Relevant bibliographies by topics / Learning by Analogy

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Learning by Analogy'

Author: Grafiati
Published: 10 September 2021
Last updated: 29 July 2025

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Journal articles on the topic "Learning by Analogy"

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Richland, Lindsey E., and Ian M. McDonough. "Learning by analogy: Discriminating between potential analogs." Contemporary Educational Psychology 35, no. 1 (2010): 28–43. http://dx.doi.org/10.1016/j.cedpsych.2009.09.001.

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Ansor, Moch Hisbul, and Abdul Haris Rosyidi. "ANALOGY TRANSFER IN LEARNING NEW MATERIAL WITH AND WITHOUT INTERMEDIATE PROBLEM." MATHEdunesa 11, no. 1 (2022): 121–33. http://dx.doi.org/10.26740/mathedunesa.v11n1.p121-133.

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Abstrak
 Transfer analogi merupakan transfer belajar yang memetakan pengetahuan dari masalah sumber ke masalah target. Transfer analogi dapat digunakan untuk mempelajari materi baru berdasarkan pengetahuan yang sudah ada sebelumnya. Penelitian ini bertujuan untuk menganalisis transfer analogi siswa dalam mempelajari materi baru dengan dan tanpa masalah antara. Masalah antara merupakan masalah yang memiliki tingkat kesulitan di antara masalah sumber dan target dengan penggunaan analogi yang sama. Penelitian ini menggunakan metode campuran, metode kuantitatif dilanjutkan dengan metode kuali
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Hinrichs, Thomas, and Kenneth D. Forbus. "Transfer Learning through Analogy in Games." AI Magazine 32, no. 1 (2011): 70. http://dx.doi.org/10.1609/aimag.v32i1.2332.

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We report on a series of transfer learning experiments in game domains, in which we use structural analogy from one learned game to speed learning of another related game. We find that a major benefit of analogy is that it reduces the extent to which the source domain must be generalized before transfer. We describe two techniques in particular, minimal ascension and metamapping, that enable analogies to be drawn even when comparing descriptions using different relational vocabularies. Evidence for the effectiveness of these techniques is provided by a large-scale external evaluation, involvin
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Podomi, Pivi Alpia, and Jailani Jailani. "Pengaruh Pendekatan Analogi Personal terhadap Prestasi, Penalaran dan Kemandirian Siswa Materi Dimensi Dua di SMK." PYTHAGORAS Jurnal Pendidikan Matematika 10, no. 1 (2015): 61–70. http://dx.doi.org/10.21831/pg.v10i1.9110.

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Tujuan penelitian ini adalah untuk (1) mendeskripsikan pengaruh pendekatan analogi personal terhadap prestasi, penalaran dan kemandirian dan (2) mendeskripsikan keunggulan pembelajaran dengan menggunakan pendekatan analogi personal dibandingkan dengan pembelajaran konvensional ditinjau dari prestasi, penalaran, dan kemandirian. Penelitian ini merupakan penelitian eksperimen semu. Populasinya adalah seluruh siswa kelas X jurusan Multimedia SMK Negeri 1 Kotamobagu yang terdiri dari 4 kelas dan dari 4 kelas tersebut diambil sampel dengan undian 2 kelas sehingga terpilih kelas X MM2 dan X MM4. Dar
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Ramdhayani, Eryuni, Muslimin Ibrahim, and Madlazim Madlazim. "PEMBELAJARAN SIKAP MELALU ANALOGI DALAM MENGAJARKAN BIOLOGI." JPPS (Jurnal Penelitian Pendidikan Sains) 5, no. 1 (2017): 874. http://dx.doi.org/10.26740/jpps.v5n1.p874-884.

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Advances technology and communications take positive and negative impacts that could affect the attitudes and thoughts of students, so it is necessary to teach positive attitude to become a fortress, selector and controller. The problem can be solved by the innovation of learning by analogy to teach attitude. The aims this research are: (1) Describe the adherence study using analogy,(2) Describe achievment test of student attitude competency (the persistent attitude, the attitude of helping, loyalty and discipline) as taught using analogy (3) Describe achievement test of students knowledge com
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Kurniawati, L., I. S. Farhana, and R. Miftah. "Improving students’ mathematical intuitive thinking ability using analogy learning model." Journal of Physics: Conference Series 2157, no. 1 (2022): 012042. http://dx.doi.org/10.1088/1742-6596/2157/1/012042.

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Abstract Analogy learning model is a learning that use analogies, namely comparing concepttarget and concept analog to find related features both of them then make conclusion. The purpose of this research was to know students’ mathematical intuitive thinking ability between students taught with Teaching with analogy model and those taught using a scientific learning. The research was conducted at senior high school in Depok. The method of research used quasi experiment method with posttest-only control group design. The samples are 71 students, they are 37 students in experimental group and 34
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Ellul, Maria, and Amanda Bezzina. "The Effectiveness of Analogy Learning as a Learning Practice Condition in Primary Physical Education." Malta Journal of Education 4, no. 2 (2023): 47–66. http://dx.doi.org/10.62695/zsnv3229.

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Analogy learning is a method of learning which encourages students to abstract new information and match it to already acquired knowledge (Richland & Simms, 2015). This study focuses on the topic of analogy learning and its effectiveness in primary Physical Education (PE) lessons. It aims to explore the perceptions of students and teachers towards the use of analogy learning in PE, as well as the benefits and limitations of this approach. The aims of the study align with the research questions which are the principal impetus for this study. The paper presents the results of a research stud
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Jayapada, Gegana, Imam Suyitno, and Suyono Suyono. "Apersepsi Guru dalam Pembelajaran Membaca Permulaan Bagi Anak Usia Dini." Jurnal Pendidikan: Teori, Penelitian, dan Pengembangan 5, no. 5 (2020): 594. http://dx.doi.org/10.17977/jptpp.v5i5.13462.

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<p class="Abstract"><strong>Abstract:</strong> This article aims to describe the form of teacher apperception in early reading and writing learning for young children in the biMBA AIUEO. This study uses a qualitative approach to the design of field studies. Based on the results of the study, it was found that in apperception teachers in learning to begin reading can be classified into three things, namely analogy, question-answer, singing. First, use an analogy. The analogy conducted by the teacher includes (1) analogy using flashcard media, (2) analogy using objects and or s
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Wang, Huayan, and Qiang Yang. "Transfer Learning by Structural Analogy." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 513–18. http://dx.doi.org/10.1609/aaai.v25i1.7907.

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Transfer learning allows knowledge to be extracted from auxiliary domains and be used to enhance learning in a target domain. For transfer learning to be successful, it is critical to find the similarity between auxiliary and target domains, even when such mappings are not obvious. In this paper, we present a novel algorithm for finding the structural similarity between two domains, to enable transfer learning at a structured knowledge level. In particular, we address the problem of how to learn a non-trivial structural similarity mapping between two different domains when they are completely
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BAILEY, TED. "Analogy, dialectics and lifelong learning." International Journal of Lifelong Education 22, no. 2 (2003): 132–46. http://dx.doi.org/10.1080/0260137032000055790.

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Dissertations / Theses on the topic "Learning by Analogy"

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Marquer, Esteban. "Reasoning over Data : Analogy-based and Transfer Learning to improve Machine Learning." Electronic Thesis or Diss., Université de Lorraine, 2024. http://www.theses.fr/2024LORR0056.

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Ces dernières années ont été marquées par un regain d'intérêt pour le potentiel de l'inférence analogique et de la détection des analogies, avec des applications fructueuses dans l'apprentissage automatique pour la découverte et la génération d'images, de textes et de données structurées telles que les graphes de connaissances, ou encore la détection des relations entre et au sein d'images, de textes, ou de données structurées. Si certains de ces travaux reposent sur une compréhension intuitive de l'analogie, des efforts considérables ont été déployés depuis l'Antiquité pour définir les analog
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Tu, Wing-yan Sheryl. "Analogy motor learning in a Chinese population : Hong Kong /." View the Table of Contents & Abstract, 2005. http://sunzi.lib.hku.hk/hkuto/record/B35505205.

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Tu, Wing-yan Sheryl, and 杜穎欣. "Analogy motor learning in a Chinese population: Hong Kong." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B45013937.

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Halstead, Daniel T. "Statistical Relational Learning through Structural Analogy and Probabilistic Generalization." NORTHWESTERN UNIVERSITY, 2012. http://pqdtopen.proquest.com/#viewpdf?dispub=3488474.

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To, Wing-hei. "Does Chinese analogy chunk with culturally relevant rules? : development and validation of new Chinese rules /." View the Table of Contents & Abstract, 2005. http://sunzi.lib.hku.hk/hkuto/record/B31941291.

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De, Vine Lance. "Analogical frames by constraint satisfaction." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/198036/1/Lance_De%20Vine_Thesis.pdf.

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This research develops a new and efficient constraint satisfaction approach to the unsupervised discovery of linguistic analogies. It shows that systems of analogies can be discovered with high confidence in natural language text by a computer program without human input. The discovery of analogies is useful for many applications such as the construction of linguistic resources, natural language processing and the automation of inference and reasoning.
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Murena, Pierre-Alexandre. "Minimum complexity principle for knowledge transfer in artificial learning." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLT019/document.

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Les méthodes classiques d'apprentissage automatique reposent souvent sur une hypothèse simple mais restrictive: les données du passé et du présent sont générées selon une même distribution. Cette hypothèse permet de développer directement des garanties théoriques sur la précision de l'apprentissage. Cependant, elle n'est pas réaliste dans un grand nombre de domaines applicatifs qui ont émergé au cours des dernières années.Dans cette thèse, nous nous intéressons à quatre problèmes différents en intelligence artificielle, unis par un point commun: tous impliquent un transfer de connaissance d'un
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To, Wing-hei, and 杜穎禧. "Does Chinese analogy chunk with culturally relevant rules?: development and validation of new Chineserules." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B45013949.

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Wagamon, Barbara J. Jinks Jerry Lee. "Life experience of sixth grade students in analog domains of sixth grade science textbooks." Normal, Ill. Illinois State University, 1999. http://wwwlib.umi.com/cr/ilstu/fullcit?p9927778.

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Thesis (Ed. D.)--Illinois State University, 1999.<br>Title from title page screen, viewed July 21, 2006. Dissertation Committee: Jerry L. Jinks (chair), Thomas T. Baer, Anthony W. Lorsbach, Susan L. Nierstheimer. Includes bibliographical references (leaves 135-144) and abstract. Also available in print.
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Murena, Pierre-Alexandre. "Minimum complexity principle for knowledge transfer in artificial learning." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLT019.

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Les méthodes classiques d'apprentissage automatique reposent souvent sur une hypothèse simple mais restrictive: les données du passé et du présent sont générées selon une même distribution. Cette hypothèse permet de développer directement des garanties théoriques sur la précision de l'apprentissage. Cependant, elle n'est pas réaliste dans un grand nombre de domaines applicatifs qui ont émergé au cours des dernières années.Dans cette thèse, nous nous intéressons à quatre problèmes différents en intelligence artificielle, unis par un point commun: tous impliquent un transfer de connaissance d'un
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Books on the topic "Learning by Analogy"

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Liao, Chu-Min. Self-focused attention, analogy learning and implicit motor learning. University of Birmingham, 2001.

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Greiner, Russell. Learning by understanding analogies. University of Toronto, Computer Systems Research Institute, 1986.

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Gentner, Dedre. Mechanisms of analogical learning. Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1987.

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Greiner, Russell. Learning by understanding analogies: A dissertation.... Stanford University, 1985.

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Russell, Stuart J. The use of knowledge in analogy and induction. Pitman, 1989.

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Falkenhainer, Brian. The structure-mapping engine: Algorithm and examples. Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1987.

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Falkenhainer, Brian. An examination of the third stage in the analogy process: Verification-based analogical learning. Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1986.

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Labra, María José González. Aprendizaje por analogía: Análisis del proceso de inferencia analógica para la adquisición de nuevos conocimientos. Trotta, 1997.

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Gerhard, Weber. Fallbasiertes Lernen und Analogien: Unterstützung von Problemlöse- und Lernprozessen in einem adaptiven Lernsystem. Beltz, 1994.

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McAuliffe, Jane. What color is Saturday?: Using analogies to enhance creative thinking in the classroom. Zephyr Press, 1993.

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Book chapters on the topic "Learning by Analogy"

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Antonietti, Alessandro. "Analogy-Based Learning." In Encyclopedia of the Sciences of Learning. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_11.

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Buehl, Doug. "Analogy Charting." In Classroom Strategies for Interactive Learning, 4th ed. Routledge, 2023. http://dx.doi.org/10.4324/9781032680842-8.

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Blenkiron, Paul. "Analogy Therapy." In Encyclopedia of the Sciences of Learning. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_645.

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Rappaport, Alain. "Goal-free Learning by Analogy." In The Kluwer International Series in Engineering and Computer Science. Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-2279-5_54.

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Antonietti, Alessandro. "Analogy/Analogies: Structure and Process." In Encyclopedia of the Sciences of Learning. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_10.

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Yoshikawa, Ryo, Ryohei Ikejiri, and Yasunobu Sumikawa. "Interactive System for Collaborative Historical Analogy." In Learning and Collaboration Technologies. Ubiquitous and Virtual Environments for Learning and Collaboration. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21817-1_28.

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Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. "Analogy-Based Preference Learning with Kernels." In KI 2019: Advances in Artificial Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30179-8_3.

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Veloso, Manuela M., and Jaime G. Carbonell. "Derivational Analogy in PRODIGY: Automating Case Acquisition, Storage, and Utilization." In Case-Based Learning. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3228-6_3.

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Hüllermeier, Eyke. "Towards Analogy-Based Explanations in Machine Learning." In Modeling Decisions for Artificial Intelligence. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57524-3_17.

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Smith, Linsey A., and Dedre Gentner. "Using Spatial Analogy to Facilitate Graph Learning." In Spatial Cognition VIII. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32732-2_13.

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Conference papers on the topic "Learning by Analogy"

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Behera, Abhinav, Ram Chandra Barik, DharmaSwarup Sahoo, Ajay Sarangi, and Soubhagya Tripathy. "An Empirical State-of-art Analogy on Heart Disease Prediction Using Machine Learning Methodologies." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724994.

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Wang, Jingyu, Zhijuan Du, and Tao Sun. "Meta-Analogy Learning Based on Dynamic Graph Neural Networks for Inductive Knowledge Graph Link Prediction." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10890317.

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Işıl, Çağatay, Tianyi Gan, F. Onuralp Ardic, et al. "Image denoising using diffractive optical processors." In CLEO: Fundamental Science. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_fs.2024.ftu3g.2.

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We report an image denoising analog processor composed of passive diffractive layers engineered through deep learning to filter out various types of noise from input images, instantly projecting denoised images at the output field-of-view.
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Rutten, Dries, Syed Jaffar Mujtaba, and Naomi-Tida Essers. "Animalogy: Learning through Analogy and Technology." In 2022 IEEE Global Humanitarian Technology Conference (GHTC). IEEE, 2022. http://dx.doi.org/10.1109/ghtc55712.2022.9911003.

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PAN, WUMING, and TIANRUI LI. "FROM ANALOGY REASONING TO INSTANCES BASED LEARNING." In Proceedings of the 7th International FLINS Conference. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812774118_0028.

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Rossiello, Gaetano, Alfio Gliozzo, Robert Farrell, Nicolas Fauceglia, and Michael Glass. "Learning Relational Representations by Analogy using Hierarchical." In Proceedings of the 2019 Conference of the North. Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/n19-1327.

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Nokes, Timothy J., Brian H. Ross, Leon Hsu, Charles Henderson, and Laura McCullough. "Facilitating Conceptual Learning Through Analogy And Explanation." In 2007 PHYSICS EDUCATION RESEARCH CONFERENCE. AIP, 2007. http://dx.doi.org/10.1063/1.2820952.

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Gupta, Gopal, Pawan Saxena, and Sanjay K. Singh. "Analogy-based Instruction for Effective Teaching of Abstract Concepts in Computer Science." In Seventh International Conference on Higher Education Advances. Universitat Politècnica de València, 2021. http://dx.doi.org/10.4995/head21.2021.13115.

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In the analogy-based learning method we map a concept that is being learned to a well-understood concept. An analogy is mainly useful when learners lack prior knowledge of the topic being learned. Computer Science (CS) is a subject whose concepts tend to be highly abstract and therefore difficult for undergraduate students to understand. Analogy-based instruction can greatly reduce a student’s burden of learning these abstract CS concepts. Role of analogy in teaching CS topics has not been adequately explored. In this paper we discuss analogy-based instruction in computer science and its advan
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Diallo, Aïssatou, Markus Zopf, and Johannes Fürnkranz. "Learning Analogy-Preserving Sentence Embeddings for Answer Selection." In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/k19-1085.

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Zhou, Linjun, Peng Cui, Shiqiang Yang, Wenwu Zhu, and Qi Tian. "Learning to Learn Image Classifiers With Visual Analogy." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.01176.

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Reports on the topic "Learning by Analogy"

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Kanivets, Oleksandr V., Irina М. Kanivets, Natalia V. Kononets, Tetyana М. Gorda, and Ekaterina O. Shmeltser. Development of mobile applications of augmented reality for projects with projection drawings. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3745.

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We conducted an analysis of the learning aids used in the study of general technical disciplines. This allowed us to draw an analogy between physical and virtual models and justify the development of a mobile application to perform tasks on a projection drawing. They showed a technique for creating mobile applications for augmented reality. The main stages of the development of an augmented reality application are shown: the development of virtual models, the establishment of the Unity3D game engine, the development of a mobile application, testing and demonstration of work. Particular attenti
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Herbert M Sauro, Herbert M. Sauro. Analog Genetic Circuits for Interactive Learning. Experiment, 2012. http://dx.doi.org/10.18258/0002.

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Bawaskar, Neerja. Analog Implicit Functional Testing using Supervised Machine Learning. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.2097.

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KLIMENKO, I. M., and O. I. GAN. EMOTIONAL ASPECTS OF TEACHING IN HIGHER SCHOOL AND THE FACTOR OF DIGITALIZATION. Science and Innovation Center Publishing House, 2022. http://dx.doi.org/10.12731/2658-4034-2022-13-1-2-80-87.

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The purpose of the article is to study the possibilities of analog and digital learning in the formation of emotional intelligence and personal-value attitudes of students. In the course of the study, the following tasks were solved: the significance of the formation of emotional intelligence among university students was analyzed; a primary analysis of the possibilities of digital and analog education in the education of personal and value attitudes of young students was carried out. The paper puts forward a hypothesis: at the current stage of digitalization of education, analog training and
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