Relevant bibliographies by topics / Incentive of learning

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

Academic literature on the topic 'Incentive of learning'

Author: Grafiati
Published: 28 May 2022
Last updated: 31 July 2025

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Journal articles on the topic "Incentive of learning"

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Liu, Jiaqi, Hucheng Xu, Xiaoheng Deng, Hui Liu, and Deng Li. "A Location-Based Crowdsensing Incentive Mechanism Based on Ensemble Learning and Prospect Theory." Mathematics 11, no. 16 (2023): 3590. http://dx.doi.org/10.3390/math11163590.

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Crowdsensing uses the participants’ smart devices to form a new perception network. The coverage of crowdsensing’s tasks determines the quality of services. Under the constraint of budget and the number of participants, the platform needs to increase the participation duration of participants through incentive mechanisms to increase the coverage of tasks. There are two problems with the existing incentive mechanisms: (1) many incentives ignore the participants’ characteristics, and using a single incentive mechanism for different participants will make the incentive effect not reach the expect
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St. Clair, Bryn, Paul Putnam, Harold L. Miller, Ross Larsen, and Jamie L. Jensen. "Test-Enhanced Learning and Incentives in Biology Education." CBE—Life Sciences Education 19, no. 3 (2020): ar40. http://dx.doi.org/10.1187/cbe.19-11-0226.

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This study examined the testing effect in a postsecondary biology course with low- and high-incentive treatments and measured student learning. Although exposure to exams predicted better learning, incentive level did not moderate this effect, an outcome that contradicted recent laboratory findings that higher incentives decreased the testing effect.
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Zhu, Chun, Mengqi Sui, Haitao Zhao, Keqi Chen, Tianyu Zhang, and Chongyu Bao. "Federated Learning Incentive Mechanism Setting in UAV-Assisted Space–Terrestrial Integration Networks." Electronics 13, no. 6 (2024): 1129. http://dx.doi.org/10.3390/electronics13061129.

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The UAV-assisted space–terrestrial integrated network provides extensive coverage and high flexibility in communication services. UAVs and ground terminals collaborate to train models and provide services. In order to protect data privacy, federated learning is widely used. However, the participation of UAVs and ground terminals is not gratuitous, and reasonable incentives for federated learning need to be set up to encourage their participation. To address the above issues, this paper proposes a federated reliable incentive mechanism based on hierarchical reinforcement learning. The mechanism
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Zhang, Hanrui, and Vincent Conitzer. "Incentive-Aware PAC Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 6 (2021): 5797–804. http://dx.doi.org/10.1609/aaai.v35i6.16726.

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We study PAC learning in the presence of strategic manipulation, where data points may modify their features in certain predefined ways in order to receive a better outcome. We show that the vanilla ERM principle fails to achieve any nontrivial guarantee in this context. Instead, we propose an incentive-aware version of the ERM principle which has asymptotically optimal sample complexity. We then focus our attention on incentive-compatible classifiers, which provably prevent any kind of strategic manipulation. We give a sample complexity bound that is, curiously, independent of the hypothesis
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Sprinkle, Geoffrey B. "The Effect of Incentive Contracts on Learning and Performance." Accounting Review 75, no. 3 (2000): 299–326. http://dx.doi.org/10.2308/accr.2000.75.3.299.

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This paper reports the results of an experiment that examines how incentive-based compensation contracts compare to flat-wage compensation contracts in motivating individual learning and performance. I use a multiperiod cognitive task where the accounting system generates information (feedback) that has both a contracting role and a belief-revision role. The results suggest that incentives enhance performance and the rate of improvement in performance by increasing both: (1) the amount of time participants devoted to the task, and (2) participants' analysis and use of information. Further, I f
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Dekel, Ofer, Felix Fischer, and Ariel D. Procaccia. "Incentive compatible regression learning." Journal of Computer and System Sciences 76, no. 8 (2010): 759–77. http://dx.doi.org/10.1016/j.jcss.2010.03.003.

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Sundaresan, Shankar, and Zuopeng Justin Zhang. "Knowledge Sharing and Learning in Organizations: Role of Incentives and Information Systems." Journal of Industrial Integration and Management 01, no. 03 (2016): 1650005. http://dx.doi.org/10.1142/s2424862216500056.

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Knowledge sharing and learning play a fundamental role in the success of knowledge management (KM). While information technologies facilitate learning and sharing of knowledge in organizations, incentive systems help to align organizational and individual goals. Applying a modeling framework that captures the characteristics of both information systems and incentives in a team-based organization, we demonstrate that an output allocation rule linear in output, and sharing-learning incentive rewards linear in the amounts being shared and learned can be optimally designed. Broad conditions for im
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Pan, Chenglu, Jiarong Xu, Yue Yu, et al. "Towards Fair Graph Federated Learning via Incentive Mechanisms." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 13 (2024): 14499–507. http://dx.doi.org/10.1609/aaai.v38i13.29365.

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Graph federated learning (FL) has emerged as a pivotal paradigm enabling multiple agents to collaboratively train a graph model while preserving local data privacy. Yet, current efforts overlook a key issue: agents are self-interested and would hesitant to share data without fair and satisfactory incentives. This paper is the first endeavor to address this issue by studying the incentive mechanism for graph federated learning. We identify a unique phenomenon in graph federated learning: the presence of agents posing potential harm to the federation and agents contributing with delays. This sta
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Thornton, Rebecca L. "The Demand for, and Impact of, Learning HIV Status." American Economic Review 98, no. 5 (2008): 1829–63. http://dx.doi.org/10.1257/aer.98.5.1829.

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This paper evaluates an experiment in which individuals in rural Malawi were randomly assigned monetary incentives to learn their HIV results after being tested. Distance to the HIV results centers was also randomly assigned. Without any incentive, 34 percent of the participants learned their HIV results. However, even the smallest incentive doubled that share. Using the randomly assigned incentives and distance from results centers as instruments for the knowledge of HIV status, sexually active HIV-positive individuals who learned their results are three times more likely to purchase condoms
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He, Jie, and Jianhua Zhang. "Human Capital Digital Incentive Mechanism Construction Based on Deep Learning." Mathematical Problems in Engineering 2022 (August 17, 2022): 1–12. http://dx.doi.org/10.1155/2022/6180883.

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The introduction of human capital can be traced back to the ancient Greek period, emphasizing the important role of knowledge and skills in the production and life process. Human capital reward mechanism promotes modern social and economic development and is an important part of social and economic growth. The core management of the enterprise is the management of human capital, and the central work of human capital management is the incentive of human capital. Many enterprises are now facing difficulties in industrial operation, serious brain drain, and lack of core competitiveness in the mar
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Dissertations / Theses on the topic "Incentive of learning"

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Balleine, Bernard Walter. "Incentive learning." Thesis, University of Cambridge, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.240892.

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Khan, Zafar Dad. "Three essays on learning-by-doing and monetary incentive." Laramie, Wyo. : University of Wyoming, 2007. http://proquest.umi.com/pqdweb?did=1338907741&sid=1&Fmt=2&clientId=18949&RQT=309&VName=PQD.

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Wasserman, James I. "Incentive learning in medicated schizophrenic patients on a nondeclarative memory task." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0007/MQ42704.pdf.

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Bradley, Curtis. "Examining the Associative Learning and Accumbal Dopaminergic Mechanisms of Caffeine Reinforcement." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etd/3457.

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Caffeine is the most consumed psychoactive substance in the world, and most caffeine consumption in coffee and energy drinks is intended to produce a psychoactive effect. However, caffeine is not a primary reinforcer in preclinical paradigms – non-human species do not reliably take the drug to produce a psychoactive effect. However, caffeine is a ‘reinforcement enhancer’ in preclinical models; the effects of caffeine increase the motivation to obtain other non-drug reinforcers. The overall goal of this project was to determine if these reinforcement enhancing effects of caffeine could promote
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De, Tommaso Matteo. "How Learning and Motivational Shifts Determine the Incentive Salience of Reward Cues: a Human Behavioral Perspective." Doctoral thesis, Università degli studi di Trento, 2017. https://hdl.handle.net/11572/368441.

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Reward cues acquire distinctive incentive properties from their association with motivationally significant outcomes. These incentive properties are manifest in an augmented salience that provides reward cues with the capacity to attract attention, also in contexts where pursuing the reward is not relevant. I will first examine the unresolved debate concerning the learning parameters that define the cue-reward association and eventually modulate attention. Specifically, whether the cue attentional salience is governed by reward expectancy or uncertainty. I will then study another incentive pro
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De, Tommaso Matteo. "How Learning and Motivational Shifts Determine the Incentive Salience of Reward Cues: a Human Behavioral Perspective." Doctoral thesis, University of Trento, 2017. http://eprints-phd.biblio.unitn.it/2707/2/Disclaimert_DeTommaso.pdf.

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Reward cues acquire distinctive incentive properties from their association with motivationally significant outcomes. These incentive properties are manifest in an augmented salience that provides reward cues with the capacity to attract attention, also in contexts where pursuing the reward is not relevant. I will first examine the unresolved debate concerning the learning parameters that define the cue-reward association and eventually modulate attention. Specifically, whether the cue attentional salience is governed by reward expectancy or uncertainty. I will then study another incentive pro
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Miller, Donna W. "The impact of teacher incentive pay programs on the learning gains of low-performing middle school students." Doctoral diss., University of Central Florida, 2010. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4668.

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President Barack Obama committed hundreds of millions of dollars to the Teacher Incentive Fund (TIF), yet a few fundamental questions remain unanswered--was the federal program effective? Did student test scores improve? Since the late 19th century, teachers have been paid for their classroom services regardless of how well--or poorly--their students performed. Nearly a century later, advocates of education reform continue to champion teacher compensation policies that link salary to student achievement. Researchers have identified two motivation theories that must be present in order to have
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Kay, Luciano. "How do prizes induce innovation? learning from the Google Lunar X-prize." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/41193.

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Inducement prizes-where cash rewards are given to motivate the attainment of targets--have been long used to encourage scientific research, develop technological innovations, or stimulate individuals, groups, and communities to accomplish diverse goals. Lately, prizes have increasingly attracted the attention of policy-makers, among others, due to their potential to induce path-breaking innovations and accomplish related goals. Academic research, however, has barely investigated these prizes in spite of their long history, recent popularity, and notable potential. This research investigates pr
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Barbosa, Solange Maria Kileber. "A competitividade das fontes energéticas em uma abordagem de learning curves: uma proposição de regulação que incentive as tecnologias renováveis." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/106/106131/tde-14042016-094140/.

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O objetivo deste estudo foi estimar os efeitos da curva de aprendizado sobre a competitividade de fontes energéticas, tais como petróleo, carvão, gás natural, biomassa (etanol), hidroeletricidade, nuclear, eólica e fotovoltaica, e propor medidas regulatórias que incentivem as tecnologias renováveis. Para tanto, se propôs a utilização da abordagem de learning curves, que considera três efeitos principais para explicar a redução dos custos de produção: o efeito especialização (chamado de learning by doing), o efeito escala (scale effect) e o efeito da pesquisa e desenvolvimento - P&D (learning b
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sg, suchan@pacific net, and Su Hoon Chan. "A Theory of Cooperative Learning as Incentive-Values–Exchange: Studies of the Effects of Task-Structures, Rewards and Ability on Academic and Social-Emotional Measures of Mathematics Learning." Murdoch University, 2004. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20051019.151329.

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This PhD thesis is concerned with the social psychology of cooperative learning and its effects in cognitive and social-emotional domains. It comprises two main studies and two exploratory studies undertaken during two 10-day, 16-hour learning intervention programmes for Maths Word Problem-Solving (MWPS), respectively for 285 and 451 Grade-5 students in Singapore. Study 1 used a quasi-experimental design to investigate the outcomes of task-structures in an Individual Learning condition and three dyadic Cooperative Learning conditions that varied in the key elements: positive interdependence,
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Books on the topic "Incentive of learning"

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Ashton, D. N. Supporting workplace learning for high performance working. International Labour Office, 2002.

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1960-, Barba Navaretti Giorgio, ed. Creation and transfer of knowledge: Institutions and incentives. Springer, 1998.

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Covington, Martin V., and Karen Manheim Teel. Overcoming student failure: Changing motives and incentives for learning. American Psychological Association, 1996. http://dx.doi.org/10.1037/10193-000.

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Manheim, Teel Karen, ed. Overcoming student failure: Changing motives and incentives for learning. American Psychological Association, 1996.

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Prendergast, Canice. What happens within firms?: A survey of empirical evidence on compensation policies. National Bureau of Economic Research, 1996.

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Gardner, J. E. Incentives for lifelong learning: Continuing studies and the B.C. forestry community : report to the Silviculture Branch, B.C. Ministry of Forests. Forestry Canada, 1993.

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Schuster, John P. The power of open book management: Releasing the true potential of people's mind, heart, and hands. John Wiley, 1996.

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Yu, Han, Qiang Yang, and Lixin Fan. Federated Learning: Privacy and Incentive. Springer International Publishing AG, 2020.

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Beninger, Richard J. Dopamine and inverse incentive learning. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198824091.003.0006.

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Dopamine and inverse incentive learning explains that dopamine determines an incentive–value continuum. Novel and intense stimuli innately produce rapid dopamine neurons activation followed by inhibition. The repeated presentation of novel stimuli leads to a loss of this effect. Aversive stimuli, biologically important by definition, often deactivate dopamine neurons and may produce inverse incentive learning, leading to conditioned inverse incentive stimuli with decreased ability to elicit approach and other responses. The offset of aversion may increase the firing of dopamine neurons produci
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Beninger, Richard J. Drug abuse and incentive learning. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198824091.003.0010.

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Drug abuse and incentive learning explains how abused drugs, including nicotine, ethanol, marijuana, amphetamine, cocaine, morphine, and heroin, produce conditioned place preference and are self-administered; dopamine receptor antagonists block these effects. Stimuli that become reliable predictors of drug reward produce burst firing in dopaminergic neurons, but the drug retains its ability to activate dopaminergic neurons. Thus, repeated drug users experience two activations of dopaminergic neurotransmission, one upon exposure to the conditioned stimuli signaling the drug and another upon tak
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Book chapters on the topic "Incentive of learning"

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Carroll, Marilyn E., Peter A. Santi, Joseph Zohar, et al. "Incentive Learning." In Encyclopedia of Psychopharmacology. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68706-1_4307.

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Warrier, Lekha C., G. K. Ragesh, and Pao-Ann Hsiung. "Incentive Mechanism for Federated Learning." In Federated Learning. Apple Academic Press, 2024. http://dx.doi.org/10.1201/9781003497196-11.

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Floresco, Stan, Robert Kessler, Ronald L. Cowan, et al. "Reward-Related Incentive Learning." In Encyclopedia of Psychopharmacology. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68706-1_1125.

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Yang, Qiang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, and Han Yu. "Incentive Mechanism Design for Federated Learning." In Federated Learning. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-01585-4_7.

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Seon Hong, Choong, Latif U. Khan, Mingzhe Chen, Dawei Chen, Walid Saad, and Zhu Han. "Incentive Mechanisms for Federated Learning." In Wireless Networks. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4963-9_4.

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Zaikin, Oleg. "Incentive Model of a Project Learning Process." In Open Distance Learning. Jenny Stanford Publishing, 2022. http://dx.doi.org/10.1201/9781003132615-7.

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Yang, Xinxin, and Bo Gu. "Toward incentive-compatible vehicular crowdsensing." In Deep Learning and Its Applications for Vehicle Networks. CRC Press, 2023. http://dx.doi.org/10.1201/9781003190691-16.

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Liu, Yuan, Mengmeng Tian, Yuxin Chen, Zehui Xiong, Cyril Leung, and Chunyan Miao. "A Contract Theory Based Incentive Mechanism for Federated Learning." In Federated and Transfer Learning. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11748-0_6.

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Zhou, Kaile, and Lulu Wen. "Incentive-Based Demand Response with Deep Learning and Reinforcement Learning." In Smart Energy Management. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9360-1_7.

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Beninger, Richard J., and Todor V. Gerdjikov. "Dopamine-Glutamate Interactions in Reward-Related Incentive Learning." In Dopamine and Glutamate in Psychiatric Disorders. Humana Press, 2005. http://dx.doi.org/10.1007/978-1-59259-852-6_14.

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Conference papers on the topic "Incentive of learning"

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Liu, Yuru, Zhicheng Bao, Weishan Zhang, et al. "HIPFL: A Hierarchical Incentive-Based Personalized Federated Learning." In 2024 IEEE 4th International Conference on Digital Twins and Parallel Intelligence (DTPI). IEEE, 2024. https://doi.org/10.1109/dtpi61353.2024.10778797.

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Khan, Ahmad Faraz, Xinran Wang, Qi Le, et al. "IP-FL: Incentive-Driven Personalization in Federated Learning." In 2025 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2025. https://doi.org/10.1109/ipdps64566.2025.00053.

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Khan, Afsana, Marijn ten Thij, Frank Thuijsman, and Anna Wilbik. "Using the Nucleolus for Incentive Allocation in Vertical Federated Learning." In 2024 2nd International Conference on Federated Learning Technologies and Applications (FLTA). IEEE, 2024. https://doi.org/10.1109/flta63145.2024.10839765.

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Liu, Xin, Rui Wang, Pengfeng Zhang, et al. "Long-Term Privacy-Preserving Incentive Scheme Design for Federated Learning." In 2024 IEEE 23rd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 2024. https://doi.org/10.1109/trustcom63139.2024.00377.

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Wen, Jinbo, Yang Zhang, Yulin Chen, et al. "Learning-based Big Data Sharing Incentive in Mobile AIGC Networks." In GLOBECOM 2024 - 2024 IEEE Global Communications Conference. IEEE, 2024. https://doi.org/10.1109/globecom52923.2024.10901493.

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Liu, Xuanzhang, Jiyao Liu, Xinliang Wei, and Yu Wang. "Incentive Mechanism Design in Semi-Asynchronous Blockchain-based Federated Learning." In 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall). IEEE, 2024. https://doi.org/10.1109/vtc2024-fall63153.2024.10757817.

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Certório, Jair, Nuno C. Martins, Richard J. La, and Murat Arcak. "Incentive Designs for Learning Agents to Stabilize Coupled Exogenous Systems." In 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10885969.

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Wang, Rongrong, Duc Van Le, Jikun Kang, Rui Tan, and Xue Liu. "Incentive Temperature Control for Green Colocation Data Centers via Reinforcement Learning." In 2024 IEEE/ACM 32nd International Symposium on Quality of Service (IWQoS). IEEE, 2024. http://dx.doi.org/10.1109/iwqos61813.2024.10682849.

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Yu, Lu, Zheng Chang, Timo Hämäläinen, and Geyong Min. "Privacy-Preserved Incentive Mechanism for Split Learning in Edge Computing System." In GLOBECOM 2024 - 2024 IEEE Global Communications Conference. IEEE, 2024. https://doi.org/10.1109/globecom52923.2024.10901308.

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Kang, Yiqun, Haozhe Xiong, Dan Liu, Wanli Feng, Zhe Li, and Yuqi Ao. "Load Aggregation Quotient Incentive Strategy Based on Multi Agent Reinforcement Learning." In 2024 4th International Conference on New Energy and Power Engineering (ICNEPE). IEEE, 2024. https://doi.org/10.1109/icnepe64067.2024.10860715.

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Reports on the topic "Incentive of learning"

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Elacqua, Gregory, Mateus Rodrigues, and Leonardo Rosa. IImpact of Monetary Incentives on Teacher Decisions to Leave and Choose Schools: Evidence from a Policy Reform in Sao Paulo. Inter-American Development Bank, 2025. https://doi.org/10.18235/0013366.

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Teacher turnover is a major challenge for human resource management in schools, adversely affecting student learning. We examine the impact of a monetary incentive program introduced in 2022 in the city of Sao Paulo, Brazil, which aims to reduce teacher turnover by allocating wage premiums ranging from 5% to 25% of base salary based on schools turnover levels. Our results show a significant reduction in turnover: an average decrease of 18% across all schools, with an even more pronounced 30% reduction in schools offering higher incentives. Notably, the program also attracted new teachers to th
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Schipper, Youdi, Isaac Mbiti, and Mauricio Romero. Designing and Testing a Scalable Teacher Incentive Programme in Tanzania. Research on Improving Systems of Education (RISE), 2022. http://dx.doi.org/10.35489/bsg-rise-ri_2022/044.

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School participation in Tanzania has increased dramatically over the past two decades: primary school enrolment increased from 4.9 million in 2001 to 10.9 million in 2020. While 81 percent of primary-school-age children are currently enrolled, over the last ten years, the primary completion rate has dropped and remains below 70 percent since 2015 (data from UNESCO Institute for Statistics).1 Despite improvements in enrolment, indicators of foundational learning remain low. According to the 2020 report of the Standard Two National Assessment (STNA), conducted by the National Examinations Counci
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Rosser, Andrew, Phil King, and Danang Widoyoko. The Political Economy of the Learning Crisis in Indonesia. Research on Improving Systems of Education (RISE), 2022. http://dx.doi.org/10.35489/bsg-rise-2022/pe01.

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Indonesia has done much to improve access to education in recent decades but it has had little success in improving learning outcomes. This paper examines the political origins of this problem. It argues that Indonesia’s learning crisis has reflected the political dominance during the New Order and post-New Order periods of predatory political, bureaucratic and corporate elites who have sought to use the country’s education system to accumulate resources, distribute patronage, mobilize political support, and exercise political control rather than produce skilled workers and critical and inquir
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Leaver, Clare, Owen Ozier, Pieter Serneels, and Andrew Zeitlin. Recruitment, Effort, and Retention Effects of Performance Contracts for Civil Servants: Experimental Evidence from Rwandan Primary Schools. Research on Improving Systems of Education (RISE), 2020. http://dx.doi.org/10.35489/bsg-rise-wp_2020/048.

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This paper reports on a two-tiered experiment designed to separately identify the selection and effort margins of pay-for-performance (P4P). At the recruitment stage, teacher labor markets were randomly assigned to a pay-for-percentile or fixed-wage contract. Once recruits were placed, an unexpected, incentive-compatible, school-level re-randomization was performed, so that some teachers who applied for a fixed-wage contract ended up being paid by P4P, and vice versa. By the second year of the study, the within-year effort effect of P4P was 0.16 standard deviations of pupil learning, with the
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Santiago, Ana, Marcelo Cabrol, and Mariana Alfonso. Aligning Learning Incentives (ALI). Inter-American Development Bank, 2010. http://dx.doi.org/10.18235/0008047.

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Mexican students score low in international learning assessments, particularly in math. To help remedy this situation, the Inter-American Development Bank is supporting a pilot study to identify to what extent performance based incentives for students, teachers and principals in upper-secondary schools can improve students¿ mathematical skills, as measured by their scores in curriculum-based mathematics tests.
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Madsen, Jens, Nikhil Kuppa, and Lucas Parra. The Brain, Body, and Behaviour Dataset - Neural Engineering Lab, CCNY. Fcp-indi, 2025. https://doi.org/10.15387/fcp_indi.retro.bbbd.

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When humans engage with video, their brain and body interact in response to sensory input. To investigate these interactions, we recorded and are releasing a dataset from N=178 participants across five experiments featuring short online educational videos. This dataset comprises approximately 110 hours of multimodal data including electrocardiogram (ECG), heart rate, respiration, breathing rate, pupil size, electrooculogram (EOG), gaze position, saccades, blinks, fixations, head movement, and electroencephalogram (EEG). Participants viewed 3-6 videos (mean total duration: 28±5 min) to test att
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Filmer, Deon, James Habyarimana, and Shwetlena Sabarwal. Teacher Performance-Based Incentives and Learning Inequality. Research on Improving Systems of Education (RISE), 2020. http://dx.doi.org/10.35489/bsg-rise-wp_2020/047.

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This study evaluates the impacts of low-cost, performance-based incentives in Tanzanian secondary schools. Results from a two-phase randomized trial show that incentives for teachers led to modest average improvements in student achievement across different subjects. Further, withdrawing incentives did not lead to a “discouragement effect” (once incentives were withdrawn, student performance did not fall below pre-baseline levels). Rather, impacts on learning were sustained beyond the intervention period. However, these incentives may have exacerbated learning inequality within and across scho
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Uchitel, Aleksandr D., Ilona V. Batsurovska, Nataliia A. Dotsenko, Olena A. Gorbenko, and Nataliia I. Kim. Implementation of future agricultural engineers' training technology in the informational and educational environment. [б. в.], 2021. http://dx.doi.org/10.31812/123456789/4440.

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The article presents the implementation of future agricultural engineers’ training technology in the informational and educational environment. To train future agricultural engineers, it is advisable to form tutorials for the study of each discipline in the conditions of informational and educational environment. Such tutorials are an assistance in mastering both theoretical material and course navigation, where interactive electronic learning tools are presented to perform tasks in the informational and educational environment. Higher education applicants perform such tasks directly in the cl
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Hwa, Yue-Yi, Sharon Kanthy Lumbanraja, Usha Adelina Riyanto, and Dewi Susanti. The Role of Coherence in Strengthening CommunityAccountability for Remote Schools in Indonesia. Research on Improving Systems of Education (RISE), 2022. http://dx.doi.org/10.35489/bsg-rise-wp_2022/090.

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Incoherence in accountability relationships can hamper the quality of education. Such incoherence can be a particular challenge in resource-constrained, remote villages where teachers tend to have higher educational capital and social status than the parents and communities that they serve. We analyze quantitative and qualitative data from a randomized controlled trial of a social accountability mechanism (SAM) for schools in remote Indonesian villages. The intervention had three treatment arms, all of which included the SAM, which engaged village-level stakeholders in a consensus-building pro
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Heinrich, Carolyn. Demand and Supply-Side Determinants of Conditional Cash Transfer Program Effectiveness: Improving the First-Generation Programs. Inter-American Development Bank, 2005. http://dx.doi.org/10.18235/0011174.

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The Programa Nacional de Becas Estudiantiles is a conditional cash transfer program designed to promote long-term human capital accumulation among Argentine youth and to reduce poverty. This study follows a 1999 cohort of students from 24 schools for five years. Propensity score matching methods are used with a comparison group of students who were excluded due to quotas to estimate the average and marginal impacts of the Becas program. Multilevel methods are also used to estimate the contributions of Becas program management and school characteristics in explaining variation between schools i
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