Matrix models of second language vocabulary acquisition
Most of the current research in L2 vocabulary acquisition has been too focused on what it is to learn a word, and has neglected how whole vocabularies grow or decline. In general, it is assumed that vocabulary gains and losses are incremental and follow a linear progression. This thesis postulates a model which considers several discrete stages of knowledge and accounts for the unstable nature of vocabulary knowledge, where words can change from one state to any other. Matrix algebra is a tool capable to operate with such a model and produce long-term forecasts of vocabulary size. Our experimental work describes the retention and the overall growth of the vocabulary of advanced learners of Spanish. These experiments show that forecasts of vocabulary size generated by the matrix model are far more accurate than those generated by a linear model. With data from two self-rating tasks containing a large number of words completed within a given lapse we build matrices which generate forecasts of vocabulary knowledge. These forecasts highly correlate to the actual knowledge measured three and four months later. This methodology is tested with subjects of various groups, using words from different frequency bands,
Rodrigeuz-Sanchez, I.
Matrix models of second language vocabulary acquisition. Most of the current research in L2 vocabulary acquisition has been too focused on what it is to learn a word, and has neglected how whole vocabularies grow or decline. In general, it is assumed that vocabulary gains and losses are incremental and follow a linear progression. This thesis postulates a model which considers several discrete stages of knowledge and accounts for the unstable nature of vocabulary knowledge, where words can change from one state to any other. Matrix algebra is a tool capable to operate with such a model and produce long-term forecasts of vocabulary size. Our experimental work describes the retention and the overall growth of the vocabulary of advanced learners of Spanish. These experiments show that forecasts of vocabulary size generated by the matrix model are far more accurate than those generated by a linear model. With data from two self-rating tasks containing a large number of words completed within a given lapse we build matrices which generate forecasts of vocabulary knowledge. These forecasts highly correlate to the actual knowledge measured three and four months later. This methodology is tested with subjects of various groups, using words from different frequency bands,
matrix model second_language vocabulary_acquisition current vocabulary_acquisition focus learn word neglect whole vocabulary grow decline general assume vocabulary gain loss incremental follow linear progression postulate model considers several discrete stage knowledge account unstable nature vocabulary_knowledge word change state other matrix tool capable operate model produce long_term forecast vocabulary size experimental_work describe retention overall growth vocabulary advance learner spanish show forecast vocabulary size generate matrix model far accurate generate linear model data self rating task contain large_number word complete within give build matrix generate forecast vocabulary_knowledge forecast highly correlate actual knowledge measure tested subject various group use_word different_frequency band