63,363 research outputs found

    Teaching and Learning by Analogy: Psychological Perspectives on the Parables of Jesus

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    Christian teachers are often encouraged to use Jesus’ teaching strategies as models for their own pedagogy. Jesus frequently utilized analogical comparisons, or parables, to help his learners understand elements of his Gospel message. Although teachers can use analogical models to facilitate comprehension, such models also can sow the seeds of confusion and misconception. Recent advances in cognitive psychology have provided new theoretical frameworks to help us understand how instructional analogies function in the teaching-learning process. The goal of this paper is to analyze Jesus’ analogical teaching from these psychological perspectives, with implications for all teachers who utilize instructional analogies. In addition to reviewing basic analogical learning processes, I explore a six-variable model to account systematically for potential analogical misconceptions

    Accelerating Innovation Through Analogy Mining

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    The availability of large idea repositories (e.g., the U.S. patent database) could significantly accelerate innovation and discovery by providing people with inspiration from solutions to analogous problems. However, finding useful analogies in these large, messy, real-world repositories remains a persistent challenge for either human or automated methods. Previous approaches include costly hand-created databases that have high relational structure (e.g., predicate calculus representations) but are very sparse. Simpler machine-learning/information-retrieval similarity metrics can scale to large, natural-language datasets, but struggle to account for structural similarity, which is central to analogy. In this paper we explore the viability and value of learning simpler structural representations, specifically, "problem schemas", which specify the purpose of a product and the mechanisms by which it achieves that purpose. Our approach combines crowdsourcing and recurrent neural networks to extract purpose and mechanism vector representations from product descriptions. We demonstrate that these learned vectors allow us to find analogies with higher precision and recall than traditional information-retrieval methods. In an ideation experiment, analogies retrieved by our models significantly increased people's likelihood of generating creative ideas compared to analogies retrieved by traditional methods. Our results suggest a promising approach to enabling computational analogy at scale is to learn and leverage weaker structural representations.Comment: KDD 201

    Automated Generation of Cross-Domain Analogies via Evolutionary Computation

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    Analogy plays an important role in creativity, and is extensively used in science as well as art. In this paper we introduce a technique for the automated generation of cross-domain analogies based on a novel evolutionary algorithm (EA). Unlike existing work in computational analogy-making restricted to creating analogies between two given cases, our approach, for a given case, is capable of creating an analogy along with the novel analogous case itself. Our algorithm is based on the concept of "memes", which are units of culture, or knowledge, undergoing variation and selection under a fitness measure, and represents evolving pieces of knowledge as semantic networks. Using a fitness function based on Gentner's structure mapping theory of analogies, we demonstrate the feasibility of spontaneously generating semantic networks that are analogous to a given base network.Comment: Conference submission, International Conference on Computational Creativity 2012 (8 pages, 6 figures

    The Latent Relation Mapping Engine: Algorithm and Experiments

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    Many AI researchers and cognitive scientists have argued that analogy is the core of cognition. The most influential work on computational modeling of analogy-making is Structure Mapping Theory (SMT) and its implementation in the Structure Mapping Engine (SME). A limitation of SME is the requirement for complex hand-coded representations. We introduce the Latent Relation Mapping Engine (LRME), which combines ideas from SME and Latent Relational Analysis (LRA) in order to remove the requirement for hand-coded representations. LRME builds analogical mappings between lists of words, using a large corpus of raw text to automatically discover the semantic relations among the words. We evaluate LRME on a set of twenty analogical mapping problems, ten based on scientific analogies and ten based on common metaphors. LRME achieves human-level performance on the twenty problems. We compare LRME with a variety of alternative approaches and find that they are not able to reach the same level of performance.Comment: related work available at http://purl.org/peter.turney
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