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Charles M Mueller

Biographic Data

ID11012017
NAMECharles M Mueller
GIVEN NAMESCharles M
FAMILY NAMEMueller
SIGNATUREMUELLER C M
AFFILIATIONSFuji Women's University
VERIFIEDNo
TOTAL WORKS3
TOTAL CITATIONS6
WORKS AS AUTHOR3
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2011
LATEST PUBLICATION YEAR2019
H-INDEX2
  • Effectiveness of force dynamic explanations of English causative verbs and the role of imagery

    Open Access•C Mueller, Charles M Mueller et al.•ARTICLE•Cognitive Linguistics•2019•References: 3

    The current study examines the effectiveness of a CL-based force dynamic (FD) lesson relative to a more conventional approach that seeks to convey the target semantics through corresponding L1 forms. Exp. 1 ( N =67) examined Japanese EFL students’ acquisition of the English verbs force, get, have, help, let, make , and prevent , comparing the effectiveness of force dynamic explanations with dynamic diagrams and a translation-based approach. Both …

  • Initial acquisition of tense-aspect morphology in an artificial language

    Open Access•C Mueller, Charles M Mueller•ARTICLE•Second language Research•2018•Cited by: 3•References: 3

    Various explanations have been put forth for the asymmetrical acquisition of tense and aspect morphology across categories of lexical aspect. This experiment tested the adequacy of a subset of such accounts by examining English native speakers’ ( n = 40) use of progressive and past tense morphology within activity and accomplishment verb frames during their early acquisition of a miniature artificial language. Participants completed a lesson in w…

  • English learners’ knowledge of prepositions

    Open Access•C Mueller, Charles M Mueller•ARTICLE•System•2011•Cited by: 3•References: 2

  • Initial acquisition of tense-aspect morphology in an artificial language

    Open Access•C Mueller, Charles M Mueller•ARTICLE•Second language Research•2018•Cited by: 3•References: 3

    Various explanations have been put forth for the asymmetrical acquisition of tense and aspect morphology across categories of lexical aspect. This experiment tested the adequacy of a subset of such accounts by examining English native speakers’ ( n = 40) use of progressive and past tense morphology within activity and accomplishment verb frames during their early acquisition of a miniature artificial language. Participants completed a lesson in w…

  • English learners’ knowledge of prepositions

    Open Access•C Mueller, Charles M Mueller•ARTICLE•System•2011•Cited by: 3•References: 2

  • English learners’ knowledge of prepositions

    Open Access•C Mueller, Charles M Mueller•ARTICLE•System•2011•Cited by: 3•References: 2

  • Initial acquisition of tense-aspect morphology in an artificial language

    Open Access•C Mueller, Charles M Mueller•ARTICLE•Second language Research•2018•Cited by: 3•References: 3

    Various explanations have been put forth for the asymmetrical acquisition of tense and aspect morphology across categories of lexical aspect. This experiment tested the adequacy of a subset of such accounts by examining English native speakers’ ( n = 40) use of progressive and past tense morphology within activity and accomplishment verb frames during their early acquisition of a miniature artificial language. Participants completed a lesson in w…

  • Effectiveness of force dynamic explanations of English causative verbs and the role of imagery

    Open Access•C Mueller, Charles M Mueller et al.•ARTICLE•Cognitive Linguistics•2019•References: 3

    The current study examines the effectiveness of a CL-based force dynamic (FD) lesson relative to a more conventional approach that seeks to convey the target semantics through corresponding L1 forms. Exp. 1 ( N =67) examined Japanese EFL students’ acquisition of the English verbs force, get, have, help, let, make , and prevent , comparing the effectiveness of force dynamic explanations with dynamic diagrams and a translation-based approach. Both …

Linguistics (3 works) · Psychology (3 works) · Computer Science (2 works) · Second Language Acquisition and Learning (2 works) · Artificial Intelligence (1 work) · Biology (1 work) · Chemistry (1 work) · Collocation (remote sensing (1 work) · EFL/ESL Teaching and Learning (1 work) · Force dynamics (1 work)

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