuous cycle](https://www.engageny.org/data-driven-instruction) of assessment, analysis and action. The systematic analysis of student assessments drives new and thoughtful action on the part of the instructor. When planning a data cycle, the instructor should ask fundamental questions regarding each stage: - “What should my students know?” - “How will I measure their knowledge or show mastery on a topic?” - “What pedagogical practices will I implement?” Each of these questions, and more, will be addressed throughout the series, but let’s dive into the most fundamental - “What type of data should I collect?” [Concordia University in Portland](http://education.cu-portland.edu/blog/teaching-strategies/collecting-data-in-the-classroom-a-teachers-guide/) provides a good starter list that we have expanded upon below. This will be different for each class, and it is important to tailor these sources of data for your own purposes. - **Short Quizzes, Q&A sessions, In-Class Activities**   - **Examinations**   - **Individual or Group Projects, Lab Reports**   - **Student-Reported Data**   - **Homework** Knowing what data to collect is an important jumping-off point when transitioning to a data-driven approach. Once you have determined the sources of data collection, the next step is to develop a plan for collecting, storing, and analyzing the data to ensure you get real-time feedback and useful insights about the progress of your students. Then in the final stage of implementing a data-driven approach, you will incorporate this feedback and integrate it into the course curriculum throughout the semester, and from year to year. As data-driven education continues to evolve, leading the way is the conversation surrounding Big Data analytics. 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