HIGH ORDER THINGKING SKILLS AND SELF REGULATED LEARNING OF JUNIOR HIGH SCHOOL STUDENT IN BANDAR LAMPUNG CITY

The development of mathematics high order thinking skills (MHOTs) is currently the main goal of learning mathematics. With these changes, the vision of school mathematics more emphasis on achieving "mathematical proficiency" that requires an integrated achievement of conceptual understanding, procedures fluency, strategic competence, adapted reasoning, and productive disposition. It encourages teachers to act more as a model, facilitator, trainer than as a conduit of information and students act as an independent learner and has thinking skills. This study describes MHOTs and self-regulated learning (SRL) of junior high school students in Bandar Lampung city before following study using interactive media based on open-ended problem. Based on the analysis of data, it is concluded that MHOTs and SRL of students on high-rank school, middle-rank school, low-rank school, and all of students before using interactive media based on open-ended problems are has not met expectations. Therefore, it is necessary to develop a learning that can improve the MHOTs and self-regulated learning of students

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.Siirretty Doriast

Classification in postural style

This article contributes to the search for a notion of postural style, focusing on the issue of classifying subjects in terms of how they maintain posture. Longer term, the hope is to make it possible to determine on a case by case basis which sensorial information is prevalent in postural control, and to improve/adapt protocols for functional rehabilitation among those who show deficits in maintaining posture, typically seniors. Here, we specifically tackle the statistical problem of classifying subjects sampled from a two-class population. Each subject (enrolled in a cohort of 54 participants) undergoes four experimental protocols which are designed to evaluate potential deficits in maintaining posture. These protocols result in four complex trajectories, from which we can extract four small-dimensional summary measures. Because undergoing several protocols can be unpleasant, and sometimes painful, we try to limit the number of protocols needed for the classification. Therefore, we first rank the protocols by decreasing order of relevance, then we derive four plug-in classifiers which involve the best (i.e., more informative), the two best, the three best and all four protocols. This two-step procedure relies on the cutting-edge methodologies of targeted maximum likelihood learning (a methodology for robust and efficient inference) and super-learning (a machine learning procedure for aggregating various estimation procedures into a single better estimation procedure). A simulation study is carried out. The performances of the procedure applied to the real data set (and evaluated by the leave-one-out rule) go as high as an 87% rate of correct classification (47 out of 54 subjects correctly classified), using only the best protocol.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS542 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

An analysis of the importance of selected applied physics concepts as prerequisites to training in automotive technology

The purpose of this study has been to develop information that may be useful in the preparation of curriculums and materials dedicated to the remediation of applied physics knowledge and skill deficits demonstrated by entering automotive technology students. The problem was to identify and rank-order the importance of applied physics concepts within the mechanical, fluidal, electrical/electronic, thermal, and chemical domains that are foundational to training in automotive technology. A questionnaire containing 77 concept statements with a corresponding graphic rating scale was administered via mailed correspondence to all 196 of California\u27s full-time community college automotive instructors. Of these, 130 (66.33%) were returned. For each survey item, a response frequency distribution, high-to-low response percentage value, mean value, and standard deviation was calculated. Concept statements were then rank-ordered by high-to-low response percentage value within groups and overall. Response frequency distributions for each survey item were then analyzed for statistical significance against a Chi square distribution at the.01 level. Rank-order amongst groups was established using grand mean values. Results found 67 questionnaire items rated as important prerequisites to training in automotive technology. Of these 21 were critically important. The category rated as having the greatest importance was electrical/electronic, followed closely by mechanical, at a distance by fluidal, with chemical and thermal nearer the bottom. Both theoretical and applied concepts within the electrical/electronic group were ranked as important. Within the remaining four areas, however, theoretical knowledges and skills were found to have low importance. Conclusions of this study are: (a) learning success of automotive technology students is positively related to pre-course knowledges and skills in applied physics; (b) the high to low rank-order of curricular emphasis amongst the five applied physics domains is electrical/electronics, mechanical, fluidal, chemical, and thermal; (c) entering automotive technology students should possess fundamental applied knowledges in all five applied physics areas while (d) important theoretical knowledges and skills appear to be concentrated in the electrical/electronic domain. Recommendations include: (a) applied physics coursework should be a prerequisite to automotive training and (b) remedial coursework in applied physics should be a part of the automotive technology curriculum

Tensorized multi-view subspace representation learning

Self-representation based subspace learning has shown its effectiveness in many applications. In this paper, we promote the traditional subspace representation learning by simultaneously taking advantages of multiple views and prior constraint. Accordingly, we establish a novel algorithm termed as Tensorized Multi-view Subspace Representation Learning. To exploit different views, the subspace representation matrices of different views are regarded as a low-rank tensor, which effectively models the high-order correlations of multi-view data. To incorporate prior information, a constraint matrix is devised to guide the subspace representation learning within a unified framework. The subspace representation tensor equipped with a low-rank constraint models elegantly the complementary information among different views, reduces redundancy of subspace representations, and then improves the accuracy of subsequent tasks. We formulate the model with a tensor nuclear norm minimization problem constrained with ℓ2,1-norm and linear equalities. The minimization problem is efficiently solved by using an Augmented Lagrangian Alternating Direction Minimization method. Extensive experimental results on diverse multi-view datasets demonstrate the effectiveness of our algorithm

Evaluating online review helpfulness based on Elaboration Likelihood Model: the moderating role of readability

It is important to understand factors affecting the perceived online review helpfulness as it helps solve the problem of information overload in online shopping. Moreover, it is also crucial to explore the factors’ relative importance in predicting review helpfulness in order to effectively detect potential helpful reviews before they exert influences. Applying Elaboration Likelihood Model (ELM), this study first investigates the effects of central cues (review subjectivity and elaborateness) and peripheral cues (reviewer rank) on review helpfulness with readability as a moderator. Second, it also explores their relative predicting power using the machine learning technique. ELM is tested in online context and the results are compared between experience and search goods. Our results provide evidence that for both types of products review subjectivity can play a more significant role when the content readability is high. Furthermore, this study reveals that the dominant predictor is varied for different product types

Information-theoretic Feature Selection via Tensor Decomposition and Submodularity

Feature selection by maximizing high-order mutual information between the selected feature vector and a target variable is the gold standard in terms of selecting the best subset of relevant features that maximizes the performance of prediction models. However, such an approach typically requires knowledge of the multivariate probability distribution of all features and the target, and involves a challenging combinatorial optimization problem. Recent work has shown that any joint Probability Mass Function (PMF) can be represented as a naive Bayes model, via Canonical Polyadic (tensor rank) Decomposition. In this paper, we introduce a low-rank tensor model of the joint PMF of all variables and indirect targeting as a way of mitigating complexity and maximizing the classification performance for a given number of features. Through low-rank modeling of the joint PMF, it is possible to circumvent the curse of dimensionality by learning principal components of the joint distribution. By indirectly aiming to predict the latent variable of the naive Bayes model instead of the original target variable, it is possible to formulate the feature selection problem as maximization of a monotone submodular function subject to a cardinality constraint - which can be tackled using a greedy algorithm that comes with performance guarantees. Numerical experiments with several standard datasets suggest that the proposed approach compares favorably to the state-of-art for this important problem