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Xin Yuan; Xuan Tang – British Journal of Educational Psychology, 2025
Aim: From the perspective of cognitive load theory, the present study examined the relative effectiveness of the sequential use of L1 and bilingual subtitles on incidental English vocabulary learning. Methods: A total of 162 upper-intermediate Chinese learners of English as a foreign language watched an English clip in one of 4 subtitling…
Descriptors: Cognitive Ability, Recall (Psychology), Native Language, Second Language Learning
Yanagisawa, Akifumi; Webb, Stuart – Studies in Second Language Acquisition, 2022
The present meta-analysis aimed to improve on Involvement Load Hypothesis (ILH) by incorporating it into a broader framework that predicts incidental vocabulary learning. Studies testing the ILH were systematically collected and 42 studies meeting our inclusion criteria were analyzed. The model-selection approach was used to determine the optimal…
Descriptors: Cognitive Ability, Vocabulary Development, Meta Analysis, Linguistic Theory
Zhan Xia; Omer Hassan Ali Mahfoodh; Malini N. G. Ganapathy – Language Teaching Research Quarterly, 2024
This study examines the effects of monologic and dialogic task sequences on incidental vocabulary learning among 115 students in four intact classes of a Chinese vocational high school. Twenty vocabulary items including words, phrases and fixed expressions ranging from high to low frequency levels were targeted. Students were required to complete…
Descriptors: Vocational Education, High School Students, English (Second Language), Second Language Instruction
Robinson, Peter – Studies in Second Language Acquisition, 2005
This paper reports replications of studies of implicit artificial grammar (AG) learning and explicit series-solution learning with experienced second language learners in order to examine their population and content generalizability. As found by Reber, Walkenfeld, and Hernstadt (1991), there was significantly greater variance in explicit compared…
Descriptors: Sentences, Test Items, Grammar, Incidental Learning