The Impact of the Data-Driven Learning Approach on ESL Writers’ Citation Patterns

  •  Ebtisam Aluthman    


This study reports the impact of the data-driven learning (DDL) approach on ESL Saudi writers’ general citation patterns that contribute to their general authorial voice. Specifically, the study examines the effects of the DDL activities on ESL writers’ use of integral and non-integral citation patterns based on Swales’ (1981, 1986, and 1990) modal of citation analysis and the extended scheme of classification set by Thompson & Tribble (2001). Guided use of both the Michigan Corpus of Upper-Level Student Papers (MICUSP) and has been designed, implemented, and assessed with a representative sample of 32 ESL upper-intermediate and advanced writers in the Department of Translation in College of Languages at Princess Nourah bint Abul Rahman University (PNU). The effectiveness of the DDL activities in improving the writers’ use of the citation patterns in composition of assignments is measured via a repeated measure paired t test. The study evaluates writers' authorial voice in terms of their use of integral and non-integral citation patterns. The quantitative analysis reveals that participants’ integral patterns (n = 398) of citation significantly outnumbered non-integral patterns (n = 126). The verb-controlling pattern occurred the most (n = 320), constituting 61% of total citation patterns. Results of the paired sample t test reveals a significant statistical difference between participants’ performances before and after the integration of the DDL activities, with the mean value being increased from 2.285 to 3.778. These results inform pedagogical implications of the DDL approach in ESL writing. The conceptual framework implementing the DDL approach in the present study provides guidance for applying corpus-informed tools when designing writing activities for upper-intermediate to advanced ESL learners.

This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1923-869X
  • ISSN(Online): 1923-8703
  • Started: 2011
  • Frequency: bimonthly

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