Replication Data for: (Re-)Constructing Questions

DOI

This is the replication data for a paper submitted to an academic journal. The abstract of the paper follows. This paper discusses the use of second language structures, particularly the use of interrogatives, as it can be understood in a construction grammar framework (cf. Goldberg 2003, Goldberg et al. 2004, Ellis and Cadierno 2009). It builds on previous research in first and second language acquisition, as well as Höder’s proposed “Diasystematic Construction Grammar” (cf. Höder 2018, Höder et al. 2021). Moreover, the paper addresses the role of language acquisition theories in the context of language teaching and teacher training. Since teachers’ knowledge is acquired in teacher education, language acquisition theories and their implications should be a fundamental part of their education. The case study is looking at the knowledge and use of interrogative constructions by learners of English. For this purpose, I analysed the language of learners in three German schools (age = 11-14, n = 100). The results of the analysis are then applied to a dynamic network approach (cf. Diessel 2019, 2020) to teaching English interrogative constructions. This means that the architecture of an emerging multilingual construct-i-con is taken into consideration. This article focusses on learners of English with one particular L1 (German) and outlines a framework for teaching English constructions and their fillers that is based on language use as observed in a corpus.

Identifier
DOI https://doi.org/10.18710/Q5S1IW
Metadata Access https://dataverse.no/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18710/Q5S1IW
Provenance
Creator Gerisch, Linda ORCID logo; Wehmeier, Christian
Publisher DataverseNO
Contributor Gerisch, Linda; Inland Norway University of Applied Sciences
Publication Year 2022
Rights CC0 1.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/publicdomain/zero/1.0
OpenAccess true
Contact Gerisch, Linda (Inland Norway University of Applied Sciences)
Representation
Resource Type Dataset
Format text/plain; text/csv; type/x-r-syntax; text/x-python-script
Size 9387; 10096; 4421; 5607; 4207; 1117; 1582; 14890; 1101; 4390; 8950; 5157; 8934; 5804; 8958; 5452
Version 2.0
Discipline Agriculture, Forestry, Horticulture, Aquaculture; Agriculture, Forestry, Horticulture, Aquaculture and Veterinary Medicine; Humanities; Life Sciences; Linguistics; Social Sciences; Social and Behavioural Sciences; Soil Sciences