Page 30 - iRead EL in Research Paper

Research Evidence and Expert Opinion
A learner’s ability to retrieve relevant knowledge and information can vary from being “e ortful” to “relatively e ortless” to “automatic” (Cohen, Dunbar, & McClelland, 1990). Research shows that the mastery of a knowledge domain, such as reading, depends on the ability to perform sub-processes unconsciously with speed and accuracy while consciously carrying out other higher-level cognitive tasks (Bloom, 1986; Hasselbring, et al., 1988; LaBerge & Samuels, 1974). However, before gaining automaticity, beginning learners must exert substantial e ort to retrieve the necessary information about a new skill from their short-term memory. This retrieval process creates a cognitive load that can inhibit their ability to engage in other learning processes at the same time (Adams, 1990).
Accordingly, beginning readers often struggle with the cognitive challenge of decoding text accurately and with uency, while simultaneously attempting to comprehend what they are reading. This is why automaticity is so critical in reading,
for only when students can decode words without having to devote much conscious e ort to the task (automaticity) and apply the proper rhythm, intonation, and phrasing ( uency), can they su ciently free up the cognitive powers necessary for comprehension (Freedman & Calfee, 1984; LaBerge & Samuels, 1974).
RECOMMENDATION
To support di erentiated early reading instruction, use a technology-based adaptive system that teaches a systematic sequence of decoding skills to build automaticity.
iRead’s Approach
HMH has collaborated with education technology experts Ted Hasselbring and Laura Goin to adapt their Fluency and Automaticity through Systematic Teaching with Technology (FASTT) model to enable explicit, systematic instruction in foundational reading skills. The FASTT model facilitates the learning transfer from e ortful practice attempts that rely on short-term memory to stable, automatic, learned elements in long-term memory, by introducing manageable sets of items, providing repeated exposures, spacing review, and shortening response time.
By providing intensive, accelerated instruction in phonological decoding skills, iRead ’s implementation of FASTT enables young learners to transfer these new skills to long-term memory, so that the act of decoding becomes automatic, accurate, and quick. Research has shown the e ectiveness of the FASTT model in multiple instructional contexts (Hasselbring, Goin, & Bransford, 1988; Scholastic Research and Validation, 2005; 2008; Slavin, Cheung, Gro , & Lake, 2008).
FASTT consists of the following sequence of instructional procedures:
1. Assessment of the learner’s current level of accuracy and response time (to individualize instruction)
2. Use of a small instruction set that is to be moved from “working memory” to long-term memory (Miller, 1956)
3. Use of an expanding recall presentation structure that gradually intersperses presentation of new skills based on continual measurement of the learner’s ongoing performance
4. Use of a stringent and controlled response time and accuracy as measures of automaticity—to adjust instruction and practice accordingly
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