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E-raamat: Computational Text Analysis and Reading Comprehension Exam Complexity: Towards Automatic Text Classification

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Liontou presents students, academics, researchers, and practitioners with an investigation of a range of linguistic features that characterize texts used at the independent user and proficient user levels of the Greek State Certificate of English Language Proficiency exams and an examination of reader variables that may impact test takers’ perception of difficulty. The main body of the text is organized in six chapters. Following an introduction, literature review, and explanation of research methodology, the author reveals her findings and conclusions. Trisevgeni Liontou is an independent researcher, focused on theoretical and practical issues of reading comprehension performance, computational linguistics, online teaching practices, and classroom-based assessment. Annotation ©2015 Ringgold, Inc., Portland, OR (protoview.com)
1 Introduction
1(14)
1.1 Rationale of the study
2(4)
1.2 Aim of the study
6(4)
1.3 Usefulness of the study
10(2)
1.4 Book Structure
12(3)
2 Literature Review
15(66)
2.1 Introduction
15(1)
2.2 Readability Formulas
16(13)
2.3 Text structural complexity
29(13)
2.3.1 Text organisation
29(6)
2.3.2 Halliday & Hasan's Model of Text Cohesion
35(7)
2.4 Lexicogrammatical complexity
42(20)
2.4.1 Lexical Density
43(1)
2.4.2 Grammatical Intricacy
44(2)
2.4.3 Lexical Diversity
46(2)
2.4.4 Prepositional Idea Density
48(2)
2.4.5 Word Frequency
50(3)
2.4.6 Idioms
53(4)
2.4.7 Phrasal Verbs
57(1)
2.4.8 Additional text variables
58(4)
2.5 Reader Variables
62(19)
2.5.1 Content schemata & reading comprehension
62(4)
2.5.2 Formal schemata & reading comprehension
66(1)
2.5.3 Topic preference & reading comprehension
67(2)
2.5.4 Background knowledge & test bias
69(1)
2.5.5 Test-takers' strategies & reading comprehension
70(7)
2.5.6 Sex-based differences & reading comprehension
77(3)
2.5.7 Additional test-takers' characteristics & reading comprehension
80(1)
2.6 Concluding remarks
81(1)
3 Research Methodology
81(50)
3.1 Introduction
83(1)
3.2 The KPG English Reading Corpus
83(1)
3.3 Automated Text Analysis Tools
84(22)
3.3.1 Basic Text Information
91(1)
3.3.2 Text genre specification
92(1)
3.3.3 Word Frequency Indices
93(1)
3.3.4 Readability Indices
94(1)
3.3.5 Prepositional Idea Density Indices
95(1)
3.3.6 Lexical Richness Indices
96(2)
3.3.7 Text Abstractness Indices
98(1)
3.3.8 Syntactic Complexity Indices
99(1)
3.3.9 Cohesion & Coherence Indices
100(2)
3.3.10 Referential & Semantic Indices
102(3)
3.3.11 Psycholinguistic Processes Indices
105(1)
3.3.12 Idioms & Phrasal Verbs Indices
105(1)
3.4 The KPG National Survey for the English Language Exams
106(23)
3.4.1 The sampling frame
109(1)
3.4.1.1 The sample size
109(2)
3.4.1.2 Sample representativeness
111(1)
3.4.1.3 Stratified random sampling
112(1)
3.4.2 The KPG English Survey: Design & Application
113(1)
3.4.2.1 Why a questionnaire?
113(2)
3.4.2.2 Operationalizing the questionnaire
115(1)
3.4.2.3 Types of questions
116(2)
3.4.2.4 The rating system
118(2)
3.4.2.5 Question wording
120(2)
3.4.2.6 Question sequencing
122(2)
3.4.2.7 Questionnaire layout
124(1)
3.4.2.8 The opening section
125(1)
3.4.2.9 Questionnaire length & language
125(1)
3.4.2.10 Ethical issues
125(1)
3.4.3 Piloting the KPG English Survey Questionnaire
126(2)
3.4.4 Administering the KPG English Survey Questionnaire
128(1)
3.4.5 Processing the KPG English Survey Data
128(1)
3.5 Reading Comprehension Task Score Analysis
129(1)
3.6 Triangulation
129(2)
4 Computational Text Analysis: Findings
131(22)
4.1 Text Analysis
132(11)
4.1.1 Basic Text Information
132(1)
4.1.2 Word Frequency Analysis
133(2)
4.1.3 Readability Formulas Scores
135(1)
4.1.4 Prepositional Idea Density & Lexical Richness Scores
136(1)
4.1.5 Text Abstractness Analysis
137(1)
4.1.6 Syntactic Complexity Analysis
138(1)
4.1.7 Reference & Cohesion Analysis
139(2)
4.1.8 Psycholinguistic Processes Analysis
141(1)
4.1.9 Additional Text Variables Analysis
142(1)
4.2 Automatic Text Classification Model
143(6)
4.3 Model Validation Procedure
149(4)
5 KPG Test-Takers' Performance & Perceptions: Research Findings
153(28)
5.1 KPG Reading Performance & Text Features
153(7)
5.1.1 Reading Performance & Text Features: An Across-Levels Analysis
154(1)
5.1.2 B2 Reading Performance & Text Features
155(2)
5.1.3 C1 Reading Performance & Text Features
157(2)
5.1.4 Construct-validity of the KPG language exams in English
159(1)
5.2 KPG Test-Takers' Perceptions
160(21)
5.2.1 KPG Test-Takers' Profile
161(1)
5.2.2 KPG test-takers' personal characteristics & reading difficulty
162(1)
5.2.3 KPG test-takers' perceptions of the Reading Comprehension Test Paper
163(2)
5.2.4 KPG test-takers' perceptions vis-a-vis text features
165(6)
5.2.5 KPG test-takers' strategies vis-a-vis text features
171(6)
5.2.6 Additional reader variables vis-a-vis text features
177(4)
6 Discussion & Conclusions
181(10)
6.1 Usefulness of the study
181(6)
6.2 Research limitations
187(2)
6.3 Suggestions for future research & for the use of the findings
189(2)
References 191(52)
Appendices 243
Trisevgeni Liontou holds a PhD in English Linguistics with specialization in Testing from the Faculty of English Studies at the National and Kapodistrian University of Athens (Greece). She holds a BA in English Language & Literature and an MA in Lexicography: Theory and Applications, both from the same faculty. She also holds a M.Sc. in Information Technology in Education from Reading University (UK). Her current research interests include theoretical and practical issues of reading comprehension performance, computational linguistics, online teaching practices and classroom-based assessment.
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