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      Developing Little Bridge as an evidence-informed English language learning platform for 6–12 year olds

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          Abstract

          English is considered by many to be the global language of business and communication and, as such, parents and educators in countries in which English is not a native language are now encouraging children to study English at a young age. Much second language teaching and learning, however, does not take into account the real-world context within which language will be put to use. Little Bridge has developed an English language learning platform for students aged 6−12 years, within which learners acquire English vocabulary and skills and are able to apply what they have learned in real conversations with other English learners around the world. As part of UCL’s EDUCATE research accelerator programme, Little Bridge worked with a mentor to design and conduct mixed-methods research into the relationship between this social aspect of their platform and students’ achievement in learning English. Findings suggested that Little Bridge users who are the most active participants in the platform’s social network also complete more of the platform’s learning activities and achieve better results than those with the lowest social participation rates. The relationship between the academic mentor and Little Bridge enabled the company to develop a research mindset, understand the value of the data that they already have, and improve their understanding of the platform.

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          Most cited references 18

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          Using thematic analysis in psychology

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            Institutional Ecology, `Translations' and Boundary Objects: Amateurs and Professionals in Berkeley's Museum of Vertebrate Zoology, 1907-39

             S. Star,  J. Griesemer (1989)
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              The importance of the normality assumption in large public health data sets.

              It is widely but incorrectly believed that the t-test and linear regression are valid only for Normally distributed outcomes. The t-test and linear regression compare the mean of an outcome variable for different subjects. While these are valid even in very small samples if the outcome variable is Normally distributed, their major usefulness comes from the fact that in large samples they are valid for any distribution. We demonstrate this validity by simulation in extremely non-Normal data. We discuss situations in which in other methods such as the Wilcoxon rank sum test and ordinal logistic regression (proportional odds model) have been recommended, and conclude that the t-test and linear regression often provide a convenient and practical alternative. The major limitation on the t-test and linear regression for inference about associations is not a distributional one, but whether detecting and estimating a difference in the mean of the outcome answers the scientific question at hand.
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                Author and article information

                Journal
                rfa
                Research for All
                UCL Press (UK )
                2399-8121
                16 February 2021
                : 5
                : 1
                : 52-66
                Affiliations
                Little Bridge
                Weatherby Education Studies, UK
                Author notes
                Corresponding author: Email: emma.rogers@ 123456littlebridge.com
                Article
                10.14324/RFA.05.1.06
                Copyright © 2021 Rogers and Weatherby

                This is an open-access article distributed under the terms of the Creative Commons Attribution Licence (CC BY) 4.0 https://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 5, Tables: 5, References: 20, Pages: 16
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                Research for All
                Volume 5, Issue 1

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