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Abstract

Abstract

Extensive reading (ER) appears to be a promising pedagogy for general literacy. Building on prior research, this corpus study examined extensive reading (ER) of digital science resources (DSR) and the potential for incidental learning of general and specialized vocabulary enclosed in general, academic, and specialized lists developed for second language (L2) learners considering specific time intervals (one day, 30 days, 90 days, 180, etc.) and reading speed (150 and 200 wpm). The results reveal that after a year of reading DSR at a rate of 6,000 words per day, learners encounter, on average, almost 91%, 85%, and 82% of the word families in the examined lists, occurring at least 12, 20, and 25 times and after 342 days of reading 8,000 words per day, learners encounter, on average, about 93%, 89%, and 85% of the target words occurring at least 12, 20, and 25 times. Noteworthy is that extensive reading for even 30 days for at least 40 minutes a day at a speed of 150–200 wpm has the potential to produce substantial vocabulary gains of general and specialized vocabulary items in the examined lists, on average, between 28.40% (25 repetitions at 150 wpm) to 48.11% (12 repetitions at 200 wpm). Looking at word families in the Academic Word List (AWL), the results suggest that extensive reading of digital science texts for 180 days at 150 wpm (reading 6000 words per day for 40 minutes) provides similar or greater opportunities for incidental vocabulary learning to occur compared with fiction for young adults and online news.

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2025-04-08
2025-04-25

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Extensive reading of digital science resources and the potential for incidental learning of vocabulary useful to language learners in secondary school
John Benjamins ; https://doi.org/10.1075/ltyl.24019.arn
/content/journals/10.1075/ltyl.24019.arn
/content/journals/10.1075/ltyl.24019.arn
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  • Article Type: Research Article
Keywords: Extensive reading ; second language learners ; secondary school ; general and specialized vocabulary ; incidental vocabulary learning ; corpus-based lists

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