Linguistic Complexity Research Articles

Teacher questions, wait time, and student output in classroom interaction in EMI science classes: An interdisciplinary view

Past research has often shown a lack of student output in English medium instruction (EMI) classes (e.g., An et al., 2021; Lo & Macaro, 2012) and this study seeks to identify possible reasons. Guided by literature on wait time (Rowe, 1986) and teacher higher-order thinking questions (Chin, 2007), this study explores whether these two pedagogical moves have the same impact on classroom interaction in EMI science classes. 30 EMI science lessons were recorded from seven EMI high school programs in China, taught by 15 native speakers of English to homogenous groups of Chinese students. Correlation tests showed that when there was more wait time after a teacher question, the students produced lengthier responses with more linguistic complexity, took up more talk time, and asked more questions. However, greater use of teacher higher-order thinking questions, coded by Chin’s (2007) framework of constructivist questions, did not correlate with any student output measures. This suggests that wait time may be a more effective factor leading to more student output in EMI classes than asking higher-order thinking questions. Qualitative analysis showed teachers’ follow-up moves may have also played a role in the limited success of higher-order thinking questions.

English and other languages in a plurilingual pedagogical approach: A case study from northern Italy

Language teaching constantly needs to be adapted to changing societal realities, such as the spread of English as an international language or the growing linguistic complexity in Europe. A key question is whether and how languages could be treated together in school programmes to cater to a multilingual milieu. This article analyses a plurilingual pedagogical approach that is adopted in a few primary schools in the Italian province of South Tyrol. The research is a qualitative case study and draws on various theoretical perspectives, including the method model developed by Richards and Rodgers (1982, 2001, 2014). The dataset comprises analogue and digital data gathered through participant observation, field notes, personal communications, audio recordings, and school documents. Results show how a multilingual turn has taken root in the context examined, while reinforcing English acquisition.

The Subjunctive as a Model of Grammatical Complexity: An Integrative Review of Issues Based on Combined Evidence from Mental Chronometry and Neurosciences.

The acquisition of a second language requires the construction or reconstruction of linguistic knowledge about the new language system. Learners of a second language have to acquire the linguistic structures of the second language by constructing or reassessing their own knowledge in the light of the new one. Some of these new linguistic structures may be more or less complex to process and/or difficult to acquire. In this review, we focus on an example of linguistic complexity in French, namely, the subjunctive. Through a discussion of some selected studies on the second language acquisition of the French subjunctive, our purpose is to argue that these findings, considered from a psycholinguistic perspective, could be fruitful for further research employing neuroscience techniques, such as electroencephalography or neuroimaging in order to better understand the neurocognitive processing of this complex structure both in French native speakers and in learners of French. Hence, we aim to contribute to exploring the question of linguistic transfer in the field of second language acquisition, the typological distance/relation between L1-L2, the syntactic acquisition of complex structures in adult second language learners, and the potential contributions of electroencephalography and functional magnetic resonance imaging to the processing of the subjunctive, selected as an example of linguistic complexity that has not yet received much attention.

Instrumental goal activation increases online petition support across languages.

Research on processing fluency and instrumental goal activation suggests people often perceive complex information positively when effort in a task is valued. The current article evaluates this idea in five online petition samples (total N = 1,047,655 petitions and over 200 million words), assessing how the linguistic fluency of a petition associates with support. Consistent with prior work, petitions with lower rates of lexical fluency (fewer common words) associated with more signatures and an increased probability of petitions making a concrete change than those with higher rates of lexical fluency (more common words). Exploratory results suggest other forms of linguistic complexity also associated with petition support: petitions with more analytic writing (e.g., more formal and complex writing patterns) and with less structural fluency (less readable writing) received more signatures than those with less analytic writing and more structural fluency. Controlling for the political leaning of the petition writers as inferred by their language patterns revealed consistent effects. Crucially, the lexical fluency results were maintained across eight languages as well. Various types of linguistic complexity are therefore instrumental to get people to support online causes. Contributions to fluency theory and the psychology of giving are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Visualizing Linguistic Complexity and Proficiency in Learner English Writings

In this article, we focus on the design of a second language (L2) formative feedback system that provides linguistic complexity graph reports on the writings of English for special purposes students at the university level. The system is evaluated in light of formative instruction features pointed out in the literature. The significance of complexity metrics is also evaluated. A learner corpus of English classified according to the Common European Framework of References for Languages (CEFR) was processed using a pipeline that computes 83 complexity metrics. By way of analysis of variance (ANOVA) testing, multinomial logistic regression, and clustering methods, we identified and validated a set of nine significant metrics in terms of proficiency levels. Validation with classification gave 67.51% (A level), 60.16% (B level), and 60.47% (C level) balanced accuracy. Clustering showed between 53.10% and 67.37% homogeneity, depending on the level. As a result, these metrics were used to create graphical reports about the linguistic complexity of learner writing. These reports are designed to help language teachers diagnose their students’ writings in comparison with prerecorded cohorts of different proficiencies.

Readability and beyond - Health literacy and numeracy and COVID-19 communications in early childhood education: Are we communicating effectively?

ObjectiveAnalyse the linguistic and numerical complexity of COVID-19-related health information communicated from Australian national and state governments and health agencies to national and local early childhood education (ECE) settings. MethodsPublicly available health information (n = 630) was collected from Australian national and state governments and health agencies, and ECE agencies and service providers. A purposive sample of documents (n = 33) from 2020 to 2021 was analysed inductively and deductively combining readability, health numeracy and linguistic analyses and focusing on the most frequent actionable health advice topics. ResultsCOVID-19 health advice most frequently related to hygiene, distancing and exclusion. Readability scores in 79% (n = 23) of documents were above the recommended grade 6 reading level for the public. Advice was delivered using direct linguistic strategies (n = 288), indirect strategies (n = 73), and frequent mitigating hedges (n = 142). Most numerical concepts were relatively simple, but lacked elaborative features (e.g., analogies) and/or required subjective interpretation. ConclusionCOVID-19 health advice available to the ECE sector included linguistic and numerical information open to mis/interpretation making it difficult to understand and implement. Practice ImplicationsCombining readability scores with measures of linguistic and numerical complexity offers a more holistic approach to assessing accessibility of health advice and improving health literacy among its recipients.

Using automated analysis to assess middle school students' competence with scientific argumentation

AbstractArgumentation is fundamental to science education, both as a prominent feature of scientific reasoning and as an effective mode of learning—a perspective reflected in contemporary frameworks and standards. The successful implementation of argumentation in school science, however, requires a paradigm shift in science assessment from the measurement of knowledge and understanding to the measurement of performance and knowledge in use. Performance tasks requiring argumentation must capture the many ways students can construct and evaluate arguments in science, yet such tasks are both expensive and resource‐intensive to score. In this study we explore how machine learning text classification techniques can be applied to develop efficient, valid, and accurate constructed‐response measures of students' competency with written scientific argumentation that are aligned with a validated argumentation learning progression. Data come from 933 middle school students in the San Francisco Bay Area and are based on three sets of argumentation items in three different science contexts. The findings demonstrate that we have been able to develop computer scoring models that can achieve substantial to almost perfect agreement between human‐assigned and computer‐predicted scores. Model performance was slightly weaker for harder items targeting higher levels of the learning progression, largely due to the linguistic complexity of these responses and the sparsity of higher‐level responses in the training data set. Comparing the efficacy of different scoring approaches revealed that breaking down students' arguments into multiple components (e.g., the presence of an accurate claim or providing sufficient evidence), developing computer models for each component, and combining scores from these analytic components into a holistic score produced better results than holistic scoring approaches. However, this analytical approach was found to be differentially biased when scoring responses from English learners (EL) students as compared to responses from non‐EL students on some items. Differences in the severity between human and computer scores for EL between these approaches are explored, and potential sources of bias in automated scoring are discussed.

Measuring language complexity: challenges and opportunities

AbstractThis special issue focuses on measuring language complexity. The contributions address methodological challenges, discuss implications for theoretical research, and use complexity measurements for testing theoretical claims. In this introductory article, we explain what knowledge can be gained from quantifying complexity. We then describe a workshop and a shared task which were our attempt to develop a systematic approach to the challenge of finding appropriate and valid measures, and which inspired this special issue. We summarize the contributions focusing on the findings which can be related to the most prominent debates in linguistic complexity research.

Precursors of Sociolinguistic Typology

Abstract This paper examines the contribution of the Norwegian historian, politician, and ethnologist Ludvig Kristensen Daa (1809–1877) to the study of the Indigenous languages of North America. We focus on his accounts of sound systems, where he argued that North American languages are characterized by greater linguistic diversity, small consonant inventories and gaps in inventories, unusual sounds, and indistinct pronunciation of consonants. Daa attributed these features to the use of the languages in small and isolated communities, thus anticipating more recent discussions in which the degree of linguistic complexity is attributed to social and demographic factors. While some of his claims reflect methodological shortcomings of pre-20th-century phonetic study, the factors which according to Daa shape languages spoken in isolation are analogous to the parameters now examined by typologists, thus providing a sense of continuity across centuries in the links sought between structural diversity among languages and external factors.

Exploration of the Effect on Continuation Task and Its Teaching Implication in High School

Using primarily a literature-based approach, this paper examines the effects of reading followed by writing on students' language performance (including accuracy, complexity, and fluency) based on the theory of reading followed by writing for learning. The results show that the read-follow-up writing task requires interaction with reading comprehension to promote synergistic effects that effectively enhance learners' language output, especially in terms of accuracy and complexity. On this basis, to explore how reading followed by writing promotes fluency, accuracy and complexity in second language writing, the study shows that 1) progressively increasing the linguistic complexity of input texts contributes to the development of second language accuracy, while simplified texts with constant complexity contribute to the improvement of fluency. 2) The effect of input text linguistic complexity on the complexity of second language writing is inconsistent across indicators, with complexity Constant complexity increases the use of subordinate clauses in linguistic output, while increasing complexity significantly increases the number of parallel sentences used. The study points out that reading followed by writing is an effective way to improve the efficiency of foreign language teaching and learning by closely integrating language output and input and aiming to accelerate students' language use.

The linguistic complexity of adult and child contextualized and decontextualized talk.

The association between decontextualized talk (DT; i.e., talk extending beyond immediate context) and child language outcomes is well-attested but not well-understood. This study tested the hypothesis that DT is more linguistically complex than contextualized talk (CT). Thirty-eight Norwegian children (Mage = 5.5 years; 25 girls; 30 Norwegian-speaking monolinguals and eight multilinguals) and their teachers were videotaped during picture book reading, story card conversations and toy play (collected 2010-2011 and 2017). Results show that DT was more complex than CT among children and teachers. Both types of talk were more complex during book reading and story conversations than during play. The conversational context should be accounted for when theorizing about the role of DT in language development.

Linguistic complexity as the predictor of EFL independent and integrated writing quality

Linguistic complexity as the predictor of EFL independent and integrated writing quality

ArSentBERT: fine-tuned bidirectional encoder representations from transformers model for Arabic sentiment classification

Sentiment analysis in the Arabic language is challenging because of its linguistic complexity. Arabic is complex in words, paragraphs, and sentence structure. Moreover, most Arabic documents contain multiple dialects, writing alphabets, and styles (e.g., Franco-Arab). Nevertheless, fine-tuned bidirectional encoder representations from transformers (BERT) models can provide a reasonable prediction accuracy for Arabic sentiment classification tasks. This paper presents a fine-tuning approach for BERT models for classifying Arabic sentiments. It uses Arabic BERT pre-trained models and tokenizers and includes three stages. The first stage is text preprocessing and data cleaning. The second stage uses transfer-learning of the pre-trained models’ weights and trains all encoder layers. The third stage uses a fully connected layer and a drop-out layer for classification. We tested our fine-tuned models on five different datasets that contain reviews in Arabic with different dialects and compared the results to 11 state-of-the-art models. The experiment results show that our models provide better prediction accuracy than our competitors. We show that the choice of the pre-trained BERT model and the tokenizer type improves the accuracy of Arabic sentiment classification.

On semi-automated extraction of causal networks from raw text

A rule-based framework, named SCANER (Semi-automated CAusal Network Extraction from Raw text) is presented. SCANER converts raw text into causal networks using a set of natural language processing rules. The raw textual data that contains useful information for informing future decisions and developing strategies in various critical domains must be converted into useful, understandable models. However, building such models manually from raw text is a labor-intensive and time-consuming process and needs to be automated. The automated extraction of causality from raw text, however, has its own set of challenges due to the linguistic complexity and ambiguity of the texts. It can also be argued that for complicated scenarios and critical domains subject matter experts cannot rely solely on such automatically generated models without sound reasoning to support them. This implies the need for a semi-automated human-in-the-loop approach. This research, therefore, is focused on combining the strengths of both manual and automated approaches and on developing a framework for a rule-based semi-automated approach to generate causal networks from raw text. SCANER is evaluated on three collections of raw text from political, food insecurity, and medical domains using F-Score as the evaluation metric. The ground truth for the causal links is generated after incorporating feedback from a group of three human judges. A comparative analysis with Eidos, an open-source causality extraction system, demonstrates the advantages of SCANER in generating dense and accurate causal networks from the raw text.

How Well are Young Deaf Children in Early Intervention Doing on their Language Acquisition? An Assessment View

Little is known about the earliest language acquisition of deaf children who have a variety of different experiences. Cochlear implants tend to be implanted between six months to 12 months and often become active around 18 months. In addition, Speech Language Pathologists (SLPs) tend to evaluate articulation rather than language acquisition. In terms of evaluating sign language, there are few normed assessments developed explicitly for this modality. Therefore, a joint research project was established with an early intervention program and Lamar University's Deaf Studies and Deaf Education department to evaluate the earliest language milestones acquired by deaf children between birth and five years of age. The project used language assessments developed for parents and teachers in both American Sign Language (ASL) and English, with norms derived from native users of each language. All languages reported as used by the family were assessed. Results found that most families selected spoken language and those results varied across participants. It was found that younger children using spoken language were more likely to demonstrate age-typical language acquisition when compared to older children. As more linguistic complexity developed, older children using spoken language tended to show delays. Future research suggests investigating bidirectional interactions between children, their families, and the broader systems with which the deaf child interacts. Doing so would create a further understanding of the child’s language acquisition and help families understand how to determine if their child’s language acquisition is progressing in a typical manner.

Exploring the Sentence Length and Age of Acquisition of Speech Recognition Test Sentences in Dutch, American English, and Canadian French.

The purpose of this study was to examine the differences between the age of acquisition (AoA) and sentence length of sentences of the speech recognition (SR) tests for adults and children in Dutch, American English, and Canadian French. The AoA and sentence length of the sentences of four SR tests for adults and children were determined. One-way analyses of variance were performed to assess differences between the tests. The AoA and sentence length of the sentences significantly differed between the SR tests for adults. These differences were also found between the SR tests for children. The AoA and the sentence length differ across the SR tests in Dutch, American English, and Canadian French. The Dutch sentences have higher AoA and are longer than the sentences in American English and Canadian French. The effect of the linguistic complexity on sentence repetition accuracy should be investigated during the development and validation of a Dutch SR test for children.

Dynamic co-movement between economic growth and language: A new perspective of technological progress

To investigate whether language complexity has significant effects on technological progress and economic growth, this paper not only improves a concise endogenous growth model with open economy in which reducing the degree of official language complexity can help one country gain rapid technological progress and economic growth in balanced growth path, but also applies stochastic frontier analysis (SFA) and two-way fixed effect model to investigate our hypothesis. Using the database ethnologue distributed by Lewis et al. (2014) and the Penn World Table 9.1 about 89 countries and regions from 1990 to 2018, we consider the number of verb conjugations of official language to be the indicator of language complexity, and examine its effects on technological change and economic growth. The empirical findings demonstrate that reducing language complexity has a significant positive effect and time-lag influence on technological change and economic output. Meanwhile, the positive effect of linguistic complexity on technological progress and economic growth is stronger for these countries with isolating languages than those with other language families, as well as for middle and low-income countries than high-income countries. For developing countries and these countries with isolating and agglutinative languages, they are urged to simplify their native language and reform the second language learning institution in schools in order to achieve a high level of development.

Comparing Interventions With Speech-Generating Devices and Other Augmentative and Alternative Communication Modes: A Meta-Analysis.

Optimal augmentative and alternative communication (AAC) systems for children with complex communication needs depend in part on child characteristics, child preferences, and features of the systems themselves. The purpose of this meta-analysis was to describe and synthesize single case design studies comparing young children's acquisition of communication skills with speech-generating devices (SGDs) and other AAC modes. A systematic search of published and gray literature was conducted. Data related to study details, rigor, participant characteristics, design information, and outcomes were coded for each study. A random effects multilevel meta-analysis was performed using log response ratios as effect sizes. Nineteen single case experimental design studies with 66 participants (M age = 4.9 years) met inclusion criteria. All but one study featured requesting as the primary dependent variable. Visual analysis and meta-analysis indicated no differences between use of SGDs and picture exchange for children learning to request. Children demonstrated preferences for and learned to request more successfully with SGDs than with manual sign. Children who preferred picture exchange also learned to request more easily with picture exchange than with SGDs. Young children with disabilities may be able to request equally well with SGDs and picture exchange systems in structured contexts. More research is needed comparing AAC modes with diverse participants, communication functions, linguistic complexity, and learning contexts. https://doi.org/10.23641/asha.22111181.

Increase in Linguistic Complexity in Older Adults During COVID-19

ABSTRACT The reported psychological impact of the COVID-19 pandemic and related public health measures included a decline in cognitive functioning in older adults. Cognitive functioning is known to correlate with the lexical and syntactic complexity of an individual’s linguistic productions. We examined written narratives from the CoSoWELL corpus (v 1.0), collected from over 1,000 U.S. and Canadian older adults (55+ y.o.) before and during the first year of the pandemic. We expected a decrease in the linguistic complexity of the narratives, given the oft-reported reduction in cognitive functioning associated with COVID-19. Contrary to this expectation, all measures of linguistic complexity showed a steady increase from the pre-pandemic level throughout the first year of the global lockdown. We discuss possible reasons for this boost in light of existing theories of cognition and offer a speculative link between the finding and reports of increased creativity during the pandemic.

A corpus-based quantitative study of numeral classifiers in Nepali

Abstract Nepali is typologically rare in terms of nominal classification systems, as it is one of the few languages of the world having simultaneously two gender systems (human/non-human, masculine/feminine) and one numeral classifier system (distinguishing features such as human, round-shaped objects, and long objects among others). Such a rare co-occurrence of different nominal classification systems is highly relevant for investigating linguistic complexity, as languages generally do not have several systems of the same type fulfilling the same functions. However, no corpus-based quantitative analyses have been conducted on the productive use of nominal classification systems in Nepali. The current paper aims at filling this gap by providing a token-based study from the Nepali National Corpus (∼20 million words). Our preliminary results show that there is in fact little formal overlap between the classifier and the gender systems.