Copying helps novice learners build orthographic knowledge: methods for teaching Devanagari akshara
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Hindi graphs, called akshara, are difficult to learn because of their visual complexity and large set of graphs. Akshara containing multiple consonants (complex akshara) are particularly difficult. In Hindi, complex akshara are formed by fusing individual consonantal graphs. Some complex akshara look similar to their component parts (transparent), whereas others do not (opaque). We taught 35 English-speaking adults a semi-artificial orthography that was modeled on the Devanagari script used for Hindi and other Indic languages. Participants were taught 80 complex akshara using 4 different methods: (1) choosing the components (from several choices) given the graph (2) choosing the correct graph (from several choices) given its components, (3) copying a graph while the graph and its components are displayed, and (4) writing a graph from memory given its components. Methods 1 and 2 compare emphasis on part-whole versus whole-part relationships, methods 1 & 2 and 3 & 4 compare motor effects, and methods 3 and 4 compare testing effects. We found that transparent graphs were better learned than opaque graphs. Testing on the akshara typically did not improve learning and there were few effects of emphasis on part-whole versus whole-part relationships. There was evidence for motor effects; copying & writing the akshara improved pure orthographic knowledge and people’s ability to produce the phonological form of a given akshara. These results corroborate other studies showing that copying and writing graphs helps beginning learners of English, Chinese, and Arabic build orthographic knowledge. Copying was more time efficient than writing, suggesting that having beginning learners copy akshara is an important pedagogical tool to use in classrooms.
KeywordsOrthographic learning Testing effect Handwriting Writing Multiple choice Akshara Alphasyllabary
This research partially fulfills the dissertation requirements for AB. I would like to thank my advisor, Charles Perfetti, for input on experimental design and feedback on manuscript drafts. I would also like to thank my committee members, Natasha Tokowicz, Julie Fiez, and Marta Ortega-Llebaria for help with experimental design and useful comments. Furthermore, I would like to thank Scott Fraundorf for his assistance with the statistical analyses. Moreover, I would like to thank my lab manager, Kimberly Muth, for assistance with IRB protocols and participant payment. Finally, I would like to thank Austin Marcus for help with data collection and entry. This research was supported by NSF PSLC [grant SBE08-36012] and the Andrew Mellon Predoctoral Fellowship awarded to AB.
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