GENDER DIFFERENCES IN PERFORMANCE MEASURES OF INDIVIDUALS WHO USE AUGMENTATIVE AND ALTERNATIVE COMMUNICATION (AAC)

Language samples from 10 adults using an augmentative and alternative communication (AAC) system were analyzed for gender differences in performance measures. Participants (5 female; 5 male) were matched on device, access method, software, experience, age, and education. Each participant was asked to describe the "cookie theft" picture from the Boston Diagnostic Aphasia Examination (BDAE; Goodglass & Kaplan, 1983). The language samples were analyzed on the following two dependent variables: frequency of Semantic Compaction™ language representation use and average communication rate. A dependent samples t-test and the equivalent non-parametric matched-pair Wilcoxon tests were conduced on both variables. The effect size and the power were also calculated and used to support the following results. There was not a significant difference in the Semantic Compaction™ dependent variable, however there was a large effect size (d=1.11). A power analysis indicated a sample size consisting of 9 pairs (4 more males and 4 more females) would increase the power to 82%. Further research with an increased sample size of 9 pairs of participants may provide more support for the current finding in relation to the use of Semantic Compaction™.No significant difference was found between the average communication rates of the genders; however the presence of a female outlier was concluded to influence these results. A dependent samples t-test was conducted on the data excluding the pair containing the outlier. The results of the dependent samples t-test indicated a significant difference between the genders in the average communication rates. Overall, for both dependent variables, the majority of males were higher on the performance measures than their paired female participants. These observations support a need for future research addressing gender differences in individuals who use AAC. Clinical implications suggest that future research is needed to determine if intervention strategies need to accommodate for differences between genders in their ability to effectively use their device to communicate as fast as they are able. Caution needs to be used when interpreting and applying these results to this population due to the limitations (i.e., small sample size and lack of control of extraneous variable) of the current study

Do women and men use language differently in spoken face-to-face interaction? A scoping review

Although the question of whether women and men speak differently is a topic of hot debate, an overview of the extent towhich empirical studies provide robust support for a relationship between sex/gender and language is lacking. Therefore, the aim of the current scoping review is to synthesize recent studies from various theoretical perspectives on the relationship between sex/gender and language use in spoken face-to-face dyadic interactions. Fifteen empirical studies were systematically selected for review, and were discussed according to four different theoretical perspectives and associated methodologies. More than thirty relevant linguistic variables were identified (e.g., interruptions and intensifiers). Overall, few robust differences between women and men in the use of linguistic variables were observed across contexts, although women seem to be more engaged in supportive turn-taking than men. Importantly, gender identity salience, institutionalized roles, and social and contextual factors such as interactional setting or conversational goal seem to play a key role in the relationship between speaker’s sex/gender and language used in spoken interaction

The Effects of Human-Computer Communication Mode, Task Complexity, and Desire for Control on Performance and Discourse Organization in an Adaptive Task

The present study examined how different communication patterns affected task performance with an adaptive interface. A Wizard-of-Oz simulation (Gould, Conti, & Hovanyecz, 1983) was used to create the impression of a talking and listening computer that acted as a teammate to help participants interact with a computer application. Four levels of communication mode were used which differed in the level of restriction placed on human-computer communication. In addition, participants completed two sets of tasks (simple and complex). Further, a personality trait, Desire for Control (DC), was measured and participants were split into high and low groups for analysis. Dependent measures included number of tasks completed in a given time period as well as subjective ratings of the interaction. In addition, participants\u27 utterances were assessed for verbosity, disfluencies, and indices of common ground

Predicting bilingual preschoolers' patterns of language development: Degree of non-native input matters

While numerous studies have recently shown that variation in input quantity predicts children’s rate of acquisition across a range of language skills, comparatively little is known about the impact of variation in input quality on (bilingual) children’s language development. This study investigated the relation between specific quality-oriented properties of bilingual children’s input and measures of children’s language development across a number of skills while at the same time taking family constellation into account. Participants were bilingual preschoolers (n = 50) acquiring Dutch alongside another language. Preschoolers’ receptive and productive vocabulary and morphosyntax in Dutch were assessed. Parental questionnaires were used to derive estimates of input quality. Family constellation was first operationalized as presence of a native-speaker parent and subsequently in terms of patterns of parental language use. Results showed that proportion of native input and having a native-speaker parent were never significant predictors of children’s language skills, whereas the degree of non-nativeness in the input, family constellation in terms of parental language use, and language richness were. This study shows that what matters is not how much exposure bilingual children have to native rather than non-native speakers, but how proficient any non-native speakers are

State of the art review : language testing and assessment (part two).

In Part 1 of this two-part review article (Alderson & Banerjee, 2001), we first addressed issues of washback, ethics, politics and standards. After a discussion of trends in testing on a national level and in testing for specific purposes, we surveyed developments in computer-based testing and then finally examined self-assessment, alternative assessment and the assessment of young learners. In this second part, we begin by discussing recent theories of construct validity and the theories of language use that help define the constructs that we wish to measure through language tests. The main sections of the second part concentrate on summarising recent research into the constructs themselves, in turn addressing reading, listening, grammatical and lexical abilities, speaking and writing. Finally we discuss a number of outstanding issues in the field

Predicting IELTS ratings using vocabulary measures

This thesis addresses the relationship between vocabulary measures and IELTS ratings. The research questions focus on the relationship between measures of lexical richness and teacher ratings. The specific question the thesis seeks to address is: Which measures of lexical richness are the best for predicting the ratings? This question has been considered central in vocabulary measurement research for the last decades particularly in relation to IELTS, one of the most popular exams in the world. Therefore, if a model can predict IELTS scores by using vocabulary measures it could be used as a predictive tool by teachers and researchers worldwide. The research was carried out through two studies, Study 1 and Study 2 and then the model was tested through a third smaller study. Study 1 was a small pilot study which looked at both oral and written data. Study 2 focused on written data only. Measures of both lexical diversity and sophistication were chosen for both studies. Both studies followed similar methodologies with the addition of an extra variable in the second study. For the first study data was collected from 42 IELTS learners whereas for the second study an existing corpus was used. The measures investigated in both studies were: Tokens, TTR, D, Guiraud, Types, Guiraud Advanced and P_Lex. The first four are measures of lexical diversity, the other three measures of lexical sophistication. However, all of the previous measures are measures of breadth of vocabulary. For the second study, a measure of formulaic count was added. This is an aspect of depth of vocabulary used to check if results would improve with this addition. Formulaic sequences were counted in each essay by using Martinez and Schmitt’s (2012) PHRASE List of the 505 most frequent non-transparent multiword expressions in English. The main findings show that all the measures correlate with the ratings but Tokens has the highest correlation of all lexical diversity measures, and Types has the highest correlation of all lexical sophistication measures. TTR, Guiraud and P_Lex can explain 52.8% of the variability in the Lexical ratings. In addition, holistic ratings can be predicted by the same two lexical diversity measures (TTR and Guiraud) but with a different measure of lexical sophistication, Guiraud Advanced. The model consisting of these three measures can explain 49.2% of the variability in the holistic ratings. The formulaic count did not seem to improve the model’s predictive validity, but further analysis from a qualitative angle seemed to explain this behaviour. In Study 3, the holistic ratings model was tested using a small sample of real IELTS data and the examiners comments’ were used for a more qualitative analysis. This revealed that the model underestimated the scores since the range of ratings from the IELTS data was wider than the range of the data from Study 2 which were used as the basis for the model. This proved to be a major hindrance to the study. However, the qualitative analysis confirmed the argument that vocabulary accounts for a high percentage of variance in ratings and provided insights to other aspects that may influence raters which could be added to the model in future research. The issues and limitations of the study and the current findings contribute to the field by stimulating further research into producing a predictive tool that could inform students of their predicted rating before they decide to take the IELTS exam. This could have potential financial benefits for students

“You’re trolling because…” – A Corpus-based Study of Perceived Trolling and Motive Attribution in the Comment Threads of Three British Political Blogs

This paper investigates the linguistically marked motives that participants attribute to those they call trolls in 991 comment threads of three British political blogs. The study is concerned with how these motives affect the discursive construction of trolling and trolls. Another goal of the paper is to examine whether the mainly emotional motives ascribed to trolls in the academic literature correspond with those that the participants attribute to the alleged trolls in the analysed threads. The paper identifies five broad motives ascribed to trolls: emotional/mental health-related/social reasons, financial gain, political beliefs, being employed by a political body, and unspecified political affiliation. It also points out that depending on these motives, trolling and trolls are constructed in various ways. Finally, the study argues that participants attribute motives to trolls not only to explain their behaviour but also to insult them

A comparative analysis of metal subgenres in terms of lexical richness and keyness

Metal music is realized under a vast variety of subgenres all of which have their unique (or shared) characteristics not only in sound but also in their lyrics. Much research has been done to distinguish or classify subgenres but little has addressed the linguistic differences across them. This study seeks to find out the lexical richness and keyness levels of heavy metal, thrash metal and death metal using a corpus of 200 songs from each subgenre with a total of 600 songs. The selection of the bands and songs was carried out finding references in the metal literature. The metal literature in the present study takes into account the academic books and articles on metal as well as noteworthy media productions, websites and metal blogs such as Metal Evolution and Encyclopaedia Metallum. The song lyrics were manually processed and meta-data, mark-ups and repeats have been removed so that the differences in repeat lengths do not affect the comparisons. Furthermore, the analyses used in the study are sensitive to repeats as they measure the frequencies and repeat ratios of the words. The song lengths – after the processing – were limited to lower and upper thresholds of 100 and 400 words. The songs were analyzed for their lexical richness levels in three aspects: 1) lexical variation, 2) lexical sophistication and 3) lexical density. Lexical variation was operationalized as TTR, Guiraud, Uber and HD-D. Lexical sophistication was measured using lexical frequency profile with two different frequency lists – the GSL and the BNC/COCA – by looking at the ratios of tokens and types which fell beyond the most frequent two thousand words (Laufer 1995). Another sophistication measure – P_Lex – which also runs on GSL, was applied. Lexical density analysis was based on the ratio of content words to all tokens in the texts. In order to complement this quantitative and data-driven approach, a keyness analysis was administered to add a qualitative dimension to the research. All lexical richness analyses pointed out to statistically significant differences between all subgenres, marking heavy metal as the least and death metal as the most lexically rich one. Keyness analysis indicated differences among all three subgenres as well. Heavy metal key words tended to be Dionysian whereas thrash and death metal keywords were more Chaotic as proposed by Weinstein (2000). Finally, a correlation analysis showed that all lexical richness measures were statistically significantly correlated to each other. Based on the findings, it could be claimed that 1) these three subgenres differ from each other not only in terms of music but also of lexical richness levels and key words and 2) lexical richness analyses, coupled with keyness, are capable of reflecting the genre differences in song lyrics. However, as a result of a discriminant analysis of the present corpus, a reverse approach whereby genres are attempted to be classified based on lexical features does not provide a pattern which fully corresponds to the existing classifications