Today’s reading programs also strong on science

Researchers have learned a great deal of information in the last 30 years about how humans acquire knowledge, and this understanding provides important insights for using technology to enhance students’ reading skills.

At the 2008 Florida Educational Technology Conference (FETC) in Orlando, Vanderbilt University professor Ted Hasselbring discussed what researchers have discovered about the science of learning–and how software providers are using this information to create reading programs that work.

Only 38 percent of the students who aren’t reading well by middle school will graduate from high school, Hasselbring warned in a Jan. 24 keynote session. He said there are two key problems that typically hold students back: an inability to decode and read connected text fluently, and an inability to comprehend text.

Hasselbring showed conference attendees a short block of text and asked them to try reading it in 20 seconds. But, by putting capital letters where they didn’t belong, he made the task extremely difficult.

Simply changing the way the text looks “interferes with our neural models for how words should appear–or what our brains have come to expect,” he said.

A functional MRI taken as students are reading shows that the brain activity for struggling readers is different, Hasselbring explained: There is not as much activity in the back of the brain, where we recognize and retrieve words very quickly from our memory.

“When students lack fluency in foundational skills, performance is likely to be painfully slow, difficult, and full of errors,” he said. This leads to comprehension problems, because kids can’t read with enough speed and flow to make sense of the words together.

The automaticity with which skillful readers recognize words is the key to successful reading, Hasselbring said, because the reader’s attention can focus on comprehension only when it is not bogged down with decoding words and letters. “So, the key to successful reading is developing neural models for quick word recognition,” he said.

Developing these neural models requires students to move from using the alphabetic principle to recognizing entire words automatically, Hasselbring said. And the more times students read a word correctly, the stronger their neural pathway becomes to retrieve that word rapidly from their long-term memory.

Dennis Pierce

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