In this dissertation, I examine how fingerspelled words and core signs in American Sign Language (ASL) reduce as they are repeated. This investigation is motivated by theories of language production that posit that reduction may be shaped not only by reducing articulatory effort, but also by accommodation to an interlocutor's understanding of the…

Systematic reviews are vital for guiding practice, research and policy, although they are often slow and labour-intensive. Large language models (LLMs) could speed up and automate systematic reviews, but their performance in such tasks has yet to be comprehensively evaluated against humans, and no study has tested Generative Pre-Trained…

Most printed Chinese words are compounds built from the combination of meaningful characters. Yet, there is a poor understanding of how individual characters contribute to the recognition of compounds. Using a megastudy of Chinese word recognition (Tse et al., 2017), we examined how the lexical decision of existing and novel Chinese compounds was…

Automatic essay scoring (AES) is an essential educational application in natural language processing. This automated process will alleviate the burden by increasing the reliability and consistency of the assessment. With the advances in text embedding libraries and neural network models, AES systems achieved good results in terms of accuracy.…

This study aims to investigate the distribution across genres and the collocation of three synonymous verbs "cease," "halt," and "stop." Data were drawn from the Corpus of Contemporary American English (COCA). The results from the distribution across genres shed light on the degree of formality of each verb. The verb…

ChatGPT and Bard, two chatbots powered by Large Language Models (LLMs), are propelling the educational sector towards a new era of instructional innovation. Within this educational paradigm, the present investigation conducts a comparative analysis of these groundbreaking chatbots, scrutinizing their distinct operational characteristics and…

This study is a corpus study on morphological features of children's books in Turkish. The purpose of the study is to determine derivational and inflectional suffixes in Turkish children books and to identify the common and productive morphemes used in child literature in the light of the information obtained; hence, reach clues about determining…

Recent advancements in large language models (LLMs) hold significant promise for improving physics education research that uses machine learning. In this study, we compare the application of various models for conducting a large-scale analysis of written text grounded in a physics education research classification problem: identifying skills in…

A strong predictor of children's language is performance on non-word repetition (NWR) tasks. However, the basis of this relationship remains unknown. Some suggest that NWR tasks measure phonological working memory, which then affects language growth. Others argue that children's knowledge of language/language experience affects NWR performance. A…

Practice in real-world settings exhibits many idiosyncrasies of scheduling and duration that can only be roughly approximated by laboratory research. Here we investigate 39,157 individuals' performance on two cognitive games on the Lumosity platform over a span of 5 years. The large-scale nature of the data allows us to observe highly varied…

Human essay grading is a laborious task that can consume much time and effort. Automated Essay Scoring (AES) has thus been proposed as a fast and effective solution to the problem of grading student writing at scale. However, because AES typically uses supervised machine learning, a human-graded essay corpus is still required to train the AES…

This study uses a diachronic corpus-based analysis to explore the topics presented in LGBTQ-related research articles published between 2001 and 2020, and to examine diachronic changes in these topics over time. The data are from 400 LGBTQ-related research articles which are divided into four time periods (2001-2005, 2006-2010, 2011-2015,…

Automating the classification of instructional strategies from a large-scale online tutorial dialogue corpus is indispensable to the design of dialogue-based intelligent tutoring systems. Despite many existing studies employing supervised machine learning (ML) models to automate the classification process, they concluded that building a…

Word segmentation is a crucial step in children's vocabulary learning. While computational models of word segmentation can capture infants' performance in small-scale artificial tasks, the examination of early word segmentation in naturalistic settings has been limited by the lack of measures that can relate models' performance to developmental…

This study examined 283 online learner dictionary definitions in terms of scores based on word frequency level and readability. Results revealed three findings. First, in terms of word frequency levels, definitions from the Cambridge learner dictionary incorporated fewer non-high frequency words (mid and low frequency words) compared to Oxford,…