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This study examined the use of ranking task exercises in physics as a means to elicit student's quantitative and/or qualitative understanding of four different physics concepts. Each ranking task exercise in physics asked students to examine several different scenarios that contain a number of quantitative features and then arrange the scenarios in an ordered sequence according to some other quantitative feature. In this study, students completed four different ranking task exercises as part of their coursework in their high school physics class. The responses of students to these ranking task exercises were scored, analyzed, and categorized according to the extent to which a student's response was primarily quantitative or primarily qualitative in nature. The results show that while students relied on a combination of both qualitative and quantitative representations as they completed the exercises, the majority of students used qualitative representations in their solutions to the ranking task exercises in physics. While the students' qualitative and quantitative representations supported the students' rankings of the scenarios in each ranking task exercise, the qualitative representations used by the students provided insight into the student's current understanding of the physics concepts being investigated. The findings suggest that regardless of the representation used by the student to complete the ranking task exercise, students had difficulty in correctly ranking the scenarios in all of the ranking task exercises used in this study. While the students used both quantitative and qualitative representations in their solutions to ranking task exercises in physics that contained two quantitative variables, the study found that students relied exclusively on qualitative representations in their solutions to the ranking task exercise in physics that contained four quantitative variables. / CITE/Mathematics and Science Education

Background and Objectives: Understanding patient and public values and preferences is essential to healthcare and policy decision making. Discrete choice experiments (DCEs) are a common tool used to capture and quantify these preferences. Recent technological advances allow for a variety of approaches to create and analyze DCEs. However, there is no optimal DCE design, nor analysis method. Our objectives were to (i) survey DCE simulation studies to determine what design features affect statistical efficiency, and assess their reporting, (ii) further investigate these findings with a de novo simulation study, and (iii) explore the sensitivity of individuals’ preference of attributes to several methods of analysis. Methods: We conducted a systematic survey of simulation studies within the health literature, created a DCE simulation study of 3204 designs, and performed two empirical comparison studies. In one empirical comparison study, we determined addiction agency employees’ preferences on knowledge translation attributes using four models, and in the second, we determined elementary school children’s choice of bullying prevention programs using nine models. Results and Conclusions: In our evaluation of DCE designs, we identified six design features that impact the statistical efficiency of a DCE, several of which were further investigated in our simulation study. The reporting quality of these studies requires improvement to ensure that appropriate inferences can be made, and that they are reproducible. In our empirical comparison of statistical models to explore the sensitivity of individuals preferences of attributes, we found similar rankings in the relative importance measures of attributes’ mean part-worth utility estimates, which differed when using latent class models. Understanding the impact of design features on statistical efficiency are useful for designing optimal DCEs. Incorporating heterogeneity in the analysis of DCEs may be important to make appropriate inferences about individuals’ preferences of attributes within a population. / Thesis / Doctor of Philosophy (PhD) / This thesis focuses on the design and analysis of preference surveys, which are referred to as discrete choice experiments. These surveys are used to capture and quantify individuals’ preferences on various characteristics describing a product or service. They are applied in various health settings to better understand a population. For example, clinicians may want to further understand a patient population’s preferences in regards to multiple treatment alternatives. Currently, there is no optimal approach for designing or analyzing preference surveys. We investigated what factors help improve the design of a preference survey by exploring the literature and conducting our own simulation study. We also investigated how sensitive the results of a preference survey were based on the statistical model used. Overall, we found that (i) increasing the amount of information presented and reducing the number of variables to explore will maximize the statistical optimality of the survey; and (ii) analyzing the data with different statistical models will yield similar results in the ranking of individuals’ preferences of the variables explored.

Some classes of Internet users have specific information needs and specialized information-seeking behaviors. For example, educators who are designing a course might create a syllabus, recommend books, create lecture slides, and use tools as lecture aid. All of these resources are available online, but are scattered across a large number of websites. Collecting, linking, and presenting the disparate items related to a given course topic within a digital library will help educators in finding quality educational material. Content quality is important for users. The results of popular search engines typically fail to reflect community input regarding quality of the content. To disseminate information related to the quality of available resources, users need a common place to meet and share their experiences. Online communities can support knowledge-sharing practices (e.g., reviews, ratings). We focus on finding the information needs of educators and helping users to identify potentially useful resources within an educational digital library. This research builds upon the existing 5S digital library (DL) framework. We extend core DL services (e.g., index, search, browse) to include information from latent user groups. We propose a formal definition for the next generation of educational digital libraries. We extend one aspect of this definition to study methods that incorporate collective knowledge within the DL framework. We introduce the concept of deduced social network (DSN) - a network that uses navigation history to deduce connections that are prevalent in an educational digital library. Knowledge gained from the DSN can be used to tailor DL services so as to guide users through the vast information space of educational digital libraries. As our testing ground, we use the AlgoViz and Ensemble portals, both of which have large collections of educational resources and seek to support online communities. We developed two applications, ranking of search results and recommendation, that use the information derived from DSNs. The revised ranking system incorporates social trends into the system, whereas the recommendation system assigns users to a specific group for content recommendation. Both applications show enhanced performance when DSN-derived information is incorporated. / Ph. D.

In this study 64 web based services (bibliographical, patron education, patron communication and patron publication services) and best practices offered by the 70 top world university libraries and 39 top Indian University libraries were studied using different data analysis techniques like cross-tabulating for average scores and Pearson correlation coefficient and tests like Chi-Square Test and T-Test were applied to the raw data collected for final results. The library rankings as per their web-based services were correlated with their university rankings as per Webometric rankings and found that library web-based services rankings are correlating with their university rankings. Therefore, developing countries like India should improve their library web-based services rankings to improve their rankings at global level. / Doctor of Philosophy

This thesis gathers some contributions to statistical pattern recognition particularly targeted at problems in which the feature vectors are high-dimensional. Three pattern recognition scenarios are addressed, namely pattern classification, regression analysis and score fusion. For each of these, an algorithm for learning a statistical model is presented. In order to address the difficulty that is encountered when the feature vectors are high-dimensional, adequate models and objective functions are defined. The strategy of learning simultaneously a dimensionality reduction function and the pattern recognition model parameters is shown to be quite effective, making it possible to learn the model without discarding any discriminative information. Another topic that is addressed in the thesis is the use of tangent vectors as a way to take better advantage of the available training data. Using this idea, two popular discriminative dimensionality reduction techniques are shown to be effectively improved. For each of the algorithms proposed throughout the thesis, several data sets are used to illustrate the properties and the performance of the approaches. The empirical results show that the proposed techniques perform considerably well, and furthermore the models learned tend to be very computationally efficient. / Villegas Santamaría, M. (2011). Contributions to High-Dimensional Pattern Recognition [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10939

El objetivo en esta Memoría ha sido el análisis de eficiencia de un determinado sector empresarial, teniendo en cuenta dos problemas casi siempre presentes, y de naturaleza muy diferente, por una parte, que los datos que se manejan pueden ser imprecisos y, por tanto, afectar al resultado de cualquier estudio de eficiencia y, por otra parte, el deseo de ordenar las empresas (Unidades De Toma de Decisión) atendiendo a la medición de su eficiencia. Para la medición de la eficiencia se ha recurrido a la metodología no paramétrica del Análisis Envolvente de datos (DEA) aplicandola a empresas del sector textil muy cercanas a nosotros. Ahora bien, dado que consideramos que siempre existe alguna incertidumbre o un posible error en la medición de algunos datos (inputs y outputs), introducimos la limitación de la certeza con el tratamiento fuzzy de los datos, métodos que no requieren conocer ni aplicar hipótesis sobre distribuciones de probabilidad de esos datos, que dicho sea de paso, podría no ser fáctible bajo determinados supuestos de incertidumbre. Pero además de la medir la eficiencia pretendemos proporcionar más información que la mera separación dicotómica entre empresas eficientes o no eficientes. Para ello desarrollamos y aplicamos los modelos de super-efficiencyfuzzy y cross-efficiency-fuzzy, que nos permiten establecer una ordenación bajo incertidumbre. Con este trabajo hemos realizado un estudio amplio de la eficiencia bajo incertidumbre. Se observa que los resultados obtenidos aplicando los distintos métodos son similares. Además, estos métodos proporcionan más información sobre las unidades estudiadas que las que proporciona un solo índice de eficiencia. Estos métodos pueden ser aplicables a otros tipos de empresas, aportando nueva información que puede ayudar u orientar en la toma de decisiones de sus gestores / Pla Ferrando, ML. (2013). Modelos flexibles para la valoración de la eficiencia [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/31521

Learning and reasoning with common sense is a challenging problem in Artificial Intelligence (AI). Humans have the remarkable ability to interpret images and text from different perspectives in multiple modalities, and to use large amounts of commonsense knowledge while performing visual or textual tasks. Inspired by that ability, we approach commonsense learning as leveraging perspectives from multiple modalities for images and text in the context of vision and language tasks. Given a target task (e.g., textual reasoning, matching images with captions), our system first represents input images and text in multiple modalities (e.g., vision, text, abstract scenes and facts). Those modalities provide different perspectives to interpret the input images and text. And then based on those perspectives, the system performs reasoning to make a joint prediction for the target task. Surprisingly, we show that interpreting textual assertions and scene descriptions in the modality of abstract scenes improves performance on various textual reasoning tasks, and interpreting images in the modality of Visual Question Answering improves performance on caption retrieval, which is a visual reasoning task. With grounding, imagination and question-answering approaches to interpret images and text in different modalities, we show that learning commonsense knowledge from multiple modalities effectively improves the performance of downstream vision and language tasks, improves interpretability of the model and is able to make more efficient use of training data. Complementary to the model aspect, we also study the data aspect of commonsense learning in vision and language. We study active learning for Visual Question Answering (VQA) where a model iteratively grows its knowledge through querying informative questions about images for answers. Drawing analogies from human learning, we explore cramming (entropy), curiosity-driven (expected model change), and goal-driven (expected error reduction) active learning approaches, and propose a new goal-driven scoring function for deep VQA models under the Bayesian Neural Network framework. Once trained with a large initial training set, a deep VQA model is able to efficiently query informative question-image pairs for answers to improve itself through active learning, saving human effort on commonsense annotations. / Ph. D. / Designing systems that learn and reason with common sense is a challenging problem in Artificial Intelligence (AI). Humans have the remarkable ability to interpret images and text from different perspectives in multiple modalities, and to use large amounts of commonsense knowledge while performing visual or textual tasks. Inspired by that ability, we approach commonsense learning as leveraging perspectives from multiple modalities for images and text in the context of vision and language tasks. Given a target task, our system first represents the input information (e.g., images and text) in multiple modalities (e.g., vision, text, abstract scenes and facts). Those modalities provide different perspectives to interpret the input information. Based on those perspectives, the system performs reasoning to make a joint prediction to solve the target task. Perhaps surprisingly, we show that imagining (generating) abstract scenes behind input textual scene descriptions improves performance on various textual reasoning tasks such as answering fill-in-the-blank and paraphrasing questions, and answering questions about images improves performance on retrieving image captions. Through the use of perspectives from multiple modalities, our system also makes use of training data more efficiently and has a reasoning process that is easy to understand. Complementary to the system design aspect, we also study the data aspect of commonsense learning in vision and language. We study active learning for Visual Question Answering (VQA). VQA is the task of answering open-ended natural language questions about images. In active learning for VQA, a model iteratively grows its knowledge through querying informative questions about images for answers. Inspired by human learning, we explore cramming (entropy), curiosity-driven (expected model change), and goal-driven (expected error reduction) active learning approaches, and propose a new goal-driven query selection function. We show that once initialized with a large training set, a VQA model is able to efficiently query informative question-image pairs for answers to improve itself through active learning, saving human effort on commonsense annotations.

While existing off-policy evaluation (OPE) methods typically estimate the value of a policy, in real-world applications, OPE is often used to compare and rank policies before deploying them in the real world. This is also known as the offline policy ranking problem. While one can rank the policies based on point estimates from OPE, it is beneficial to estimate the full distribution of outcomes for policy ranking and selection. This paper introduces Probabilistic Offline Policy Ranking that works with expert trajectories. It introduces rigorous statistical inference capabilities to offline evaluation, which facilitates probabilistic comparisons of candidate policies before they are deployed. We empirically demonstrate that POPR is effective for evaluating RL policies across various environments.