This thesis investigates whether reflection after tutoring with the Itspoke qualitative physics tutoring system can improve both near and far transfer learning and retention. This question is formalized in three major hypotheses. H1: that reading a post-tutoring reflective text will improve learning compared to reading a non-reflective text. H2: that a more cohesive reflective text will produce higher learning gains for most students. And H3: that students with high domain knowledge will learn more from a less cohesive text. In addition, this thesis addresses the question of which mechanisms affect learning from a reflective text. Secondary hypotheses H4 and H5 posit that textual cohesion and student motivation, respectively, each affect learning by influencing the amount of inference performed while reading. These hypotheses were tested by asking students to read a reflective/abstractive text after tutoring with the Itspoke tutor. This text compared dialog parts in which similar physics principles had been applied to different situations. Students were randomly assigned among two experimental conditions which got ``high' or ``low' cohesion versions of this text, or a control condition which read non-reflective physics material after tutoring. The secondary hypotheses were tested using two measures of cognitive load while reading: reading speeds and a self-report measure of reading difficulty. Near and far transfer learning was measured using sets of questions that were mostly isomorphic vs. non-isomorphic the tutored problems, and retention was measured by administering both an immediate and a delayed post-test. Motivation was measured using a questionnaire. Reading a reflective text improved learning, but only for students with a middle amount of motivation, confirming H1 for that group. These students also learned more from a more cohesive reflective text, supporting H2. Cohesion also affected high and low knowledge students significantly differently, supporting H3, except that high knowledge students learned best from high, not low cohesion text. Students with higher amounts of motivation did have higher cognitive load, confirming hypothesis H5 and suggesting that they engaged the text more actively. However, secondary hypothesis H4 failed to show a role for cognitive load in explaining the learning interaction between knowledge and cohesion demonstrated in H3.

The challenge of translation varies from one sentence to another, or even between phrases of a sentence. We investigate whether variations in difficulty can be located automatically for Statistical Machine Translation (SMT). Furthermore, we hypothesize that customization of a SMT system based on difficulty information, improves the translation quality. We assume a binary categorization for phrases: easy vs. difficult. Our focus is on the Difficult to Translate Phrases (DTPs). Our experiments show that for a sentence, improving the translation of the DTP improves the translation of the surrounding non-difficult phrases too. To locate the most difficult phrase of each sentence, we use machine learning and construct a difficulty classifier. To improve the translation of DTPs, we introduce customization methods for three components of the SMT system: I. language model; II. translation model; III. decoding weights. With each method, we construct a new component that is dedicated for the translation of difficult phrases. Our experiments on Arabic-to-English translation show that DTP-specific system customization is mostly successful. Overall, we demonstrate that translation difficulty is an important source of information for machine translation and can be used to enhance its performance.

Biomarkers are a critical tool for the detection, diagnosis, monitoring and prognosis of diseases, and for understanding disease mechanisms in order to create treatments. Unfortunately, finding reliable biomarkers is often hampered by a number of practical problems, including scarcity of samples, the high dimensionality of the data, and measurement error. An important opportunity to make the most of these scarce data is to combine information from multiple related data sets for more effective biomarker discovery. Because the costs of creating large data sets for every disease of interest are likely to remain prohibitive, methods for more effectively making use of related biomarker data sets continues to be important. This thesis develops TRL, a novel framework for integrative biomarker discovery from related but separate data sets, such as those generated for similar biomarker profiling studies. TRL alleviates the problem of data scarcity by providing a way to validate knowledge learned from one data set and simultaneously learn new knowledge on a related data set. Unlike other transfer learning approaches, TRL takes prior knowledge in the form of interpretable, modular classification rules, and uses them to seed learning on a new data set. We evaluated TRL on 13 pairs of real-world biomarker discovery data sets, and found TRL improves accuracy twice as often as degrading it. TRL consists of four alternative methods for transfer and three measures of the amount of information transferred. By experimenting with these methods, we investigate the kinds of information necessary to preserve for transfer learning from related data sets. We found it is important to keep track of the relationships between biomarker values and disease state, and to consider during learning how rules will interact in the final model. If the source and target data are drawn from the same distribution, we found the performance improvement and amount of transfer increase with increasing size of the source compared to the target data.

Bioinformatics is rapidly advancing through the "post-genomic" era following the sequencing of the human genome. In preparation for studying the inner workings behind genes, proteins and even smaller biological elements, several subdivisions of bioinformatics have developed. The subdivision of proteomics, concerning the structure and function of proteins, has been aided by the mass spectrometry data source. Biofluid or tissue samples are rapidly assayed for their protein composition. The resulting mass spectra are analyzed using machine learning techniques to discover reliable patterns which discriminate samples from two populations, for example, healthy or diseased, or treatment responders versus non-responders. However, this data source is imperfect and faces several challenges: unwanted variability arising from the data collection process, obtaining a robust discriminative model that generalizes well to future data, and validating a predictive pattern statistically and biologically. This thesis presents several techniques which attempt to intelligently deal with the problems facing each stage of the analytical process. First, an automatic preprocessing method selection system is demonstrated. This system learns from data and selects a combination of preprocessing methods which is most appropriate for the task at hand. This reduces the noise affecting potential predictive patterns. Our results suggest that this method can help adapt to data from different technologies, improving downstream predictive performance. Next, the issues of feature selection and predictive modeling are revisited with respect to the unique challenges posed by proteomic profile data. Approaches to model selection through kernel learning are also investigated. Key insights are obtained for designing the feature selection and predictive modeling portion of the analytical framework. Finally, methods for interpreting the results of predictive modeling are demonstrated. These methods are used to assure the user of various desirable properties: validation of the strength of a predictive model, validation of reproducible signal across multiple data generation sessions and generalizability of predictive models to future data. A method for labeling profile features with biological identities is also presented, which aids in the interpretation of the data. Overall, these novel techniques give the protein profiling community additional support and leverage to aid the predictive capability of the technology.

Students who are solving open-ended problems would benefit from formative assessment, i.e., from receiving helpful feedback and from having an instructor who is informed about their level of performance. Open-ended problems challenge existing assessment techniques. For example, such problems may have reasonable alternative solutions, or conflicting objectives. Analyses of open-ended problems are often presented as free-form text since they require arguments and justifications for one solution over others, and students may differ in how they frame the problems according to their knowledge, beliefs and attitudes. This dissertation investigates how peer review may be used for formative assessment. Computer-Supported Peer Review in Education, a technology whose use is growing, has been shown to provide accurate summative assessment of student work, and peer feedback can indeed be helpful to students. A peer review process depends on the rubric that students use to assess and give feedback to each other. However, it is unclear how a rubric should be structured to produce feedback that is helpful to the student and at the same time to yield information that could be summarized for the instructor. The dissertation reports a study in which students wrote individual analyses of an open-ended legal problem, and then exchanged feedback using Comrade, a web application for peer review. The study compared two conditions: some students used a rubric that was relevant to legal argument in general (the domain-relevant rubric), while others used a rubric that addressed the conceptual issues embedded in the open-ended problem (the problem-specific rubric). While both rubric types yield peer ratings of student work that approximate the instructor's scores, feedback elicited by the domain-relevant rubric was redundant across its dimensions. On the contrary, peer ratings elicited by the problem-specific rubric distinguished among its dimensions. Hierarchical Bayesian models showed that ratings from both rubrics can be fit by pooling information across students, but only problem-specific ratings are fit better given information about distinct rubric dimensions.

Inspired by the recent developments, we studied some recent developments and research trends in intelligent vehicle sensing and control tasks. We emphasize on advanced vehicle motion control techniques and intelligent tires. The main research motivation is to improve drivers/passengers' comfort and safety as well as highway capacity and efficiency.

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Thesis (M.Sc.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 64-65) Also available in print.

Thesis (Ph.D.)--University of Delaware, 2006. / Principal faculty advisor: Keith S. Decker, Dept. of Computer and Information Sciences. Includes bibliographical references.