Journal articles: 'Learning and Forgetting'

1

Kamuangu, Giasuma. "Learning and Forgetting." International Journal of Learning: Annual Review 12, no. 4 (2007): 45–52. http://dx.doi.org/10.18848/1447-9494/cgp/v14i04/45310.

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Li, Zhizhong, and Derek Hoiem. "Learning without Forgetting." IEEE Transactions on Pattern Analysis and Machine Intelligence 40, no. 12 (December 1, 2018): 2935–47. http://dx.doi.org/10.1109/tpami.2017.2773081.

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Carmona, Salvador, and Anders Gronlund. "Learning from Forgetting." Management Learning 29, no. 1 (March 1998): 21–38. http://dx.doi.org/10.1177/1350507698291002.

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Unterstell, Rembert. "Learning, Remembering and Forgetting." German Research 41, no. 2 (September 2019): 14–15. http://dx.doi.org/10.1002/germ.201970205.

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Villas-Boas, Sofia Berto, and J. Miguel Villas-Boas. "Learning, Forgetting, and Sales." Management Science 54, no. 11 (November 2008): 1951–60. http://dx.doi.org/10.1287/mnsc.1080.0909.

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Ishikawa, Masumi. "Structural learning with forgetting." Neural Networks 9, no. 3 (April 1996): 509–21. http://dx.doi.org/10.1016/0893-6080(96)83696-3.

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Globerson, Shlomo. "Incorporating Forgetting into Learning Curves." International Journal of Operations & Production Management 7, no. 4 (April 1987): 80–94. http://dx.doi.org/10.1108/eb054802.

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Gold, James M., Gina Rehkemper, Sidney W. Binks, Constance J. Carpenter, Kirsten Fleming, Terry E. Goldberg, and Daniel R. Weinberger. "Learning and forgetting in schizophrenia." Journal of Abnormal Psychology 109, no. 3 (2000): 534–38. http://dx.doi.org/10.1037/0021-843x.109.3.534.

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WATANABE, Eiji. "Selective Learning Algorithms with Forgetting Taking Account of the Balance between Learning and Forgetting." Transactions of the Institute of Systems, Control and Information Engineers 10, no. 12 (1997): 628–36. http://dx.doi.org/10.5687/iscie.10.628.

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Leung, Chi Sing, and Lai Wan Chan. "The Behavior of Forgetting Learning in Bidirectional Associative Memory." Neural Computation 9, no. 2 (February 1, 1997): 385–401. http://dx.doi.org/10.1162/neco.1997.9.2.385.

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Forgetting learning is an incremental learning rule in associative memories. With it, the recent learning items can be encoded, and the old learning items will be forgotten. In this article, we analyze the storage behavior of bidirectional associative memory (BAM) under the forgetting learning. That is, “Can the most recent k learning item be stored as a fixed point?” Also, we discuss how to choose the forgetting constant in the forgetting learning such that the BAM can correctly store as many as possible of the most recent learning items. Simulation is provided to verify the theoretical analysis.

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Rupčić, Nataša. "Learning-forgetting-unlearning-relearning – the learning organization’s learning dynamics." Learning Organization 26, no. 5 (July 8, 2019): 542–48. http://dx.doi.org/10.1108/tlo-07-2019-237.

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Madhoushi, Mehrdad, and Azam Sazvar. "The effects of chaos edge management on intentional organizational forgetting with emphasis on quantum learning (case study: information technology-based organizations)." Problems and Perspectives in Management 14, no. 3 (September 15, 2016): 356–63. http://dx.doi.org/10.21511/ppm.14(3-si).2016.08.

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Unlike learning process, the critical phenomenon of organizational forgetting is not entirely understood. There are two categories of forgetting: accidental forgetting (not-preferred) and intentional forgetting (preferred). Therefore, all the variables that influence intentional forgetting can be important for organizational learning. One of them, which have been neglected thus far, is the edge of chaos in quantum learning. It is the point that a balance is achieved between stability and chaos. Organizational innovation, learning, and creativity all reach a proper level at this point. Along with emphasizing on these variables and surveying an IT-based organization, the present study is an attempt to discover the causal relationships between the variables. Based on the data from 289 filled out questionnaires, of which reliability and validity have been confirmed, structural equations model was developed in AMOS. The results showed that the all path coefficients were significant. In addition, comparison of goodness of fit indices and the standard range showed that all indices were acceptable and the main hypothesis regarding effectiveness of quantum learning on organizational forgetting was supported. The effect of quantum learning on organizational forgetting in non-standard and standard conditions was 0.51 and 0.28, respectively. Keywords: quantum learning, edge of chaos management, intentional organizational forgetting. JEL Classification: D83, D23

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Wu, Wenqing, Saixiang Ma, Yuzheng Su, and Chia-Huei Wu. "Double-Layer Learning, Leaders' Forgetting, and Knowledge Performance in Online Work Community Organizations." Journal of Organizational and End User Computing 33, no. 1 (January 2021): 92–117. http://dx.doi.org/10.4018/joeuc.2021010105.

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This paper constructs an online community organizational double-layer learning structure model based on exploration-exploitation models. In this way, the authors examine the effect how double-layer online community learning as well as heterogeneous teams affects online work community organizational knowledge performance (OWCOKP) with leaders forgetting and without leaders forgetting. First, the results suggest an inverted-U relationship between the degree of different team member connectivity and OWCOKP. Second, as the leaders forgetting rate increases, the degree of different team member connectivity, which leads to the optimum OWCOKP also increases. Third, with or without leaders forgetting, moderate learning between members and that between the leader and members can improve OWCOKP within a team of online community. Fourth, in different teams, slow learning between leaders produces higher OWCOKP without leaders forgetting while moderate learning between leaders produces higher OWCOKP with their forgetting.

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Steiner, Gerhard. "Forgetting While Learning: A Plea for Specific Consolidation." Journal of Cognitive Education and Psychology 8, no. 2 (July 2009): 117–27. http://dx.doi.org/10.1891/1945-8959.8.2.117.

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One of the most striking observations during our longitudinal studies in six vocational schools with more than 50 classes and teachers was the tremendous loss of knowledge from one teaching unit to the next one: sometimes over as short a time as from the beginning of a lecture to its end, sometimes from one week to the next one. So, it is not only mid- or long-term forgetting but also very short-term forgetting, or “forgetting while learning.” An exemplary analysis of an example from conceptual learning is given, and some causes for this striking form of forgetting—at a moment where forgetting is extremely detrimental for any kind of continuing learning—are presented. This article refers to some theoretical considerations about certain conditions of conceptual learning and presents the conclusions of the longitudinal studies rather than the description of the research and development processes. It may be interpreted as a direct contribution to teachers’ further education.

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Blech, Christine, and Robert Gaschler. "Assessing Students’ Knowledge about Learning and Forgetting Curves with a Free Production Technique: Measures and Implications for the Development of Learning Aids." Psychology Learning & Teaching 17, no. 3 (June 12, 2018): 308–22. http://dx.doi.org/10.1177/1475725718779684.

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Learning and forgetting curves are not only integral issues for courses in introductory psychology, they are also of high practical relevance to students when it comes to the formation of realistic goals and expectations on learning outcomes. A paper-and-pencil-study investigated how well students of psychology ( N = 82) have internalized the concepts of learning and forgetting curves. We developed a vignette-based assessment technique: drawing a hypothetical learning or forgetting curve in an empty coordinate system with time on the x-axis and performance on the y-axis, the starting point and endpoint being fixed. In spite of the free production format answers were quantified in a way that would allow for automated feedback in online teaching tools. For instance, learning which decelerates over time implies a curve above the diagonal while decelerated forgetting implies a curve below the diagonal. Deviating from this optimal solution, about 60% of the drawn learning and forgetting curves were classified as being close to the diagonal axis. Analyses on the individual level also documented poor consistency of knowledge. Students drawing a deceleration in learning were not more likely to also draw a deceleration in forgetting. Implications for future learning aids, for example, online feedback systems are discussed.

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Jaber, M. Y., and H. V. Kher. "The dual-phase learning–forgetting model." International Journal of Production Economics 76, no. 3 (April 2002): 229–42. http://dx.doi.org/10.1016/s0925-5273(01)00169-4.

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Kleiner, Morris M., Jerry Nickelsburg, and Adam M. Pilarski. "Organizational and Individual Learning and Forgetting." ILR Review 65, no. 1 (January 2012): 68–81. http://dx.doi.org/10.1177/001979391206500104.

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18

Miller, G. "FORGETTING AND REMEMBERING: Learning to Forget." Science 304, no. 5667 (April 2, 2004): 34–36. http://dx.doi.org/10.1126/science.304.5667.34.

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Shuai, Yichun, Areekul Hirokawa, Yulian Ai, Min Zhang, Wanhe Li, and Yi Zhong. "Dissecting neural pathways for forgetting in Drosophila olfactory aversive memory." Proceedings of the National Academy of Sciences 112, no. 48 (November 16, 2015): E6663—E6672. http://dx.doi.org/10.1073/pnas.1512792112.

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Recent studies have identified molecular pathways driving forgetting and supported the notion that forgetting is a biologically active process. The circuit mechanisms of forgetting, however, remain largely unknown. Here we report two sets of Drosophila neurons that account for the rapid forgetting of early olfactory aversive memory. We show that inactivating these neurons inhibits memory decay without altering learning, whereas activating them promotes forgetting. These neurons, including a cluster of dopaminergic neurons (PAM-β′1) and a pair of glutamatergic neurons (MBON-γ4>γ1γ2), terminate in distinct subdomains in the mushroom body and represent parallel neural pathways for regulating forgetting. Interestingly, although activity of these neurons is required for memory decay over time, they are not required for acute forgetting during reversal learning. Our results thus not only establish the presence of multiple neural pathways for forgetting in Drosophila but also suggest the existence of diverse circuit mechanisms of forgetting in different contexts.

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Brainerd, C. J., and V. F. Reyna. "Learning rate, learning opportunities, and the development of forgetting." Developmental Psychology 31, no. 2 (1995): 251–62. http://dx.doi.org/10.1037/0012-1649.31.2.251.

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Liu, Qing, and Ru Yi. "Impact of the Learning-Forgetting Effect on Mixed-Model Production Line Sequencing." International Journal of Information Technologies and Systems Approach 14, no. 1 (January 2021): 97–115. http://dx.doi.org/10.4018/ijitsa.2021010106.

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In this study, the learning-forgetting (L-F) effect is considered in a mixed-model sequencing problem to investigate its impact on makespan minimization. To this end, mathematical models of the learning and forgetting effects are modified in accordance with a mixed-model production environment, and the L-F functions for a serial workstation and multiple products are established. Subsequently, their impact on production is demonstrated via data experiments. The relationships between the learning effect, forgetting effect, product model combination, and makespan are also discussed based on the experimental results. The results show that the learning and forgetting functions can significantly affect the work time in the mathematical scheduling model and that a balanced product model combination and small MPS (minimum part set) batch can help to reduce the L-F effect.

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Dai, Baolin, Jun Gong, Cuiming Li, and Huifeng Ning. "Iterative learning control realized using an iteration-varying forgetting factor based on optimal gains." Transactions of the Institute of Measurement and Control 43, no. 10 (March 9, 2021): 2334–44. http://dx.doi.org/10.1177/0142331221996507.

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Iterative learning control with forgetting factor (ILCFF) is widely used in control engineering. However, choosing the optimal parameters of ILCFF to improve system-output characteristics has been a challenging issue for controller designers. This paper proposes an iterative learning control (ILC) algorithm that involves a variable forgetting factor based on optimal gains for a class of discrete linear time-invariant systems with aperiodic disturbances. The convergence of the algorithm is analyzed, and the necessary and sufficient condition for its convergence is derived in terms of proportional–integral–derivative coefficients. A design method based on optimal gains is established to determine the algorithm coefficients and to accelerate system convergence. Furthermore, the influence of the forgetting factor on both the system-output error and the scope of the proposed algorithm is analyzed. Additionally, the most suitable system type for the application of the forgetting factor is determined. The effectiveness of the algorithm is verified by performing a theoretical analysis and a case-based simulation. The proposed iteration-varying optimal forgetting-factor-based ILC algorithm undergoes fast convergence with a small system-output error. The findings disrupt the conventional view that the use of the forgetting factor increases system-output error. In fact, in a system with small trajectory and increased disturbances, the error induced by the forgetting factor may be smaller than that of the traditional optimal ILC algorithm.

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Yeung, Dennis, Dario Farina, and Ivan Vujaklija. "Directional Forgetting for Stable Co-Adaptation in Myoelectric Control." Sensors 19, no. 9 (May 13, 2019): 2203. http://dx.doi.org/10.3390/s19092203.

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Conventional myoelectric controllers provide a mapping between electromyographic signals and prosthetic functions. However, due to a number of instabilities continuously challenging this process, an initial mapping may require an extended calibration phase with long periods of user-training in order to ensure satisfactory performance. Recently, studies on co-adaptation have highlighted the benefits of concurrent user learning and machine adaptation where systems can cope with deficiencies in the initial model by learning from newly acquired data. However, the success remains highly dependent on careful weighting of these new data. In this study, we proposed a function driven directional forgetting approach to the recursive least-squares algorithm as opposed to the classic exponential forgetting scheme. By only discounting past information in the same direction of the new data, local corrections to the mapping would induce less distortion to other regions. To validate the approach, subjects performed a set of real-time myoelectric tasks over a range of forgetting factors. Results show that directional forgetting with a forgetting factor of 0.995 outperformed exponential forgetting as well as unassisted user learning. Moreover, myoelectric control remained stable after adaptation with directional forgetting over a range of forgetting factors. These results indicate that a directional approach to discounting past training data can improve performance and alleviate sensitivities to parameter selection in recursive adaptation algorithms.

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Cropper, Andrew. "Forgetting to Learn Logic Programs." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3676–83. http://dx.doi.org/10.1609/aaai.v34i04.5776.

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Most program induction approaches require predefined, often hand-engineered, background knowledge (BK). To overcome this limitation, we explore methods to automatically acquire BK through multi-task learning. In this approach, a learner adds learned programs to its BK so that they can be reused to help learn other programs. To improve learning performance, we explore the idea of forgetting, where a learner can additionally remove programs from its BK. We consider forgetting in an inductive logic programming (ILP) setting. We show that forgetting can significantly reduce both the size of the hypothesis space and the sample complexity of an ILP learner. We introduce Forgetgol, a multi-task ILP learner which supports forgetting. We experimentally compare Forgetgol against approaches that either remember or forget everything. Our experimental results show that Forgetgol outperforms the alternative approaches when learning from over 10,000 tasks.

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ISHIKAWA, Masumi. "Structural Learning with Forgetting of Neural Networks." Journal of Japan Society for Fuzzy Theory and Systems 9, no. 1 (1997): 2–9. http://dx.doi.org/10.3156/jfuzzy.9.1_2.

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Zakaryan, Arusyak, Ji-Yub (Jay) Kim, and Francisco Polidoro. "Learning from and Forgetting Successes and Failures." Academy of Management Proceedings 2018, no. 1 (August 2018): 16953. http://dx.doi.org/10.5465/ambpp.2018.16953symposium.

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Lee, Hae-In, Hyo-Sang Shin, and Antonios Tsourdos. "Concurrent Learning Adaptive Control With Directional Forgetting." IEEE Transactions on Automatic Control 64, no. 12 (December 2019): 5164–70. http://dx.doi.org/10.1109/tac.2019.2911863.

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Faglioni, P., C. Botti, M. Scarpa, V. Ferrari, and M. C. Saetti. "Learning and forgetting processes in Parkinson's disease:." Neuropsychologia 35, no. 6 (May 1997): 767–79. http://dx.doi.org/10.1016/s0028-3932(96)00125-x.

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Tarakci, Hakan, Kwei Tang, and Sunantha Teyarachakul. "Learning and forgetting effects on maintenance outsourcing." IIE Transactions 45, no. 4 (April 2013): 449–63. http://dx.doi.org/10.1080/0740817x.2012.706734.

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Kyllonen, Patrick C., and William C. Tirre. "Individual differences in associative learning and forgetting." Intelligence 12, no. 4 (October 1988): 393–421. http://dx.doi.org/10.1016/0160-2896(88)90004-9.

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Yang, Hong, and Sheng Ming Li. "PD-Type ILC Algorithm Research with Forgetting Factor for a Class of Linear Systems with Multiple Time Delays." Applied Mechanics and Materials 220-223 (November 2012): 1125–30. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.1125.

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Based on iterative learning control (ILC) algorithm with forgetting factor, the thought that the forgetting factor is a function of iteration numbers is proposed in this paper, which has simplified the convergence conditions. And the convergence analysis is given. Then, the study results of this paper are applied to a class of linear systems with multiple time delays and simulation results show that, under the improvements of the convergence conditions and the reasonable choice of forgetting factor function, the PD-type iterative learning control algorithm with forgetting factor applied to the linear systems with multiple time delays in this paper has effectiveness and superiority.

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Agostini, Alejandro, and Enric Celaya. "Online Reinforcement Learning Using a Probability Density Estimation." Neural Computation 29, no. 1 (January 2017): 220–46. http://dx.doi.org/10.1162/neco_a_00906.

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Function approximation in online, incremental, reinforcement learning needs to deal with two fundamental problems: biased sampling and nonstationarity. In this kind of task, biased sampling occurs because samples are obtained from specific trajectories dictated by the dynamics of the environment and are usually concentrated in particular convergence regions, which in the long term tend to dominate the approximation in the less sampled regions. The nonstationarity comes from the recursive nature of the estimations typical of temporal difference methods. This nonstationarity has a local profile, varying not only along the learning process but also along different regions of the state space. We propose to deal with these problems using an estimation of the probability density of samples represented with a gaussian mixture model. To deal with the nonstationarity problem, we use the common approach of introducing a forgetting factor in the updating formula. However, instead of using the same forgetting factor for the whole domain, we make it dependent on the local density of samples, which we use to estimate the nonstationarity of the function at any given input point. To address the biased sampling problem, the forgetting factor applied to each mixture component is modulated according to the new information provided in the updating, rather than forgetting depending only on time, thus avoiding undesired distortions of the approximation in less sampled regions.

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Sodhani, Shagun, Sarath Chandar, and Yoshua Bengio. "Toward Training Recurrent Neural Networks for Lifelong Learning." Neural Computation 32, no. 1 (January 2020): 1–35. http://dx.doi.org/10.1162/neco_a_01246.

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Catastrophic forgetting and capacity saturation are the central challenges of any parametric lifelong learning system. In this work, we study these challenges in the context of sequential supervised learning with an emphasis on recurrent neural networks. To evaluate the models in the lifelong learning setting, we propose a curriculum-based, simple, and intuitive benchmark where the models are trained on tasks with increasing levels of difficulty. To measure the impact of catastrophic forgetting, the model is tested on all the previous tasks as it completes any task. As a step toward developing true lifelong learning systems, we unify gradient episodic memory (a catastrophic forgetting alleviation approach) and Net2Net (a capacity expansion approach). Both models are proposed in the context of feedforward networks, and we evaluate the feasibility of using them for recurrent networks. Evaluation on the proposed benchmark shows that the unified model is more suitable than the constituent models for lifelong learning setting.

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Lan, Tianyi, Fei Yan, and Hui Lin. "Iterative Learning Control with Forgetting Factor for Urban Road Network." Journal of Control Science and Engineering 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/9269187.

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In order to improve the traffic condition, a novel iterative learning control (ILC) algorithm with forgetting factor for urban road network is proposed by using the repeat characteristics of traffic flow in this paper. Rigorous analysis shows that the proposed ILC algorithm can guarantee the asymptotic convergence. Through iterative learning control of the traffic signals, the number of vehicles on each road in the network can gradually approach the desired level, thereby preventing oversaturation and traffic congestion. The introduced forgetting factor can effectively adjust the control input according to the states of the system and filter along the direction of the iteration. The results show that the forgetting factor has an important effect on the robustness of the system. The theoretical analysis and experimental simulations are given to verify the validity of the proposed method.

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Stroganov, Victor Yu, and Yurii B. Tsvetkov. "The Optimization Methodology of the Digital Model of the Educational Program Based on Learning-Forgetting Functions." ITM Web of Conferences 35 (2020): 01017. http://dx.doi.org/10.1051/itmconf/20203501017.

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A methodology for curriculum optimizing based on the construction of learning-forgetting terms of educational content is proposed. In the formation of the curriculum, it is important not only to minimize the intervals between educational modules that use the same terms, but also to take into account the forgetting of the studied educational material. The article offers a generalized representation of the optimization methodology of the digital model of the educational program based on modeling the learning-forgetting function of the terms throughout the educational program, which poses the task of optimizing the curriculum in a multicriteria setting.

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Merriman, William E., Margarita Azmitia, and Marion Perlmutter. "Rate of Forgetting in Early Childhood." International Journal of Behavioral Development 11, no. 3 (September 1988): 359–67. http://dx.doi.org/10.1177/016502548801100305.

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The relation between age and rate of forgetting was investigated with a task that eliminated differences in level of initial learning. Three-, four-, and six-year-olds were shown 40 pictures, then were tested for their recognition of 20 pictures immediately, followed by a recognition test of all pictures 24 hours later. Rate of forgetting was nearly identical in every age group. The results are discussed in terms of the interference theory of forgetting and hypotheses about the relation of forgetting to neurological maturation.

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Merriman, William E., Margarita Azmitia, and Marion Perlmutter. "Rate of Forgetting in Early Childhood." International Journal of Behavioral Development 11, no. 4 (December 1988): 467–74. http://dx.doi.org/10.1177/016502548801100405.

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The relation between age and rate of forgetting was investigated with a task that eliminated differences in level of initial learning. Three-, four-, and six-year-olds were shown 40 pictures, then were tested for their recognition of 20 pictures immediately, followed by a recognition test of all pictures 24 hours later. Rate of forgetting was nearly identical in every age group. The results are discussed in terms of the interference theory of forgetting and hypotheses about the relation of forgetting to neurological maturation.

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Bjork, Robert A., and Elizabeth L. Bjork. "Forgetting as the friend of learning: implications for teaching and self-regulated learning." Advances in Physiology Education 43, no. 2 (June 1, 2019): 164–67. http://dx.doi.org/10.1152/advan.00001.2019.

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One of the “important peculiarities” of human learning (Bjork RA and Bjork EL. From Learning Processes to Cognitive Processes: Essays in Honor of William K. Estes, 1992, p. 35–67) is that certain conditions that produce forgetting—that is, impair access to some to-be-learned information studied earlier—also enhance the learning of that information when it is restudied. Such conditions include changing the environmental context from when some to-be-learned material is studied to when that material is restudied; increasing the delay from when something is studied to when it is tested or restudied; and interleaving, rather than blocking, the study or practice of the components of to-be-learned knowledge or skills. In this paper, we provide some conjectures as to why conditions that produce forgetting can also enable learning, and why a misunderstanding of this peculiarity of how humans learn can result in nonoptimal teaching and self-regulated learning.

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Wang, Hongbin, Jian Dong, and Yueling Wang. "High-Order Feedback Iterative Learning Control Algorithm with Forgetting Factor." Mathematical Problems in Engineering 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/826409.

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A novel iterative learning control (ILC) algorithm is proposed to produce output curves that pass close to the desired trajectory. The key advantage of the proposed algorithm is introducing forgetting factor, which is a function of the number of iterations. Due to the forgetting factor characteristic of ILC, the proposed scheme not only stabilizes the nonlinear system with uncertainties but also weakens interference on the tracking desired trajectory. Simulation examples are included to demonstrate feasibility and effectiveness of the proposed algorithm.

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Davidovitch, Lior, Avi Parush, and Avy Shtub. "Simulation-based learning: The learning–forgetting–relearning process and impact of learning history." Computers & Education 50, no. 3 (April 2008): 866–80. http://dx.doi.org/10.1016/j.compedu.2006.09.003.

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Adi-Japha, Esther, and Haia Abu-Asba. "Learning, Forgetting, and Relearning: Skill Learning in Children With Language Impairment." American Journal of Speech-Language Pathology 23, no. 4 (November 2014): 696–707. http://dx.doi.org/10.1044/2014_ajslp-13-0031.

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Purpose The current study tested whether the difficulties of children with specific language impairment (SLI) in skill acquisition are related to learning processes that occur while practicing a new skill or to the passage of time between practice and later performance. Method The acquisition and retention of a new complex grapho-motor symbol were studied in 5-year-old children with SLI and peers matched for age and nonverbal IQ. The children practiced the production of the symbol for 4 consecutive days. Retention testing took place 10 days later. Results Children with SLI began each practice day slower than their peers but attained similar levels of performance by its end. Although they increased their performance speed within sessions more than their peers, they did not retain their learning as well between sessions. The loss in speed was largest in the 10-day retention interval. They were also less accurate, but accuracy differences decreased over time. Between-session group differences in speed could not fully be accounted for based on fine motor skills. Conclusions In spite of effective within-session learning, children with SLI did not retain the new skill well. The deficit may be attributed to task forgetting in the presence of delayed consolidation processes.

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Endress, Ansgar D., and Scott P. Johnson. "When forgetting fosters learning: A neural network model for statistical learning." Cognition 213 (August 2021): 104621. http://dx.doi.org/10.1016/j.cognition.2021.104621.

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Biruk, Sławomir, and Łukasz Rzepecki. "Wpływ zjawiska uczenia i zapominania na czas realizacji powtarzalnych procesów budowlanych realizowanych w warunkach losowych." Przegląd Naukowy Inżynieria i Kształtowanie Środowiska 26, no. 2 (June 2, 2017): 202–9. http://dx.doi.org/10.22630/pniks.2017.26.2.18.

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Construction projects encompass repetitive works carried out on the same or similar object parts, called working units. Due to the cyclical nature of construction processes it is possible to use the learning-forgetting theory to construction projects scheduling. The article shows an example of using learning-forgetting theory in the planning of implementation multi-storey residential building in random conditions.

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Tanaka, Chiaki, and Tohru Taniuchi. "Directed forgetting in spatial serial learning in rats." Proceedings of the Annual Convention of the Japanese Psychological Association 82 (September 25, 2018): 1AM—081–1AM—081. http://dx.doi.org/10.4992/pacjpa.82.0_1am-081.

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Carpenter, Shana K., Harold Pashler, John T. Wixted, and Edward Vul. "The effects of tests on learning and forgetting." Memory & Cognition 36, no. 2 (March 2008): 438–48. http://dx.doi.org/10.3758/mc.36.2.438.

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Gent, Ian P., Ian Miguel, and Neil C. A. Moore. "An empirical study of learning and forgetting constraints." AI Communications 25, no. 2 (2012): 191–208. http://dx.doi.org/10.3233/aic-2012-0524.

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Pashler, Harold, Doug Rohrer, Nicholas J. Cepeda, and Shana K. Carpenter. "Enhancing learning and retarding forgetting: Choices and consequences." Psychonomic Bulletin & Review 14, no. 2 (April 2007): 187–93. http://dx.doi.org/10.3758/bf03194050.

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Jaber, M. Y., M. Bonney, and I. Moualek. "Lot sizing with learning, forgetting and entropy cost." International Journal of Production Economics 118, no. 1 (March 2009): 19–25. http://dx.doi.org/10.1016/j.ijpe.2008.08.006.

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Jaber, Mohamad Y., and Maurice Bonney. "A comparative study of learning curves with forgetting." Applied Mathematical Modelling 21, no. 8 (August 1997): 523–31. http://dx.doi.org/10.1016/s0307-904x(97)00055-3.

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Fogel, Gerald I. "Rediscovering Psychoanalysis: Thinking and Dreaming, Learning and Forgetting." International Journal of Psychoanalysis 90, no. 6 (December 2009): 1471–76. http://dx.doi.org/10.1111/j.1745-8315.2009.00225_5.x.

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Journal articles on the topic 'Learning and Forgetting'

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