Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1ELT511Corpus Linguistics and Language Teaching3+0+03707.10.2026
Course Details
Language of Instruction English
Level of Course Unit Master's Degree
Department / Program English Language Teaching
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course To develop a systematic understanding of the concepts, principles, types, and applications of corpus linguistics; use corpus tools and techniques to investigate authentic language data and identify meaningful linguistic patterns; critically interpret corpus evidence, considering representativeness, sampling, frequency, association measures, limitations, and appropriate use of findings; apply corpus-informed approaches and Data-Driven Learning principles to vocabulary, grammar, discourse, and language skills teaching; and design corpus-informed lesson plans, materials, assessment tasks, and a small-scale ELT project based on authentic corpus evidence.
Course Content This course introduces the theoretical and practical foundations of corpus linguistics and examines how corpus evidence can inform English language teaching, learning, materials development, assessment, and classroom research. Students explore different types and designs of corpora, corpus tools and analytical techniques, including frequency analysis, concordancing, collocations, keywords, clusters and n-grams. Particular emphasis is placed on corpus-informed pedagogy and Data-Driven Learning (DDL), with students using accessible corpora and software to investigate authentic language patterns and translate findings into pedagogically meaningful activities, materials, and assessment tasks. The course also develops students ` critical awareness of corpus representativeness, data interpretation, methodological limitations, and ethical considerations in corpus use.
Course Methods and Techniques Concept mapping; corpus comparison and evaluation; guided corpus searches; frequency, collocation and keyword analysis; critical and pedagogical interpretation of findings; Data-Driven Learning activity design; materials adaptation; assessment task design; project workshops, reporting and presentations.
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Asist Prof.Dr. Zekeriya DURMAZ zekeriya.durmaz@hku.edu.tr
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Meyer, C. F. (2023). English corpus linguistics: An introduction. Cambridge University Press.
Bennett, G. R. (2010). Using corpora in the language learning classroom: Corpus linguistics for teachers. University of Michigan Press.
McEnery, T., & Hardie, A. (2012). Corpus linguistics: Method, theory and practice. Cambridge University Press.
O ` Keeffe, A., McCarthy, M., & Carter, R. (2007). From corpus to classroom: Language use and language teaching. Cambridge University Press.
Reppen, R. (2010). Using corpora in the language classroom. Cambridge University Press.
Supplementary readings: Sinclair (1991); Biber, Conrad, & Reppen (1998); Aijmer (2009); Hunston (2002); selected recent corpus-based ELT studies and instructor-provided materials.
Course Notes Required textbook:
Meyer, C. F. (2023). English corpus linguistics: An introduction. Cambridge University Press.

Core texts:
Bennett, G. R. (2010). Using corpora in the language learning classroom: Corpus linguistics for teachers. University of Michigan Press.
McEnery, T., & Hardie, A. (2012). Corpus linguistics: Method, theory and practice. Cambridge University Press.
O ` Keeffe, A., McCarthy, M., & Carter, R. (2007). From corpus to classroom: Language use and language teaching. Cambridge University Press.
Reppen, R. (2010). Using corpora in the language classroom. Cambridge University Press.

Supplementary readings:
Sinclair (1991); Biber, Conrad, & Reppen (1998); Aijmer (2009); Hunston (2002); selected recent corpus-based ELT studies and instructor-provided materials.

Course Category
Field %100

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Assignment 1 % 40
Project 1 % 60
Total
2
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 6 84
Assignments 1 30 30
Presentation 1 8 8
Project 1 46 46
Total Work Load   Number of ECTS Credits 7 210

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Explain and critically evaluate the theoretical foundations, types, designs, and applications of corpus linguistics in English language teaching.
2 Apply corpus tools and analytical techniques, including frequency analysis, concordancing, collocation, keyword, cluster, and n-gram analysis, to authentic language data.
3 Interpret corpus evidence critically by considering sampling, representativeness, frequency and association measures, methodological limitations, and the reliability of pedagogical inferences.
4 Apply corpus-informed pedagogy and Data-Driven Learning principles to design context-sensitive language teaching activities, materials, and assessment tasks.
5 Design, conduct, and report a small-scale corpus-informed ELT project that connects a research question, corpus evidence, analysis, interpretation, and pedagogical implications.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Orientation and introduction to corpus linguistics: Course overview; what is a corpus?; corpus linguistics and its applications in ELT; corpus vs. intuition-based evidence. Meyer (2023), Chapter 1. -
2 Types, purposes, and designs of corpora: Reference, learner, pedagogic, specialized, spoken and written corpora; corpus size, balance, representativeness and sampling. McEnery & Hardie (2012). -
3 Building and evaluating corpora: Corpus design principles; metadata; annotation; sampling; representativeness; reliability and limitations. Meyer (2023). -
4 Corpus tools and concordancers: Online corpora and software; AntConc/Sketch Engine-type tools; searching, concordance lines and KWIC. Bennett (2010). -
5 Frequency and distribution: Word frequency; normalization; frequency lists; dispersion; interpreting frequent vs. salient items. McEnery & Hardie (2012). -
6 Collocation, clusters and lexical patterning: Collocations; association measures; clusters/n-grams; lexical bundles and pedagogical implications. O ` Keeffe et al. (2007). -
7 Keywords and contrastive corpus analysis: Keyword analysis; comparing corpora; learner vs. reference data; interpreting patterns critically. Selected corpus studies. -
8 Mid-term week: Mid-term examination / individual corpus analysis task. - -
9 Corpus-informed language pedagogy: Corpus-informed vs. corpus-based teaching; teacher mediation; linking corpus evidence to classroom decisions. Reppen (2010). -
10 Data-Driven Learning (DDL): Discovery learning; inductive language learning; direct and indirect corpus use; learner autonomy and scaffolding. Bennett (2010). -
11 Corpora for vocabulary and grammar teaching: Lexical patterns, collocations, phraseology, grammar in context; authentic examples and noticing. O ` Keeffe et al. (2007). -
12 Corpus-informed materials development: Selecting corpus examples; adapting textbook materials; authenticity, level, difficulty and contextualization. Reppen (2010). -
13 Corpora, discourse and language skills: Corpus evidence for reading, writing, speaking and discourse; register and genre; pedagogical applications. Biber & Conrad (2009). -
14 Corpus-based language assessment and classroom research: Corpus-informed assessment; item/content decisions; learner corpus analysis; small-scale classroom research. Selected recent studies. -
15 Corpus project workshop and presentations: Research question; data selection; analysis; interpretation; pedagogical application; reporting and presentation. Project guidelines. -
16 Final exam week: Final project submission / final examination. - -

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
C1 5 4 4 2 1 2 4 3 2 3 3 2
C2 3 3 5 4 1 5 4 3 1 3 3 2
C3 4 3 5 5 2 4 4 3 4 4 4 2
C4 5 4 4 3 4 4 5 4 3 4 4 3
C5 4 5 5 5 2 5 5 5 4 5 5 4

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant