A methodology for developing an error taxonomy for a computer assisted language learning tool for second language learners Export

@misc{suri93methodology, abstract = {This paper discusses linguistic issues that one must address in order to design an effective CALL system for second language learners. We focus on how one should develop an error taxonomy and indicate how that taxonomy can affect the design of the entire system. This work was done in the context of designing a CALL tool to help native signers of American Sign Language (ASL) learn written English. Here we report the methodology used in developing the error taxonomy for the system. Our analysis ...}, author = {Suri, L. and Mccoy, K.}, citeulike-article-id = {598996}, citeulike-linkout-0 = {http://citeseer.ist.psu.edu/suri93methodology.html}, citeulike-linkout-1 = {http://citeseer.lcs.mit.edu/suri93methodology.html}, citeulike-linkout-2 = {http://citeseer.ifi.unizh.ch/suri93methodology.html}, citeulike-linkout-3 = {http://citeseer.comp.nus.edu.sg/suri93methodology.html}, comment = {Paper looks at language transfer between L1 ASL and L2 English. Tries to develop a taxonomy of errors based on a feature-level analysis of L1 and L2. Doesn't give an explicit taxonomy, though. ---=note-separator=--- Features in a language: 1. When they mark a feature (redundancy or conciseness, always required or hardly required) 2. How they mark a feature 3. Whether they have a feature 4. One-to-one mapping between lexicon in L1 and L2? ---=note-separator=--- Corpus size: 21 writing samples (370 sentences, 3490 words) ---=note-separator=--- Classification/counting of errors is hard: 1. Ambiguity: (multiple errors explain surface level--"the dress look pretty") 2. Ambiguity (multiple error sources explain surface error--no marked tense == bad tense knowledge or bad knowledge of context?) 3. Errors interact \& cascade ---=note-separator=--- And, most damningly, error counts don't directly map to level of language acquisition.}, keywords = {asl, call, icicle, learner\_errors, pedagogy, sla, taxonomy}, posted-at = {2006-04-25 00:12:44}, priority = {0}, title = {A methodology for developing an error taxonomy for a computer assisted language learning tool for second language learners}, url = {http://citeseer.ist.psu.edu/suri93methodology.html}, year = {1993} }

TY - GEN ID - suri93methodology L3 - citeulike-article-id:598996 N2 - This paper discusses linguistic issues that one must address in order to design an effective CALL system for second language learners. We focus on how one should develop an error taxonomy and indicate how that taxonomy can affect the design of the entire system. This work was done in the context of designing a CALL tool to help native signers of American Sign Language (ASL) learn written English. Here we report the methodology used in developing the error taxonomy for the system. Our analysis ... TI - A methodology for developing an error taxonomy for a computer assisted language learning tool for second language learners KW - asl KW - call KW - icicle KW - learner_errors KW - pedagogy KW - sla KW - taxonomy N1 - Paper looks at language transfer between L1 ASL and L2 English. Tries to develop a taxonomy of errors based on a feature-level analysis of L1 and L2. Doesn't give an explicit taxonomy, though. N1 - Features in a language: 1. When they mark a feature (redundancy or conciseness, always required or hardly required) 2. How they mark a feature 3. Whether they have a feature 4. One-to-one mapping between lexicon in L1 and L2? N1 - Corpus size: 21 writing samples (370 sentences, 3490 words) N1 - Classification/counting of errors is hard: 1. Ambiguity: (multiple errors explain surface level--"the dress look pretty") 2. Ambiguity (multiple error sources explain surface error--no marked tense == bad tense knowledge or bad knowledge of context?) 3. Errors interact & cascade N1 - And, most damningly, error counts don't directly map to level of language acquisition. AU - Suri, L AU - Mccoy, K PY - 1993/// UR - http://citeseer.ist.psu.edu/suri93methodology.html ER -