Thesis (Ph. D.)--Rutgers University, 2008. / "Graduate Program in Education." Includes bibliographical references (p. 120-129).

Thesis supervisor: Tarrell Awe Agahe Portman. Thesis supervisor: Susannah M. Wood. Includes bibliographic references (p. 154-158).

Thesis (Ph. D.)--University of Texas at Austin, 2000. / Vita. Includes bibliographical references (leaves 205-233). Available also in a digital version from Dissertation Abstracts.

Thesis (M. Ed.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 93-97).

Thesis (Ph. D.)--University of Washington, 2003. / Vita. Includes bibliographical references (leaves 179-191).

Thesis (M. Ed.)--York University, 1999. Graduate Programme in Education. / Typescript. Includes bibliographical references (leaves 88-94). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://wwwlib.umi.com/cr/yorku/fullcit?pMQ56178.

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (p. 101-108).

Thesis (M. Ed.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 117-122).

Critical thinking is widely acknowledged as crucial for 21st century learners to be able to tackle the complex tasks arising every day in a rapidly changing world. Earlier critical thinking research has mostly focused on the related micro-skills, which have been criticized as inadequate. Recent studies have placed more emphasis on nurturing the dispositions towards critical thinking. Another trend in critical thinking research is the realization that good performance on generic critical thinking skills tests does not guarantee critical thinking performance in the real world. In line with the recent trends in critical thinking development, educators have shown growing interest in fostering critical thinking skills and dispositions in students through collaborative inquiry in authentic problem-solving contexts. However, empirical research that examines students’ critical thinking behavior while they engage in extended collaborative inquiry is rare. Further, it is not clear whether differences in students’ critical thinking abilities contribute to differences in their engagement and learning outcomes in collaborative inquiry. This study aims to investigate the relationship between students’ critical thinking and the quality of their collaborative inquiry. The study involved a class of secondary two students studying a humanities module through collaborative inquiry involving the use of an online platform. Given the debates over the value of different methods of measuring critical thinking, this study first seeks to explore the relationship between three different critical thinking tests: two standardized surveys on generic skills and dispositions, and a constructed-response test on context-specific skills. Second, this study examines whether students with higher critical thinking scores are better able to apply critical thinking to real-life situations. This is done through analyzing students’ critical thinking behaviors exhibited in the context of authentic problem solving. Third, this study explores the relationship between students’ critical thinking behavior and their knowledge building engagement in the learning process. Finally, this study uses the standardized tests to determine whether there is any significant change in the students’ critical thinking ability after a sustained engagement in collaborative inquiry to tackle an authentic problem. The analysis revealed a number of interesting findings. First, the three critical thinking tests are related but measure different aspects of critical thinking. Second, compared with traditional standardized surveys, the context-specific critical thinking test is a better indicator of the likelihood of the students to autonomously apply critical thinking in authentic problem-solving contexts. Third, critical thinking behaviors exhibited by students contributed to their engagement in knowledge building. Fourth, there was a statistically significant increase in students’ critical thinking dispositions after the extended collaborative inquiry of an authentic problem. The study has both theoretical and methodological implications. The study has both theoretical and educational implications. It deepens our understanding of the relationship between critical thinking and computer-supported collaborative inquiry. This study has provided strong evidence that traditional measures of critical thinking cannot predict the quality of students’ critical thinking engagement in authentic problem-solving contexts. Further, it also provides empirical support the theoretical claim that engaging students in knowledge building activities will enhance their critical thinking skills and dispositions. / published_or_final_version / Education / Doctoral / Doctor of Philosophy

This study aims to investigate the automated question detection and classification methods to support teachers in monitoring the progression of discussion in Computer-Supported Collaborative Learning (CSCL) discourse of Hong Kong students. Questioning is an important component of CSCL. Through the analysis of question types in CSCL discourse, teachers may probably get a general idea of how an inquiry is constructed. This study is an attempt to take up this time-consuming task of question classification with the techniques developed from machine learning. In general, the performance of machine learning algorithms will improve by increasing the amount of empirical data for training. The amount of training data is a determining factor for the performance of machine learning algorithms. The machine learning based question classification algorithms may not able to detect those question types with a small amount of training data. In order not to miss out those questions, an extra step to detect the occurrence of all question types might be needed. One Chinese and one English datasets are collected from an online discussion platform. These datasets are selected for comparing the performance of question detection and classification in the two languages, and a sentence is defined as the unit of analysis. Question detection is a process to distinguish questions from other types of discourse act. A hybrid method is proposed to combine the rule-based question mark method and machine-learning-based syntax method for question detection. This method achieves 94.8% f1-score and 98.9% accuracy in English question detection and 94.8% f1-score and 93.9% accuracy in Chinese question detection. While question detection focuses mainly on the identification of questions, question classification concentrates on the categorization of questions. The literature showed that the tree kernel method is almost a standardized method for question classification. The classification of English verification and reason questions using tree kernel method can both attained f1-score above 80%. Though the precision of Chinese question classification using the same settings remains at a similar level, the recall drops greatly. This result indicates that the syntax-based tree kernel method may not be appropriate for classifying questions in Chinese languages. In order to improve on the Chinese question classification result, Case-Based Reasoning (CBR) is introduced. CBR is a method to retrieve example case(s) which shares the maximum percentage of similarity with the test case from a database. In this study, the similarity is measured by the lexemes that composed a question. Although the implementation of the CBR method can improve the recall, it also causes the great drop of precision. Considering the high precision of tree kernel method and wide coverage of CBR method, a hybrid method is proposed to combine the two methods. The experiment result shows that f1-score of the hybrid method for multi-class classification surpasses the tree kernel and CBR methods. This indicates that the implementation of hybrid method can generally improve the result of Chinese question classification. / published_or_final_version / Education / Master / Master of Philosophy