No One Strategy Is Best For Teaching Reading, Study Shows

Carol M. Connor is an assistant professor in the FSU College of Education and a researcher with the Florida Center for Reading Research. Along with colleagues from FSU and the University of Michigan, she wrote "Algorithm-Guided Individualized Reading Instruction," published in Science's Jan. 26 issue.

Connor's paper shows that lots of individualized instruction, combined with the use of diagnostic tools that help teachers match each child with the amounts and types of reading instruction that are most effective for him or her, is vastly preferable to the standard "one size fits all" approach to reading education that is prevalent in many American elementary schools.

"There is too much of a tendency in education to go with what 'sounds' really good," Connor said of various educational trends that come into and fall out of fashion. "What we haven't done very well is conduct comprehensive field trials and perform the rigorous research that are the norm in other fields of science. With this study, we sought to do just that - to take a systematic approach to what works, what doesn't, and why" when teaching students to read.

The researchers found that "the efficacy of any particular instructional practice may depend on the skill level of the student. Instructional strategies that help one student may be ineffective when applied to another student with different skills." The trick, then, is to more precisely determine the reading skill level of each child and then find a way to cater the curriculum to each student's individual needs.

"Instead of viewing the class as an organism, we're trying to get teachers to view the students as individuals," Connor said.

While that may sound daunting to the typical first- or second-grade teacher, Connor has turned to technology to offer a helping hand. She, Frederick J. Morrison and Barry Fishman, professors at the University of Michigan, have developed "Assessment to Instruction," or A2i, a Web-based software program. A2i uses students' vocabulary and reading scores and their desired reading outcome (i.e. their grade level by the end of first grade) to create algorithms that compute the recommended amounts and types of reading instruction for each child in the classroom. The software then groups students based on learning goals and allows teachers to regularly monitor their progress and make changes to individual curricula as needed.

A2i currently is being tested by about 60 elementary-school teachers in one Florida county. However, "right now A2i is just a research tool," Connor said. "Hopefully we'll be able to make it available more widely as time goes on."

Source: Florida State University