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Data-Driven Problem-Solving in International Business Communication

Examining the Use of Bilingual Web-Based Tools for Text Production with Advanced English as a Foreign Language Professionals

by Alexander Zielonka (Author)
Thesis XXV, 254 Pages

Summary

This study challenges the traditional approach of focusing on English as a foreign language learning in international business settings. The primary objective in such settings is to successfully create a linguistically correct document. Rather than relying on accumulated incomplete individual language knowledge, an alternative approach is to «solve a written language problem» by employing online tools to search for certain unknown technical terms. The author of this study advocates that the use of bilingual text search engines as a more viable problem-solving tool than traditional online dictionaries. Therefore, he examines how well participants are able to select correct verb-object expression using either an online dictionary or a bilingual text search engine.

Table Of Contents

  • Cover
  • Title
  • Copyright
  • About the author(s)/editor(s)
  • About the book
  • This eBook can be cited
  • Vorwort
  • Foreword
  • Preface and Acknowledgments
  • Table of Contents
  • Table of Figures
  • Table of Tables
  • Abbreviations
  • 1 Object of Research
  • 1.1 Introduction
  • 1.2 Research Question and Outline of this Study
  • 2 Native Language Acquisition and Foreign Language Learning
  • 2.1 Terminology and Definitions
  • 2.2 Native Language Acquisition
  • 2.2.1 Early Stages of Native Language Acquisition
  • 2.2.2 Behaviorism
  • 2.2.3 Innatism
  • 2.2.4 Social Interactionism
  • 2.3 Foreign Language Learning
  • 2.3.1 Approaches in Foreign Language Learning
  • 2.3.2 Cognitive Factors in Foreign Language Learning
  • 2.3.2.1 Attention, Noticing and Awareness Raising
  • 2.3.2.2 Developmental Sequence
  • 2.3.2.3 Fossilization
  • 2.3.2.4 Concepts and Categories
  • 2.3.2.5 Native Language Transfer
  • 2.3.3 Affective Factors Affecting Foreign Language Learning
  • 2.3.4 Business-related Factors Affecting Foreign Language Learning
  • 2.3.5 A Model of Foreign Language Learning for Business Professionals
  • 2.4 Toward a Model for Written Problem-solving and Foreign Language Learning during Text Production
  • 3 Using Corpus-Based Online Tools for and Data-Driven Text Production and Learning in Business English
  • 3.1 The role of Data-Driven Learning in CALL
  • 3.2 Employing Computer-Based Applications as Cognitive Tools to Foster Problem-Solving
  • 3.3 Research on Monolingual and Bilingual Corpora
  • 3.4 The Use of the OnDic “LEO” and the BiTeSeN “Linguee” as two freely available online tools for Problem-Solving in Text Production
  • 4 Empirical Study
  • 4.1 Description of Research Tools
  • 4.2 Sample Design
  • 4.3 Criteria for Judging the Soundness of Quantitative Research
  • 4.3.1 Objectivity
  • 4.3.2 Reliability
  • 4.3.3 Validity
  • 4.3.4 Item Analysis
  • 4.4 Elaboration of Hypotheses
  • 4.5 Data Analysis and Interpretation
  • 4.5.1 Analysis of between-group test scores
  • 4.5.2 Analysis of Within-Group Correlations of Two Variables
  • 4.5.3 Analysis of Within-Group Correlations of Multiple Variables
  • 5 Discussion of Findings and Outlook
  • Reference List
  • Appendix
  • Konzepte des Lehrens und Lernens

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Table of Figures

Figure 1: True Knowledge of a Word (adapted from Nation, 2001, p. 27)

Figure 2: Conflict between employees’ English language communication requirements and companies’ ability to properly invest in long-term education (author’s figure)

Figure 3: Illustrative process of native and foreign language development (author’s figure)

Figure 4: Bridge between approaches in L1 acquisition and L2 learning and the relevant factors in adult Business EFL learning (author’s figure)

Figure 5: The three levels of awareness as described by Schmidt, 1990 (author’s figure)

Figure 6: Kihlstom’s multistore model of memory (adapted from Schmidt, 1990, p. 135)

Figure 7: A computational model of L2 acquisition (adapted from Ellis, 1997, p. 35)

Figure 8: A comprehensive attention-awareness model of foreign language learning (author’s figure)

Figure 9: L1 category formation of “kennen” and “wissen” in German children (author’s figure)

Figure 10: Two-part representational system of German L1 and English L2 speakers (author’s figure)

Figure 11: Interactive complexity model (adapted from Hillen, Breuer & Tennyson, 2011)

Figure 12: Model of adult foreign language learning in business settings (author’s figure)

Figure 13: Model of foreign language problem-solving and learning (author’s figure)

Figure 14: Example of monolingual concordances of the word “abandon” (Cobb & Horst, 2001, p. 201)

Figure 15: Example of bilingual concordances of the expression “to take a ride” (Retrieved December 06, 2013 from http://rali.iro.umontreal.ca/rali/?q=en/TransSearch)

Figure 16: The use of corpora in second language learning and teaching (adapted from Römer, 2011, p. 207)

Figure 17: Screenshot of sample query on http://www.linguee.com ← xiii | xiv →

Figure 18: Screenshot of sample query on http://dict.leo.org

Figure 19: Overview of the process of designing the cloze test (author’s figure)

Figure 20: Boxplot of the general English-language aptitude test scores of the OnDic and the BiTeSeN group (cited from SPSS)

Figure 21: Placement of the sample regarding age and level of proficiency in foreign language development (author’s figure)

Figure 22: Research design of BiTeSeN and OnDic tests (author’s figure)

Figure 23: Random sampling of the 10 human resources expressions

Figure 24: Random sampling of the 10 marketing expressions

Figure 25: Random sampling of the 10 manufacturing expressions

Figure 26: Random sampling of the 10 management expressions

Figure 27: Random sampling of the 10 finance expressions

Figure 28: Linear regression scatter plot of general aptitude test and online problem-solving test for the OnDic group

Figure 29: Linear regression scatter plot of general aptitude test and recall test for the OnDic group

Figure 30: Linear regression general aptitude test and recall test for OnDic – histogram of residuals

Figure 31: Linear regression general aptitude test and recall test for OnDic – Q-Q plot of residuals

Figure 32: Linear regression scatter plot of general aptitude test and offline problem-solving test for the OnDic group

Figure 33: Linear regression of general aptitude test and offline problem-solving test for the OnDic group – histogram of residuals

Figure 34: Linear regression of general aptitude test and offline problem-solving test for the OnDic group – Q-Q plot of residuals

Figure 35: Linear regression scatter plot of online problem-solving test and recall test for the OnDic group

Figure 36: Linear regression scatter plot of online problem-solving test and offline problem-solving test for the OnDic group

Figure 37: Linear regression scatter plot of recall test and offline problem-solving test for the OnDic group

Figure 38: Linear regression of recall test and offline problem-solving test for the OnDic group – histogram of residuals

Figure 39: Linear regression of recall test and offline problem-solving test for the OnDic group – Q-Q plot of residuals ← xiv | xv →

Figure 40: Linear regression scatter plot of general aptitude test and online problem-solving test for the BiTeSeN group

Figure 41: Linear regression scatter plot of general aptitude test and recall test for the BiTeSeN group

Figure 42: Linear regression of general aptitude test and recall test for the BiTeSeN group – histogram of residuals

Figure 43: Linear regression of general aptitude test and recall test for the BiTeSeN group – Q-Q plot of residuals

Figure 44: Linear regression scatter plot of general aptitude test and offline problem-solving test for the BiTeSeN group

Figure 45: Linear regression of general aptitude test and offline problem-solving test for the BiTeSeN group – histogram of residuals

Figure 46: Linear regression of general aptitude test and offline problem-solving test for the BiTeSeN group – Q-Q plot of residuals

Figure 47: Linear regression scatter plot of online problem-solving test and recall test for the BiTeSeN group

Figure 48: Linear regression scatter plot of online problem-solving test and offline problem-solving test for the BiTeSeN group

Figure 49: Linear regression scatter plot of recall test and offline problem-solving test for the BiTeSeN group

Figure 50: Linear regression of recall test and offline problem-solving test for the BiTeSeN group – histogram of residuals

Figure 51: Linear regression of recall test and offline problem-solving test for the BiTeSeN group – Q-Q plot of residuals

Figure 52: Multiple regression of group, general aptitude test and online problem-solving test – histogram of residuals

Figure 53: Multiple regression of group, general aptitude test and online problem-solving test – Q-Q plot of residuals

Details

Pages
XXV, 254
ISBN (ePUB)
9783631712528
ISBN (PDF)
9783653065787
ISBN (MOBI)
9783631712535
ISBN (Hardcover)
9783631674451
Language
English
Publication date
2017 (July)
Tags
Online Tasks Learning Global Commerce Non-Native-Speakers
Published
Frankfurt am Main, Bern, Bruxelles, New York, Oxford, Warszawa, Wien, 2017. XXV, 254 pp., 63 b/w ill., 123 b/w tables

Biographical notes

Alexander Zielonka (Author)

Alexander Zielonka had been working in international corporate management and controlling. He finished his doctorate at the University of Mainz and is working as an Assistant and Substitute Professor at the University of Applied Sciences Mainz and Darmstadt.

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Title: Data-Driven Problem-Solving in International Business Communication