Computer Assisted Learning for Young Bilinguals
Bibliographic record
Abstract
Abstract Few educational systems have been developed to specifically address the needs of young children who are acquiring two languages at the same time. In this paper, we present a prototype of a CALL (Computer Assisted Language Learning) system for English and Japanese bilingual children aged between 6 and 8. The prototype recreates a bilingual learning environment and was tested with 4 bilingual children and 8 language teachers. The study indicates that a CALL system appropriately designed for bilinguals can help children achieve balanced bilingualism and biliteracy. Introduction Bilingualism is extremely common around the world. Some nations, for example Canada and India, are officially bi- or multilingual. However, most bilinguals do not live in bilingual countries and most of them receive very little support in achieving bilingualism. The acquisition and development of bilingualism by children is in fact a complicated process that requires a lot of commitment from both the children and the people who influence them linguistically (Taeschner & Volterra, 1978). The term balanced bilingualism is often used to describe individuals who, compared to monolinguals, possess about the same level of fluency in two languages. For a number of different reasons, few people are truly balanced bilinguals: one language is usually dominant, at least in some aspects of language use (for example reading), or in some specific domains (for example in the domain of professional activity) (Bialystok, 2001). In bilingual children, this imbalance is often linked to an imbalance in the amount and/or quality of input that they receive in each language (McLaughlin, Blanchard & Osanai, 1995). Moreover, when one language is used at the exclusion of the other in some specific domains or for some specific purposes, a specialization of the languages operates: each language becomes specialized in the domain in which it is mostly used. For example, a bilingual child may be fluent in the minority language for speaking with his or her family about family matters, but functionally unable to use the same language to talk about school matters. Finally, literacy in one language rarely develops without formal education (Cummins, 1989). When formal education is exclusively provided in the majority language, literacy in the minority language is not achieved. Recent studies have shown that bilinguals may have cognitive advantages over monolinguals (Bialystok, 2001) and that cross-linguistic transfers operate concurrently between learnt languages (Odlin, 1989). Despite this, bilingual education is often considered a controversial issue (Baker, 2001). In addition to the lack of formal bilingual education, it is also surprising to note that few attempts have been made to develop educational tools specifically designed for bilinguals. Typically, multilingual families and communities rely on a multiplicity of independent and ill-adapted educational tools, each of which addresses only one of their children's languages and cultures. For example, CALL (Computer Assisted Language Learning) systems for first or second language acquisition have gained a tremendous momentum, following the recent developments in multimedia personal computers' capabilities and interconnectivity. But unfortunately, in current CALL systems, the world is still essentially a monolingual world where children are not expected to exhibit a variety of proficiency levels in several languages. The purpose of this paper is to contribute ideas towards the development of CALL systems for bilinguals that recreate the richness and complexity of a multilingual learning environment. First we outline the requirements for a bilingual CALL system, and then we propose a new CALL paradigm (the model) and a prototype implementation. Finally, we present the results of a preliminary study on the OCOL model and the prototype's use. Ideally, a CALL system dedicated to bilinguals should use the same level of linguistics in both languages throughout the application. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".