teacher language
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2022 ◽  
pp. 159-178
Author(s):  
Arthur McNeill

Within the field of TESOL, opinions often differ about the role of learners' first language (L1) in second language learning. When teachers are aware of their students' L1, this awareness can increase their understanding of second language acquisition processes and issues. It can also provide teachers with insights into learners' backgrounds and cultures that may influence their approach to studying English and attitudes towards multilingualism. Specifically, the chapter proposes that the notion of teacher language awareness (TLA) should be expanded to include awareness of students' language backgrounds. TLA is regarded as an important component of the knowledge base of a language teacher. Two questionnaires are provided to assist teachers with the elicitation of information about students' L1: (1) a language-focused set of questions to allow comparison between a learner's L1 and English and (2) a sociolinguistic-oriented questionnaire that explores issues related to status and use.


2022 ◽  
Vol 59 ◽  
pp. 186-202
Author(s):  
Elizabeth Burke Hadley ◽  
Erica M. Barnes ◽  
Brenton M. Wiernik ◽  
Mukhunth Raghavan

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257130
Author(s):  
Yang Li ◽  
Yuqing Sun ◽  
Nana Zhu

In recent years, text sentiment analysis has attracted wide attention, and promoted the rise and development of stance detection research. The purpose of stance detection is to determine the author’s stance (favor or against) towards a specific target or proposition in the text. Pre-trained language models like BERT have been proven to perform well in this task. However, in many reality scenes, they are usually very expensive in computation, because such heavy models are difficult to implement with limited resources. To improve the efficiency while ensuring the performance, we propose a knowledge distillation model BERTtoCNN, which combines the classic distillation loss and similarity-preserving loss in a joint knowledge distillation framework. On the one hand, BERTtoCNN provides an efficient distillation process to train a novel ‘student’ CNN structure from a much larger ‘teacher’ language model BERT. On the other hand, based on the similarity-preserving loss function, BERTtoCNN guides the training of a student network, so that input pairs with similar (dissimilar) activation in the teacher network have similar (dissimilar) activation in the student network. We conduct experiments and test the proposed model on the open Chinese and English stance detection datasets. The experimental results show that our model outperforms the competitive baseline methods obviously.


Author(s):  
Nicole Sparapani ◽  
Vanessa P. Reinhardt ◽  
Jessica L. Hooker ◽  
Lindee Morgan ◽  
Christopher Schatschneider ◽  
...  

AbstractThis study examined how teachers and paraprofessionals in 126 kindergarten-second grade general and special education classrooms talked with their 194 students with autism, and further, how individual student characteristics in language, autism symptoms, and social abilities influenced this talk. Using systematic observational methods and factor analysis, we identified a unidimensional model of teacher language for general and special education classrooms yet observed differences between the settings, with more language observed in special education classrooms—much of which included directives and close-ended questions. Students’ receptive vocabulary explained a significant amount of variance in teacher language beyond its shared covariance with social impairment and problem behavior in general education classrooms but was non-significant within special education classrooms. Research implications are discussed.


2021 ◽  
Vol 11 (6) ◽  
pp. 283
Author(s):  
Brooke Rumper ◽  
Elizabeth Frechette ◽  
Daryl B. Greenfield ◽  
Kathy Hirsh-Pasek

The present study examined the roles that language of assessment, language dominance, and teacher language use during instruction play in Dual Language Learner (DLL) science scores. A total of 255 Head Start DLL children were assessed on equated science assessments in English and Spanish. First overall differences between the two languages were examined, then associations between performance on science assessments were compared and related to children’s language dominance, teacher quantity of English and Spanish, and teachers’ academic science language. When examined as a homogeneous group, DLLs did not perform differently on English or Spanish science assessments. However, when examined heterogeneously, Spanish-dominant DLLs performed better on Spanish science assessments. The percentage of English and Spanish used by teachers did not affect children’s science scores. Teachers’ use of Spanish academic science language impacted children’s performance on science assessments, but English did not. The results have implications for the assessment of DLLs and teacher language use during instruction.


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