Drafts by Galbadrakh Enkhbayar
English teachers often have difficulty matching the complexity of fiction texts with students' r... more English teachers often have difficulty matching the complexity of fiction texts with students' reading levels. Texts that seem appropriate for students of a given level can turn out to be too difficult. Furthermore, it is difficult to choose a series of texts that represent a smooth gradation of text difficulty. This paper attempts to address both problems by providing a complexity ranking of a corpus of 200 fiction texts consisting of 100 adults' and 100 children's texts. Using machine learning, several standard readability measures are used as variables to create a classifier which is able to classify the corpus with an accuracy of 84%. A classifier created with linguistic variables is able to classify the corpus with an accuracy of 89%. The 'latter classifier is then used to provide a linear complexity rank for each text. The resulting ranking instantiates a fine-grained increase in complexity. This can be used by an English teacher to select a sequence of texts that represent an increasing challenge to a student without there being a frustratingly discrete rise in difficulty.
Papers by Galbadrakh Enkhbayar
Linguistic Research, 2018
English teachers often have difficulty matching the complexity of fiction texts with students'... more English teachers often have difficulty matching the complexity of fiction texts with students' reading levels. Texts that seem appropriate for students of a given level can turn out to be too difficult. Furthermore, it is difficult to choose a series of texts that represent a smooth gradation of text difficulty. This paper attempts to address both problems by providing a complexity ranking of a corpus of 200 fiction texts consisting of 100 adults' and 100 children's texts. Using machine learning, several standard readability measures are used as variables to create a classifier which is able to classify the corpus with an accuracy of 84%. A classifier created with linguistic variables is able to classify the corpus with an accuracy of 89%. The latter classifier is then used to provide a linear complexity rank for each text. The resulting ranking instantiates a fine-grained increase in complexity. This can be used by a reading or ESL teacher to select a sequence of texts ...
SSRN Electronic Journal
English teachers often have difficulty matching the complexity of fiction texts with students' re... more English teachers often have difficulty matching the complexity of fiction texts with students' reading levels. Texts that seem appropriate for students of a given level can turn out to be too difficult. Furthermore, it is difficult to choose a series of texts that represent a smooth gradation of text difficulty. This paper attempts to address both problems by providing a complexity ranking of a corpus of 200 fiction texts consisting of 100 adults' and 100 children's texts. Using machine learning, several standard readability measures are used as variables to create a classifier which is able to classify the corpus with an accuracy of 84%. A classifier created with linguistic variables is able to classify the corpus with an accuracy of 89%. The 'latter classifier is then used to provide a linear complexity rank for each text. The resulting ranking instantiates a fine-grained increase in complexity. This can be used by a reading or ESL teacher to select a sequence of texts that represent an increasing challenge to a student without there being a frustratingly perceptible increase in difficulty.
SSRN Electronic Journal
English teachers often have difficulty matching the complexity of fiction texts with students' re... more English teachers often have difficulty matching the complexity of fiction texts with students' reading levels. Texts that seem appropriate for students of a given level can turn out to be too difficult. Furthermore, it is difficult to choose a series of texts that represent a smooth gradation of text difficulty. This paper attempts to address both problems by providing a complexity ranking of a corpus of 200 fiction texts consisting of 100 adults' and 100 children's texts. Using machine learning, several standard readability measures are used as variables to create a classifier which is able to classify the corpus with an accuracy of 84%. A classifier created with linguistic variables is able to classify the corpus with an accuracy of 89%. The 'latter classifier is then used to provide a linear complexity rank for each text. The resulting ranking instantiates a fine-grained increase in complexity. This can be used by a reading or ESL teacher to select a sequence of texts that represent an increasing challenge to a student without there being a frustratingly perceptible increase in difficulty.
Linguistic Research, 2018
Dalvean, Michael and Galbadrakh Enkhbayar. 2018. Standard readability measures are based on the r... more Dalvean, Michael and Galbadrakh Enkhbayar. 2018. Standard readability measures are based on the readability of non-fiction texts. Linguistic Research 35(Special Edition), 137-170. This means that the validity of the measures when applied to fiction texts is questionable. Thus, the scores given to fiction texts using such indices may be invalid when used by English teachers to identify fiction texts of appropriate difficulty for students with various reading ability levels. This paper attempts to address this problem by 1) developing a readability measure specifically designed for fiction texts and 2) applying it to 200 English fiction texts. A corpus, consisting of 100 adults' and 100 children's texts, is used for the analysis. In the initial modeling, several standard readability measures are used as variables, and machine learning is used to create a classifier which is able to classify the corpus with an accuracy of 84%. A second classifier is then created using linguistic variables rather than standard readability measures. The latter classifier is able to classify the corpus with an accuracy of 89%, indicating that the standard readability measures are less accurate in classifying fiction texts than linguistic variables. Due to its higher accuracy, the latter classifier is then used to provide a linear complexity or 'readability' rank for each text. The ranking using the linguistic-based classifier provides an more accurate method of determining which texts to choose for students according to their reading levels than the standard readability measures. Importantly, the ranking instantiates a fine-grained increase in complexity. This means that the ranking can be used by an English teacher to select a sequence of texts that represent an increasing challenge to a student without there being a frustratingly discrete rise in difficulty. (Canberra College ･ Southern Taiwan University of Science and Technology)
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Drafts by Galbadrakh Enkhbayar
Papers by Galbadrakh Enkhbayar