Learning to read
Can factors that affect skill learning be harnessed to improve reading? Beginning readers must decode letters on the page into the sounds of language. These decoded sounds can then be linked meaningful ideas and concepts, allowing children to leverage what they know (spoken language) to learn a new skill (reading). Traditionally, decoding was taught as a process of memorizing a large set of rules that translate letters to sounds (e.g., “A with a silent E makes the ‘long A’, as in LATE”). However, while rules are clearly an important educational tool, many psychologists now view learning to read as a process of encoding probabilistic relationships between the written letters and the sounds rather than explicit rules. As a result, learning to read may be more like acquiring a skill, like shooting a basketball, than like memorizing a list of rules. It is therefore potentially significant for the instruction of reading that basic research has uncovered several principles that improve skill acquisition. We test these principles in first-graders learning to read by partnering with a private-sector reading-technology company to develop short-term studies in which students learn a handful of decoding skills. To examine the correspondence between reading and skill acquisition, we have devised a motor task that captures the same kind of probabilistic input-output relationships that are required for decoding in word reading.
Translating this science to education is difficult and principles from cognitive science are rarely applied in the classroom. For reading, instructional curricula emphasizing decoding and phonics are more successful than others. Such approaches give children mastery of the letter->sound mappings or Grapheme Phoneme Correspondence (GPC) regularities. This can lead to fluent word recognition and eventually comprehension. These letter->sound mappings are not so different than the SR mappings studied in learning theory, making reading and cognitive science an apt comparison. Yet, the GPC mappings used in decoding are full of exceptions and quasi-regularities and are used in a complex array of tasks. Thus, applying the science of learning to the classroom is not straightforward.
Numerous laboratory studies show that variability in irrelevant stimulus components improves statistical learning; we applied this to reading in a short term learning study examining GPC regularities involving vowels. The results were clear. Across a wide range of factors, including initial performance level and learner gender, practice with variable items led to significantly greater improvements in the decoding skills we taught than practice with similar items, and to greater generalization to novel words and tasks (Apfelbaum, Hazeltine, & McMurray, 2012).