What Machine Learning Can Tell Us About the Role of Language Dominance in the Diagnostic Accuracy of German LITMUS Non-word and Sentence Repetition Tasks

Using a monolingual screening test for assessing bilingual children

Bilingual language development is different from monolingual language development. The lack of appropriate assessment tools geared to the bilingual population has led to inaccurate over-diagnosis of bilingual children with typical language development (TLD) as children with Developmental Language Disorder (DLD) and under-diagnosis of bilingual children with DLD. The present paper addresses this challenge by focusing on Hebrew as a second language (L2) of bilingual preschool children whose first language (L1) is either English or Russian, taking into consideration both chronological age (CA) and age of onset of bilingualism (AOB). This study aimed to generate bilingual standards for a monolingual screening test, Goralnik Screening Test for Hebrewby arriving at appropriate bilingual typical development cut-off points. A total of 443 bilingual Hebrew speaking children (397 with TLD and 46 with DLD), ages 61-78 months (M = 70; SD = 4), 199 with L1 English and 244 with L1 Russian, took part in the study. The results demonstrate low diagnostic accuracy when a monolingual test with monolingual norms is used for bilingual children, in contrast with increased diagnostic accuracy when bilingual standards are used for bilingual children. The paper concludes by showing the importance of bilingual standards when assessing clinical populations with varying ages of acquisition, and in particular, for those who were exposed to their second language after the age of four.

Russian-German five-year-olds: What omissions in sentence repetition tell us about linguistic knowledge, memory skills and their interrelation

In error analyses using sentence repetition data, most authors focus on word types of omissions. The current study considers serial order in omission patterns independent of functional categories. Data was collected from Russian and German sentence repetition tasks performed by 53 five-year-old bilingual children. Number and positions of word omissions were analyzed. Serial order effects were found in both languages: medial errors made up the largest percentage of errors. Then, the position of omissions was compared to visuo-verbal n-back working memory and non-verbal visual forward short-term memory scores using stepwise hierarchical linear regression models, taking into account demographic variables and receptive language. The interaction differed between languages: there was a significant negative association between omissions in the medial position in German and the final position in Russian and the visuo-verbal n-back memory score. Our study contributes to the understanding of how working memory and language are intertwined in sentence repetition.

The role of age and timing in bilingual assessment: non-word repetition, subject-verb agreement and case marking in L1 and eL2 children with and without SLI

Diagnostics in bilingual children is challenging, due to an overlap of production patterns in typically developing (TD) bilingual children and monolingual children with specific language impairment (SLI). To screen bilingual children effectively, the Language Impairment Testing in Multilingual Settings (LITMUS) tools were developed in an international project. The present study tests three of these tools for their suitability and diagnostic accuracy for early second language learners (eL2) of German, aged six to eight years. The study focuses on the timing in first language (L1) TD acquisition, investigating early and late acquisition phenomena of the morphosyntactic domain (subject-verb agreement [SVA], and case marking), combined with a non-word repetition (NWR) task targeting phonological complexity. The study aims at evaluating these three LITMUS-tools regarding their diagnostic accuracy, compared to a standardised assessment tool (LiSe-DaZ).To this end, forty-two children were tested using the LITMUS-tools, namely, contrastive case marking (CCM), supplemented by an elicitation task for the prepositional case, SVA and NWR. Four groups of children participated: eL2 children with SLI (mean age 7;6, mean age of onset 3;1), eL2 children with TD (mean age 7;10, mean age of onset 2;11), L1 TD children (mean age 7;3) and L1 SLI children (mean age 7;2). Results show NWR and SVA as suitable markers and the LITMUS-tools as suitable screenings. Conversely, CCM does not disentangle SLI from TD in the investigated bilingual population by this age.

Special Needs Assessment in Bilingual School-Age Children in Germany

Educational and (psycho-)linguistic research on L1 and L2 acquisition in bilingual children sketches them as a group of language learners varying in many aspects. However, most studies to date have based evaluations of language proficiency or new assessment tools on data from heritage children, while studies on the appropriateness of assessment tools for school-age refugee children remain a notable exception. This study focuses on the standardized assessment tool BUEGA for primary school children, which is, among others, a widespread tool for the assessment of pedagogical support or special needs (SN) in Germany. We compare the performance of 12 typically developing monolinguals (MoTD: 7;3–12;1), 14 heritage-bilinguals (BiTD: 7;1–13;4, L1 Turkish and Arabic), 12 refugee- students (BiTD: 8;7–13;1, L1 Arabic), and 7 children with developmental language disorders (DLD: 7;7–13;9) on the subtests of grammar, word-reading, and spelling. Overall results show that refugee-BiTDs perform in the (monolingual) pathology range. No significant differences emerged between students with DLD and typically developing (TD) refugee students. Considering the assessment of school-related language performance, bilingual refugees are at risk of misdiagnosis, along with the well-known effects of educational disadvantage. This particularly applies to children with low socioeconomic status (SES). Looking beyond oral language competencies and using test combinations can help exclude language disorders in school-age children with limited L2 proficiency.

Challenges and Future Directions of Big Data and Artificial Intelligence in Education

We discuss the new challenges and directions facing the use of big data and artificial intelligence (AI) in education research, policy-making, and industry. In recent years, applications of big data and AI in education have made significant headways. This highlights a novel trend in leading-edge educational research. The convenience and embeddedness of data collection within educational technologies, paired with computational techniques have made the analyses of big data a reality. We are moving beyond proof-of-concept demonstrations and applications of techniques, and are beginning to see substantial adoption in many areas of education. The key research trends in the domains of big data and AI are associated with assessment, individualized learning, and precision education. Model-driven data analytics approaches will grow quickly to guide the development, interpretation, and validation of the algorithms. However, conclusions from educational analytics should, of course, be applied with caution. At the education policy level, the government should be devoted to supporting lifelong learning, offering teacher education programs, and protecting personal data. With regard to the education industry, reciprocal and mutually beneficial relationships should be developed in order to enhance academia-industry collaboration. Furthermore, it is important to make sure that technologies are guided by relevant theoretical frameworks and are empirically tested. Lastly, in this paper we advocate an in-depth dialog between supporters of "cold" technology and "warm" humanity so that it can lead to greater understanding among teachers and students about how technology, and specifically, the big data explosion and AI revolution can bring new opportunities (and challenges) that can be best leveraged for pedagogical practices and learning.