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Relevant bibliographies by topics / Problem solving Computer simulation

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Academic literature on the topic 'Problem solving Computer simulation'

Author: Grafiati

Published: 11 December 2022

Last updated: 25 January 2023

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Journal articles on the topic "Problem solving Computer simulation"

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Garrett, Michael, and Mark McMahon. "Computer-Generated Three-Dimensional Training Environments." International Journal of Gaming and Computer-Mediated Simulations 2, no. 3 (July 2010): 43–60. http://dx.doi.org/10.4018/jgcms.2010070103.

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Problem-based learning is an instructional strategy that emphasises the accumulation and development of knowledge via an active and experiential based approach to solving problems. This pedagogical framework can be instantiated using gaming technology to provide learners with the ability to control their learning experience within a dynamic, responsive, and visually rich three-dimensional virtual environment. In this regard, a conceptual framework referred to as the Simulation, User, and Problem-based Learning (SUPL) approach has been developed in order to inform the design of 3D simulation environments based on gaming technology within a problem-based learning pedagogy. The SUPL approach identifies a series of design factors relative to the user, the problem-solving task, and the 3D simulation environment that guide the learning process and facilitate the transfer of knowledge. This paper will present a simulation environment design according to this conceptual framework for a problem-solving task within the context of an underground mine emergency evacuation. The problem-solving task will be designed to satisfy learning objectives that relate to the development of knowledge and skills for emergency evacuation of the Dominion Mining’s Challenger mining operation located in South Australia.

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Axelsson, O., V. A. Barker, M. Neytcheva, and B. Polman. "SOLVING THE STOKES PROBLEM ON A MASSIVELY PARALLEL COMPUTER." Mathematical Modelling and Analysis 6, no. 1 (June 30, 2001): 7–27. http://dx.doi.org/10.3846/13926292.2001.9637141.

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We describe a numerical procedure for solving the stationary two-dimensional Stokes problem based on piecewise linear finite element approximations for both velocity and pressure, a regularization technique for stability, and a defect‐correction technique for improving accuracy. Eliminating the velocity unknowns from the algebraic system yields a symmetric positive semidefinite system for pressure which is solved by an inner‐outer iteration. The outer iterations consist of the unpreconditioned conjugate gradient method. The inner iterations, each of which corresponds to solving an elliptic boundary value problem for each velocity component, are solved by the conjugate gradient method with a preconditioning based on the algebraic multi‐level iteration (AMLI) technique. The velocity is found from the computed pressure. The method is optimal in the sense that the computational work is proportional to the number of unknowns. Further, it is designed to exploit a massively parallel computer with distributed memory architecture. Numerical experiments on a Cray T3E computer illustrate the parallel performance of the method.

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Peng, Yamian, Chunfeng Liu, and Dianxuan Gong. "Numerical Simulation Techniques Research and Application in Genetic Algorithm Design." Open Mechanical Engineering Journal 8, no. 1 (March 21, 2014): 63–68. http://dx.doi.org/10.2174/1874155x01408010063.

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Numerical simulation techniques are also called computer simulation, which take the computer as a means to study all kinds of engineering and physical problems even natural objective through numerical calculation method and image display. This paper studied the numerical simulation techniques and try to solve two-dimensional convectiondiffusion equation parameter identification inverse problem by the genetic algorithm. Firstly, the finite element method was illustrated to solve the steady problem of two-dimensional convection-diffusion equation before it compute parameter identification inverse problem each time. Subsequently, it can search the best approximate solution from many initial points and obtained the global optimum solution by means of crossover operator and mutation operator. Finally, the paper discussed the computer simulation of GA for solving the inverse problem, and puts forward a new method for solving inverse problem: Genetic algorithm based on the best disturbed iteration. The results of numerical simulation show that the genetic algorithm has the higher accuracy and the quicker convergent speed. And it is easy to program and calculate and is of great application.

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Dellarosa, Denise. "A computer simulation of children’s arithmetic word-problem solving." Behavior Research Methods, Instruments, & Computers 18, no. 2 (March 1986): 147–54. http://dx.doi.org/10.3758/bf03201014.

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Shen, Xiaojian, W. L. Scheller, and R. Kenneth Wolfe. "Solving a cafeteria dish circulation problem by computer simulation." Computers & Industrial Engineering 29, no. 1-4 (September 1995): 555–59. http://dx.doi.org/10.1016/0360-8352(95)00133-l.

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Allami, Ahmed J., Maria Grazia Concilio, Pavan Lally, and Ilya Kuprov. "Quantum mechanical MRI simulations: Solving the matrix dimension problem." Science Advances 5, no. 7 (July 2019): eaaw8962. http://dx.doi.org/10.1126/sciadv.aaw8962.

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We propose a solution to the matrix dimension problem in quantum mechanical simulations of MRI (magnetic resonance imaging) experiments on complex molecules. This problem is very old; it arises when Kronecker products of spin operators and spatial dynamics generators are taken—the resulting matrices are far too large for any current or future computer. However, spin and spatial operators individually have manageable dimensions, and we note here that the action by their Kronecker products on any vector may be computed without opening those products. This eliminates large matrices from the simulation process. MRI simulations for coupled spin systems of complex metabolites in three dimensions with diffusion, flow, chemical kinetics, and quantum mechanical treatment of spin relaxation are now possible. The methods described in this paper are implemented in versions 2.4 and later of the Spinach library.

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Mumby, Stephen J. "Solving materials problems by computer simulation." Advanced Materials 9, no. 1 (January 1997): 9–11. http://dx.doi.org/10.1002/adma.19970090102.

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Woodward, John, Douglas Carnine, and Russell Gersten. "Teaching Problem Solving Through Computer Simulations." American Educational Research Journal 25, no. 1 (January 1988): 72–86. http://dx.doi.org/10.3102/00028312025001072.

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JOHNSON, TRISTAN E., and CLARK GEDNEY. "Learning Support Assessment Study of a Computer Simulation for the Development of Microbial Identification Strategies." Microbiology Education 2, no. 1 (May 2001): 18–24. http://dx.doi.org/10.1128/me.2.1.18-24.2001.

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This paper describes a study that examined how microbiology students construct knowledge of bacterial identification while using a computer simulation. The purpose of this study was to understand how the simulation affects the cognitive processing of students during thinking, problem solving, and learning about bacterial identification and to determine how the simulation facilitates the learning of a domain-specific problem-solving strategy. As part of an upper-division microbiology course, five students participated in several simulation assignments. The data were collected using think-aloud protocol and video action logs as the students used the simulation. The analysis revealed two major themes that determined the performance of the students: Simulation Usage—how the students used the software features and Problem-Solving Strategy Development—the strategy level students started with and the skill level they achieved when they completed their use of the simulation. Several conclusions emerged from the analysis of the data: (i) The simulation affects various aspects of cognitive processing by creating an environment that makes it possible to practice the application of a problem-solving strategy. The simulation was used as an environment that allowed students to practice the cognitive skills required to solve an unknown. (ii) Identibacter (the computer simulation) may be considered to be a cognitive tool to facilitate the learning of a bacterial identification problem-solving strategy. (iii) The simulation characteristics did support student learning of a problem-solving strategy. (iv) Students demonstrated problem-solving strategy development specific to bacterial identification. (v) Participants demonstrated an improved performance from their repeated use of the simulation.

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Mandinach, Ellen B., and Marcia C. Linn. "The Cognitive Effects of Computer Learning Environments." Journal of Educational Computing Research 2, no. 4 (November 1986): 411–27. http://dx.doi.org/10.2190/tvfd-b7t8-gubf-fw86.

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The potential cognitive consequences of computers in education are just starting to emerge. The power and capabilities of computers change rapidly. Researchers often working with outmoded computer learning environments have just begun to catalog and analyze the activities that occur in these situations. There is clear agreement that computers can help students learn to solve problems and that computers might help to ameliorate the dearth of problem-solving activities in classrooms. A useful way to think about the potential advantages of the computer learning environments is in terms of a chain of cognitive accomplishments culminating in problem-solving skill. Such a chain can be identified and used to assess instruction using computers for programming, simulations, and tools such as spreadsheets. The chain emphasizes the need for students to learn templates or stereotypic sequences of actions for solving problems. It also makes clear that planning and testing are central components of problem solving. The articles in this issue illustrate how far along the chain students in typical computer learning environments progress. They offer conjectures about why students fail to proceed further and suggestions about how the situation can be improved.

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Dissertations / Theses on the topic "Problem solving Computer simulation"

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Jávor, András. "Problem solving by simulation IMACS European Simulation Meeting, Esztergom, Hungary, 28-30 August 1990 /." Budapest : Scientific Society of Measurement and Automation, 1990. http://catalog.hathitrust.org/api/volumes/oclc/79077197.html.

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DeSa, Colin Joseph. "Distributed problem solving environments for scientific computing." Thesis, This resource online, 1991. http://scholar.lib.vt.edu/theses/available/etd-08042009-040307/.

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Hicks, Dixon Kendall. "Applicability of computer spreadsheet simulation for solving resource allocations problems." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from the National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA267436.

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Stilson, Frederick R. B. "Does agreeableness help a team perform a problem solving task?" [Tampa, Fla] : University of South Florida, 2005. http://purl.fcla.edu/usf/dc/et/SFE0001302.

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Tajvidi, Seyed Mohammad. "Effects of Computer Simulation and Animation (CSA) on Students’ Problem Solving in Engineering Dynamics: What and How." DigitalCommons@USU, 2017. https://digitalcommons.usu.edu/etd/5805.

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The application of Computer Simulation and Animation (CSA) in the instruction of engineering dynamics has shown a significant growth in the recent years. The two foremost methods to evaluate the effectiveness of CSA tools, including student feedback and surveys and measuring student change in performance, suggest that CSA modules improve student learning in engineering dynamics. However, neither method fully demonstrates the quality of students’ cognitive changes. This study examined the quality of effects of application of CSA modules on student learning and problem solving in particle dynamics. It also compared CSA modules with textbook-style problem-solving regarding the changes they cause in students’ cognitive process. A qualitative methodology was adopted to design and implement a study to explore the changes in participants’ learning and problem-solving behavior caused by using a CSA module. Collected data were coded and analyzed using the categories of cognitive process based on the Revised Bloom’s Taxonomy. An analysis of the results revealed that the most significant effects were observed in understanding, analyzing, and evaluating. The high frequency of “inference” behavior after working with modules indicated a significant increase in participants’ understanding activity after working with computer modules. Comparing behavior changes of computer-simulation group students with those who worked with a textbook-style example demonstrated that the CSA modules ignited more analytical behavior among students than did textbook-style examples. This study illustrated that improvement in learning due to the application of CSA is not limited to conceptual understanding; CSA modules enhance students’ skills in applying, organizing, and evaluating as well. The interactive characteristics of CSA play a major role in stimulating students’ analytical reasoning and critical thinking in engineering dynamics.

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Guo, Yongquing. "Interactive Computer Simulation and Animation Learning Modules: A Mixed-method Study of Their Effects on Students' Problem Solving in Particle Dynamics." DigitalCommons@USU, 2015. https://digitalcommons.usu.edu/etd/4493.

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Computer simulation and animation (CSA) has been receiving growing attention and wide application in the engineering education community. The goal of this dissertation research was to improve students' conceptual understanding and procedural skills for solving particle dynamics problems, by developing, implementing and assessing 12 interactive computer simulation and animation learning modules. The developed CSA learning modules integrate visualization with mathematical modeling to help students directly connect engineering dynamics with mathematics. These CSA modules provide a constructivist environment where students can study physical laws, demonstrate mental models, make predictions, derive conclusions, and solve problems. A mixed-method research was conducted in this study: quasi-experimental method (quantitative), and survey questionnaires and interviews (qualitative and quantitative). Quasi-experimental research involving an intervention group and a comparison group was performed to investigate the extent that the developed CSA learning modules improved students' conceptual understanding and procedural skills in solving particle dynamics problems. Surveys and interviews were administrated to examine students' learning attitudes toward and experiences with the developed CSA learning modules. The results of quasi-experimental research show that the 12 CSA learning modules developed for this study increased students' class-average conceptual and procedural learning gains by 29% and 40%, respectively. Therefore, these developed CSA modules significantly improved students' conceptual understanding and procedural skills for solving particle dynamics problems. The survey and interview results show that students had a positive experience with CSA learning.

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Nafkha, Amor. "A geometrical approach detector for solving the combinatorial optimisation problem: Application to wireless communication systems." Phd thesis, Université de Bretagne Sud, 2006. http://tel.archives-ouvertes.fr/tel-00106708.

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Cette thèse s'intéresse à la résolution du problème classique de décodage d'un mélange linéaire entaché d'un bruit additif gaussien. A partir d'une observation
bruitée: y = Hx+b, d'un signal x ∈ {±1}n mélangé linéairement par une matrice H connue, b étant un vecteur de bruit, on cherche le vecteur x minimisant la distance Euclidienne entre y et le vecteur Hx. Ce problème est réputé NP-complet. Il intervient dans un grand nombre de systèmes de télécommunications (CDMA,MIMO, MC-CDMA, etc.). Nous proposons dans cette thèse un algorithme de résolution quasi optimal de ce problème et bien adapté à une implémentation
matérielle.
Notre démarche s'appuie sur l'utilisation des méthodes classiques de recherche opérationnelle : trouver des points initiaux répartis sur l'espace des solutions
possibles et potentiellement proches de la solution optimale (diversification) et effectuer une recherche locale au voisinage des ces points (intensification). Dans
ce travail, la diversification est basée sur une approche géométrique utilisant les axes dominants de concentration du bruit (vecteurs singuliers associés aux
valeurs singulires minimales de la matrice H). Les performances en terme de taux d'erreur par bit de la méthode proposée sont proches de l'optimum tout en
gardant une complexité constante et un degré de parallélisme important (même pour des matrices H de taille très importantes, de l'ordre de 100). Nous avons
étendu cette méthode à la constellation MAQ-16 d'une part, et à la génération d'une décision souple d'autre part.
Nous avons étudié l'algorithme proposé du point de vue implémentation matérielle.
La sensibilité à l'utilisation de la précision finie et des normes sous optimales est décrite. Une étude de complexité de l'algorithme est présentée ainsi que les effets d'une mauvaise estimation de la matrice H.
L'algorithme proposé présente d'une part une nouvelle alternative pour le 11 décodage quasi optimal du mélange linéaire bruité et d'autre part un important
degré de parallélisme permettant une implémentation matérielle efficace et rapide.

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Park, John Charles. "The effects of simulation modes on the number of alternatives generated by fourth graders in a decision-making task /." The Ohio State University, 1985. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487262513407234.

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Tahvili, Sahar. "Solving complex maintenance planning optimization problems using stochastic simulation and multi-criteria fuzzy decision making." Thesis, SICS, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-24416.

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Krumpe, Norman Joseph. "A COMPARISON OF SIMULATION OPTIMIZATION TECHNIQUES IN SOLVING SINGLE-OBJECTIVE, CONSTRAINED, DISCRETE VARIABLE PROBLEMS." Miami University / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=miami1129749397.

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Books on the topic "Problem solving Computer simulation"

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F, Perry Ronald, ed. Simulation: A problem-solving approach. Reading, Mass: Addison-Wesley, 1989.

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Szymankiewicz, J. Z. Solving business problems by simulation. 2nd ed. London: McGraw-Hill, 1988.

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1947-, Smith Karl A., and Bleloch A. L, eds. How to model it: Problem solving for the computer age. New York: McGraw-Hill, 1990.

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1947-, Smith Karl A., and Bleloch A. L, eds. How to model it: Problem solving for the computer age. Edina, MN, USA: Burgess International Group, 1994.

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Problem solving models of scientific discovery learning processes. Frankfurt am Main: P. Lang, 1990.

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José, Páez Mejía Manuel, ed. Computational physics: Problem solving with computers. New York: Wiley, 1997.

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José, Páez Mejía Manuel, and Bordeianu Cristian C, eds. Computational physics: Problem solving with computers. 2nd ed. Weinheim: Wiley-VCH, 2007.

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Introduction to engineering: Modeling and problem solving. Hoboken, N.J: Wiley, 2009.

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Introduction to elementary computation modeling: Essential concepts, principles, and problem solving. Boca Raton: CRC Press, 2012.

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Managerial spreadsheet modeling and analysis. Chicago: Irwin, 1997.

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Book chapters on the topic "Problem solving Computer simulation"

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Reimann, Peter, and Sieghard Beller. "Computer-Based Support for Analogical Problem Solving and Learning." In Simulation-Based Experiential Learning, 91–104. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-78539-9_7.

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Moar, Magnus, Fiona Spensley, Tim O’Shea, Ronnie Singer, Sara Hennessey, and Eileen Scanlon. "Two Uses of Computers in Science Teaching: Horizontal Motion Simulation and Simulation Building." In Computer-Based Learning Environments and Problem Solving, 429–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77228-3_20.

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de Jong, Ton, and Melanie Njoo. "Learning and Instruction with Computer Simulations: Learning Processes Involved." In Computer-Based Learning Environments and Problem Solving, 411–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77228-3_19.

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Goodyear, Peter. "The Provision of Tutorial Support for Learning with Computer-Based Simulations." In Computer-Based Learning Environments and Problem Solving, 391–409. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77228-3_18.

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Cuzme-Rodríguez, Fabián, Ana Umaquinga-Criollo, Luis Suárez-Zambrano, Henry Farinango-Endara, Hernán Domínguez-Limaico, and Mario Mediavilla-Valverde. "Simulation Tools for Solving Engineering Problems. Case Study." In Communications in Computer and Information Science, 271–85. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42517-3_21.

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Ahmadi, Atefeh, Antonija Mitrovic, Badroddin Najmi, and Julia Rucklidge. "TARLAN: a Simulation Game to Improve Social Problem-Solving Skills of ADHD Children." In Lecture Notes in Computer Science, 328–37. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19773-9_33.

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Thammabut, Thawach, Sumalee Chaijaroen, and Suchat Wattanachai. "The Development of Simulation Web-Based Learning Environment to Enhance Ill-Structured Problem Solving for Engineering Students." In Lecture Notes in Computer Science, 328–37. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63885-6_37.

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Thammabut, Thawach, Sumalee Chaijaroen, and Suchat Wattanachai. "The Development of Simulation-Based Laboratory Lessons in Electronics Industrial Instrumentation to Enhance Ill-Structured Problem Solving for Engineering Students." In Lecture Notes in Computer Science, 847–53. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-35343-8_88.

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Tryputen, Mykola, Vitaliy Kuznetsov, Alisa Kuznetsova, Maksym Tryputen, Yevheniia Kuznetsova, and Tetiana Serdiuk. "Improving the Reliability of Simulating the Operation of an Induction Motor in Solving the Technical and Economic Problem." In Advances in Computer Science for Engineering and Education III, 143–52. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55506-1_13.

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Fox, William P., and William C. Bauldry. "Problem Solving with Simulation." In Advanced Problem Solving with Maple™, 297–329. Boca Raton : Taylor & Francis, CRC Press, 2020.: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429469633-7.

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Conference papers on the topic "Problem solving Computer simulation"

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Poehlman, W. F. S., and Wm J. Garland. "ENGINEERING PROBLEM SOLVING WITH KNOWLEDGE-BASED SYSTEMS." In Second International Forum on Expert System and Computer Simulation in Energy Engineering. Connecticut: Begellhouse, 1992. http://dx.doi.org/10.1615/ichmt.1992.intforumexpsyscompsimee.20.

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Fu, Chunhua, Lijun Zhang, Xiaojing Wang, and Liying Qiao. "Solving TSP problem with improved genetic algorithm." In 6TH INTERNATIONAL CONFERENCE ON COMPUTER-AIDED DESIGN, MANUFACTURING, MODELING AND SIMULATION (CDMMS 2018). Author(s), 2018. http://dx.doi.org/10.1063/1.5039131.

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Abdolzadeh, Masoud, and Hassan Rashidi. "Solving Job Shop Scheduling Problem Using Cellular Learning Automata." In 2009 Third UKSim European Symposium on Computer Modeling and Simulation. IEEE, 2009. http://dx.doi.org/10.1109/ems.2009.68.

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Garland, Wm J., and W. F. S. Poehlman. "Engineering Problem Solving with Knowledge-Based Systems." In Expert Systems and Computer Simulation in Energy Engineering: Selected Papers from the Second International Forum. Connecticut: Begellhouse, 1992. http://dx.doi.org/10.1615/ichmt.1992.expsystcomputsimenergengin.100.

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Draa, Amer, and Souham Meshoul. "A Quantum Inspired Learning Cellular Automaton for Solving the Travelling Salesman Problem." In 2010 12th International Conference on Computer Modelling and Simulation. IEEE, 2010. http://dx.doi.org/10.1109/uksim.2010.17.

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Moaven, Shahrouz, Jafar Habibi, Hamed Ahmadi, and Ali Kamandi. "A Fuzzy Model for Solving Architecture Styles Selection Multi-Criteria Problem." In 2008 Second UKSIM European Symposium on Computer Modeling and Simulation (EMS). IEEE, 2008. http://dx.doi.org/10.1109/ems.2008.45.

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Asgarian, Ehsan, M.-Hossein Moeinzadeh, Jafar Habibi, Sarah Sharifian-R, Ammar Rasooli-V, and Amir Najafi-A. "Solving Haplotype Reconstruction Problem in MEC Model with Hybrid Information Fusion." In 2008 Second UKSIM European Symposium on Computer Modeling and Simulation (EMS). IEEE, 2008. http://dx.doi.org/10.1109/ems.2008.97.

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Podlesny, Maxim, and Carey Williamson. "Solving the TCP-Incast Problem with Application-Level Scheduling." In 2012 IEEE 20th International Symposium on Modelling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS). IEEE, 2012. http://dx.doi.org/10.1109/mascots.2012.21.

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Bustillo, Andrés, Javier Sedano, José Ramón Villar, Leticia Curiel, and Emilio Corchado. "Conventional Methods and AI models for Solving an Industrial an Industrial Problem." In 2008 Second UKSIM European Symposium on Computer Modeling and Simulation (EMS). IEEE, 2008. http://dx.doi.org/10.1109/ems.2008.106.

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Mohammadzadeh, J., A. Ghazinezhad, A. Rasooli Valaghozi, A. Nadi, E. Asgarian, V. Salmani, A. Najafi-Ardabili, and M.-H. Moeinzadeh. "Observations on Using Probabilistic C-Means for Solving a Typical Bioinformatics Problem." In 2008 Second UKSIM European Symposium on Computer Modeling and Simulation (EMS). IEEE, 2008. http://dx.doi.org/10.1109/ems.2008.96.

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Reports on the topic "Problem solving Computer simulation"

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Semerikov, Serhiy O., Illia O. Teplytskyi, Yuliia V. Yechkalo, and Arnold E. Kiv. Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot. [б. в.], November 2018. http://dx.doi.org/10.31812/123456789/2648.

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The article substantiates the necessity to develop training methods of computer simulation of neural networks in the spreadsheet environment. The systematic review of their application to simulating artificial neural networks is performed. The authors distinguish basic approaches to solving the problem of network computer simulation training in the spreadsheet environment, joint application of spreadsheets and tools of neural network simulation, application of third-party add-ins to spreadsheets, development of macros using the embedded languages of spreadsheets; use of standard spreadsheet add-ins for non-linear optimization, creation of neural networks in the spreadsheet environment without add-ins and macros. After analyzing a collection of writings of 1890-1950, the research determines the role of the scientific journal “Bulletin of Mathematical Biophysics”, its founder Nicolas Rashevsky and the scientific community around the journal in creating and developing models and methods of computational neuroscience. There are identified psychophysical basics of creating neural networks, mathematical foundations of neural computing and methods of neuroengineering (image recognition, in particular). The role of Walter Pitts in combining the descriptive and quantitative theories of training is discussed. It is shown that to acquire neural simulation competences in the spreadsheet environment, one should master the models based on the historical and genetic approach. It is indicated that there are three groups of models, which are promising in terms of developing corresponding methods – the continuous two-factor model of Rashevsky, the discrete model of McCulloch and Pitts, and the discrete-continuous models of Householder and Landahl.

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White, Steven, and John Lyman. Distributed Problem Solving: Adaptive Networks with a Computer Intermediary Resource. Fort Belvoir, VA: Defense Technical Information Center, June 1991. http://dx.doi.org/10.21236/ada238949.

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Lyman, John, and Carla J. Conaway. Distributed Problem Solving: Adaptive Networks with a Computer Intermediary Resource. Intelligent Executive Computer Communication. Fort Belvoir, VA: Defense Technical Information Center, June 1991. http://dx.doi.org/10.21236/ada242797.

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Dierolf, David A., and Karen J. Richter. Computer-Aided Group Problem Solving for Unified Life Cycle Engineering (ULCE). Fort Belvoir, VA: Defense Technical Information Center, February 1989. http://dx.doi.org/10.21236/ada209446.

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Morehead, Leslie. Determining the Factors Influential in the Validation of Computer-based Problem Solving Systems. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.1244.

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Kondratenko, Larysa O., Hanna T. Samoylenko, Arnold E. Kiv, Anna V. Selivanova, Oleg I. Pursky, Tetyana O. Filimonova, and Iryna O. Buchatska. Computer simulation of processes that influence adolescent learning motivation. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4452.

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In order for the learning process to always retain personal value for the learner, it is necessary that his or her motivation be maintained through an awareness of his or her purpose and goals. This article presents a local model (at the individual object level) of enhancing external motivation, which give to determine students’ efforts to get rewards. The concept of this model based on describing the behavior of agents (in our case students). The characteristics of the phenomenon in the motivation of learning at different stages of adolescent development are analyzed. The problem of computer modeling of educational processes with the help of agent modeling on the example of studying student motivation is considered. Internal and external factors that may strengthen or weaken the adolescent’s motivation to study have been studied. The expediency of using information technologies of agent modeling to study the dynamics of strengthening or weakening student motivation is substantiated. Using the AnyLogic Cloud computing environment the change of dynamics of strengthening of motivation of teenagers on an example of model of strengthening of external motivation is defined.

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Semerikov, Serhiy, Illia Teplytskyi, Yuliia Yechkalo, Oksana Markova, Vladimir Soloviev, and Arnold Kiv. Computer Simulation of Neural Networks Using Spreadsheets: Dr. Anderson, Welcome Back. [б. в.], June 2019. http://dx.doi.org/10.31812/123456789/3178.

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The authors of the given article continue the series presented by the 2018 paper “Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot”. This time, they consider mathematical informatics as the basis of higher engineering education fundamentalization. Mathematical informatics deals with smart simulation, information security, long-term data storage and big data management, artificial intelligence systems, etc. The authors suggest studying basic principles of mathematical informatics by applying cloud-oriented means of various levels including those traditionally considered supplementary – spreadsheets. The article considers ways of building neural network models in cloud-oriented spreadsheets, Google Sheets. The model is based on the problem of classifying multi-dimensional data provided in “The Use of Multiple Measurements in Taxonomic Problems” by R. A. Fisher. Edgar Anderson’s role in collecting and preparing the data in the 1920s-1930s is discussed as well as some peculiarities of data selection. There are presented data on the method of multi-dimensional data presentation in the form of an ideograph developed by Anderson and considered one of the first efficient ways of data visualization.

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Бакум, З. П., and В. В. Ткачук. Open Education Space: Computer-Aided Training of the Future Engineer-Teacher. Криворізький державний педагогічний університет, 2015. http://dx.doi.org/10.31812/0564/426.

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One of the challenges facing higher education is training of graduates for professional activity in the information society. The solution of this problem is connected with students’ competence formation in the field of computer science, computer engineering and information and communication technology. Activation of the formation process of "engineer-teacher" profession requires solving the problem of computer-aided training of specialists in the field of engineering and pedagogy, taking into account global experience, as well as issues of training specialists, that are common to the national higher school. In the article the computer-based disciplines for the field of training 6.010104 "Vocational Education (according to specialty) have been analyzed as professional. The attention is focused on the open education space as one of the means of optimization of these subjects teaching in view of modern popularization of continuous open access to the educational process.

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Henrick, Erin, Steven McGee, Lucia Dettori, Troy Williams, Andrew Rasmussen, Don Yanek, Ronald Greenberg, and Dale Reed. Research-Practice Partnership Strategies to Conduct and Use Research to Inform Practice. The Learning Partnership, April 2021. http://dx.doi.org/10.51420/conf.2021.3.

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This study examines the collaborative processes the Chicago Alliance for Equity in Computer Science (CAFÉCS) uses to conduct and use research. The CAFÉCS RPP is a partnership between Chicago Public Schools (CPS), Loyola University Chicago, The Learning Partnership, DePaul University, and University of Illinois at Chicago. Data used in this analysis comes from three years of evaluation data, and includes an analysis of team documents, meeting observations, and interviews with 25 members of the CAFÉCS RPP team. The analysis examines how three problems are being investigated by the partnership: 1) student failure rate in an introductory computer science course, 2) teachers’ limited use of discussion techniques in an introductory computer science class, and 3) computer science teacher retention. Results from the analysis indicate that the RPP engages in a formalized problem-solving cycle. The problem-solving cycle includes the following steps: First, the Office of Computer Science (OCS) identifies a problem. Next, the CAFÉCS team brainstorms and prioritizes hypotheses to test. Next, data analysis clarifies the problem and the research findings are shared and interpreted by the entire team. Finally, the findings are used to inform OCS improvement strategies and next steps for the CAFÉCS research agenda. There are slight variations in the problem-solving cycle, depending on the stage of understanding of the problem, which has implications for the mode of research (e.g hypothesis testing, research and design, continuous improvement, or evaluation).

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Vakaliuk, Tetiana, Valerii Kontsedailo, Dmytro Antoniuk, Olha Korotun, Serhiy Semerikov, and Iryna Mintii. Using Game Dev Tycoon to Create Professional Soft Competencies for Future Engineers-Programmers. [б. в.], November 2020. http://dx.doi.org/10.31812/123456789/4129.

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The article presents the possibilities of using game simulator Game Dev Tycoon to develop professional soft competencies for future engineer programmers in higher education. The choice of the term “gaming simulator” is substantiated, a generalization of this concept is given. The definition of such concepts as “game simulation” and “professional soft competencies” are given. Describes how in the process of passing game simulations students develop the professional soft competencies. Professional soft competencies include: the ability to work in a team; ability to cooperate; ability to problem-solving; ability to communicative; ability to decision-making; ability to orientation to the result; ability to support of interpersonal relations; ability to use of rules and procedures; ability to reporting; ability to attention to detail; ability to customer service; ability to sustainability; ability to the manifestation of professional honesty and ethics; ability to planning and prioritization; ability to adaptation; ability to initiative; ability to Innovation; ability to external and organizational awareness.

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