Possibilities and Drawbacks of Using an Online Application for Semi-automatic Corpus Analysis to Investigate Discourse Markers and Alternative Fluency Variables

Abstract

To overcome planning phases in spontaneous speech production, learners and native speakers use strategies such as (un)filled pauses, smallwords or discourse markers. Small scale studies in this vein have demonstrated that learners differ from native speakers in that they underuse smallwords and discourse markers, and rely on other fluency-enhancing strategies instead. In the present paper, we present a corpusbased study, which investigates fluency-enhancing strategies in four components of the Louvain International Database of Spoken English Interlanguage (LINDSEI; Gilquin et al. 2010), covering four learner English varieties, namely Spanish, German, Bulgarian and Japanese. We investigate 216 different fluencemes (i.e. fluencyenhancing features; Götz in Fluency in native and nonnative English speech, John Benjamins, Amsterdam, 2013) in 200 transcribed interviews with advanced learners of English. An online coding application, which was specially designed and programmed for this project, enables us to cover such a large amount of data. We report on the design, functionality and (dis-)advantages of the online application, the multilevel-coding system we implemented, and the methodological challenges we face in detail. We will also present the findings of one first pilot study where we exhibit considerable variation between and within learners of particular native languages concerning fluenceme frequencies, while distributional patterns of fluencemes are rather similar across varieties.

The Use of Discourse Markers in Academic Writing by In-Service Primary School Teachers of English

“…e frequencies were normalised by the program based upon a 1 000-word cut-off to enable cross-comparison. e frequency of 1 000 words was deemed to be a standard measure in normalising data (Aijmer 2002, Polat 2011, Wolk, Götz, and Jäschke 2020. e data analysis involved the following procedure.…”

Discourse Markers in Peer Reviews of Academic Essays By Future Teachers of English as a Foreign Language

“…In line with Povolná (2013), the data in the present study were normalised for the frequency of the occurrence of DMs per 1 000 words per group of participants. In this regard, it should be observed that the frequency of DMs per 1000 words was deemed to be a standard measure in normalising the data (Aijmer 2002;Wolk, Götz & Jäschke 2021). Given that the corpora in this study differed in the total number of words (see Table 1), the frequencies of DMs in the corpora were calculated by the program WordSmith (Scott 2008) based upon the 1000 word cut-off in order to enable cross-comparison of DMs in all tasks, i.e., E1 (N = 12), E2 (N = 12), as well as all R1 (N = 12) and R2 (N = 12).…”

“We Use English But Not Like All the Time Like”—Discourse Marker Like in UAE English

“…Fuller (2003, p. 370) further argued that like is "pragmatically useful" for creating closeness, placing focus on something, or implying approximation, which are typical contexts of (personal and somewhat informal) interviews. Even though like (among other discourse markers) is a way to accommodate planning and continuation in spontaneous speech (Hasselgren 2002;Wolk et al, 2021), it is frequently considered as something negative. Rüdiger (2021, p. 1) even talked about "public language stigmatization" and presents a number of quite strong, negative attitudes towards the use of like (Rüdiger, 2021, p. 2).…”

“…In a study by Wolk et al (2021), like was the fifth most frequently used discourse marker, but it generally appeared relatively infrequently (Wolk et al, 2021, p. 23). Their findings are based on English major university students with German, Spanish, Bulgarian, or Japanese as native languages.…”