It’s all about data—sorry, Spock

Four students in a classroom talking with a male professor

Most people would agree that the primary job of an educator is to support student learning. In a world awash in data, it also appears to them that whether you consider the K-12 system or higher ed, educators are fixated on grades or results from tests. And, yes, that is data that can be useful;  however, K-12 teachers spend an entire year collecting all sorts of information about students, not just one standardized test. In higher education, it may be a boot camp, five-week summer course, ten-week term, or traditional 14 or 15 week semester, but the same fact holds true for us. We collect information that influences how we refine what we do, and it’s not just based on a midterm or final exam.

Not all of us are statistics “nerds,” but educators do need to have a solid understanding of data. We are increasingly called upon to support decision making with good data; we all know data can be manipulated and misinterpreted, so we want to be sure to truly grasp the meaning of all that information. We need to be able to use this data to make decisions that best support learning in our classrooms. How do we find the scientific evidence for what we do every day?

In Integrating Cognitive Science with Innovative Teaching in STEM Disciplines by Mark A. McDaniel, Regina F. Frey, Susan M. Fitzpatrick, and Henry L. Roediger III, the authors discuss the fact that learning happens outside of formal instruction, and learners need to know what to study, how to study, etc. Technology tools offer a variety of options for how to manage learning. Because it’s important to know how to learn not just during “school” but for a lifetime, it is critical that we use the data and information we have as educators to develop and fine-tune methods that not only teach content, but cognition skills. Research articles suggest, however, that we often mismanage our own learning. In these articles, they explore the evidence that intuition and standard use of data are not always the best guides to optimize learning. They argue that there are tools to help learners and educators both develop practices to support effective learning. Later in the text, the authors explore strategies to help students get the most learning in limited time. They explore the use of  successive relearning as a valuable strategy for achieving learning. Here data clearly plays a role. Practice tests and spaced study are both highly critical for enhancing learning and memory.  If you combine these two methods (i.e., use practice tests that invoke successful retrieval from long-term memory and space study across days), you have the promising technique referred to as successive relearning.

Of course, the concept of personalized learning is also a current focus in education. Brian Fleming wrote in March 2015 on the EduVentures site,  that  adaptive learning is “both a concept and a tool that enhances learning through highly sophisticated technology platforms that enable rapid personalization and the collection of learning analytics.” The data collected from the adaptive software helps researchers and faculty alike look for trends in where students succeed and struggle. Researchers at Carnegie Mellon, through the Open Learning Initiative (OLI), have called our attention to “learning mechanisms,” or the patterns in human learning. And they, as well as many other researchers, have looked at studies about multiple intelligences, learning behaviors, impulses, memory and recognition, and the effects of social learning. These studies are fascinating, as we learn more about how complex learning is; yet, in the complexity, the data shows us some very clear, simplistic things that work.

Visit a site like the Center for Integrative Research in Cognition, Learning, and Education, and you will find a wealth of articles and research to help you think about data. Consider the power of well-designed graphs to display data. Have you seen the work of Edward Tufte?  His resources are not just eye-catching; he addresses the need for quality graphics. If that resonates with you, and you find yourself needing a user-friendly tool to work with data and create some graphs, try StatCrunch. StatCrunch makes it just as easy to import web-based data as to open a saved data file from your computer. StatCrunch has a thorough help site and its own YouTube channel, too. In addition, if you are using a Pearson MyLab & Mastering product, you can use your Reporting Dashboard tool to create some meaningful graphics about mastery, item analysis, trends, and assignment performance in your courses.

So, Spock, take a look. The scientific analysis of data has met the art of teaching. It’s the future – and the here and now – of education.


About the Author
Diane Hollister

Diane Hollister

Diane Hollister has been teaching college courses since 1992. In June 2015, she resigned from her full-time position at Reading Area Community College in Reading, Pennsylvania, where all the math courses have undergone some level of redesign. She still teaches online there and now is part of Pearson’s Efficacy team, helping instructors to implement programs and strategies that bolster student success.

She is intrigued by neurobiological research and learning theory, and she was quick to adopt adaptive learning as a new tool in her courses. Not only does she strive to help her students succeed, but Diane enjoys the collaboration with her peers. She has taught a variety of courses and loves learning how new technology and resources can help students be more successful.

Read more of her articles about math, ICTCM, and quantitative reasoning.