Lau Tak Pang. / Thesis submitted in: October 2006. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 110-122). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation and Major Contributions --- p.1 / Chapter 1.1.1 --- Chinese Readability Analysis --- p.1 / Chapter 1.1.2 --- Web Readability Analysis --- p.3 / Chapter 1.2 --- Thesis Chapter Organization --- p.6 / Chapter 2 --- Related Work --- p.7 / Chapter 2.1 --- Readability Assessment --- p.7 / Chapter 2.1.1 --- Assessment for Text Document --- p.8 / Chapter 2.1.2 --- Assessment for Web Page --- p.13 / Chapter 2.2 --- Support Vector Machine --- p.14 / Chapter 2.2.1 --- Characteristics and Advantages --- p.14 / Chapter 2.2.2 --- Applications --- p.16 / Chapter 2.3 --- Chinese Word Segmentation --- p.16 / Chapter 2.3.1 --- Difficulty in Chinese Word Segmentation --- p.16 / Chapter 2.3.2 --- Approaches for Chinese Word Segmentation --- p.17 / Chapter 3 --- Chinese Readability Analysis --- p.20 / Chapter 3.1 --- Chinese Readability Factor Analysis --- p.20 / Chapter 3.1.1 --- Systematic Analysis --- p.20 / Chapter 3.1.2 --- Feature Extraction --- p.30 / Chapter 3.1.3 --- Limitation of Our Analysis and Possible Extension --- p.32 / Chapter 3.2 --- Research Methodology --- p.33 / Chapter 3.2.1 --- Definition of Readability --- p.33 / Chapter 3.2.2 --- Data Acquisition and Sampling --- p.34 / Chapter 3.2.3 --- Text Processing and Feature Extraction . --- p.35 / Chapter 3.2.4 --- Regression Analysis using Support Vector Regression --- p.36 / Chapter 3.2.5 --- Evaluation --- p.36 / Chapter 3.3 --- Introduction to Support Vector Regression --- p.38 / Chapter 3.3.1 --- Basic Concept --- p.38 / Chapter 3.3.2 --- Non-Linear Extension using Kernel Technique --- p.41 / Chapter 3.4 --- Implementation Details --- p.42 / Chapter 3.4.1 --- Chinese Word Segmentation --- p.42 / Chapter 3.4.2 --- Building Basic Chinese Character / Word Lists --- p.47 / Chapter 3.4.3 --- Pull Sentence Detection --- p.49 / Chapter 3.4.4 --- Feature Selection Using Genetic Algorithm --- p.50 / Chapter 3.5 --- Experiments --- p.55 / Chapter 3.5.1 --- Experiment 1: Evaluation on Chinese Word Segmentation using the LMR-RC Tagging Scheme --- p.56 / Chapter 3.5.2 --- Experiment 2: Initial SVR Parameters Searching with Different Kernel Functions --- p.61 / Chapter 3.5.3 --- Experiment 3: Feature Selection Using Genetic Algorithm --- p.63 / Chapter 3.5.4 --- Experiment 4: Training and Cross-validation Performance using the Selected Feature Subset --- p.67 / Chapter 3.5.5 --- Experiment 5: Comparison with Linear Regression --- p.74 / Chapter 3.6 --- Summary and Future Work --- p.76 / Chapter 4 --- Web Readability Analysis --- p.78 / Chapter 4.1 --- Web Page Readability --- p.79 / Chapter 4.1.1 --- Readability as Comprehension Difficulty . --- p.79 / Chapter 4.1.2 --- Readability as Grade Level --- p.81 / Chapter 4.2 --- Web Site Readability --- p.83 / Chapter 4.3 --- Experiments --- p.85 / Chapter 4.3.1 --- Experiment 1: Web Page Readability Analysis -Comprehension Difficulty --- p.87 / Chapter 4.3.2 --- Experiment 2: Web Page Readability Analysis -Grade Level --- p.92 / Chapter 4.3.3 --- Experiment 3: Web Site Readability Analysis --- p.98 / Chapter 4.4 --- Summary and Future Work --- p.101 / Chapter 5 --- Conclusion --- p.104 / Chapter A --- List of Symbols and Notations --- p.107 / Chapter B --- List of Publications --- p.110 / Bibliography --- p.113

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In 2016 employers hiring four-year college graduates indicate that 27.8% have deficiencies in written communication. Postsecondary learning objectives should focus on improving specific writing skills like grammar, sentence structure, and vocabulary usage for individual students and monitoring text readability as an overall score to measure learning outcomes. Web-based applications and the tools integrated into them have the potential to serve as a diagnostic solution for analyzing the text readability and writing skills of students. Organization and structuring of Canvas data was required before adding text readability and other writing skills analytics as part of the process to develop diagnostic learning analytics that interprets student writing skills in the learning management system. Decision modeling was used to capture and describe the specifics of literacy improvement decisions for instructors and administrators in a graphical notation and structured format.