Details
| Original language | English |
|---|---|
| Pages (from-to) | 385-417 |
| Number of pages | 33 |
| Journal | Reading and writing |
| Volume | 37 |
| Issue number | 2 |
| Early online date | 8 Aug 2022 |
| Publication status | Published - Feb 2024 |
Abstract
Recurrence quantification analysis (RQA) is a time-series analysis method that uses autocorrelation properties of typing data to detect regularities within the writing process. The following paper first gives a detailed introduction to RQA and its application to time series data. We then apply RQA to keystroke logging data of first and foreign language writing to illustrate how outcome measures of RQA can be understood as skill-driven constraints on keyboard typing performance. Forty native German students performed two prompted writing assignments, one in German and one in English, a standardized copy task, and a standardized English placement test. We assumed more fluent and skilled writing to reveal more structured typing time series patterns. Accordingly, we expected writing in a well-mastered first language to coincide with higher values in relevant RQA measures as compared to writing in a foreign language. Results of mixed model ANOVAs confirmed our hypothesis. We further observed that RQA measures tend to be higher, thus indicating more structured data, whenever parameters of pause, burst, and revision analyses indicate more fluent writing. Multiple regression analyses revealed that, in addition to typing skills, language proficiency significantly predicts outcomes of RQA. Thus, the present data emphasize RQA being a valuable resource for studying time series data that yields meaningful information about the effort a writer must exert during text production.
Keywords
- Foreign writing research, Language proficiency, Recurrence quantification analysis, Time course data, Writing fluency
ASJC Scopus subject areas
- Psychology(all)
- Neuropsychology and Physiological Psychology
- Social Sciences(all)
- Education
- Social Sciences(all)
- Linguistics and Language
- Health Professions(all)
- Speech and Hearing
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In: Reading and writing, Vol. 37, No. 2, 02.2024, p. 385-417.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Global temporal typing patterns in foreign language writing: exploring language proficiency through recurrence quantification analysis (RQA)
AU - Haake, Lisa
AU - Wallot, Sebastian
AU - Tschense, Monika
AU - Grabowski, Joachim
N1 - Funding information: Open Access funding enabled and organized by Projekt DEAL. The research was funded by the affiliations of the authors.
PY - 2024/2
Y1 - 2024/2
N2 - Recurrence quantification analysis (RQA) is a time-series analysis method that uses autocorrelation properties of typing data to detect regularities within the writing process. The following paper first gives a detailed introduction to RQA and its application to time series data. We then apply RQA to keystroke logging data of first and foreign language writing to illustrate how outcome measures of RQA can be understood as skill-driven constraints on keyboard typing performance. Forty native German students performed two prompted writing assignments, one in German and one in English, a standardized copy task, and a standardized English placement test. We assumed more fluent and skilled writing to reveal more structured typing time series patterns. Accordingly, we expected writing in a well-mastered first language to coincide with higher values in relevant RQA measures as compared to writing in a foreign language. Results of mixed model ANOVAs confirmed our hypothesis. We further observed that RQA measures tend to be higher, thus indicating more structured data, whenever parameters of pause, burst, and revision analyses indicate more fluent writing. Multiple regression analyses revealed that, in addition to typing skills, language proficiency significantly predicts outcomes of RQA. Thus, the present data emphasize RQA being a valuable resource for studying time series data that yields meaningful information about the effort a writer must exert during text production.
AB - Recurrence quantification analysis (RQA) is a time-series analysis method that uses autocorrelation properties of typing data to detect regularities within the writing process. The following paper first gives a detailed introduction to RQA and its application to time series data. We then apply RQA to keystroke logging data of first and foreign language writing to illustrate how outcome measures of RQA can be understood as skill-driven constraints on keyboard typing performance. Forty native German students performed two prompted writing assignments, one in German and one in English, a standardized copy task, and a standardized English placement test. We assumed more fluent and skilled writing to reveal more structured typing time series patterns. Accordingly, we expected writing in a well-mastered first language to coincide with higher values in relevant RQA measures as compared to writing in a foreign language. Results of mixed model ANOVAs confirmed our hypothesis. We further observed that RQA measures tend to be higher, thus indicating more structured data, whenever parameters of pause, burst, and revision analyses indicate more fluent writing. Multiple regression analyses revealed that, in addition to typing skills, language proficiency significantly predicts outcomes of RQA. Thus, the present data emphasize RQA being a valuable resource for studying time series data that yields meaningful information about the effort a writer must exert during text production.
KW - Foreign writing research
KW - Language proficiency
KW - Recurrence quantification analysis
KW - Time course data
KW - Writing fluency
UR - http://www.scopus.com/inward/record.url?scp=85135574284&partnerID=8YFLogxK
U2 - 10.1007/s11145-022-10331-0
DO - 10.1007/s11145-022-10331-0
M3 - Article
VL - 37
SP - 385
EP - 417
JO - Reading and writing
JF - Reading and writing
SN - 0922-4777
IS - 2
ER -