Testing the Limits of Data-driven Learning: Language Proficiency and Training. Note: This Is a Pre-print Version and May Contain Differences with the Final Published Version

The potential for corpora in language learning has attracted a significant amount of attention in recent years, including in the form of data-driven learning (DDL). Careful not to appear to over-promote the field, enthusiasts have urged caution in its application, in particular with regard to lower-level learners, and have argued that extensive learner-training in corpus techniques is an essential condition for DDL to be successful. Such limits seem eminently reasonable, but there is a notable dearth of empirical studies to support them. This paper describes a simple experiment to see how lower-level learners cope with corpus data with no prior training. The language focus here is on linking adverbials in English, which are renowned to be difficult to teach using traditional methods. The subjects are 132 first-year students at an engineering college in France of roughly intermediate and lower levels of English. They were divided into random groups to compare their ability to deal with the target items using traditional sources (extracts from a bilingual dictionary or a grammar/usage manual) or corpus data (short contexts or truncated concordances). Performance was tested prior to the experiment, subsequently to check ability to use the different information sources as a reference, and later to test recall. No evidence was found that traditional sources promote better recall, and corpus data seemed to be more effective for reference purposes. While the results of any single experiment must be treated with caution, these findings suggest the need for more empirical studies to complement the theoretical arguments and qualitative data which currently dominate the discussions of DDL.