Preference learning (26 Ergebnisse)

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8pp, Printed Card Cover, VGC.

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Paperback. Zustand: Brand New. 2nd edition. 205 pages. 9.00x5.75x0.50 inches. In Stock.

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Taschenbuch. Zustand: Neu. Neuware.

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Seminar paper from the year 2014 in the subject Communications - Research, Studies, Enquiries, grade: 9.0, University of Dodoma (College of Education), course: Research Methods and Statistical Analysis, language: English, abstract: The intrinsic nature of learning process where a learner manifests changes in behavior as result of experience has provoked serial debates in Education Research. The discussions range from 'what', 'when', 'how' and 'where' would a learner be able to perform; which not only view a learner as subject of knowledge but also an ultimate object for knowledge generation. The differences in performance among learners are evident and require an extensive investigation to improve and maintain. However, the fundamental question remains as to 'why' students' performance is different; this is a crucial starting point to understand the different ways 'style' which a learner use in the daily learning processs (Brown, 1994; Sorenson, 2011).In this article, the study sought to analyze the hypothesis that 'accommodating students' learning-style preferences increase academic achievement'. The study analysis delimits to understanding of how various factors influence how students learn and their consequential effect on academic achievement. The study extarcted research studies through computer-based survey; fourty two studies out of 138 were rated by Lytton and Romney's rating scales, then 6 of 42 were discarded following inherent validity threat. The sample of 3181 from 36 studies was thereafter subjected to meta-analysis.Through Meta-analysis cummulative effect size r of .353 was calculated with residual variance of 0.79, the latter converted to Mean Standard Deviation (MSD) d of .755. The measures complemented to standard normal curve, revealing that the academic achievement of students will increase three fourths (3/4) when learning learning style are accommodated than those whose learning styles have not been accommodated.Generally the findings from the study established the fact that to ensure optimal academic achievement, it is inevitably necessary to understand and account (i.e. implication for instructional interventions) for the factors that best provide or compliment the students' affection and passion (style) to learning of particular subject (s). …

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Academic Paper from the year 2024 in the subject Pedagogy - School System, Educational and School Politics, , language: English, abstract: The study examined learning style preference in a traditional learning environment. The specific aim is to determine the influence of individual learning styles on students¿ performance in Technical Drawing Subjects.The study adopted a quasi-experimental approach to determine learning style preferences in a class of 45 students. Learning style inventory was used as an instrument for data collection which was centered on isometric projection. The outcome of the treatments was analyzed using Multiple Regression and Pearson Product Moment Correlation Coefficient. The findings revealed that kinesthetic learners scored higher in the isometric projection test conducted followed by visual learners and auditory learners came last. Hypothetically, the result shows a significant difference in the three learning styles (visual, auditory, and kinesthetic).However, teachers¿ poor understanding of students' learning styles seems to be a source of concern to many education experts in Nigeria. Hence, the study recommends teachers' proper understanding and consideration of students¿ visual, auditory, and kinesthetic learning styles in a traditional learning environment in senior secondary schools in Nigeria.…

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Taschenbuch. Zustand: Neu. Categories of Perceptual Learning Style Preference | A study on engineering students at a polytechnic | Yeoh Poh Chin | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2009 | VDM Verlag Dr. Müller | EAN 9783639177657 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu.…

Zustand: New. This work provides counsellors, classroom teachers and parents with an understanding of homework, as well as an introduction to homework methods and materials, in order to aid them in the challenge of helping children to do homework more effectively. Num Pages: 208 pages, black & white illustrations. BIC Classification: JNT; JNZ; VFXC. Category: (G) General (US: Trade); (P) Professional & Vocational. Dimension: 229 x 152 x 15. Weight in Grams: 478. . 2000. hardcover. . . . . Books ship from the US and Ireland.…

Hardcover. Zustand: Brand New. 191 pages. 9.50x6.25x1.00 inches. In Stock.

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Taschenbuch. Zustand: Neu. Preference Learning | Johannes Fürnkranz (u. a.) | Taschenbuch | ix | Englisch | 2014 | Springer | EAN 9783642422300 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Hardcover. Zustand: Brand New. 454 pages. 9.50x6.50x1.25 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The topic of preferences is a new branch of machine learning and data mining, and it has attracted considerable attention in artificial intelligence research in previous years. It involves learning from observations that reveal information about the preferences of an individual or a class of individuals. Representing and processing knowledge in terms of preferences is appealing as it allows one to specify desires in a declarative way, to combine qualitative and quantitative modes of reasoning, and to deal with inconsistencies and exceptions in a flexible manner. And, generalizing beyond training data, models thus learned may be used for preference prediction.This is the first book dedicated to this topic, and the treatment is comprehensive. The editors first offer a thorough introduction, including a systematic categorization according to learning task and learning technique, along with a unified notation. The first half of the book is organized into parts on label ranking, instance ranking, and object ranking; while the second half is organized into parts on applications of preference learning in multiattribute domains, information retrieval, and recommender systems.The book will be of interest to researchers and practitioners in artificial intelligence, in particular machine learning and data mining, and in fields such as multicriteria decision-making and operations research.…

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The topic of preferences is a new branch of machine learning and data mining, and it has attracted considerable attention in artificial intelligence research in previous years. It involves learning from observations that reveal information about the preferences of an individual or a class of individuals. Representing and processing knowledge in terms of preferences is appealing as it allows one to specify desires in a declarative way, to combine qualitative and quantitative modes of reasoning, and to deal with inconsistencies and exceptions in a flexible manner. And, generalizing beyond training data, models thus learned may be used for preference prediction.This is the first book dedicated to this topic, and the treatment is comprehensive. The editors first offer a thorough introduction, including a systematic categorization according to learning task and learning technique, along with a unified notation. The first half of the book is organized into parts on label ranking, instance ranking, and object ranking; while the second half is organized into parts on applications of preference learning in multiattribute domains, information retrieval, and recommender systems.The book will be of interest to researchers and practitioners in artificial intelligence, in particular machine learning and data mining, and in fields such as multicriteria decision-making and operations research.…