Language and numbers must be encoded in visual symbols in order to render them accessible through print. That raises a new demand - decoding the symbols - which is trivial for many students but raises significant barriers for some. The majority of the experimental studies listed here focus upon the effectiveness of providing automatic text-to-speech for students who have especial difficulty decoding text. Studies find that students' lack of fluency acts as a barrier to comprehension and that decoding support can provide students access to content. Research on automatic text-to-speech continues to grow, and we hope to expand this list as more studies become available. Furthermore, there is limited research on the effectiveness of providing support for decoding mathematical notation (Mathematical Markup Language, or "Math ML") as this is an emerging area. Again, we hope to add to this list as more research is completed. The scholarly reviews and opinion pieces provide more classroom-based perspectives on proving decoding support to students. The specifications defining MathML are also included in this listing.
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Last Updated: 02/01/2011