Relevant bibliographies by topics / Phenomena-based learning

Academic literature on the topic 'Phenomena-based learning'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Phenomena-based learning"

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李, 鹏. "Automatic Recognition Method of Precipitation Phenomena Based on Deep Learning." Journal of Sensor Technology and Application 09, no. 04 (2021): 256–62. http://dx.doi.org/10.12677/jsta.2021.94031.

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Hongyim, K., and E. Brunsell. "Identifying teacher understanding of phenomena-based learning after professional development." Journal of Physics: Conference Series 1957, no. 1 (July 1, 2021): 012039. http://dx.doi.org/10.1088/1742-6596/1957/1/012039.

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Maskey, Manil, Rahul Ramachandran, and Jeffrey Miller. "Deep learning for phenomena-based classification of Earth science images." Journal of Applied Remote Sensing 11, no. 04 (September 1, 2017): 1. http://dx.doi.org/10.1117/1.jrs.11.042608.

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Imron, Ilmawati Fahmi, and Kukuh Andri Aka. "Peningkatan Kemampuan Menganalisis Fenomena Sosial dengan Penerapan Model Problem Based Learning." PEDAGOGIA: Jurnal Pendidikan 7, no. 2 (December 13, 2018): 102. http://dx.doi.org/10.21070/pedagogia.v7i2.1569.

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Conducted on the students of UN PGRI Kediri on IPS learning, found some obstacles in the practice of IPS learning that many students who play games, then when the lecturer asked questions about social phenomena in the surrounding community, students are less responsive to the problems that occur today. In learning the students do a little questioning about social phenomena, they just sit, shut up, listen and many are out of the classroom. Students' learning outcomes about social phenomena are also less satisfactory. Seen from some grades of student assignment, still at 52% or below KKM (70%). Observing the constraints, the researchers identified that the ability to analyze the social phenomenon of students on IPS learning is low, because the use of learning models that are less in accordance with the study materials of social phenomena, so that students are tired in learning and find difficulties in understanding the content and less able to analyze the causes, further impacts, and solutions of social phenomena presented by lecturers. Based on the above, the researcher intends to conduct classroom action research to improve the ability to analyze social phenomena by applying Problem Based Learning model (PBL)
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Cunningham, Billie M. "Using Action Research to Improve Learning and the Classroom Learning Environment." Issues in Accounting Education 23, no. 1 (February 1, 2008): 1–30. http://dx.doi.org/10.2308/iace.2008.23.1.1.

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To a large extent, research in business and the social sciences is based on theoretical constructs about existing organizations, phenomena, or behavior, followed by tests of hypotheses derived from these constructs. The goal usually is to describe or explain the organizations, phenomena, or behavior being studied and/or to generalize the findings to future organizations, phenomena, or behavior. Conversely, the goal of action research is to effect a desirable change within a specific social setting—one in which the researcher is an active participant. It is a value-driven, cyclical, and transformative process that uses intervention in a setting, based on observation and theoretical constructs, to alleviate an observed problem or to increase the effectiveness of a practice in the setting. This paper describes action research and provides an example of how faculty can use it to help them diminish observed classroom problems or increase the effectiveness of their classroom strategies.
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Haryadi, Rudi, and Heni Pujiastuti. "Discovery Learning based on Natural Phenomena to Improve Students' Science Process Skills." Jurnal Penelitian & Pengembangan Pendidikan Fisika 5, no. 2 (December 28, 2019): 183–92. http://dx.doi.org/10.21009/1.05214.

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The purpose of this research is to find out the science process skills of students through Discovery learning. The process of science is essential for students to have because it is the process of forming science. Science process skills can accustom students to learning through scientific work processes and systematic work. The research method uses a quasi-experiment with one group pretest-posttest design. This study does not use a comparison class, because it already uses a preliminary test so that the magnitude of the effect or effect of using discovery learning can be known with certainty. In this study, research subjects were first given a pre-test (pretest) to find out the extent of the students' initial abilities before being given physics learning by using discovery learning. After the initial test is given, then the student is given treatment, namely learning physics by using discovery learning. After completing physics learning with discovery learning, then all students are given a final test (posttest) to determine the extent of the effect of physics learning by using discovery learning on students' science process skills. The instrument used in this study was a science process skills test on the material elasticity and Hooke law, in the form of 20 multiple-choice tests and tested on 30 students in Prisma City High School. Each question evaluates aspects of conclusions, observations, identification, predictions, and interpretation of data. The results of the analysis show an increase in observational indicators from 39.95 to 66.62, concluding from 53.86 to 69.4, identifying from 36.6 to 73.3, predicting from 39.43 to 68.31 and interpreting from 73, 3 to 98.3. Based on the results of the analysis, the learning of physics-based on natural phenomena through discovery learning models can improve students' science process skills.
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Castro Garcia, Abraham, Cheng Shuo, and Jeffrey S. Cross. "Machine learning based analysis of reaction phenomena in catalytic lignin depolymerization." Bioresource Technology 345 (February 2022): 126503. http://dx.doi.org/10.1016/j.biortech.2021.126503.

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Ningrum, Epon. "LEARNING MODEL BASED ON GEOSFER PHENOMENA FOR UNDERSTANDING THE DISASTER CONCEPT." Jurnal Geografi Gea 17, no. 1 (June 14, 2017): 38. http://dx.doi.org/10.17509/gea.v17i1.5995.

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Balykbaeva, G. T., A. S. Tapalova, G. M. Abyzbekova, Sh O. Espenbetova, and K. Sh Arynova. "INORGANIC CHEMISTRY PROBLEM-BASED LEARNING." Bulletin of the Korkyt Ata Kyzylorda University 58, no. 3 (2021): 63–73. http://dx.doi.org/10.52081/bkaku.2021.v58.i3.072.

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The resolution of problem situations under the teacher’s guidance makes students compare, generalize, analyze phenomena, and not only memorize them mechanically. The processes of advancing and resolving problem situations are an unbroken chain, since when a problem is advanced, its solution begins simultaneously, which leads to the formulation of new problems. That is, a contradictory and continuous process of new scientific concepts active cognition is carried out. We see from the experience that using the methods of problem-based learning in the lessons that they promote development of cognitive activity, creative students’ independence, the formation of their worldview, intellectual development, and as a result, the improvement of the knowledge’s quality. Today, it is necessary when learning future specialists, in addition to the implementation of existing educational state standards in this specialty, to focus on the development of their creative qualities, creative thinking, which, ultimately, will promote to the formation of highly professional competent personnel.
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Kaldybaeva, Aichuruk, and Gulnur Dzhumagulova. "PROBLEM LEARNING AS AN ACTIVE LEARNING METHOD." Alatoo Academic Studies 20, no. 1 (January 30, 2020): 18–24. http://dx.doi.org/10.17015/aas.2020.201.02.

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Active learning technologies include problem-based learning. It is based on the solution of a problem or task. In a broad sense, a problem is a complex theoretical and practical issue that requires study and resolution; in science, it is a contradictory situation that appears as opposite positions in the explanation of any phenomena, objects, or processes and requires an adequate theory to resolve it. The advantages of problem learning are, first of all, great opportunities for the development of attention, observation, activation of thinking, activation of cognitive activity of students; it develops independence, responsibility, criticism and self-criticism, non-standard thinking.
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Dissertations / Theses on the topic "Phenomena-based learning"

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Boutkhamouine, Brahim. "Stochastic modelling of flood phenomena based on the combination of mechanist and systemic approaches." Thesis, Toulouse, INPT, 2018. http://www.theses.fr/2018INPT0142/document.

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Les systèmes de prévision des crues décrivent les transformations pluie-débit en se basant sur des représentations simplifiées. Ces représentations modélisent les processus physiques impliqués avec des descriptions empiriques, ou basées sur des équations de la mécanique classique. Les performances des modèles actuels de prévision des crues sont affectées par différentes incertitudes liées aux approximations et aux paramètres du modèle, aux données d’entrée et aux conditions initiales du bassin versant. La connaissance de ces incertitudes permet aux décideurs de mieux interpréter les prévisions et constitue une aide à la décision lors de la gestion de crue. L’analyse d’incertitudes dans les modèles hydrologiques existants repose le plus souvent sur des simulations de Monte-Carlo (MC). La mise en œuvre de ce type de techniques requiert un grand nombre de simulations et donc un temps de calcul potentiellement important. L'estimation des incertitudes liées à la modélisation hydrologique en temps réel reste donc une gageure. Dans ce projet de thèse, nous développons une méthodologie de prévision des crues basée sur les réseaux Bayésiens (RB). Les RBs sont des graphes acycliques dans lesquels les nœuds correspondent aux variables caractéristiques du système modélisé et les arcs représentent les dépendances probabilistes entre ces variables. La méthodologie présentée propose de construire les RBs à partir des principaux facteurs hydrologiques contrôlant la génération des crues, en utilisant à la fois les observations disponibles de la réponse du système et les équations déterministes décrivant les processus concernés. Elle est conçue pour prendre en compte la variabilité temporelle des différentes variables impliquées. Les dépendances probabilistes entre les variables (paramètres) peuvent être spécifiées en utilisant des données observées, des modèles déterministes existants ou des avis d’experts. Grâce à leurs algorithmes d’inférence, les RBs sont capables de propager rapidement, à travers le graphe, différentes sources d'incertitudes pour estimer leurs effets sur la sortie du modèle (ex. débit d'une rivière). Plusieurs cas d’études sont testés. Le premier cas d’étude concerne le bassin versant du Salat au sud-ouest de la France : un RB est utilisé pour simuler le débit de la rivière à une station donnée à partir des observations de 3 stations hydrométriques localisées en amont. Le modèle présente de bonnes performances pour l'estimation du débit à l’exutoire. Utilisé comme méthode inverse, le modèle affiche également de bons résultats quant à la caractérisation de débits d’une station en amont par propagation d’observations de débit sur des stations en aval. Le deuxième cas d’étude concerne le bassin versant de la Sagelva situé en Norvège, pour lequel un RB est utilisé afin de modéliser l'évolution du contenu en eau de la neige en fonction des données météorologiques disponibles. Les performances du modèle sont conditionnées par les données d’apprentissage utilisées pour spécifier les paramètres du modèle. En l'absence de données d'observation pertinentes pour l’apprentissage, une méthodologie est proposée et testée pour estimer les paramètres du RB à partir d’un modèle déterministe. Le RB résultant peut être utilisé pour effectuer des analyses d’incertitudes sans recours aux simulations de Monte-Carlo. Au regard des résultats enregistrés sur les différents cas d’études, les RBs se révèlent utiles et performants pour une utilisation en support d’un processus d'aide à la décision dans le cadre de la gestion du risque de crue
Flood forecasting describes the rainfall-runoff transformation using simplified representations. These representations are based on either empirical descriptions, or on equations of classical mechanics of the involved physical processes. The performances of the existing flood predictions are affected by several sources of uncertainties coming not only from the approximations involved but also from imperfect knowledge of input data, initial conditions of the river basin, and model parameters. Quantifying these uncertainties enables the decision maker to better interpret the predictions and constitute a valuable decision-making tool for flood risk management. Uncertainty analysis on existing rainfall-runoff models are often performed using Monte Carlo (MC)- simulations. The implementation of this type of techniques requires a large number of simulations and consequently a potentially important calculation time. Therefore, quantifying uncertainties of real-time hydrological models is challenging. In this project, we develop a methodology for flood prediction based on Bayesian networks (BNs). BNs are directed acyclic graphs where the nodes correspond to the variables characterizing the modelled system and the arcs represent the probabilistic dependencies between these variables. The presented methodology suggests to build the RBs from the main hydrological factors controlling the flood generation, using both the available observations of the system response and the deterministic equations describing the processes involved. It is, thus, designed to take into account the time variability of different involved variables. The conditional probability tables (parameters), can be specified using observed data, existing hydrological models or expert opinion. Thanks to their inference algorithms, BN are able to rapidly propagate, through the graph, different sources of uncertainty in order to estimate their effect on the model output (e.g. riverflow). Several case studies are tested. The first case study is the Salat river basin, located in the south-west of France, where a BN is used to simulate the discharge at a given station from the streamflow observations at 3 hydrometric stations located upstream. The model showed good performances estimating the discharge at the outlet. Used in a reverse way, the model showed also satisfactory results when characterising the discharges at an upstream station by propagating back discharge observations of some downstream stations. The second case study is the Sagelva basin, located in Norway, where a BN is used to simulate the accumulation of snow water equivalent (SWE) given available weather data observations. The performances of the model are affected by the learning dataset used to train the BN parameters. In the absence of relevant observation data for learning, a methodology for learning the BN-parameters from deterministic models is proposed and tested. The resulted BN can be used to perform uncertainty analysis without any MC-simulations to be performed in real-time. From these case studies, it appears that BNs are a relevant decisionsupport tool for flood risk management
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(8848631), Nadra M. Guizani. "Prediction of disease spread phenomena in large dynamic topology with application to malware detection in ad hoc networks." Thesis, 2020.

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Prediction techniques based on data are applied in a broad range of applications such as bioinformatics, disease spread, and mobile intrusion detection, just to name a few. With the rapid emergence of on-line technologies numerous techniques for collecting and storing data for prediction-based analysis have been proposed in the literature. With the growing size of global population, the spread of epidemics is increasing at an alarming rate. Consequently, public and private health care officials are in a dire need of developing technological solutions for managing epidemics. Most of the existing syndromic surveillance and disease detection systems deal with a small portion of a real dataset. From the communication network perspective, the results reported in the literature generally deal with commonly known network topologies. Scalability of a disease detection system is a real challenge when it comes to modeling and predicting disease spread across a large population or large scale networks. In this dissertation, we address this challenge by proposing a hierarchical aggregation approach that classifies a dynamic disease spread phenomena at different scalability levels. Specifically, we present a finite state model (SEIR-FSM) for predicting disease spread, the model manifests itself into three different levels of data aggregation and accordingly makes prediction of disease spread at various scales. We present experimental results of this model for different disease spread behaviors on all levels of granularity. Subsequently, we present a mechanism for mapping the population interaction network model to a wireless mobile network topology. The objective is to analyze the phenomena of malware spread based on vulnerabilities. The goal is to develop and evaluate a wireless mobile intrusion detection system that uses a Hidden Markov model in connection with the FSM disease spread model (HMM-FSM). Subsequently, we propose a software-based architecture that acts as a network function virtualization (NFV) to combat malware spread in IoT based networks. Taking advantage of the NFV infrastructure's potential to provide new security solutions for IoT environments to combat malware attacks. We propose a scalable and generalized IDS that uses a Recurrent Neural Network Long Short Term Memory (RNN-LSTM) learning model for predicting malware attacks in a timely manner for the NFV to deploy the appropriate countermeasures. The analysis utilizes the susceptible (S), exposed (E), infected (I), and resistant (R) (SEIR) model to capture the dynamics of the spread of the malware attack and subsequently provide a patching mechanism for the network. Our analysis focuses primarily on the feasibility and the performance evaluation of the NFV RNN-LSTM proposed model.
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Books on the topic "Phenomena-based learning"

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Delogu, Cristina, ed. Tecnologia per il web learning. Florence: Firenze University Press, 2008. http://dx.doi.org/10.36253/978-88-8453-571-9.

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This book maps out a course through the methodological and technological innovations of internet-based training, setting the emphasis on the collaborative character of experiences of learning and on the interactivity of the virtual workshops. On the one hand, this underscores the possibilities offered by the net to make available educational modes centred on the social process that enables learning in an active manner, rather than on the centrality of contents to be passively transferred to the students. On the other hand, it also shows how in the virtual workshops it is possible to develop one's understanding of the phenomena that are the subject of learning as a result of the interaction with the phenomena themselves, reproduced in the computer, acting upon them and observing the consequences of one's own actions. The effect is to underline how this type of model of learning can help to overcome the technology gap between different countries and social groups (the digital divide) and also to make learning more accessible even to disabled students.
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Dietetic and Nutrition Case Studies. Wiley & Sons, Limited, John, 2016.

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Douglas, Pauline, Judy Lawrence, and Joan Gandy. Dietetic and Nutrition Case Studies. Wiley & Sons, Incorporated, John, 2016.

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Douglas, Pauline, Judy Lawrence, and Joan Gandy. Dietetic and Nutrition Case Studies. Wiley & Sons, Incorporated, John, 2016.

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Dietetic and Nutrition Case Studies. Chichester, West Sussex: John Wiley & Sons, 2016.

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Iori, Giulia, and James Porter. Agent-based Modeling for Financial Markets. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.43.

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This chapter discusses a step in the evolution of agent-based model (ABM) research in finance. Agent-based modeling has concentrated on the development of stylized market models, which have been extremely useful for understanding how complex macro-scale phenomena emerge from micro-rules. In order to further develop ABMs from proof of concept into robust tools for policy makers, to control and forecast complex real-world financial markets, it is essential to permit agents to behave as active data-gathering decision makers with sophisticated learning capabilities. The main focus of this chapter is to show how agent based models (ABMs) in financial markets have evolved from simple zero- intelligence agents that follow arbitrary rules of thumb into sophisticated agents described by microfounded rules of behavior. The chapter then briefly looks at the challenges posed by and approaches to model calibration and provides examples of how ABMs have been successful at offering useful insights for policy making.
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Head, Paul D. The Choral Experience. Edited by Frank Abrahams and Paul D. Head. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199373369.013.3.

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Much has changed in the choral rehearsal room over the past two generations, particularly in regard to the role the choral conductor assumes—or commands—in the rehearsal process. This chapter discusses the ever-evolving stereotypical roles of the conductor, while examining alternatives to traditional leadership models with particular emphasis on the encouragement of student engagement and peer-based learning. In addition to the facilitation of collaborative learning exercises, the chapter outlines a specific process of written interaction with the choral ensemble. This section is inspired by the renowned “Dear People” letters of Robert Shaw. Finally, in response to the recently revised National Standards for Music Education in the United States, the author discusses possible implementation of the Standards in a performance-based classroom. In the shadow of the relatively recent phenomena of collegiate a cappella groups, these student ensembles have created a new paradigm for peer-led instruction.
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Steels, Luc. Fluid Construction Grammar. Edited by Thomas Hoffmann and Graeme Trousdale. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780195396683.013.0009.

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This chapter focuses on Fluid Construction Grammar (FCG), a formalism that allows Construction Grammar researchers to formulate their findings in a precise manner and to test the implications of their theories for language parsing, production, and learning. It explains that FCG is not intended to displace other linguistic proposals for Construction Grammar but to be an open instrument which can be used by construction grammarians who want to formulate their intuitions and analyses in a precise way and who want to test the implications of their grammar designs for language parsing, production, and learning. The chapter furthermore shows that the construction-based approach is also relevant for the investigation of language processing, and discusses the methods and techniques adopted for the implementation of complex linguistic phenomena.
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Book chapters on the topic "Phenomena-based learning"

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Stankova, Elena N., Irina A. Grechko, Yana N. Kachalkina, and Evgeny V. Khvatkov. "Hybrid Approach Combining Model-Based Method with the Technology of Machine Learning for Forecasting of Dangerous Weather Phenomena." In Computational Science and Its Applications – ICCSA 2017, 495–504. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62404-4_37.

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Crooks, Andrew, Alison Heppenstall, Nick Malleson, and Ed Manley. "Agent-Based Modeling and the City: A Gallery of Applications." In Urban Informatics, 885–910. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_46.

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AbstractAgent-based modeling is a powerful simulation technique that allows one to build artificial worlds and populate these worlds with individual agents. Each agent or actor has unique behaviors and rules which govern their interactions with each other and their environment. It is through these interactions that more macro-phenomena emerge: for example, how individual pedestrians lead to the emergence of crowds. Over the past two decades, with the growth of computational power and data, agent-based models have evolved into one of the main paradigms for urban modeling and for understanding the various processes which shape our cities. Agent-based models have been developed to explore a vast range of urban phenomena from that of micro-movement of pedestrians over seconds to that of urban growth over decades and many other issues in between. In this chapter, we introduce readers to agent-based modeling from simple abstract applications to those representing space utilizing geographical data not only for the creation of the artificial worlds but also for the validation and calibration of such models through a series of example applications. We will then discuss how big data, data mining, and machine learning techniques are advancing the field of agent-based modeling and demonstrate how such data and techniques can be leveraged into these models, giving us a new way to explore cities.
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Rüttgers, Mario, Seong-Ryong Koh, Jenia Jitsev, Wolfgang Schröder, and Andreas Lintermann. "Prediction of Acoustic Fields Using a Lattice-Boltzmann Method and Deep Learning." In Lecture Notes in Computer Science, 81–101. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59851-8_6.

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Abstract Using traditional computational fluid dynamics and aeroacoustics methods, the accurate simulation of aeroacoustic sources requires high compute resources to resolve all necessary physical phenomena. In contrast, once trained, artificial neural networks such as deep encoder-decoder convolutional networks allow to predict aeroacoustics at lower cost and, depending on the quality of the employed network, also at high accuracy. The architecture for such a neural network is developed to predict the sound pressure level in a 2D square domain. It is trained by numerical results from up to 20,000 GPU-based lattice-Boltzmann simulations that include randomly distributed rectangular and circular objects, and monopole sources. Types of boundary conditions, the monopole locations, and cell distances for objects and monopoles serve as input to the network. Parameters are studied to tune the predictions and to increase their accuracy. The complexity of the setup is successively increased along three cases and the impact of the number of feature maps, the type of loss function, and the number of training data on the prediction accuracy is investigated. An optimal choice of the parameters leads to network-predicted results that are in good agreement with the simulated findings. This is corroborated by negligible differences of the sound pressure level between the simulated and the network-predicted results along characteristic lines and by small mean errors.
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Pramling Samuelsson, Ingrid. "A Retrospective View on Researchers’ and Preschool Teachers’ Collaboration: The Case of Developing Children’s Learning in Preschool." In Methodology for Research with Early Childhood Education and Care Professionals, 21–32. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14583-4_2.

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AbstractThis chapter reports on research that is foundational to much of the work carried out by the members of the network responsible for this volume (Wallerstedt, Brooks, Ødegaard & Pramling, this volume). The aim of the two studies I will discuss here was to determine whether a metacognitive approach to children’s learning supported children’s sensemaking in preschool. Four preschools were followed, of which two received feedback on their metacognitive dialogues with children from the researcher and two were followed with no feedback, serving as the comparison group. A replication with more groups and teachers was later conducted, with similar results. The development approach consisted of teachers and researchers meeting regularly to jointly discuss the approach to teaching and the content to work with. The content was based on earlier research on how children make sense of different phenomena and content areas. The researchers visited the participating preschools and video-recorded when the teachers carried out activities with children. Afterwards, the recordings were discussed with the teachers. The participants also met once a month to discuss central questions. What development research means in this case will be discussed, as will what contributions the studies made to research (theory) and the development of pedagogy (preschool). There is also a parallel process between teachers and children that will be highlighted. Perhaps one can see this kind of developmental study as the first step towards praxis-oriented research.
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Malavena, Gerardo. "Modeling of GIDL–Assisted Erase in 3–D NAND Flash Memory Arrays and Its Employment in NOR Flash–Based Spiking Neural Networks." In Special Topics in Information Technology, 43–53. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85918-3_4.

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AbstractSince the very first introduction of three-dimensional (3–D) vertical-channel (VC) NAND Flash memory arrays, gate-induced drain leakage (GIDL) current has been suggested as a solution to increase the string channel potential to trigger the erase operation. Thanks to that erase scheme, the memory array can be built directly on the top of a $$n^+$$ n + plate, without requiring any p-doped region to contact the string channel and therefore allowing to simplify the manufacturing process and increase the array integration density. For those reasons, the understanding of the physical phenomena occurring in the string when GIDL is triggered is important for the proper design of the cell structure and of the voltage waveforms adopted during erase. Even though a detailed comprehension of the GIDL phenomenology can be achieved by means of technology computer-aided design (TCAD) simulations, they are usually time and resource consuming, especially when realistic string structures with many word-lines (WLs) are considered. In this chapter, an analysis of the GIDL-assisted erase in 3–D VC nand memory arrays is presented. First, the evolution of the string potential and GIDL current during erase is investigated by means of TCAD simulations; then, a compact model able to reproduce both the string dynamics and the threshold voltage transients with reduced computational effort is presented. The developed compact model is proven to be a valuable tool for the optimization of the array performance during erase assisted by GIDL. Then, the idea of taking advantage of GIDL for the erase operation is exported to the context of spiking neural networks (SNNs) based on NOR Flash memory arrays, which require operational schemes that allow single-cell selectivity during both cell program and cell erase. To overcome the block erase typical of nor Flash memory arrays based on Fowler-Nordheim tunneling, a new erase scheme that triggers GIDL in the NOR Flash cell and exploits hot-hole injection (HHI) at its drain side to accomplish the erase operation is presented. Using that scheme, spike-timing dependent plasticity (STDP) is implemented in a mainstream NOR Flash array and array learning is successfully demonstrated in a prototype SNN. The achieved results represent an important step for the development of large-scale neuromorphic systems based on mature and reliable memory technologies.
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Durak, Benzegül, and Mustafa Sami Topçu. "Socio-Scientific Issues and Model-Based Learning." In Socioscientific Issues-Based Instruction for Scientific Literacy Development, 279–97. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4558-4.ch010.

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This chapter aims to provide a literature analysis on socio-scientific issues and model-based learning. The position of socio-scientific issues in the process of raising science literate students is indisputable. On the other hand, modeling gives students opportunities to construct their own models and use them through the learning process to formulate hypothesis, make investigations, explain scientific phenomena, and communicate and justify their ideas. Therefore, embedding modeling practice to SSI-based instruction through a framework is an innovative tool for scientific literacy in science education.
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"Learning Computational Thinking in Phenomena-Based Co-creation Projects: Perspectives from Finland." In Computational Thinking Education in K–12. The MIT Press, 2022. http://dx.doi.org/10.7551/mitpress/13375.003.0008.

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Rieber, Lloyd P. "Supporting Discovery-Based Learning within Simulations." In Cognitive Effects of Multimedia Learning, 217–36. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-158-2.ch012.

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This chapter presents a review of research on the use and role of interactive simulations for learning. Contemporary theories of learning, instruction, and media, suggest that learning involves a complex relationship and dependency between a learner’s prior knowledge, a learner’s motivation, the context, the task, and the resources (e.g., simulations) provided to, and used by the learner to support or enable the task. Given this perspective, and data from an evolving research program, simulations are best used to help learners construct knowledge and make meaning by giving them control over phenomena modeled by the simulation. Several theoretical frameworks have guided this research program: dual coding theory, mental models, and constructivist learning theory. An overall result of this research is that learning should be based on experience, such as that derived from interacting with a simulation, and supported with explanations. This is counter to traditional educational wisdom where explanations rule instructional strategies.
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Isaacs, Steven R., Erik Leitner, Laylah Bulman, Rick Marlatt, and Miles M. Harvey. "The Role of Minecraft Build Challenges in Esports." In Advances in Game-Based Learning, 85–102. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7069-2.ch006.

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In this case study, a team of educators explored the power of Minecraft Education so that students could advance their learning in core academic subjects. This study examined what happened when students utilized Minecraft Education challenges and scholastic esports in a classroom, across a school district, and around the world. The authors share a variety of challenges that demonstrate the power of Minecraft esports as a powerful pedagogical strategy for engaging students and building an interest in STEM-based initiatives that align with the National Council of Teachers of English and the Partnership for 21st Century Skills. As Steve, Erik, and Laylah worked to create challenges for students to compete in Minecraft, Rick and Miles examined the participation of each event, examined the quotes from students, and analyzed the data for clues into what phenomena or processes occurred as students navigated Minecraft challenges. This study examined the evolution of competitive Minecraft challenges during its early phases of integration.
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Adah Miller, E. C., T. Li, I. C. Chen, and S. K. Codere. "Using flexible thinking to assess student sensemaking of phenomena in project-based learning." In International Encyclopedia of Education(Fourth Edition), 444–57. Elsevier, 2023. http://dx.doi.org/10.1016/b978-0-12-818630-5.13047-7.

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Conference papers on the topic "Phenomena-based learning"

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Putri, Anissa Rakhma, and Lia Yuliati. "Analysis of conceptual changes of static fluid topic through authentic learning based on phenomena." In THE 3RD INTERNATIONAL CONFERENCE ON MATHEMATICS AND SCIENCE EDUCATION (ICOMSE) 2019: Strengthening Mathematics and Science Education Research for the Challenge of Global Society. AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0001556.

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Stanescu, Denis, Angela Digulescu, Cornel Ioana, and Alexandru Serbanescu. "Transient power grid phenomena classification based on phase diagram features and machine learning classifiers." In 2022 30th European Signal Processing Conference (EUSIPCO). IEEE, 2022. http://dx.doi.org/10.23919/eusipco55093.2022.9909687.

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Prasianakis, Nikolaos. "Towards Digital Twins: Machine Learning Based Process Coupling and Multiscale Modelling of Reactive Transport Phenomena." In Goldschmidt2020. Geochemical Society, 2020. http://dx.doi.org/10.46427/gold2020.2116.

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Zheng, Wei, Yong Lei, and Qing Chang. "Reinforcement Learning Based Real-Time Control Policy for Two-Machine-One-Buffer Production System." In ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/msec2017-2771.

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It is attractive to reduce the total cost of a manufacture system with real-time control of the production. The total cost mainly consists of the production cost, the penalty of the permanent production loss, and the Work-In-Process (WIP) inventory level cost. However, it is difficult to derive an analytical model of manufacture system due to the complexity of starved and blocked phenomena, the random failure and maintenance processes. Therefore, finding a real-time control policy for the manufacture system without exact analytical model is dearly needed. In this paper, a novel reinforcement learning based control decision policy is proposed based on the action of switching the machines on or off at the start of each time slot. Firstly, a simulation model is developed with MTBF and MTTR evaluated from the history data to collect samples. Then, a reinforcement learning method, specifically, Least-Square-Policy-Iteration method, is applied to obtain a sub-optimal policy. The simulation results show that the proposed method performs well in reducing the total cost.
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Zilletti, Michele, and Ermanno Fosco. "Damper Model Identification Using Hybrid Physical and Machine Learning Based Approach." In Vertical Flight Society 78th Annual Forum & Technology Display. The Vertical Flight Society, 2022. http://dx.doi.org/10.4050/f-0078-2022-17523.

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In this paper the identification of a time domain model of a helicopter main rotor lead-lag damper is discussed. Previous studies have shown that lead-lag dampers have a significant contribution to the overall aircraft dynamics, therefore an accurate damper model is essential to predict complex phenomena such as, instabilities, limit cycles, etc. Due to the inherently nonlinear dynamics and the complex internal architecture of these components, the model identification can be a challenging task. In this paper, a hybrid physical/machine learning based approach, has been used to identify a damper model based on experimental test data. The model, called grey box, consists of a combination of a white box, i.e. a physical model described by differential equations and a black box, i.e. regression numerical model. The white box approximates the core physical behaviour of the damper while the black box improves the overall accuracy by capturing the complex dynamic not included in the white box. The paper shows that, at room temperature, the grey box is able to predict the damper force when either a multi-frequency harmonic or a random input displacement is imposed. The model is validated up to 20 Hz and for the entire damper dynamic stroke.
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Mohd Razak, Syamil, Jodel Cornelio, Atefeh Jahandideh, Behnam Jafarpour, Young Cho, Hui-Hai Liu, and Ravimadhav Vaidya. "Integrating Deep Learning and Physics-Based Models for Improved Production Prediction in Unconventional Reservoirs." In SPE Middle East Oil & Gas Show and Conference. SPE, 2021. http://dx.doi.org/10.2118/204864-ms.

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Abstract The physics of fluid flow and transport processes in hydraulically fractured unconventional reservoirs are not well understood. As a result, the predicted production behavior using conventional simulation often does not agree with the observed field performance data. The discrepancy is caused by potential errors in the simulation model and the physical processes that take place in complex fractured rocks subjected to hydraulic fracturing. Additionally, other field data such as well logs and drilling parameters containing important information about reservoir condition and reservoir characteristics are not conveniently integrated into existing simulation models. In this paper, we discuss the development of a deep learning model to learn the errors in simulation-based performance prediction in unconventional reservoirs. Once trained, the model is expected to forecast the performance response of a well by augmenting physics-based predictions with the learned prediction errors from the deep learning model. To learn the discrepancy between simulated and observed production data, a simulation dataset is generated by using formation, completion, and fluid properties as input to an imperfect physics-based simulation model. The difference between the resulting simulated responses and observed field data, together with collected field data (i.e. well logs, drilling parameters), is then used to train a deep learning model to learn the prediction errors of the imperfect physical model. Deep convolutional autoencoder architectures are used to map the simulated and observed production responses into a low-dimensional manifold, where a regression model is trained to learn the mapping between collected field data and the simulated data in the latent space. The proposed method leverages deep learning models to account for prediction errors originating from potentially missing physical phenomena, simulation inputs, and reservoir description. We illustrate our approach using a case study from the Bakken Play in North Dakota.
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Aristizabal, Jaime, Carlos Motta, Nelson Obregon, Carlos Capachero, Leonardo Real, and Julian Chaves. "Supervised Learning Algorithms Applied in the Zoning of Susceptibility by Hydroclimatological Geohazards." In ASME-ARPEL 2021 International Pipeline Geotechnical Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/ipg2021-65003.

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Abstract Cenit Transporte y Logística de Hidrocarburos (CENIT), operator of about 7000 km of hydrocarbon transport systems, which constitutes it the largest operator in Colombia, has developed a strategic alliance to structure an adaptive geotechnical susceptibility zoning using supervised learning algorithms. Through this exercise, has been implemented operational decision inferences with simple linguistic values. The difficulties proposed by the method considers the hydroclimatology of Colombia, which is conditioned by several phenomena of Climate Variability that affect the atmosphere at different scales such as the Oscillation of the Intertropical Convergence Zone - ITCZ (seasonal scale) and the occurrence of macroclimatic phenomena such as El Niño-La Niña Southern Oscillation (ENSO) (interannual scale). Likewise, it considers the geotechnical complexity derived from the different geological formation environments, the extension and geographical dispersion of the infrastructure, and its interaction with the climatic regimes, to differentiate areas of interest based on the geohazards of hydrometeorological origin, when grouped into five clusters. The results of this exercise stand out the importance of keep a robust record of the events that affect the infrastructure of hydrocarbon transportation systems and using data-guided intelligence techniques to improve the tools that support decision-making in asset management.
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Niu, Xueyan, Xiaoyun Li, and Ping Li. "Learning Cluster Causal Diagrams: An Information-Theoretic Approach." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/675.

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Many real-world phenomena arise from causal relationships among a set of variables. As a powerful tool, Bayesian Network (BN) has been successful in describing high-dimensional distributions. However, the faithfulness condition, enforced in most BN learning algorithms, is violated in the settings where multiple variables synergistically affect the outcome (i.e., with polyadic dependencies). Building upon recent development in cluster causal diagrams (C-DAGs), we initiate the formal study of learning C-DAGs from observational data to relax the faithfulness condition. We propose a new scoring function, the Clustering Information Criterion (CIC), based on information-theoretic measures that represent various complex interactions among variables. The CIC score also contains a penalization of the model complexity under the minimum description length principle. We further provide a searching strategy to learn structures of high scores. Experiments on both synthetic and real data support the effectiveness of the proposed method.
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Kang, Hyun-Su, and Youn-Jea Kim. "Machine Learning-Based Multi-Disciplinary Optimization of Transonic Axial Compressor Blade Considering Aeroelasticity." In ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/gt2022-80876.

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Abstract The optimal design of the turbomachinery field can produce the best results when considering multiple fields (e.g., structural vibration, aerodynamic, and aeroelasticity). Since multi-disciplinary phenomena are very complex and nonlinear, a higher level of optimization is needed. In this study, the transonic axial compressor blade was optimized using machine learning-based multi-disciplinary optimization techniques. The entire simulation includes important three factors to consider when designing the axial compressor blade: vibration, aerodynamic performance, and aeroelasticity. In the first step of the optimization, six variables that make up the blade design were used in a design of experiments to explore the design space. Frequency, efficiency, pressure ratio, and aerodynamic damping ratio were used as the output parameter used in the optimal design. The first to third blade modes frequency from the modal analysis were used for parameters of vibration fields. The efficiency and pressure ratio are values that can be obtained from steady CFD calculations for evaluating aerodynamic performance. The aerodynamic damping coefficient was obtained from transient CFD, which is widely used to determine the presence or absence of fluid-induced vibration and flutter. As a result of machine learning-based optimization, the optimized blade shape has a lower risk of blade resonance. Also, the aerodynamic performance of the axial compressor has been improved compared to the reference model. In addition, the aerodynamic damping coefficient, an important indicator in terms of aeroelasticity, rose by up to 15%, completing optimization considering the aeroelastic effect.
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Alonzo, Alice. "Learning progressions as models and tools for supporting classroom assessment." In Research Conference 2021: Excellent progress for every student. Australian Council for Educational Research, 2021. http://dx.doi.org/10.37517/978-1-74286-638-3_5.

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Like all models, learning progressions (LPs) provide simplified representations of complex phenomena. One key simplification is the characterisation of student thinking in terms of levels. This characterisation is both essential for large-scale applications, such as informing standards, but potentially problematic for smaller-scale applications. In this paper, I describe a program of research designed to explore the smaller-scale use of LPs as supports for teacher classroom assessment practices in light of this simplification. Based on this research, I conclude that LP levels may serve as a generative heuristic, particularly when teachers are engaged with evidence of the limitations of LP levels and supported to use LPs in ways that do not rely on their levels.
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Reports on the topic "Phenomena-based learning"

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McGee, Steven, Randi McGee-Tekula, and Jennifer Duck. Does a Focus on Modeling and Explanation of Molecular Interactions Impact Student Learning and Identity? The Learning Partnership, April 2017. http://dx.doi.org/10.51420/conf.2017.1.

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The Interactions curriculum and professional development program is designed to support high school teachers in their transition to the physical science Next Generation Science Standards. Through curriculum materials, an online portal for delivering the digital materials, interactive models of molecular phenomena, and educative teacher guide, teachers are able to support students in bridging the gap between macroscopic and sub-microscopic ideas in physical science by focusing on a modeling and explanation-oriented exploration of attractions and energy changes at the atomic level. During the fall semester of the 2015-16 school year, The Learning Partnership conducted a field test of Interactions with eleven teachers who implemented the curriculum across a diverse set of school districts. As part of the field test, The Learning Partnership examined the impact of teachers’ inquiry-based teaching practices on student learning and identification with the scientific enterprise. The results indicate that students had statistically significant growth in learning from the beginning to end of unit 2 and that the extent to which teachers engaged students in inquiry had a positive statistically significant influence on the growth rate and a statistically significant indirect impact on students’ identification with the scientific enterprise.
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Shamonia, Volodymyr H., Olena V. Semenikhina, Volodymyr V. Proshkin, Olha V. Lebid, Serhii Ya Kharchenko, and Oksana S. Lytvyn. Using the Proteus virtual environment to train future IT professionals. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3760.

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Based on literature review it was established that the use of augmented reality as an innovative technology of student training occurs in following directions: 3D image rendering; recognition and marking of real objects; interaction of a virtual object with a person in real time. The main advantages of using AR and VR in the educational process are highlighted: clarity, ability to simulate processes and phenomena, integration of educational disciplines, building an open education system, increasing motivation for learning, etc. It has been found that in the field of physical process modelling the Proteus Physics Laboratory is a popular example of augmented reality. Using the Proteus environment allows to visualize the functioning of the functional nodes of the computing system at the micro level. This is especially important for programming systems with limited resources, such as microcontrollers in the process of training future IT professionals. Experiment took place at Borys Grinchenko Kyiv University and Sumy State Pedagogical University named after A. S. Makarenko with students majoring in Computer Science (field of knowledge is Secondary Education (Informatics)). It was found that computer modelling has a positive effect on mastering the basics of microelectronics. The ways of further scientific researches for grounding, development and experimental verification of forms, methods and augmented reality, and can be used in the professional training of future IT specialists are outlined in the article.
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