This is the fourth (and last) in a series on “Super Hero” teachers, and what it might take for anyone to become a super hero for all learners.

Superheroes…those larger than life beings with special powers who beat tremendous odds to make great things happen, saving the world along the way. We love our superheroes and their extraordinary ability to help individuals, all of us, live a better life in a better world. For some, what’s missing from being a Superhero Teacher are a handful of distinct skills and techniques that any educator can learn and use, creating a world where every child comes to school eager to learn, able to learn, with teachers eager and able to teach. That’s the kind of world Superhero Teachers with these special powers can create. Previous posts revealed superpowers 1-5 (High Rates of Real Reinforcement, Effective Planned Ignoring, Excellent Antecedent Control, Frequent Active Student Responding and Meaningful Measurement). And finally, below are habits 6 and 7 of Superhero Teachers.

Superpower 6: Informed (Data-Based) Decisions

Data-based decision making (DBDM) is fundamental to instruction. Student performance is frequently measured and graphed, and timely instructional decisions (e.g., whether to continue as is or make changes in instruction) are made based on the resulting picture of student learning (Heward, 2003). If the picture indicates that the student is learning, the superhero teacher knows to continue as is. If the picture indicates insufficient or no progress, the superhero teacher knows to make an immediate change in the learning scenario. The change may be instructional (e.g., establishing more foundational skills) or motivational (e.g., increasing personalized learning), or a combination of both.

The picture painted by the visual display of data can help inform the type of change that might need to be made, and will later show the effects of the change (to indicate continuation or further change). The term “data-based,” or “data-driven,” in educational decision making is used to describe the collection and analysis of input, process, outcome, achievement, and satisfaction data, to guide a range of decisions to inform accountability and, most important, to help improve the success of students, teachers, and schools (Marsh, Pane, & Hamilton, 2006).

What is the evidence base?
Repeated measures graphed on standard displays allow for sophisticated data analysis and decision-making protocols (see Greer, 2002; Horner et al., 2004), and help educators analyze their curricula, resources, and professional development to produce more efficient and effective programs (Kekahio & Baker, 2013). Teachers who learn methods to examine student data (and adjust instruction accordingly) produce higher gains in learning (Wayman & Stringfield, 2006), an effect that holds up in a meta-analysis of 250 studies (Black & William, 1998).

Why is making informed (data-based) decisions a superpower?
Shorr (2003) is among those who have used the following maxim to stress the importance of data: “If you’re not using data to make decisions, you’re flying blind.” The ability to see exactly where their students are in their learning and motivation, and what effect their teaching is having, allows superhero teachers to rapidly adapt, make decisions, and course-correct whenever needed—not only at the end of a week, unit, semester, or worse, school year—but at any moment in time.

Where can I get more “how to” information?

Superpower 7: Fluency (in All of the Above)

Superheroes don’t fumble around. They’re quick to know what to do and how to respond. Their superhero powers are at the ready whenever needed, and they know which power to use and when to use it. Superhero teachers are the same. They are fluent, automatic, and smooth in all their superpowers. They can praise quickly, and seemingly almost automatically ignore inappropriate behavior. They can provide high rates of active student responding across various subject matter and topics as long as needed, and prevent clear antecedents even while distracted. They can measure behavior and analyze data even while doing other things. They can even use their superpowers again after long periods of non-use, although what superhero teacher wouldn’t always use his or her superpowers?

Being at the ready is a benefit of being fluent. Being fluent at something means it’s easy, effortless, and almost automatic. We think of people as experts when they do things automatically. Superhero teachers are fluent in all their powers. And superhero teachers do one more thing: They make sure their students are fluent in all the things they need to do as well. They ensure fluency in basic math operations before moving on to more complex ones; they build fluency in short blocks on writing or public speaking before moving on to essays and speeches; they ensure fluency in asking good questions and logical thinking before advancing to debating and scientific problem-solving.

What is the evidence base?
There is strong evidence that fluent performance produces additional learning outcomes, including longer retention and application of skills, greater stability under disruptive conditions, and ease of application of that skill in more complex and even initially unrelated skills (Johnson & Layng, 1992). In addition, accuracy at appropriate speed has been found to be a significant indicator of expertise and thus helps educators differentiate between learners who have or have not mastered a particular skill (Binder, 1990, 1996).

Why is fluency a superpower?
Closely related to active student responding (Superpower #4), fluency almost always requires lots of practice and time spent in the skill. With fluency, teachers not only get better at what they do, they get faster, with more resistance to distraction and greater endurance. Once teachers are fluent at something, nothing can stop them. Fluency is a superpower because it helps superhero teachers become invincible in the things important to them.

Where can I get more “how to” information?

Each of these seven superpowers is achievable by each and every teacher in each and every classroom, in every school and learning environment in the world. Let every teacher be a superhero teacher.

Binder, C. (1990). Precision teaching and curriculum based measurement. Journal of Precision Teaching, 7(2), 33–35.
Binder, C. (1996). Behavioral fluency: Evolution of a new paradigm. The Behavior Analyst, 19(2), 163–197.
Black, P. J., & William, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, policy and practice, 5(1), 7–73.
Greer, R. D. (2002). Designing teaching strategies: An applied behavior analysis systems approach. New York: Academic Press.
Heward, W. L. (2003). Exceptional Children: An Introductory Survey of Special Education (7th ed., p. 40). Upper Saddle River, NJ: Merrill/Prentice Hall.
Horner, R. H., Todd, A. W., Lewis-Palmer, T., Irvin, L. K., Sugai, G., & Boland, J. B. (2004). The school-wide evaluation tool (SET): A research instrument for assessing school-wide positive behavior support. Journal of Positive Behavior Interventions, 6(1), 3–12.
Johnson, K. R., & Layng, T. V. J. (1994). The Morningside model of generative instruction. In R. Gardner, III, D. M. Sainato, J. O. Cooper, T. E. Heron, W. L. Heward, J. Eshleman, & T. A. Grossi (Eds.), Behavior analysis in education: Focus on measurably superior instruction (pp. 173–197). Pacific Grove, CA: Brooks/Cole Publishing.
Kekahio, W., & Baker, M. (2013). Five steps for structuring data-informed conversations and action in education (REL 2013–001). Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Regional Educational Laboratory Pacific. Retrieved from http://ies.ed.gov/ncee/edlabs.
Marsh, J. A., Pane, J. F., & Hamilton, L. S. (2006). Making sense of data-driven decision making in education. Santa Monica, CA: The RAND Corporation
Shorr, P. W. (2003, September). 10 things you always wanted to know about data-driven decision making. Scholastic Administ@r. Retrieved from http://www.scholastic.com/browse/article.jsp?id=423
Wayman, J. C., & Stringfield, S. (2006, August). Technology-supported involvement of entire faculties in examination of student data for instructional improvement. American Journal of Education, 112(4), 549–571.


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