Data analysis is huge. So much can be learned about a lesson, a student, and the teacher, by analyzing the the test results. In looking at quiz results, I can determine whether the lesson wasn’t learned well and-more commonly-whether the lesson wasn’t taught well.
It’s easy to blame the students for not learning the material; it’s almost cathartic to realize I may not have taught the lesson well. An effective teacher will look not only at the performance of his or her students but also at the performance of him/herself. For years, I have understood that how I present the information is just as, if not more, important than how the students receive my instruction.
If my instruction is poorly delivered, then I can hardly blame the student for not learning the information. An effective teacher is a reflective teacher. I must look at my efforts, how effectively I prepared the lesson, how well I set up the structure of the lesson, how well I executed the lesson.
It’s far easier to modify my lesson than to modify how my students learn. With seven different learning styles and three different modes of learning among my 15 to 30 students, I would have to have super-teacher powers to effect some miraculous molding of minds to create an easily-taught class of several students!
Rather, I can effect far greater change for better teaching when I look upon my own styles and methods of instructing the students. In fact, I have found that creating a learning environment–conducive to students teaching themselves, in such a way that I provide the information to be learned and a variety of ways to acquire the knowledge-works more meaningfully for the students.
Meaningful learning for the students: that’s the crux of data analysis. Using the data-written assignments, test papers, oral presentation results-to break down how well a student learned a concept has been far more effective as a method of teaching than the old-fashioned method of letting scores stand as they are, and telling the students, “Better luck next time!”
My first step in data analysis is to look at the performance of the top students in the class. All classes have a spectrum of achievement. Certain students are masters of learning. Regardless of the class or the subject within the class, certain students know how to get A’s. Time and again, they figure out how to get the high scores. By looking at their performances first, I can get a fair gauge of whether my lesson worked.
It’s when these top performers do poorly that I immediately question my part in the difficulty of a task. If the students who typically get A’s can’t do my work, then I’ve made the assignment too difficult. Generally speaking, this is a trustworthy gauge. On the other hand, if the top performers succeeded moderately well, but the rest of the class bombed on the task, I have to consider my methods. Were my methods too strict, allowing for only certain learners to get the point? Is it possible I should have employed other methods?
At this point in data analysis, frustration creeps in on some teachers, but the effective instructors thrive on the frustration. Effective teachers embrace learning/teaching challenges. I love it! Can’t say that makes me an effective teacher, necessarily, but I’d like to think it helps! I love to analyze the results of what my students have learned. I love to see that they get it. That earns me my own bonus point. But, honestly, I love seeing possible ways to reteach lessons that dogged even the top performers. Data analysis makes all this possible; it is the crux of good teaching.