Peer interactions in early childhood education and care (ECEC) contribute to young children’s development. Earlier studies show that children’s linguistic resources might relate to how much they interact with peers, and with whom they interact. However, it is unclear to what extent interaction preferences are shaped by young children’s language background, language proficiency, and communicative competence. In this exploratory study, we tried to examine this question using the latest innovations in social network analysis, namely generalized exponential graph models and observation apps. We investigated to what extent children’s linguistic resources are related to whom they interact with and how much. We observed a free play episode (30-60 minutes) in twelve linguistically diverse toddler groups in the Netherlands (N=124) with a specifically designed observation app and looked at which children interacted and how much. Exploratory analyses across nine playgroups showed that children who shared a home language had more interactions with each other. There was no effect of multilingualism or ECEC language proficiency on peer interactions. We found that teacher involvement in free play was positively related to the quantity of peer interactions. In terms of practical implications, this suggests that teachers play an important role in stimulating (multilingual) children’s peer interactions in ECEC. Teachers might be advised to connect multilingual children to same-language peers and encourage translanguaging practices. • We studied toddlers’ peer interactions in relation to their linguistic resources • A dedicated observation app and generalized exponential graph models were used • Children who shared a home language had more interactions with each other • There was no effect of multilingualism or language proficiency on peer interactions • Teacher involvement during free play was positively related to peer interactions

The main aim of this study was to examine the effects of dialogue journal writing on the writing performance of EFL first-year students at Hawassa University. Two intact classes were randomly selected from the Social Science stream to participate in the study. A quasi-experimental pretest-posttest control group design was employed. After administering a pre-test to both the experimental and comparison groups at the onset of the intervention and after checking their comparability, the two groups were randomly assigned to experimental and comparison conditions. Following thirteen calendar weeks of intervention using dialogue journal writing for the experimental group and the conventional method for the comparison group, a post-test was administered to both groups. To analyze the data, both descriptive statistics (mean and standard deviation) and inferential statistics, including a one-way MANOVA and paired samples t-test, were employed, with data analyzed using SPSS version 26. The findings of the study revealed that the intervention significantly improved the experimental group students' writing performance as a combined dependent variable, with a moderate effect size. A follow-up ANOVA indicated that the intervention significantly improved all aspects of writing, including content, organization, vocabulary, and language use (with a modest effect size). However, the treatment did not significantly improve the experimental group's writing performance in terms of mechanics, although they scored a higher mean (M = 2.93) than the comparison group (M = 2.82). Therefore, EFL university instructors are encouraged to integrate dialogue journal writing into their writing classes to enhance students' writing performance. • Examines the impacts of dialogue journal writing on students' writing performance. • Reveals significant improvement for the experimental group after the treatment. • Suggests adopting dialogue journal writing in EFL writing instruction.

The aim of this study is to conduct a linguistic analysis of English essays produced by intermediate-level second language (L2) learners compared to those generated by AI, across five linguistic dimensions: lexical richness, syntactic complexity, semantic similarity, discourse cohesion, and surface-level errors. A parallel corpus of 160 essays, 80 AI-generated and 80 learner-written, was collected and analyzed using Natural Language Processing (NLP) techniques. The results revealed that AI essays tend to be longer and syntactically more complex, with significantly higher lexical diversity and greater use of content words. While both types of essays share similar sentiment and cohesion patterns, the AI essays demonstrate more advanced sentence structures and deeper syntactic tree depths. Readability metrics show that the learners’ essays are simpler and more accessible. Error analysis revealed that the human essays contain four times more errors, particularly in spelling and stylistic choices. The study highlights how AI-generated language diverges from learner-produced writing and offers insights into how AI tools can be effectively leveraged to support language development at this proficiency level. • A comparative linguistic analysis of English essays produced by intermediate-level second language (L2) learners • A total of 160 essays, 80 AI-generated and 80 human-written, were analyzed using NLP tools • Focusing on five linguistic dimensions: lexical richness, syntactic complexity, semantic similarity, discourse cohesion, and surface-level errors • AI essays were longer, lexically denser, and syntactically more complex, with deeper parse trees and more frequent use of content words • The human-authored essays demonstrated simpler sentence structures and greater readability, containing 4 times more surface-level errors