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12 Mid-Western Educational Researcher Volume 22, Number 1 · Winter 2009

mind·set or mind-set (mndst) n A fixed mental attitude or disposition that predeter-• mines a person’s responses to and interpretations of situations. An inclination or a habit.•

Introduction

In this address, I plan to discuss some things that I am very passionate about; some things I think are very important for professional educators—and education, in general—and hopefully give you some food for thought as you go forward in practicing in your specific chosen field. Specifically what I want to do initially is to “dissect” the notion of data-driven in- structional decision making by first discussing “instructional decision making,” then talking about the data-driven aspect. I’m also going to incorporate a discussion of merging these concepts as a single broad educational process and argue for the inclusion of three critical components in that process. I think I’m correct in assuming that we are all familiar with these critical components, but that we’ve not really looked at these concepts as integral parts of a single process. This is one of the key aspects that I hope you take away from my talk this morning. Finally, I am going to try to integrate dis- cussions of the roles and responsibilities of both researchers and practitioners in these educational processes.

Instructional Decision Making: The Art of Teaching

Let’s begin by taking a look at instructional decision making. My working definition for this term is the notion that all educators are constantly making decisions about

educational programs, curriculum, instructional styles, and instructional materials. You name it…we make decisions about it. Hopefully, the reason that we are making those decisions has its basis in our continuing effort to maximize student learning. Let’s face it…that’s why we’re in this business. In the past—and, probably, the not so distant past—a lot of instructional decision making was based on “gut instinct.” It was based on that feeling or that sense that, as educators, we know what works with students; we know what doesn’t work with students. Let me put that in a more specific context. We know what works with our students and we know what doesn’t work with our students. The fact that we are talking about the students that we teach or of whom we are in charge is really a key feature of what I want to fo- cus on. The problem with relying on gut instinct as the basis for instructional decisions is that it is not a very systematic process. Teachers, or educators in general, often try different instructional approaches. Sometimes they work, but most of the time they do not. Therefore, what we end up with is a sort of “trial-and-error” process that often results in a good deal of frustration. I am sure that you can recall examples from your own teaching. We have sketched ideas out on paper and they looked great. However, when we ultimately try them with our students, our ideas have failed miserably. Please note that I said that our “ideas failed,” not that we failed. The reason that I say that is because we have to remember that we still learned something through our trial-and-error process. We have learned what not to do, what did not work. It is crucial to remember that this is still beneficial to the teaching and learning process.

Simply put, what I am referring to with this practice of “gut instinct decision making” is the art of teaching. Now,

2008 MWERA Presidential Address

A Systematic Approach to Transforming the Art of Teaching Into the Science of Teaching:

Developing a D-DIDM Mindset Craig A. Mertler

Bowling Green State University

Abstract Data-driven instructional decision making (or D-DIDM) is a “process by which educators examine [data] in order to identify student strengths and deficiencies” (Mertler, 2007). My view of the process of D-DIDM merges three critical educational practices: classroom-based (or site-based) action research, assessment of student learning, and reflective practice. Each of these practices are discussed individually, followed by an examination of the union of the three into a comprehensive approach to D-DIDM. The roles and responsibilities of researchers and practitioners in this process is also integrated throughout the discussion. My address is intended to motivate educators at all levels to seriously and conscientiously consider integrating D-DIDM into their classroom practice.

(Presidential Address continued on page 17.)

Volume 22, Number 1 · Winter 2009 Mid-Western Educational Researcher 17

I firmly believe that teaching, at any and all levels, is an art form. There are some skills that just cannot be taught; there are some skills that cannot be learned. I am sure that if you think back, you can recall a teacher that you have had so much respect for because that teacher just “got” you, helped you, reached you. When you walked into that teachers’ classroom or out of that classroom on a given day, you were inspired. You were taught something that you did not know before and that was a great feeling, wasn’t it? Now, try to recall a teacher who might be located at the opposite end of that spectrum. All of us have had teachers who we knew just did not get it. They were not that “artist” in their respective classrooms. As students, we could sense that. But remember how we are sensing that. It is sort of that gut feeling; we just know it when we see it.

Now, rest assured, I do not ever mean to take anything away from teachers who possess that art of teaching because it is a very important and integral part of the educational pro- cess. In contrast, what I want to do is to take “teaching as an art form” a little bit further than that and suggest some things that hopefully build and extend this notion of good classroom teaching. When it comes to the art of teaching, I believe that both researchers and practitioners have responsibilities. I believe that researchers have responsibilities for suggesting alternatives for educators to examine and consider trying as part of their trial-and-error process. The reason that I think that this is an important responsibility for researchers is be- cause oftentimes, as researchers, we know were to find these resources; sometimes practicing educators may not know all of the resource capability and availability that we might. I think as researchers, we have a responsibility to work with educators and to suggest various ideas and alternatives, hope- fully based on existing research. Of course, whenever we do this and suggest that educators use these alternatives in their particular settings, we immediately have issues of generaliz- ability, along with a host of other potential implementation problems. Simply because an idea worked in the setting in which we read about it obviously does not mean that it is go- ing to work in our setting. Unfortunately, this is not a perfect blending of the responsibilities of researchers and the task at hand (i.e., helping educators to be more effective).

I think that practitioners also have similar responsibili- ties, in that they need to consider research-based alternatives, and to be willing to try them in their settings. Eventually, educators still must engage in the trial-and-error process, and this continues to be a frustrating part of the process. However, I think that both researchers and practitioners have to be willing to examine resources that they may not have examined in the past. For example, if there is a great Web site that you go to for ideas, that is great, but you do not want to limit yourself to just that one Web site. You want to expand your options and look at other resources. I think that both researchers and practitioners have a responsibility to do

these things and to do them collaboratively (I will revisit this notion of collaboration later…).

Data-Driven: The Science of Teaching

Let’s shift to the other component of “data-driven in- structional decision making” (i.e., the data driven part). As I define it, data-driven is the notion that questions or problems require information in order to be answered appropriately and to the best of our abilities, and that the decisions that result from those questions and actions are based on evidence. In other words, they are based on information that we gather so that they are not just our gut instincts or reactions. There is more to it than just gut instinct. Historically, when you see the term “data-driven” in most of its contexts, it has a very, very narrow definition. That definition is limited to data in the form of standardized testing results. Why has there been such a narrow view? I believe that is because we tend to equate “data” with numbers, and test scores are numbers and therefore that’s data-driven. I believe that this is a very, very limited perspective. Part of the reason that I view this as a very limited perspective has a lot to do with the types of things that all of us have likely experienced when it comes to standardized testing, as a student taking a test, a teacher trying to prepare students to take a test, an administrator trying to motivate our teachers to prepare students to take tests, parents who have to deal with the results of the tests, etc. It just sort of makes you want to pull your hair out on a regular basis!

I personally do not hold this narrow view of data-driven evidence. My approach to the notion of data-driven can be summarized in the following quote:

I honestly don’t know anyone who loves standardized testing! But the standardized testing movement is not going away anytime soon. An ex- amination of its impact on this country’s educational system over the past 40 years will confirm that. Therefore I approach it from this perspective…and I strongly suggest that all professional educators adopt a similar attitude. Anytime we are given the responsibility of making decisions about children, we need as much information as possible in order for those decisions to be as accurate as possible. We ask students questions; we ask them to read to us; we require them to write for us; we test them over units of instruction; we observe them; we encourage them to be creative; we engage them in performance based tasks; etc. The results from standardized tests are just another source of information—about stu- dent learning, about our teaching, and about our curriculum. Please use them as such—add them to your long list of various sorts of information about student learning. They can only help improve the accuracy of the decisions that we make about our students, as well as our own instruction. (Mertler, 2007)

(Presidential Address continued from page 12.)

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Therefore, I do not have the limited view that the only things that can guide data-driven decisions are test scores. In fact, the way that I view all of this is that nothing should limit you in terms of the kinds of data that you collect in order to guide data-driven decisions, provided they are sound data. They can be based on a wide variety of sources of informa- tion about students. They can certainly be based on teacher- developed classroom tests, performance-based assessments, and informal classroom assessments techniques or tasks. Consider one of several informal classroom assessment tech- niques, called a “one-minute paper.” A minute or two before students leave the classroom, the teacher says “Take out a note card and complete this sentence: One thing I learned today that I didn’t know coming in is ___________,” or “The one thing that I’m still confused about is ___________.” If you think about it for a moment, a technique such as this provides a very efficient means of collecting some highly valuable information. If a teacher did not take that little bit of effort to collect this information at the end of a class period, there are potentially lots of things that he or she walked out of class not knowing about the students and vice versa. Other sources of meaningful student information include student journals, student reflections, interviews with students, and surveys of students (whether they be content-based surveys, attitudinal surveys, or affective surveys). All of these sources provide potential information about students and their learning that can be very beneficial. What I am really encouraging you to do is to develop an assessment system that includes both formative and summative assessments. You should not limit yourself in terms of the kinds of things that you can incorpo- rate in this overall broad system of data-driven evidence.

Earlier, I talked about “instructional decision making” comprising the gut reaction aspect in the art of teaching. To me, the “data-driven” component is the science of teaching. It provides a more scientific and systematic approach to this decision making process. I do not think that those two things—the art of teaching and the science of teaching—are mutually exclusive. I hope that, as educators, we would do both of these. First, I would never want to take anything away from the teacher who is a true artist in his or her classroom, because that is a rare entity. I would never encourage some- body not to do those things. However, there are a lot of other things that we can also incorporate into that process, in order to improve that process. I believe that both researchers and practitioners have a great deal of responsibility here as well.

We need to promote the notion of the data-driven science of teaching from the researcher perspective.

If we extend the idea of considering classroom alter- natives and options and do so from a data-driven (i.e., the science of teaching) perspective, what I am really referring to is focusing on a more systematic approach to weighing alternatives and options. Employing a systematic approach implies that we utilize the scientific method. This means that we’re going to generate ideas, develop hypotheses, design a scientific investigation, collect data, analyze those data, draw conclusions, and then start that cycle all over again by developing new hypotheses. (One of the other things that I will revisit later is the whole notion of all of these things being cyclical—this is not a “one time thing and then we stop” type of approach.) If we examine this from the prac- titioner perspective again, we will consider alternatives and options, but will do so in a more systematic fashion. This improved trial-and-error process is shown in Figure 1. It is still a “trial-and-error” process, but the “trial” piece becomes a lot more systematic and incorporates a good deal of profes- sional reflection. During the process of reflection, several questions should be addressed:

How well did the idea work?• Next time I do this, how am I going to do it dif-• ferently? What do I need to do to extend what I have already • tried? If my idea did not work, what am I going to do • differently?

Contrary to the figure, this is not an “end-of-the-road” kind of process (note the arrow at the bottom). Based on their relative effectiveness, ideas should be revised and the revisions implemented again. It is important to recognize that sometimes the time frame from the first cycle to the next cycle maybe a year apart, especially if you are teaching in a K-12 setting. A benefit of finding yourself in this situa- tion is that you have a year to reflect and generate ideas for the revised implementation during the subsequent cycle. It should be fairly obvious that this is a much more systematic process than just finding ideas on the Internet, throwing them together, and seeing how they fly. Therefore, the proverbial bottom line for me is that teaching, and education in general,

Figure 1. A more “systematic” process of trial-and-error.

Figure 1. A more “systematic” process of trial-and-error.