Screens and Preschools: The Bilingual English Language Learner Assessment as a Curriculum-Compliant Digital Application.

BACKGROUND/OBJECTIVES: The increase in digital tools in early childhood education highlights the need for evidence-based assessments that support cognitive development and align with educational requirements and technological advances. This study contributes to the evaluation of the Bilingual English Language Learner Assessment (BELLA), designed to enhance early learning through curriculum-aligned tasks in preschool-aged children. METHODS: Data were collected from 17 schools, including 506 preschool children, using a mixed-model approach to assess BELLA's capacity to appraise early numeracy, literacy, science, and social/emotional development. Analyses included a three-way ANOVA to examine the effects of sex, age, and sub-domain on pass rates and mixed-effects models to evaluate interactions between age and domain. RESULTS: The results indicated a significant effect of age on performance across all domains, with older children demonstrating higher pass rates (p < 0.0001). No significant gender bias was detected. The interaction between age and domain was also significant (p < 0.0001), suggesting domain-specific age-related performance trends, which aligns with internal validity requirements. CONCLUSION: These findings position BELLA within the growing body of literature on digital media use in early childhood assessment and education, highlighting its potential as a curriculum-compliant digital assessment tool that evaluates and supports cognitive development without a gender bias. This study contributes to the field by providing empirical evidence of BELLA's effectiveness and suggesting future research directions, including the exploration of its bilingual (and potentially multilingual) applications and external validation against existing evidence-based assessments.

Assessing Cognitive Skills in Early Childhood Education Using a Bilingual Early Language Learner Assessment Tool.

In this article, we propose that basic cognitive skills may be fostered and assessed in early childhood educational (pre-K) settings using a technology-based approach to assessment. BELLA (Bilingual English Language Learner Assessment), designed for use with both monolingual (English or Spanish speaking) and bilingual (English and Spanish speaking) children, is designed to attend to cognitive skill development in addition to (pre-)academic knowledge. Specifically, BELLA assesses analytical, creative, and practical thinking in 3-5-year-old children through unique item content and delivery. BELLA is among the first tablet-based pre-K assessments designed to assess cognitive skills needed for the era of the Anthropocene.

Who are the haters? A corpus-based demographic analysis of authors of hate speech.

Introduction: We examine the profiles of hate speech authors in a multilingual dataset of Facebook reactions to news posts discussing topics related to migrants and the LGBT+ community. The included languages are English, Dutch, Slovenian, and Croatian. Methods: First, all utterances were manually annotated as hateful or acceptable speech. Next, we used binary logistic regression to inspect how the production of hateful comments is impacted by authors' profiles (i.e., their age, gender, and language). Results: Our results corroborate previous findings: in all four languages, men produce more hateful comments than women, and people produce more hate speech as they grow older. But our findings also add important nuance to previously attested tendencies: specific age and gender dynamics vary slightly in different languages or cultures, suggesting that distinct (e.g., socio-political) realities are at play. Discussion: Finally, we discuss why author demographics are important in the study of hate speech: the profiles of prototypical "haters" can be used for hate speech detection, for sensibilization on and for counter-initiatives to the spread of (online) hatred.

Language: Its Origin and Ongoing Evolution.

With the present paper, we sought to use research findings to illustrate the following thesis: the evolution of language follows the principles of human evolution. We argued that language does not exist for its own sake, it is one of a multitude of skills that developed to achieve a shared communicative goal, and all its features are reflective of this. Ongoing emerging language adaptations strive to better fit the present state of the human species. Theories of language have evolved from a single-modality to multimodal, from human-specific to usage-based and goal-driven. We proposed that language should be viewed as a multitude of communication techniques that have developed and are developing in response to selective pressure. The precise nature of language is shaped by the needs of the species (arguably, uniquely H. sapiens) utilizing it, and the emergence of new situational adaptations, as well as new forms and types of human language, demonstrates that language includes an act driven by a communicative goal. This article serves as an overview of the current state of psycholinguistic research on the topic of language evolution.

Improving Feature Representation Based on a Neural Network for Author Profiling in Social Media Texts.

We introduce a lexical resource for preprocessing social media data. We show that a neural network-based feature representation is enhanced by using this resource. We conducted experiments on the PAN 2015 and PAN 2016 author profiling corpora and obtained better results when performing the data preprocessing using the developed lexical resource. The resource includes dictionaries of slang words, contractions, abbreviations, and emoticons commonly used in social media. Each of the dictionaries was built for the English, Spanish, Dutch, and Italian languages. The resource is freely available.