Relevant bibliographies by topics / Discovery Learning model

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

Academic literature on the topic 'Discovery Learning model'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Discovery Learning model"

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Putri, Ayu Setiyo, Bambang Riadi, and Iswanti Wahyuni. "PENGEMBANGAN MODUL PEMBELAJARAN TEKS PROSEDUR BERBASIS MODEL DISCOVERY LEARNING DI SMP KELAS VII." Jurnal Kata : Bahasa, Sastra, dan Pembelajarannya 10, no. 1 (2022): 21–32. http://dx.doi.org/10.23960/kata.v10.i1.202203.

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This research was This research aims to produce, develop, and describe the feasibility of teaching material product in the form of discovery learning-based procedure text learning module in SMP class VII. This research used the Research and Development (R&D) method by Borg and Gall. The product research results in the form of modules which consists of four modules containing learning materials, learning activities, practice questions, summaries, and formative tests. Each module has learning activities by implementing the steps in the discovery learning model, namely providing stimulation, identifying problems, collecting data, processing data, verifying data, and drawing conclusions. The module feasibility test was carried out by material expert and educator of Indonesian subject. The results of the validation test by material expert obtained an average percentage of 81.25% in very feasible category. The results of validation by Indonesian language educator obtained an average percentage of 82.5% in very feasible category.
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Satiti, Abidah Dwi Rahmi. "Pengaruh Model Pembelajaran Discovery Learning terhadap Hasil Belajar Akuntansi." JPEK (Jurnal Pendidikan Ekonomi dan Kewirausahaan) 4, no. 1 (2020): 66–81. http://dx.doi.org/10.29408/jpek.v4i1.2195.

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Erna, Mena Niman, Yansen Edison Arnoldus, and Momang Benedikta. "The Effectiveness of the Discovery Learning Model on Student Learning Outcomes." INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH AND ANALYSIS 07, no. 07 (2024): 3454–58. https://doi.org/10.5281/zenodo.12803761.

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Discovery learning directs students to discover something through their learning process. Learning becomes active when students use their thoughts and actions during the learning process. This study aims to determine the effect of discovery learning model on student learning outcomes. This type of research is an experimental study using posttest only control design. Data collection techniques in the form of tests using question instruments, then data analysis using prerequisite tests, namely homogeneity test, normality test and hypothesis testing. The results showed that learning by using discovery learning can significantly improve student learning outcomes and can significantly improve student activeness.
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Sit, Masganti, Putri Lestari, and Yusnaili Budianti. "Improving The Understanding of Science Concept Through Guided Discovery Learning Model in Azzahra Preschool Kindergarten." Unnes Science Education Journal 9, no. 3 (2020): 128–36. http://dx.doi.org/10.15294/usej.v9i3.39590.

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Understanding simple concepts (scientific concepts) was one of important understanding in aspects of cognitive development for early childhood. Children were trained to think actively and critically in order to understand they activities, namely by applying a constructivist approach or guided discovey learning. However in Azzahra Preschool Kindergarten has not applied a guided discovey learning. It considered to be a factor in children’s low understanding of science concepts. Therefore, this research aimed to improve the children's understanding of science concepts by applying guided discovery learning. This research used a classroom action research with two cycles. Subject of this research is all of children group B in Azzahra Preschool Kindergarten. And the results show that the guided discovery learning can improved children’s understanding of science concept. This is based on percentage score of children’s understanding of science concept which is increasing in each cycle. Other findings of this research showed that children were eager to learn, curiosity was increasing, and active to conduct experiments to discover various simple concepts. So, this research was recommended that apply the guided discovery learning model to develop various aspects of the child.
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Jovita Lantang Anetha, Gisela. "Development of a Discovery Learning Learning Model Combined with Literacy Activities and Educandy Games in Learning Mathematics." International Journal of Science and Research (IJSR) 12, no. 6 (2023): 1850–57. http://dx.doi.org/10.21275/sr23612160341.

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Asyari, Dian Noer. "KEEFEKTIFAN MODEL GUIDED DISCOVERY LEARNING UNTUK MENINGKATKAN KETERAMPILAN KETERAMPILAN BERPIKIR KRITIS." Edupedia 3, no. 2 (2019): 17–27. http://dx.doi.org/10.35316/edupedia.v3i2.257.

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Discovery learning is a learning situation on wich the principle content of what is tobe learned is not give but must be independly discovered by student. The effectiveness of guided discovery learning model to improve students’ critical thinking skills uses 6 indicators, are: formulating problems, formulating hypotheses, analyzing date, providing alternatives, summing up, communicating and applying principle. The guided discovery learning model was included in the effective category in terms of: (a) Improvement (N-gain) of students’ critical thinking skills by 0.65 with moderate criteria and (b) Students respond positively to learning using guided discovery learning model and its learning tools on implementation.
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Yuniawati, Tesa Lonika, and Friska Juliana Purba. "Discovery learning model to optimize students’ critical thinking skills on hydrocarbon material." Jurnal Pendidikan Kimia 13, no. 3 (2021): 269–77. http://dx.doi.org/10.24114/jpkim.v13i3.30208.

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Learning in 21st century requires an advance human mindset, especially critical thinking skills. Critical thinking skills improve the learning process to be more optimal in taking the core of the learning. However, data shows that the critical thinking skills of class XI IPA 2 students are still lacking because they have not been able to answer the analytical questions which is this is the important skills for the students. This paper aims to describe the use of discovery learning models to optimize students' critical thinking skills. The research method used is descriptive qualitative. The research subjects were 28 students of class XI IPA 2 in one of the senior high schools in Jakarta in the academic year 2021/2022 odd semester. Based on the study and data on each indicator, it was found that the use of discovery learning models was able to optimize students' critical thinking skills. Each step in the discovery learning model facilitates the achievement of indicators of critical thinking skills.
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Udin, S. F., and Syamsia. "A Study of the Implementation of the Discovery Learning Model on the Speaking Skills of Class VIII Students at Madrasah Tsanawiyah Darul Ulum Sasa, Ternate City." Langua: Journal of Linguistics, Literature, and Language Education 5, no. 2 (2022): 93–105. https://doi.org/10.5281/zenodo.7145556.

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This research aims to find out the percentage of students’ speaking skills completed before and after using the discovery learning model at the students of Madrasah Tsanawiyah Darul Ulum Sasa and to find out whether the discovery learning model is effective in improving students' speaking skills. The method used in this research is an experimental method of Quasi-Experimental Research with a research design of One Groups Pretest-Posttest Design. The result of the percentage of students' mastery speaking skills value is very high after the application of the discovery learning model, 97,96% compared to the percentage of students speaking skills mastery scores before the implementation of the discovery learning model. The average value of the students' speaking skills after the application of the discovery learning model is 84,41 was higher than the average value of the students' speaking skills before the application of the learning model is 53,47. There is a relationship between the students' speaking skills scores before (pretest) and after (posttest) the application of the discovery learning model is 55.2%. The percentage of the effectiveness of the application of the discovery learning model in increasing the value of students' speaking skills is 66.95%.
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Prihatin, Tri Aprini. "Differences in Learning Outcomes Using Discovery Learning and Problem-Based Learning Models on Indonesia's Strategic Position as The World's Maritime Axis." Jurnal Penelitian Pendidikan IPA 10, no. 5 (2024): 2721–26. http://dx.doi.org/10.29303/jppipa.v10i5.6866.

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Discovery Learning is a model that invites students to actively learn to discover their knowledge. Meanwhile, Problem-Based Learning is a learning model where students are required to be active in acquiring concepts by solving problems. This research aims to analyze geography learning outcomes using the discovery learning model; determine the results of learning geography using the problem-based learning model; determine differences in geography learning outcomes using discovery learning models and problem-based learning models. The research method used was a quasi-experiment with two classes, namely the experimental class and the control class. Based on the research results, the learning outcomes for class XI IPS 1 were obtained with an average score of 85.8, and the learning outcomes for class learning on geography learning outcomes in class.
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Yuliati, Maridi, and Masykuri Mohammad. "The Influence of Biology Learning Using Concept Attainment Model on Student's Cognitive Learning Achievement." Journal of Education and Learning (EduLearn) 12, no. 4 (2018): 767–74. https://doi.org/10.11591/edulearn.v12i4.9296.

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This research aimed to find out the difference of cognitive learning achievement between students taught with Concept Attainment Model and those taught with Discovery Learning model. This study was a quasi experimental research. The population of research was the 12th Science  graders of SMAN 1 Karas of Magetan Regency in school year of 2016/2017. The sample was taken using cluster random sampling technique, consisting of two grades: the 12 th Science 4 grade as the first experiment class using  Concept Attainment Model and the 12 th Science 3 grade as the second  experiment class using Discovery Learning model. Technique of collecting data used was t-test technique for data of students’ cognitive learning outcome. Data analysis was carried out using unpaired two-sample variance analysis. The result of research showed there was a difference of cognitive learning outcome between the students treated with learning using Concept Attainment Model and those treated with learning using Discovery Learning. The cognitive learning achievement of students taught with Concept Attainment Model was higher than that of those taught with Discovery Learning.
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Dissertations / Theses on the topic "Discovery Learning model"

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Hedden, Chet. "A guided exploration model of problem-solving discovery learning /." Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/7683.

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Miller, Chreston. "Structural Model Discovery in Temporal Event Data Streams." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/19341.

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This dissertation presents a unique approach to human behavior analysis based on expert guidance and intervention through interactive construction and modification of behavior models. Our focus is to introduce the research area of behavior analysis, the challenges faced by this field, current approaches available, and present a new analysis approach: Interactive Relevance Search and Modeling (IRSM). More intelligent ways of conducting data analysis have been explored in recent years. Ma- chine learning and data mining systems that utilize pattern classification and discovery in non-textual data promise to bring new generations of powerful "crawlers" for knowledge discovery, e.g., face detection and crowd surveillance. Many aspects of data can be captured by such systems, e.g., temporal information, extractable visual information - color, contrast, shape, etc. However, these captured aspects may not uncover all salient information in the data or provide adequate models/patterns of phenomena of interest. This is a challenging problem for social scientists who are trying to identify high-level, conceptual patterns of human behavior from observational data (e.g., media streams). The presented research addresses how social scientists may derive patterns of human behavior captured in media streams. Currently, media streams are being segmented into sequences of events describing the actions captured in the streams, such as the interactions among humans. This segmentation creates a challenging data space to search characterized by non- numerical, temporal, descriptive data, e.g., Person A walks up to Person B at time T. This dissertation will present an approach that allows one to interactively search, identify, and discover temporal behavior patterns within such a data space. Therefore, this research addresses supporting exploration and discovery in behavior analysis through a formalized method of assisted exploration. The model evolution presented sup- ports the refining of the observer\'s behavior models into representations of their understanding. The benefit of the new approach is shown through experimentation on its identification accuracy and working with fellow researchers to verify the approach\'s legitimacy in analysis of their data.<br>Ph. D.
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Kayesh, Humayun. "Deep Learning for Causal Discovery in Texts." Thesis, Griffith University, 2022. http://hdl.handle.net/10072/415822.

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Causality detection in text data is a challenging natural language processing task. This is a trivial task for human beings as they acquire vast background knowledge throughout their lifetime. For example, a human knows from their experience that heavy rain may cause flood or plane accidents may cause death. However, it is challenging to automatically detect such causal relationships in texts due to the availability of limited contextual information and the unstructured nature of texts. The task is even more challenging for social media short texts such as Tweets as often they are informal, short, and grammatically incorrect. Generating hand-crafted linguistic rules is an option but is not always effective to detect causal relationships in text because they are rigid and require grammatically correct sentences. Also, the rules are often domain-specific and not always portable to another domain. Therefore, supervised learning techniques are more appropriate in the above scenario. Traditional machine learning-based model also suffers from the high dimensional features of texts. This is why deep learning-based approaches are becoming increasingly popular for natural language processing tasks such as causality detection. However, deep learning models often require large datasets with high-quality features to perform well. Extracting deeply-learnable causal features and applying them to a carefully designed deep learning model is important. Also, preparing a large human-labeled training dataset is expensive and time-consuming. Even if a large training dataset is available, it is computationally expensive to train a deep learning model due to the complex structure of neural networks. We focus on addressing the following challenges: (i) extracting highquality causal features, (ii) designing an effective deep learning model to learn from the causal features, and (iii) reducing the dependency on large training datasets. Our main goals in this thesis are as follows: (i) we aim to study the different aspects of causality and causal discovery in text in depth. (ii) We aim to develop strategies to model causality in text, (iii) and finally, we aim to develop frameworks to design effective and efficient deep neural network structures to discover causality in texts.<br>Thesis (PhD Doctorate)<br>Doctor of Philosophy (PhD)<br>School of Info & Comm Tech<br>Science, Environment, Engineering and Technology<br>Full Text
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Rijn, Dirk Hendrik van. "Exploring the limited effect of inductive discovery learning computational models and model-based analyses /." [Amsterdam : Amsterdam : EPOS, experimenteel-psychologische onderzoekschool] ; Universiteit van Amsterdam [Host], 2003. http://dare.uva.nl/document/68567.

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Tuovinen, L. (Lauri). "From machine learning to learning with machines:remodeling the knowledge discovery process." Doctoral thesis, Oulun yliopisto, 2014. http://urn.fi/urn:isbn:9789526205243.

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Abstract Knowledge discovery (KD) technology is used to extract knowledge from large quantities of digital data in an automated fashion. The established process model represents the KD process in a linear and technology-centered manner, as a sequence of transformations that refine raw data into more and more abstract and distilled representations. Any actual KD process, however, has aspects that are not adequately covered by this model. In particular, some of the most important actors in the process are not technological but human, and the operations associated with these actors are interactive rather than sequential in nature. This thesis proposes an augmentation of the established model that addresses this neglected dimension of the KD process. The proposed process model is composed of three sub-models: a data model, a workflow model, and an architectural model. Each sub-model views the KD process from a different angle: the data model examines the process from the perspective of different states of data and transformations that convert data from one state to another, the workflow model describes the actors of the process and the interactions between them, and the architectural model guides the design of software for the execution of the process. For each of the sub-models, the thesis first defines a set of requirements, then presents the solution designed to satisfy the requirements, and finally, re-examines the requirements to show how they are accounted for by the solution. The principal contribution of the thesis is a broader perspective on the KD process than what is currently the mainstream view. The augmented KD process model proposed by the thesis makes use of the established model, but expands it by gathering data management and knowledge representation, KD workflow and software architecture under a single unified model. Furthermore, the proposed model considers issues that are usually either overlooked or treated as separate from the KD process, such as the philosophical aspect of KD. The thesis also discusses a number of technical solutions to individual sub-problems of the KD process, including two software frameworks and four case-study applications that serve as concrete implementations and illustrations of several key features of the proposed process model<br>Tiivistelmä Tiedonlouhintateknologialla etsitään automoidusti tietoa suurista määristä digitaalista dataa. Vakiintunut prosessimalli kuvaa tiedonlouhintaprosessia lineaarisesti ja teknologiakeskeisesti sarjana muunnoksia, jotka jalostavat raakadataa yhä abstraktimpiin ja tiivistetympiin esitysmuotoihin. Todellisissa tiedonlouhintaprosesseissa on kuitenkin aina osa-alueita, joita tällainen malli ei kata riittävän hyvin. Erityisesti on huomattava, että eräät prosessin tärkeimmistä toimijoista ovat ihmisiä, eivät teknologiaa, ja että heidän toimintansa prosessissa on luonteeltaan vuorovaikutteista eikä sarjallista. Tässä väitöskirjassa ehdotetaan vakiintuneen mallin täydentämistä siten, että tämä tiedonlouhintaprosessin laiminlyöty ulottuvuus otetaan huomioon. Ehdotettu prosessimalli koostuu kolmesta osamallista, jotka ovat tietomalli, työnkulkumalli ja arkkitehtuurimalli. Kukin osamalli tarkastelee tiedonlouhintaprosessia eri näkökulmasta: tietomallin näkökulma käsittää tiedon eri olomuodot sekä muunnokset olomuotojen välillä, työnkulkumalli kuvaa prosessin toimijat sekä niiden väliset vuorovaikutukset, ja arkkitehtuurimalli ohjaa prosessin suorittamista tukevien ohjelmistojen suunnittelua. Väitöskirjassa määritellään aluksi kullekin osamallille joukko vaatimuksia, minkä jälkeen esitetään vaatimusten täyttämiseksi suunniteltu ratkaisu. Lopuksi palataan tarkastelemaan vaatimuksia ja osoitetaan, kuinka ne on otettu ratkaisussa huomioon. Väitöskirjan pääasiallinen kontribuutio on se, että se avaa tiedonlouhintaprosessiin valtavirran käsityksiä laajemman tarkastelukulman. Väitöskirjan sisältämä täydennetty prosessimalli hyödyntää vakiintunutta mallia, mutta laajentaa sitä kokoamalla tiedonhallinnan ja tietämyksen esittämisen, tiedon louhinnan työnkulun sekä ohjelmistoarkkitehtuurin osatekijöiksi yhdistettyyn malliin. Lisäksi malli kattaa aiheita, joita tavallisesti ei oteta huomioon tai joiden ei katsota kuuluvan osaksi tiedonlouhintaprosessia; tällaisia ovat esimerkiksi tiedon louhintaan liittyvät filosofiset kysymykset. Väitöskirjassa käsitellään myös kahta ohjelmistokehystä ja neljää tapaustutkimuksena esiteltävää sovellusta, jotka edustavat teknisiä ratkaisuja eräisiin yksittäisiin tiedonlouhintaprosessin osaongelmiin. Kehykset ja sovellukset toteuttavat ja havainnollistavat useita ehdotetun prosessimallin merkittävimpiä ominaisuuksia
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Zhang, Xuan. "Product Defect Discovery and Summarization from Online User Reviews." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/85581.

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Product defects concern various groups of people, such as customers, manufacturers, government officials, etc. Thus, defect-related knowledge and information are essential. In keeping with the growth of social media, online forums, and Internet commerce, people post a vast amount of feedback on products, which forms a good source for the automatic acquisition of knowledge about defects. However, considering the vast volume of online reviews, how to automatically identify critical product defects and summarize the related information from the huge number of user reviews is challenging, even when we target only the negative reviews. As a kind of opinion mining research, existing defect discovery methods mainly focus on how to classify the type of product issues, which is not enough for users. People expect to see defect information in multiple facets, such as product model, component, and symptom, which are necessary to understand the defects and quantify their influence. In addition, people are eager to seek problem resolutions once they spot defects. These challenges cannot be solved by existing aspect-oriented opinion mining models, which seldom consider the defect entities mentioned above. Furthermore, users also want to better capture the semantics of review text, and to summarize product defects more accurately in the form of natural language sentences. However, existing text summarization models including neural networks can hardly generalize to user review summarization due to the lack of labeled data. In this research, we explore topic models and neural network models for product defect discovery and summarization from user reviews. Firstly, a generative Probabilistic Defect Model (PDM) is proposed, which models the generation process of user reviews from key defect entities including product Model, Component, Symptom, and Incident Date. Using the joint topics in these aspects, which are produced by PDM, people can discover defects which are represented by those entities. Secondly, we devise a Product Defect Latent Dirichlet Allocation (PDLDA) model, which describes how negative reviews are generated from defect elements like Component, Symptom, and Resolution. The interdependency between these entities is modeled by PDLDA as well. PDLDA answers not only what the defects look like, but also how to address them using the crowd wisdom hidden in user reviews. Finally, the problem of how to summarize user reviews more accurately, and better capture the semantics in them, is studied using deep neural networks, especially Hierarchical Encoder-Decoder Models. For each of the research topics, comprehensive evaluations are conducted to justify the effectiveness and accuracy of the proposed models, on heterogeneous datasets. Further, on the theoretical side, this research contributes to the research stream on product defect discovery, opinion mining, probabilistic graphical models, and deep neural network models. Regarding impact, these techniques will benefit related users such as customers, manufacturers, and government officials.<br>Ph. D.<br>Product defects concern various groups of people, such as customers, manufacturers, and government officials. Thus, defect-related knowledge and information are essential. In keeping with the growth of social media, online forums, and Internet commerce, people post a vast amount of feedback on products, which forms a good source for the automatic acquisition of knowledge about defects. However, considering the vast volume of online reviews, how to automatically identify critical product defects and summarize the related information from the huge number of user reviews is challenging, even when we target only the negative reviews. People expect to see defect information in multiple facets, such as product model, component, and symptom, which are necessary to understand the defects and quantify their influence. In addition, people are eager to seek problem resolutions once they spot defects. Furthermore, users also want to better summarize product defects more accurately in the form of natural language sentences. These requirements cannot be satisfied by existing methods, which seldom consider the defect entities mentioned above, or hardly generalize to user review summarization. In this research, we develop novel Machine Learning (ML) algorithms for product defect discovery and summarization. Firstly, we study how to identify product defects and their related attributes, such as Product Model, Component, Symptom, and Incident Date. Secondly, we devise a novel algorithm, which can discover product defects and the related Component, Symptom, and Resolution, from online user reviews. This method tells not only what the defects look like, but also how to address them using the crowd wisdom hidden in user reviews. Finally, we address the problem of how to summarize user reviews in the form of natural language sentences using a paraphrase-style method. On the theoretical side, this research contributes to multiple research areas in Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning. Regarding impact, these techniques will benefit related users such as customers, manufacturers, and government officials.
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Liang, Wen. "Integrated feature, neighbourhood, and model optimization for personalised modelling and knowledge discovery." Click here to access this resource online, 2009. http://hdl.handle.net/10292/749.

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“Machine learning is the process of discovering and interpreting meaningful information, such as new correlations, patterns and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques” (Larose, 2005). From my understanding, machine learning is a process of using different analysis techniques to observe previously unknown, potentially meaningful information, and discover strong patterns and relationships from a large dataset. Professor Kasabov (2007b) classified computational models into three categories (e.g. global, local, and personalised) which have been widespread and used in the areas of data analysis and decision support in general, and in the areas of medicine and bioinformatics in particular. Most recently, the concept of personalised modelling has been widely applied to various disciplines such as personalised medicine, personalised drug design for known diseases (e.g. cancer, diabetes, brain disease, etc.) as well as for other modelling problems in ecology, business, finance, crime prevention, and so on. The philosophy behind the personalised modelling approach is that every person is different from others, thus he/she will benefit from having a personalised model and treatment. However, personalised modelling is not without issues, such as defining the correct number of neighbours or defining an appropriate number of features. As a result, the principal goal of this research is to study and address these issues and to create a novel framework and system for personalised modelling. The framework would allow users to select and optimise the most important features and nearest neighbours for a new input sample in relation to a certain problem based on a weighted variable distance measure in order to obtain more precise prognostic accuracy and personalised knowledge, when compared with global modelling and local modelling approaches.
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Prokopp, Christian Werner. "Semantic service discovery in the service ecosystem." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/50872/1/Christian_Prokopp_Thesis.pdf.

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Electronic services are a leitmotif in ‘hot’ topics like Software as a Service, Service Oriented Architecture (SOA), Service oriented Computing, Cloud Computing, application markets and smart devices. We propose to consider these in what has been termed the Service Ecosystem (SES). The SES encompasses all levels of electronic services and their interaction, with human consumption and initiation on its periphery in much the same way the ‘Web’ describes a plethora of technologies that eventuate to connect information and expose it to humans. Presently, the SES is heterogeneous, fragmented and confined to semi-closed systems. A key issue hampering the emergence of an integrated SES is Service Discovery (SD). A SES will be dynamic with areas of structured and unstructured information within which service providers and ‘lay’ human consumers interact; until now the two are disjointed, e.g., SOA-enabled organisations, industries and domains are choreographed by domain experts or ‘hard-wired’ to smart device application markets and web applications. In a SES, services are accessible, comparable and exchangeable to human consumers closing the gap to the providers. This requires a new SD with which humans can discover services transparently and effectively without special knowledge or training. We propose two modes of discovery, directed search following an agenda and explorative search, which speculatively expands knowledge of an area of interest by means of categories. Inspired by conceptual space theory from cognitive science, we propose to implement the modes of discovery using concepts to map a lay consumer’s service need to terminologically sophisticated descriptions of services. To this end, we reframe SD as an information retrieval task on the information attached to services, such as, descriptions, reviews, documentation and web sites - the Service Information Shadow. The Semantic Space model transforms the shadow's unstructured semantic information into a geometric, concept-like representation. We introduce an improved and extended Semantic Space including categorization calling it the Semantic Service Discovery model. We evaluate our model with a highly relevant, service related corpus simulating a Service Information Shadow including manually constructed complex service agendas, as well as manual groupings of services. We compare our model against state-of-the-art information retrieval systems and clustering algorithms. By means of an extensive series of empirical evaluations, we establish optimal parameter settings for the semantic space model. The evaluations demonstrate the model’s effectiveness for SD in terms of retrieval precision over state-of-the-art information retrieval models (directed search) and the meaningful, automatic categorization of service related information, which shows potential to form the basis of a useful, cognitively motivated map of the SES for exploratory search.
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Kučera, Petr. "Meta-učení v oblasti dolování dat." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236213.

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This paper describes the use of meta-learning in the area of data mining. It describes the problems and tasks of data mining where meta-learning can be applied, with a focus on classification. It provides an overview of meta-learning techniques and their possible application in data mining, especially  model selection. It describes design and implementation of meta-learning system to support classification tasks in data mining. The system uses statistics and information theory to characterize data sets stored in the meta-knowledge base. The meta-classifier is created from the base and predicts the most suitable model for the new data set. The conclusion discusses results of the experiments with more than 20 data sets representing clasification tasks from different areas and suggests possible extensions of the project.
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Akpakpan, Nsikak Etim. "Analytic Extensions to the Data Model for Management Analytics and Decision Support in the Big Data Environment." ScholarWorks, 2018. https://scholarworks.waldenu.edu/dissertations/5538.

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From 2006 to 2016, an estimated average of 50% of big data analytics and decision support projects failed to deliver acceptable and actionable outputs to business users. The resulting management inefficiency came with high cost, and wasted investments estimated at $2.7 trillion in 2016 for companies in the United States. The purpose of this quantitative descriptive study was to examine the data model of a typical data analytics project in a big data environment for opportunities to improve the information created for management problem-solving. The research questions focused on finding artifacts within enterprise data to model key business scenarios for management action. The foundations of the study were information and decision sciences theories, especially information entropy and high-dimensional utility theories. The design-based research in a nonexperimental format was used to examine the data model for the functional forms that mapped the available data to the conceptual formulation of the management problem by combining ontology learning, data engineering, and analytic formulation methodologies. Semantic, symbolic, and dimensional extensions emerged as key functional forms of analytic extension of the data model. The data-modeling approach was applied to 15-terabyte secondary data set from a multinational medical product distribution company with profit growth problem. The extended data model simplified the composition of acceptable analytic insights, the derivation of business solutions, and the design of programs to address the ill-defined management problem. The implication for positive social change was the potential for overall improvement in management efficiency and increasing participation in advocacy and sponsorship of social initiatives.
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Books on the topic "Discovery Learning model"

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1968-, Džeroski Sašo, Todorovski Ljupčo 1969-, and International Symposium on Computational Discovery of Communicable Knowledge (2001 : Stanford, Calif.), eds. Computational discovery of scientific knowledge: Introduction, techniques, and applications in environmental and life sciences. Springer, 2007.

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Krimins, Rebecca A. Learning from Disease in Pets: A 'One Health' Model for Discovery. Taylor & Francis Group, 2020.

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Krimins, Rebecca A. Learning from Disease in Pets: A 'One Health' Model for Discovery. Taylor & Francis Group, 2020.

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Krimins, Rebecca A. Learning from Disease in Pets: A 'One Health' Model for Discovery. Taylor & Francis Group, 2020.

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Krimins, Rebecca A. Learning from Disease in Pets: A 'One Health' Model for Discovery. Taylor & Francis Group, 2020.

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P, Smitha V. Inquiry Training Model and Guided Discovery Learning for Fostering Critical Thinking and Scientific Attitude. Lulu Press, Inc., 2012.

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On Data Mining in Context: Cases, Fusion and Evaluation. Leiden Institute of Advanced Computer Science (LIACS), Faculty of Science, Leiden University, 2010.

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Figdor, Carrie. Cases. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198809524.003.0003.

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Chapter 3 introduces the use of mathematical models and modeling practices in contemporary biological and cognitive sciences. The familiar Lotka–Volterra model of predator–prey relations is used to explain these practices and show how they promote the extensions of predicates, including psychological predicates, into new and often unexpected domains. It presents two models of cognitive capacities that were developed to explain human behavioral data: Ratcliff’s drift-diffusion model of decision-making and Sutton and Barto’s temporal difference model of reinforcement learning. These are now used for fruit flies and neural populations. It also discusses contemporary and ongoing attempts to revise psychological concepts in response to empirical discovery.
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Problem solving models of scientific discovery learning processes. P. Lang, 1990.

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CONNERR, HELYN. Learning Without Tears: Discover How the Mercury Model Can. Watkins Publishing, 2008.

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Book chapters on the topic "Discovery Learning model"

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Ikonomovska, Elena, and Joao Gama. "Learning Model Trees from Data Streams." In Discovery Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88411-8_8.

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Krishnan, N. M. Anoop, Hariprasad Kodamana, and Ravinder Bhattoo. "Model Refinement." In Machine Learning for Materials Discovery. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-44622-1_7.

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Bravo, Crescencio, Miguel A. Redondo, Manuel Ortega, and M. Felisa Verdejo. "Collaborative Discovery Learning of Model Design." In Intelligent Tutoring Systems. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-47987-2_68.

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Kveton, Branislav, Hung Bui, Mohammad Ghavamzadeh, Georgios Theocharous, S. Muthukrishnan, and Siqi Sun. "Graphical Model Sketch." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46128-1_6.

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Brence, Jure, Jovan Tanevski, Jennifer Adams, Edward Malina, and Sašo Džeroski. "Learning Surrogates of a Radiative Transfer Model for the Sentinel 5P Satellite." In Discovery Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61527-7_15.

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Wu, Dianbin, Weijie Jiang, Zhiyong Huang, et al. "Model Inversion-Based Incremental Learning." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-20738-9_133.

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Camilleri, Michael P. J., and Christopher K. I. Williams. "The Extended Dawid-Skene Model." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43823-4_11.

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Strobl, Michael, Jörg Sander, Ricardo J. G. B. Campello, and Osmar Zaïane. "Model-Based Clustering with HDBSCAN*." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67661-2_22.

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Rogers, Eoin, John D. Kelleher, and Robert J. Ross. "Language Model Co-occurrence Linking for Interleaved Activity Discovery." In Machine Learning for Networking. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45778-5_6.

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Ting, Chia-Hsin, Hung-Yi Lo, and Shou-De Lin. "Transfer-Learning Based Model for Reciprocal Recommendation." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31750-2_39.

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Conference papers on the topic "Discovery Learning model"

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Hardi, Muhammad, Rahmad Husein, and Meisuri. "The Design of Discovery Learning Model." In 6th Annual International Seminar on Transformative Education and Educational Leadership (AISTEEL 2021). Atlantis Press, 2021. http://dx.doi.org/10.2991/assehr.k.211110.144.

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Oliveira, Guilherme S., Fabrício A. Silva, and Ricardo V. Ferreira. "Model and Algorithm-Agnostic Clustering Interpretability." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/kdmile.2023.232618.

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Data clustering through unsupervised algorithms is an important technique in several applications, both in research and industrial projects, allowing similar elements to be associated with each other for knowledge extraction. After grouping, the interpretation and understanding of the created clusters is a crucial step so that they can be used in decision-making. However, this is not a trivial task, since it requires manual and repetitive analyses, which consume time and resources of those involved. In the present work, a solution for the interpretability of clusters generated by unsupervised learning is proposed. Unlike existing solutions in the literature, the proposed approach is independent of the model and algorithm used for clustering, and generates easy-to-understand descriptions for end users, facilitating their use by teams from different areas of the companies. The results showed that the solution was able to provide a friendly description to interpret the 13 clusters created to segment 263,684 customers of a company.
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Salvi, Andrey De Aguiar, and Rodrigo Coelho Barros. "An Experimental Analysis of Model Compression Techniques for Object Detection." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/kdmile.2020.11958.

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Recent research on Convolutional Neural Networks focuses on how to create models with a reduced number of parameters and a smaller storage size while keeping the model’s ability to perform its task, allowing the use of the best CNN for automating tasks in limited devices, with reduced processing power, memory, or energy consumption constraints. There are many different approaches in the literature: removing parameters, reduction of the floating-point precision, creating smaller models that mimic larger models, neural architecture search (NAS), etc. With all those possibilities, it is challenging to say which approach provides a better trade-off between model reduction and performance, due to the difference between the approaches, their respective models, the benchmark datasets, or variations in training details. Therefore, this article contributes to the literature by comparing three state-of-the-art model compression approaches to reduce a well-known convolutional approach for object detection, namely YOLOv3. Our experimental analysis shows that it is possible to create a reduced version of YOLOv3 with 90% fewer parameters and still outperform the original model by pruning parameters. We also create models that require only 0.43% of the original model’s inference effort.
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Ensina, L. A., P. E. M. Karvat, E. C. de Almeida, and L. E. S. de Oliveira. "Fault Location in Transmission Lines based on LSTM Model." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/kdmile.2022.227805.

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Transmission lines are fundamental components of the electric power system, demanding special attention from the protection system due to the vulnerability of these lines. This paper presents a method for fault location in transmission lines using data for a single terminal without requiring explicit feature engineering by a domain expert. The fault location task provides an approximate position of the point of the line where the failure occurred, serving as information to the operators to dispatch a maintenance staff to this location to reclose the transmission line with better reliability and safety. In our method, we extract two post-fault cycles of the three-phase current and voltage signals to serve as input to a model based on the LSTM algorithm. We defined the model's architecture with empirical experiments searching for the best structure to estimate the fault distance. For this purpose, we used a dataset with diversified failure events, also available to the scientific community. The results demonstrate the effectiveness of the proposed method with a mean error of 0.1309 km +- 0.4897 km, representing 0.0316% +- 0.1183% of the transmission line extension.
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Kurnia, Meisa, Irwan Irwan, and Yerizon Yerizon. "Validity of learning devices based on guided discovery learning model." In International Conferences on Educational, Social Sciences and Technology. Fakultas Ilmu Pendidikan, 2018. http://dx.doi.org/10.29210/2018178.

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Ventura, N., P. Loures, C. Nicola, et al. "An Interpretable Classification Model for Identifying Individuals with Attention Defict Hyperactivity Disorder." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/kdmile.2022.227962.

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Attention Deficit Hyperactivity Disorder (ADHD) is a psychiatric condition that affects around 5% of children around the world. The primary attention procedure is traditionally based on analysis of ratings collected in questionnaires called psychometrics. This work aims to investigate interpretable classification models capable of not only accurately identifying individuals with ADHD, but also explain it, by providing the evidences that lead to the outcome. We compare the performance of Explainable Boosting Machine (EBM) with 3 other classical decision tree-based models and observed similar results, with the distinction of EBM being a more interpretable model. We also assess explanations quantitative and qualitatively, demonstrating how they may actually help psychiatrists in their practice.
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Khairatunnisa, Khairatunnisa. "PENGEMBANGAN MODUL DIGITAL INTERAKTIF DENGAN MODEL DISCOVERY LEARNING PADA MATERI PENGUKURAN." In SEMINAR NASIONAL FISIKA 2016 UNJ. PRODI Pendidikan Fisika dan Fisika UNJ, 2024. http://dx.doi.org/10.21009/03.1201.pf29.

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Kaliffe, Kalil Saldanha, and Reginaldo Santos. "Finding the Best Tennis Serves with K-Means and GMM Clusters of Ball Tracking data to Interpret Serve Strategies." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2024. http://dx.doi.org/10.5753/kdmile.2024.244569.

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The serve is a crucial shot in tennis, that dictates a player’s advantage. However, there has been a noticeable gap in recent data analysis focused on player behavior during serves, when compared to data analysis adoption in other sports. With high speeds, precision, and small margins, ball-tracking systems like Hawkeye are essential for capturing serve steps with fidelity. This data is crucial for decision-making improvements, performance enhancement, and knowledge discovery. However, the Full Hawkeye data is not publicly available. In this manner, this article uses scraping techniques to harness Hawk-Eye serve tracking data from the Australian Open (2020-2024) and Roland Garros (2019-2024), consisting of 152.761 serves from 951 matches. K-Means and Gaussian Mixture Model (GMM) clustering models were employed to discover clusters that summarize thousands of servers into interpretable serve strategies. The best serve strategies optimize success percentages, risk of missing the serve (fault), and may vary from first to second serves, or be affected by pressure in breakpoints, thus the best serve is a serve that best fits a situation and matches a desired outcome. The relation between the serve success and best players was checked, by correlating the server ranking with cluster success using serves from these clusters in different context scenarios. We discovered that the success rate in the clusters increases with player ranking points in high-pressure situations, such as breakpoints and tiebreaks, also that, the hard courts at the Australian Open have greater success rates, while the slower clay courts at Roland Garros have lower first and second serve success rates, despite using similar serve strategies, and that rankings had little bearing on serve performance on these slower courts, indicating that in this surface, other factors may matter more for player advantage in the end than just winning points with the serve right away.
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Wu, Lan'an. "A Knowledge Discovery Process Model Based on Reverse Engineering." In 2016 International Conference on Education, E-learning and Management Technology. Atlantis Press, 2016. http://dx.doi.org/10.2991/iceemt-16.2016.17.

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Wang, Xiaoqiang, Yali Du, Shengyu Zhu, et al. "Ordering-Based Causal Discovery with Reinforcement Learning." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/491.

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It is a long-standing question to discover causal relations among a set of variables in many empirical sciences. Recently, Reinforcement Learning (RL) has achieved promising results in causal discovery from observational data. However, searching the space of directed graphs and enforcing acyclicity by implicit penalties tend to be inefficient and restrict the existing RL-based method to small scale problems. In this work, we propose a novel RL-based approach for causal discovery, by incorporating RL into the ordering-based paradigm. Specifically, we formulate the ordering search problem as a multi-step Markov decision process, implement the ordering generating process with an encoder-decoder architecture, and finally use RL to optimize the proposed model based on the reward mechanisms designed for each ordering. A generated ordering would then be processed using variable selection to obtain the final causal graph. We analyze the consistency and computational complexity of the proposed method, and empirically show that a pretrained model can be exploited to accelerate training. Experimental results on both synthetic and real data sets shows that the proposed method achieves a much improved performance over existing RL-based method.
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Reports on the topic "Discovery Learning model"

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Japar, M., Heni Rochimah, and Etin Solihatin. Implementasi Model Discovery Learning di Sekolah. Minhaj Pustaka, 2024. https://doi.org/10.71457/591058.

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Zheng, Jian. Relational Patterns Discovery in Climate with Deep Learning Model. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2021. http://dx.doi.org/10.7546/crabs.2021.01.05.

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Pasupuleti, Murali Krishna. Mathematical Modeling for Machine Learning: Theory, Simulation, and Scientific Computing. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv125.

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Abstract Mathematical modeling serves as a fundamental framework for advancing machine learning (ML) and artificial intelligence (AI) by integrating theoretical, computational, and simulation-based approaches. This research explores how numerical optimization, differential equations, variational inference, and scientific computing contribute to the development of scalable, interpretable, and efficient AI systems. Key topics include convex and non-convex optimization, physics-informed machine learning (PIML), partial differential equation (PDE)-constrained AI, and Bayesian modeling for uncertainty quantification. By leveraging finite element methods (FEM), computational fluid dynamics (CFD), and reinforcement learning (RL), this study demonstrates how mathematical modeling enhances AI-driven scientific discovery, engineering simulations, climate modeling, and drug discovery. The findings highlight the importance of high-performance computing (HPC), parallelized ML training, and hybrid AI approaches that integrate data-driven and model-based learning for solving complex real-world problems. Keywords Mathematical modeling, machine learning, scientific computing, numerical optimization, differential equations, PDE-constrained AI, variational inference, Bayesian modeling, convex optimization, non-convex optimization, reinforcement learning, high-performance computing, hybrid AI, physics-informed machine learning, finite element methods, computational fluid dynamics, uncertainty quantification, simulation-based AI, interpretable AI, scalable AI.
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Krachunov, Milko, Milena Sokolova, Valeriya Simeonova, Maria Nisheva, Irena Avdjieva, and Dimitar Vassilev. Quality of Different Machine Learning Models in Error Discovery for Parallel Genome Sequencing. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2018. http://dx.doi.org/10.7546/crabs.2018.07.08.

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Orbán, Levente. Associative Learning in Psychology: Classical and Operant Conditioning Revisited. Orban Foundation, 2025. https://doi.org/10.69642/5323.

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Learning is a central concept in psychology, governing how organisms adapt to their environments. This paper revisits foundational theories of associative learning, with an emphasis on classical conditioning, as discovered by Ivan Pavlov, and operant conditioning, as developed by B.F. Skinner. Drawing from a transcript of a live undergraduate lecture, we explore theoretical models, empirical evidence, practical examples, and mathematical structures underlying these learning mechanisms. The aim is to distill key insights while highlighting the contemporary relevance of behaviorist principles in domains like behavioral design, education, and machine learning.
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Vesselinov, Velimir. Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1781345.

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Frash, Luke, Bulbul Ahmmed, Maruti Mudunuru, and Daniel Tartakovsky. Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources. Office of Scientific and Technical Information (OSTI), 2024. https://doi.org/10.2172/2480434.

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Frash, Luke, Bulbul Ahmmed, Maruti Mudunuru, and Daniel Tartakovsky. GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2290287.

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Dyulicheva, Yulia Yu, Yekaterina A. Kosova, and Aleksandr D. Uchitel. he augmented reality portal and hints usage for assisting individuals with autism spectrum disorder, anxiety and cognitive disorders. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/4412.

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The augmented reality applications are effectively applied in education and therapy for people with special needs. We propose to apply the augmented reality portal as a special tool for the teachers to interact with people at the moment when a panic attack or anxiety happens in education process. It is expected that applying the augmented reality portal in education will help students with ASD, ADHD and anxiety disorder to feel safe at discomfort moment and teachers can interact with them. Our application with the augmented reality portal has three modes: for teachers, parents, and users. It gives the ability to organize personalized content for students with special needs. We developed the augmented reality application aimed at people with cognitive disorders to enrich them with communication skills through associations understanding. Applying the augmented reality application and the portal discovers new perspectives for learning children with special needs. The AR portal creates illusion of transition to another environment. It is very important property for children with ADHD because they need in breaks at the learning process to change activity (for example, such children can interact with different 3D models in the augmented reality modes) or environment. The developed AR portal has been tested by a volunteer with ASD (male, 21 years old), who confirmed that the AR portal helps him to reduce anxiety, to feel calm down and relaxed, to switch attention from a problem situation.
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Vesselinov, Velimir, Daniel O'Malley, Luke Frash, et al. Geo Thermal Cloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1782607.

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