Contrast set learning

Abstract: Rules the clearest, most explored and best understood form of knowledge representation are particularly important for data mining, as they offer the best tradeoff between human and machine understandability. This book presents the fundamentals of rule learning as investigated in classical machine learning and modern data mining. It introduces a feature-based view, as a unifying framework for propositional and relational rule learning, thus bridging the gap between attribute-value learning and inductive logic programming, and providing complete coverage of most important elements of rule learning. The book can be used as a textbook for teaching machine learning, as well as a comprehensive reference to research in the field of inductive rule learning. As such, it targets students, researchers and developers of rule learning algorithms, presenting the fundamental rule learning concepts in sufficient breadth and depth to enable the reader to understand, develop and apply rule learning techniques to real-world data.

Abstract: Active learning is a supervised machine learning technique in which the learner is in control of the data used for learning. That control is utilized by the learner to ask an oracle, typically a human with extensive knowledge of the domain at hand, about the classes of the instances for which the model learned so far makes unreliable predictions. The active learning process takes as input a set of labeled examples, as well as a larger set of unlabeled examples, and produces a classifier and a relatively small set of newly labeled data. The overall goal is to create as good a classifier as possible, without having to mark-up and supply the learner with more data than necessary. The learning process aims at keeping the human annotation effort to a minimum, only asking for advice where the training utility of the result of such a query is high. Active learning has been successfully applied to a number of natural language processing tasks, such as, information extraction, named entity recognition, text categorization, part-of-speech tagging, parsing, and word sense disambiguation. This report is a literature survey of active learning from the perspective of natural language processing.

Abstract: This investigation examined ways in which student beliefs and goals distinguish different styles of engagement with learning and how such styles are associated with both the strategies students report using when preparing for exams and school achievement. Cluster analysis was used to identify groups of students with similar patterns of beliefs about their own learning. Within a cohort of 137 female llth-grade students, 6 styles of engagement were identified. Analysis of the influence of these styles on strategies adopted for exam preparation indicated differences in the strategies reported. Styles of engagement were also significantly related to school achievement. Findings are discussed in terms of insights achieved through adopting methods of analysis that preserve the multidimensional character of student engagement with learning. This investigation is concerned with examination of the relationship between an individual's general motivational orientation and some features of learning behavior. It emphasizes the interdependence of the sets of goals that guide learning. Students bring to the learning context a personal construction of the purposes of their learning and a set of beliefs about themselves as learners. These beliefs are thought to exert a powerful influence on learning (Paris & Newman, 1990). Typically, variables representing students' beliefs and goals in learning have been studied as separate variables, and their influence on learning has been assessed as independent effects. An important complementary view involves considering these variables as interdependent sets (Corno & Snow, 1986; Iran-Nejad, McKeachie, & Berliner, 1990). This investigation is concerned with a number of general student goals and beliefs about learning and with the ways in which combinations of those goals and beliefs, referred to here as styles of engagement, are associated with learning strategies and academic achievement. Groups of llth-grade students with similar patterns of goals and beliefs about learning were identified, and differences in their learning strategies and learning outcomes were examined. The learning strategy measures were based on strategies students reported using when preparing for midyear exams, and school achievement measures consisted of final grades awarded at the end of the students' 11th- and 12th-grade school years. The model of motivation in learning that informs the current investigation assumes that characteristics that the individual learner brings to the learning context shape and combine with the learner's construction of the task and its I wish to thank the students who cooperated in the collection of these data and acknowledge the helpful comments of Robert Reeve, Suzanne Hidi, Krystyna Gilowska, and two anonymous reviewers. The data analysis was supported by a Special Initiatives

Abstract: With the growing demand in e-learning, numerous research works have been done to enhance teaching quality in e-learning environments. Among these studies, researchers have indicated that adaptive learning is a critical requirement for promoting the learning performance of students. Adaptive learning provides adaptive learning materials, learning strategies and/or courses according to a student's learning style. Hence, the first step for achieving adaptive learning environments is to identify students' learning styles. This paper proposes a learning style classification mechanism to classify and then identify students' learning styles. The proposed mechanism improves k-nearest neighbor (k-NN) classification and combines it with genetic algorithms (GA). To demonstrate the viability of the proposed mechanism, the proposed mechanism is implemented on an open-learning management system. The learning behavioral features of 117 elementary school students are collected and then classified by the proposed mechanism. The experimental results indicate that the proposed classification mechanism can effectively classify and identify students' learning styles.