Bibliografías temáticas / Progressive learning

Literatura académica sobre el tema "Progressive learning"

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Publicado: 4 de mayo de 2024

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Artículos de revistas sobre el tema "Progressive learning"

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Acharya, Avidit y Juan Ortner. "Progressive Learning". Econometrica 85, n.º 6 (2017): 1965–90. http://dx.doi.org/10.3982/ecta14718.

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Yu, Zhengxu, Dong Shen, Zhongming Jin, Jianqiang Huang, Deng Cai y Xian-Sheng Hua. "Progressive Transfer Learning". IEEE Transactions on Image Processing 31 (2022): 1340–48. http://dx.doi.org/10.1109/tip.2022.3141258.

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Daniels, Jonathan S., David Moreau y Brooke N. Macnamara. "Learning and Transfer in Problem Solving Progressions". Journal of Intelligence 10, n.º 4 (12 de octubre de 2022): 85. http://dx.doi.org/10.3390/jintelligence10040085.

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Do individuals learn more effectively when given progressive or variable problem-solving experience, relative to consistent problem-solving experience? We investigated this question using a Rubik’s Cube paradigm. Participants were randomly assigned to a progression-order condition, where they practiced solving three progressively more difficult Rubik’s Cubes (i.e., 2 × 2 × 2 to 3 × 3 × 3 to 4 × 4 × 4), a variable-order condition, where they practiced solving three Rubik’s Cubes of varying difficulty (e.g., 3 × 3 × 3 to 2 × 2 × 2 to 4 × 4 × 4), or a consistent-order condition, where they consistently practiced on three 5 × 5 × 5 Rubik’s Cubes. All the participants then attempted a 5 × 5 × 5 Rubik’s Cube test. We tested whether variable training is as effective as progressive training for near transfer of spatial skills and whether progressive training is superior to consistent training. We found no significant differences in performance across conditions. Participants’ fluid reasoning predicted 5 × 5 × 5 Rubik’s Cube test performance regardless of training condition.
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Yu, Zhiwen, Daxing Wang, Jane You, Hau-San Wong, Si Wu, Jun Zhang y Guoqiang Han. "Progressive subspace ensemble learning". Pattern Recognition 60 (diciembre de 2016): 692–705. http://dx.doi.org/10.1016/j.patcog.2016.06.017.

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Fayek, Haytham M., Lawrence Cavedon y Hong Ren Wu. "Progressive learning: A deep learning framework for continual learning". Neural Networks 128 (agosto de 2020): 345–57. http://dx.doi.org/10.1016/j.neunet.2020.05.011.

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Ahmed, Naveed, JeeWoong Park, Cristian Arteaga y Haroon Stephen. "Investigation of Progressive Learning within a Statics Course: An Analysis of Performance Retention, Critical Topics, and Active Participation". Education Sciences 13, n.º 6 (2 de junio de 2023): 576. http://dx.doi.org/10.3390/educsci13060576.

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Previous research has demonstrated a link between prior knowledge and student success in engineering courses. However, while course-to-course relations exist, researchers have paid insufficient attention to internal course performance development. This study aims to address this gap—designed to quantify and thus extract meaningful insights—by examining a fundamental engineering course, Statics, from three perspectives: (1) progressive learning reflected in performance retention throughout the course; (2) critical topics and their influence on students’ performance progression; and (3) student active participation as a surrogate measure of progressive learning. By analyzing data collected from 222 students over five semesters, this study draws insights on student in-course progressive learning. The results show that early learning had significant implications in building a foundation in progressive learning throughout the semester. Additionally, insufficient knowledge on certain topics can hinder student learning progression more than others, which eventually leads to course failure. Finally, student participation is a pathway to enhance learning and achieve excellent course performance. The presented analysis approach provides educators with a mechanism for diagnosing and devising strategies to address conceptual lapses for STEM (science, technology, engineering, and mathematics) courses, especially where progressive learning is essential.
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Boo-Ho Yang y H. Asada. "Progressive learning and its application to robot impedance learning". IEEE Transactions on Neural Networks 7, n.º 4 (julio de 1996): 941–52. http://dx.doi.org/10.1109/72.508937.

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Gitlin, Andrew. "Global learning & progressive school change". Literacy Information and Computer Education Journal 6, n.º 4 (1 de diciembre de 2015): 2064–68. http://dx.doi.org/10.20533/licej.2040.2589.2015.0275.

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Zhang, Hui, Danqing Kang, Haibo He y Fei-Yue Wang. "APLNet: Attention-enhanced progressive learning network". Neurocomputing 371 (enero de 2020): 166–76. http://dx.doi.org/10.1016/j.neucom.2019.08.086.

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Han, Bo, Ivor W. Tsang, Ling Chen, Celina P. Yu y Sai-Fu Fung. "Progressive Stochastic Learning for Noisy Labels". IEEE Transactions on Neural Networks and Learning Systems 29, n.º 10 (octubre de 2018): 5136–48. http://dx.doi.org/10.1109/tnnls.2018.2792062.

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Tesis sobre el tema "Progressive learning"

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Cox, Graham. "Communities of practice : learning in progressive ensembles". Thesis, University of Huddersfield, 2003. http://eprints.hud.ac.uk/id/eprint/4711/.

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This study examines the learning of ensemble skills by young musicians in progressive ensembles. Data collection took place in three music centres which form part of an LEA music service's ensemble provision. This study uniquely approaches ensembles as'socio-musical' phenomena. It finds description and explanation in the constantly changing and developing socio-musical interactions that form ensembles. Using an ethnographic approach it examines the practice of ensembles that are part of musical learning pyramids, through the eyes and actions of the ensemble members. There is an examination of ensemble membership and the social structures and interactions that form ensembles. This study explores, and for the first time identifies, a set of ensemble specific skills. These are the skills that a musician uses to negotiate, integrate and cooperate with other participants in the production of ensemble performance. It goes on to examine how ensemble specific skills are acquired and suggests that the learning process is one of serial performative responsibility transfer created within stratified centripetal progression. New, or novice, members of an ensemble start by participating at a peripheral level leaving it to others to take performative responsibility for the production of a negotiated collaborative realization of the musical intentions of the composer. This study has been influenced by the work of Lave and Wenger and social theories of learning. However, it departs from these theories by suggesting that the learning process within an ensemble is responsibility led and stratified.
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Yang, Boo Ho. "Progressive learning and its application to robotic assembly". Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/35038.

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Yu, Jyu-fang. "Learning and teaching the English Progressive in Taiwan /". Full text (PDF) from UMI/Dissertation Abstracts International, 2001. http://wwwlib.umi.com/cr/utexas/fullcit?p3008480.

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Archibald, Douglas Bollon. "Global education, an alternative program of study for progressive learning". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0013/MQ53397.pdf.

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Li, Shih-Hung. "Progressive learning of endpoint feedback systems with model uncertainty and sensor noise". Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/38158.

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Wan, Lisa Chi Yan. "Implementation of Professional Learning Communities at the Progressive Academy of Southeast Asia". Thesis, University of Southern California, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10749206.

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There is a large body of research that suggests the concept of a professional learning community (PLC) can promote improved student learning by increasing collective teacher capacity to meet the diverse learning needs of students (Reeves, 2016; Battersby & Verdi, 2013; Marzano & DuFour, 2011; Fullan 2010, Senge, 2006; Morrissey, 2000; Hord, 1997). Many schools have implemented PLCs around the world. Despite the well-documented benefits of PLCs, there is inconsistency to the extent with which PLCs are implemented within schools and districts. The purpose of this study was to (a) understand how the elementary division at Progressive Academy of Southeast Asia (PASA), an independent private school, has implemented PLCs; (b) learn about promising practices utilized by high performing PLCs to promote teacher collaboration and high productivity; and (c) understand barriers confronted by PLCs during the PLC process. A qualitative research design was used to understand the implementation process through focus group discussions. Focus group discussions were conducted with all grade levels, kindergarten to grade 5 in the elementary division. Ten themes emerged from the study (1) an alignment of belief in the mission and vision and purpose of PLCs by faculty and administration, (2) the elementary school has implemented systems and structures to support the PLC process, (3) there is a collaborative culture for the PLC process, (4) collective responsibility for student learning is still at its infancy stage, (5) PLCs are results oriented and have clearly established SMART goals that align with the elementary school’s strategic plan, (6) instructional assistants are a strong system of support for learning for students, (7) trust is a key to high performing PLCs, (8) there is a low level of trust in some PLCs, (9) there is a transient faculty, and (10) there are an overwhelming number of initiatives that require the time of PLCs at the school.

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Sah, Shubham. "A Virtual Reality Based Progressive Learning Paradigm For Supply Chain Management Education". University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1282059998.

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Wu, Wenbo. "Object Recognition with Progressive Refinement for Collaborative Robots Task Allocation". Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41581.

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With the rapid development of deep learning techniques, the application of Convolutional Neural Network (CNN) has benefited the task of target object recognition. Several state-of-the-art object detectors have achieved excellent performance on the precision for object recognition. When it comes to applying the detection results for the real world application of collaborative robots, the reliability and robustness of the target object detection stage is essential to support efficient task allocation. In this work, collaborative robots task allocation is based on the assumption that each individual robotic agent possesses specialized capabilities to be matched with detected targets representing tasks to be performed in the surrounding environment which impose specific requirements. The goal is to reach a specialized labor distribution among the individual robots based on best matching their specialized capabilities with the corresponding requirements imposed by the tasks. In order to further improve task recognition with convolutional neural networks in the context of robotic task allocation, this thesis proposes an innovative approach for progressively refining the target detection process by taking advantage of the fact that additional images can be collected by mobile cameras installed on robotic vehicles. The proposed methodology combines a CNN-based object detection module with a refinement module. For the detection module, a two-stage object detector, Mask RCNN, for which some adaptations on region proposal generation are introduced, and a one-stage object detector, YOLO, are experimentally investigated in the context considered. The generated recognition scores serve as input for the refinement module. In the latter, the current detection result is considered as the a priori evidence to enhance the next detection for the same target with the goal to iteratively improve the target recognition scores. Both the Bayesian method and the Dempster-Shafer theory are experimentally investigated to achieve the data fusion process involved in the refinement process. The experimental validation is conducted on indoor search-and-rescue (SAR) scenarios and the results presented in this work demonstrate the feasibility and reliability of the proposed progressive refinement framework, especially when the combination of adapted Mask RCNN and D-S theory data fusion is exploited.
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Friedle, Robert E. "Assessment of learning potential using a modified version of the coloured progressive matrices". Virtual Press, 1986. http://liblink.bsu.edu/uhtbin/catkey/468076.

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An assessment procedure to measure the learning potential of mildly and moderately mentally handicapped adults was investigated in this study. The procedure was developed to overcome the limitations of previously established learning potential assessment procedures by meeting the specific orientation necessary when assessing the mentally handicapped, by avoiding practice and mechanical memory effects, and by requiring the subjects to show the ability to retain and apply new cognitive skills. Thirty institutionalized mentally handicapped adults were matched on IG and then randomly assigned to an assessment or control group. For the assessment group the intervention procedure involved the Coloured Progressive Matrices test as a pretest measure, in four treatment sessions each two to three days apart, and as a posttest measure. Verbal and/or figural aids were provided when an incorrect response was given on a test item during the treatment sessions. The control group was provided the same number of administrations of the test but without the treatment intervention. Significant differences were found between the pre and post test performance levels of the assessment group. The control group showed no significant gains. The learning potential assessment procedure used in this study provided a measure of the learning potential of mentally handicapped institutionalized adults.The results indicate that the mentally handicapped can perform at much higher levels than those assessed by standardly used intellectual measures. Implications are that through the use of this approach a clearer discrimination of intellectual ability within the broad classifications of the mentally handicappped can be gained. Such information could be used to aid in the selection of peers, developmental programming decisions, and provide prescriptive information about the most efficacious modes of learning for an individual.
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Lai, Matteo. "Conditional MR image synthesis with Auxiliary Progressive Growing GANs". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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L'addestramento di algotritmi di deep learning (DL) richiede una grande quantità di dati, che però spesso non sono disponibili in ambito medico. In questa tesi viene proposto un modello per la generazione di dataset sintetici etichettati nell'ambito dell'imaging medico ad alta risoluzione. Dopo aver presentato vantaggi e limiti dell'uso delle tecniche di DL in radiologia, vengono proposte le Generative Adversarial Networks (GANs) come possibile soluzione per superare tali limiti. Illustrando lo stato dell'arte relativo alle GAN, viene focalizzata l'attenzione sulle Progressive Growing GAN, capaci di generare immagini ad alta risoluzione, e sulle Auxiliary Classifier GAN (ACGAN), capaci di generare immagini target. Sulla base di questi modelli, vengono proposte le innovative Progressive ACGAN (PACGAN), progettate per generare immagini target ad elevata risoluzione. L'obiettivo di questo lavoro di tesi è sfruttare la capacità delle GAN di creare una rappresentazione nello spazio latente dei dati del training set, sia per generare immagini target ad alta risoluzione (256 x 256), che per effettuare una classificazione. Il modello proposto viene testato su un dataset contenente 200 immagini di risonanza magnetica (RM) cerebrale di soggetti sani e pazienti con malattia di Alzheimer. I risultati del modello sono molto promettenti. La qualità delle immagini generate è stata valutata sia visivamente che quantitativamente, tramite FID (Fréchet Inception Distance) e MS-SSIM (Multi-Scale Structural Similarity Index), evidenziando una maggiore capacità delle PACGAN di rappresentare immagini target ad alta risoluzione rispetto alle ACGAN. Le performance di classificazione risultano ottime nel training set, con discreta capacità di generalizzare su nuovi dati. Il modello proposto consente quindi di generare immagini target ad alta risoluzione che possono essere usate per ottenere dataset sintetici.
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Libros sobre el tema "Progressive learning"

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1947-, Sutherland Rosamund, Claxton Guy y Pollard Andrew 1949-, eds. Learning and teaching where worldviews meet. Stoke on Trent, Staffordshire, England: Trentham Books, 2003.

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R, Payne Carla, ed. Information technology and constructivism in higher education: Progressive learning frameworks. Hershey, PA: Information Science Reference, 2009.

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Boehm, Ingrid. Productive learning: An educational opportunity for young people in Europe. Berlin: Schrbri-Verlag, 1996.

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Norris, Norman Dale. The promise and failure of progressive education. Lanham, Maryland: Scarecrow Press/Rowman & Littlefield Pub., 2004.

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Lachance, Julie. French connections: Progressive French language learning for adult and continuing education students. Montréal: Guérin, 2011.

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Midwives of progressive education: The Bureau of Educational Experiments, 1916-1919. Nijmegen: Integraal (Werkgroep Integrerende Wetenschapsbeoefening), 2013.

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1957-, Bogner Drew y Buli-Holmberg Jorun 1951-, eds. Teaching and learning: A model for academic and social cognition. Lanham: Rowman & Littlefield Education, 2011.

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1973-, Lytras Miltiadis D. y Sheth A. 1959-, eds. Progressive concepts for Semantic Web evolution: Applications and developments. Hershey PA: Information Science Reference, 2010.

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Hughes, Martin, 1949 May 15-, ed. Progression in learning. Clevedon [England]: Multilingual Matters, 1996.

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1948-, Evers Williamson M., ed. What's gone wrong in America's classrooms. Stanford, Calif: Hoover Institution Press, Stanford University, 1998.

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Capítulos de libros sobre el tema "Progressive learning"

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Pyles, Loretta. "Learning from Social Movements". En Progressive Community Organizing, 85–112. Third edition. | New York, NY: Routledge, 2021.: Routledge, 2020. http://dx.doi.org/10.4324/9780429294075-4.

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Huang, Xin y Yuxin Peng. "Progressive Cross-Media Correlation Learning". En Image and Graphics Technologies and Applications, 201–11. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1702-6_20.

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Wang, Yiwei, Wei Wang, Yuxuan Liang, Yujun Cai y Bryan Hooi. "Progressive Supervision for Node Classification". En Machine Learning and Knowledge Discovery in Databases, 266–81. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67658-2_16.

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Poonam Chaudhari y Himanshu Agarwal. "Progressive Review Towards Deep Learning Techniques". En Proceedings of the International Conference on Data Engineering and Communication Technology, 151–58. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1675-2_17.

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Sadid, Md Waselul Haque, Md Nazrul Islam Mondal, Md Shamsul Alam, Abu Sayeed Md Sohail y Boshir Ahmed. "Progressive Boosting for Classifier Committee Learning". En Lecture Notes in Computer Science, 52–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30176-9_7.

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Leite, Rui y Pavel Brazdil. "Improving Progressive Sampling via Meta-learning". En Progress in Artificial Intelligence, 313–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24580-3_37.

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Hu, Jiagao, Zhengxing Sun, Kewei Yang y Yiwen Chen. "Progressive Image Segmentation Using Online Learning". En Lecture Notes in Computer Science, 181–91. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24075-6_18.

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B�ntig, W. E. "Learning from Cancer Patients". En Body Psychotherapy in Progressive and Chronic Disorders, 121–44. Basel: KARGER, 2002. http://dx.doi.org/10.1159/000065902.

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Achanta, Yashasvi, P. Ramesh Babu y D. Sandeep. "Progressive Damage Analysis of Laminated Composites". En Learning and Analytics in Intelligent Systems, 517–25. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24314-2_62.

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Collier, Caleb. "Dewey, Montessori, and Progressive Reformers". En Theoretical and Historical Evolutions of Self-Directed Learning, 53–60. New York: Routledge, 2023. http://dx.doi.org/10.4324/9781003357094-8.

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Actas de conferencias sobre el tema "Progressive learning"

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Ningsih, Sulia y Anita Adesti. "Android-Based Mobile Learning: Its Effect on Students’ Learning Achievement". En International Conference on Progressive Education (ICOPE 2019). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/assehr.k.200323.099.

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Triastuti, Rini, Winarno y Wijayanti. "The Strategy of Improving Student Learning Interest Through the Use of Video as Learning Media in Civic Education Learning". En International Conference on Progressive Education (ICOPE 2019). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/assehr.k.200323.124.

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Ndiung, S. "The Treffinger Learning Model with RME Principles on Mathematics Learning Outcome by Considering Numerical Ability". En International Conference on Progressive Education (ICOPE 2019). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/assehr.k.200323.080.

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Church, Joshua y Amihai Motro. "Learning service behavior with progressive testing". En 2011 IEEE International Conference on Service-Oriented Computing and Applications (SOCA). IEEE, 2011. http://dx.doi.org/10.1109/soca.2011.6166199.

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Lynn, Eric C., Shih-Ying Chen, Tyng-Hao Hsu, Chang-Cheng Hung y Chin-Hsiang Lin. "Progressive self-learning photomask defect classification". En Photomask and Next Generation Lithography Mask Technology IX, editado por Hiroichi Kawahira. SPIE, 2002. http://dx.doi.org/10.1117/12.477012.

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Wang, Wufan, Lei Zhang y Hua Huang. "Progressive Unsupervised Learning of Local Descriptors". En MM '22: The 30th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3503161.3547792.

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Zhao, Chen, Yixiao Ge, Feng Zhu, Rui Zhao, Hongsheng Li y Mathieu Salzmann. "Progressive Correspondence Pruning by Consensus Learning". En 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.00640.

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Back, Philipp. "Real-World Reinforcement Learning: Observations from Two Successful Cases". En Digital Support from Crisis to Progressive Change. University of Maribor Press, 2021. http://dx.doi.org/10.18690/978-961-286-485-9.20.

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Reinforcement Learning (RL) is a machine learning technique that enables artificial agents to learn optimal strategies for sequential decision-making problems. RL has achieved superhuman performance in artificial domains, yet real-world applications remain rare. We explore the drivers of successful RL adoption for solving practical business problems. We rely on publicly available secondary data on two cases: data center cooling at Google and trade order execution at JPMorgan. We perform thematic analysis using a pre-defined coding framework based on the known challenges to real-world RL by DulacArnold, Mankowitz, & Hester (2019). First, we find that RL works best when the problem dynamics can be simulated. Second, the ability to encode the desired agent behavior as a reward function is critical. Third, safety constraints are often necessary in the context of trial-and-error learning. Our work is amongst the first in Information Systems to discuss the practical business value of the emerging AI subfield of RL.
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Yulianti, Tien y Ari Sulistiyawati. "The Blended Learning for Student’s Character Building". En International Conference on Progressive Education (ICOPE 2019). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/assehr.k.200323.089.

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Smith, James, Cameron Taylor, Seth Baer y Constantine Dovrolis. "Unsupervised Progressive Learning and the STAM Architecture". En Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/410.

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We first pose the Unsupervised Progressive Learning (UPL) problem: an online representation learning problem in which the learner observes a non-stationary and unlabeled data stream, learning a growing number of features that persist over time even though the data is not stored or replayed. To solve the UPL problem we propose the Self-Taught Associative Memory (STAM) architecture. Layered hierarchies of STAM modules learn based on a combination of online clustering, novelty detection, forgetting outliers, and storing only prototypical features rather than specific examples. We evaluate STAM representations using clustering and classification tasks. While there are no existing learning scenarios that are directly comparable to UPL, we compare the STAM architecture with two recent continual learning models, Memory Aware Synapses (MAS) and Gradient Episodic Memories (GEM), after adapting them in the UPL setting.
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Informes sobre el tema "Progressive learning"

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Pearce, Andrea, Richard Byrne, David Grover, Benjamin Leavitt y Kevin Powell. Progressive Automation of Minirhizotron Image Processing Through Advanced Contextualization and Machine Learning (Final Report). Office of Scientific and Technical Information (OSTI), febrero de 2021. http://dx.doi.org/10.2172/1767440.

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Rosser, Andrew, Phil King y Danang Widoyoko. The Political Economy of the Learning Crisis in Indonesia. Research on Improving Systems of Education (RISE), julio de 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 inquiring minds. Technocratic and progressive elements, who have supported a stronger focus on basic skills acquisition, have contested this orientation, with occasional success, but generally contestation has been settled in favour of predatory elites. The analysis accordingly suggests that efforts to improve learning outcomes in Indonesia are unlikely to produce significant results unless there is a fundamental reconfiguration of power relations between these elements. In the absence of such a shift, moves to increase funding levels, address human resource deficits, eliminate perverse incentive structures, and improve education management in accordance with technocratic templates of international best practice or progressive notions of equity and social justice—the sorts of measures that have been the focus of education reform efforts in Indonesia so far—are unlikely to produce the intended results.
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Steil, Benn. Regional Financial Market Integration: Learning from the European Experience. Inter-American Development Bank, diciembre de 1997. http://dx.doi.org/10.18235/0011547.

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The so-called "European model" of market integration has evolved over many decades. In particular, the original plan to integrate Europe economically via a progressive program of harmonizing national legislation has, particularly in the area of financial markets, given way to a radical alternative based upon Member State "mutual recognition" of existing national legislation and regulation. Whereas this shift had been initiated largely on pragmatic grounds, the mutual recognition approach has since taken on an ideological and strategic dimension in political negotiations which makes the study of its effects on the ground all the more important.
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Singh, Abhijeet, Mauricio Romero y Karthik Muralidharan. COVID-19 Learning Loss and Recovery: Panel Data Evidence from India. Research on Improving Systems of Education (RISE), septiembre de 2022. http://dx.doi.org/10.35489/bsg-risewp_2022/112.

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We use a near-representative household panel survey of ∼19,000 primary-school-aged children in rural Tamil Nadu to study the extent of ‘learning loss’ after COVID-19 school closures, the pace of recovery in the months after schools reopened, and the role of a flagship compensatory intervention introduced by the state government. Students tested in December 2021, after 18 months of school closures, displayed severe deficits in learning of about 0.7 standard deviations (σ) in math and 0.34σ in language compared to identically-aged students in the same villages in 2019. Using multiple rounds of in-person testing, we find that two-thirds of this deficit was made up in the 6 months after school reopening. Using value-added models, we attribute ∼24% of the cohort-level recovery to a government-run after-school remediation program which improved test scores for attendees by 0.17σ in math and 0.09σ in Tamil after 3-4 months. Further, while learning loss was regressive, the recovery was progressive, likely reflecting (in part) the greater take up of the remediation program by more socioeconomically disadvantaged students. These positive results from a state-wide program delivered at scale by the government may provide a useful template for both recovery from COVID-19 learning losses, and bridging learning gaps more generally in low-and-middle-income countries.
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Rosser, Andrew. Welfare Regimes and the Political Economy of Learning in Developing Countries. Research on Improving Systems of Education (RISE), marzo de 2023. http://dx.doi.org/10.35489/bsg-rise-2023/pe11.

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This paper synthesises the findings of the RISE Political Economy of Adoption (PET-A) country studies by interpreting their findings in light of the findings of comparative research on the evolution of ‘welfare regimes’. This paper suggests that PET-A countries have not, for the most part, developed the sorts of political settlements identified as conducive to change in the welfare regimes literature or indeed others that might be expected to produce similar gains. They are instead dominated by predatory coalitions which seek to use education systems for rent-seeking, ideological, or other non-developmental purposes while technocratic and progressive elements are marginalised. In all cases, they consequently lack coalitions that are capable of driving education development. At the same time, the PET-A studies also provide some evidence to suggest that democratic reform can trigger improvements in education policy and learning outcomes by shifting the balance of power between elements within political settlements. In this respect, the PET-A studies suggest that the key to promoting educational development/learning in the developing world may be to think beyond narrow education-focused interventions to broader programs of democratic rights-oriented reform.In presenting this argument, this paper begins by providing an overview of the welfare regimes literature’s findings with regards to the political dynamics that have facilitated education and learning gains. It then assesses the extent to which the PET-A countries have developed such political settlements or indeed any others that might produce learning gains and reviews the evidence in the PET-A studies as to the effects of democratic reform. The final section of the paper presents the conclusions.
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6

Bergeron, Augustin, Arnaud Fournier, John Kabeya Kabeya, Gabriel Tourek y Jonathan L. Weigel. Using Machine Learning to Create a Property Tax Roll: Evidence from the City of Kananga, DR Congo'. Institute of Development Studies, octubre de 2023. http://dx.doi.org/10.19088/ictd.2023.053.

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Developing countries often lack the financial resources to provide public goods. Property taxation has been identified as a promising source of local revenue, because it is relatively efficient, captures growth in real estate value, and can be progressive. However, many low income countries do not collect property taxes effectively due to missing or incomplete property tax rolls. We use machine learning and computer vision models to construct a property tax roll in a large Congolese city. To train the algorithm and predict the value of all properties in the city, we rely on the value of 1,654 randomly chosen properties assessed by government land surveyors during in-person property appraisal visits, and property characteristics from administrative data or extracted from property photographs. The best machine learning algorithm, trained on property characteristics from administrative data, achieves a cross validated R2 of 60 per cent, and 22 per cent of the predicted values are within 20 per cent of the target value. The computer vision algorithms, trained on property picture features, perform less well, with only 9 per cent of the predicted values within 20 per cent of the target value for the best algorithm. We interpret the results as suggesting that simple machine learning methods can be used to construct a property tax roll, even in a context where information about properties is limited and the government can only collect a small number of property values using in-person property appraisal visits.
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Duong, Bich-Hang y Joan DeJaeghere. From Student-Centered to Competency-Based Reform: Exploring Teachers’ Perspective of Meaningful Participation. Research on Improving Systems of Education (RISE), febrero de 2022. http://dx.doi.org/10.35489/bsg-rise-wp_2022/089.

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Student-centered pedagogy has been widely advocated in many contexts with student active participation in learning being a central element. Vietnam has adopted innovative pedagogies including child-centered and competency-based teaching to further active learning and develop students’ full potential. This study explores Vietnamese teachers’ views about student participation and teaching roles as they implement these progressive reforms. It also examines pedagogical practices that teachers planned to use and actually employed to support student learning through meaningful participation. Drawing on qualitative analysis of interviews and classroom observations conducted over three years with 47 secondary-level literature teachers throughout Vietnam, we found that student participation as expected by teachers broadly falls into three categories: participation as attention; participation as contribution and collaboration; and participation as autonomy and engagement. Each of these modes characterizes what teachers’ envision of students’ overall engagement, but these modes coexisted in the data in classroom practices. Our analysis shows how ‘hybrid pedagogy,’ a mix of teacher-directed and student-centered approaches, was most used to support students’ active contribution and collaboration. This research contributes to the literature on student-centered learning and student participation in transitional contexts, highlighting the complex processes of how teachers perceive and enact these pedagogical reforms.
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Savedoff, William D. Is Anybody Listening?: Ignoring Evidence in the Latin American Health Reform Debates. Inter-American Development Bank, octubre de 2000. http://dx.doi.org/10.18235/0008923.

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Throughout Latin America and the Caribbean, countries are in the midst of debates about their health systems. Frequently, the characteristics of reforms in one country are used to promote or criticize particular proposals in another. Despite the great importance and potential from learning from external models, evidence frequently makes very little difference to the way models are perceived and described in the political and social arena. To illustrate this point, the author takes Chile as an example, a country whose health reform has been widely decried as the most horrible of neo-liberal health reforms. However, the author states that Chile has one of the most progressive health systems in the world when measured by the combination of tax incidence and public health spending.
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London, Jonathan D. Adoption, Adaption, and the Iterative Challenges of Scaling up in Vietnam: Policy Entrepreneurship and System Coherence in a Major Pedagogical Reform. Research on Improving Systems of Education (RISE), marzo de 2023. http://dx.doi.org/10.35489/bsg-rise-misc_2023/11.

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Đặng Tự Ân played a pivotal role in the genesis, adoption, and diffusion of pedagogical and curricular reforms that are transforming teaching and learning in the Socialist Republic of Vietnam. His is a fascinating story of a career that began with the paralyzing disappointment of being assigned to study in a seemingly lowly teacher training college only to culminate, decades later, in his central role in the research, design, piloting, and scaling up of a reform that, despite numerous difficulties, would shape the most far-reaching and progressive curricular reforms in Vietnam’s long educational history. This essay uses the case of VNEN, a pedagogical and curricular reform adapted to Vietnam from the Colombian Escuela Nueva (EN) model, to advance our understanding of the challenges policy entrepreneurs and networks of policy stakeholders can encounter in efforts to institute pathbreaking reforms and of the formidable challenges they can encounter in bringing such reforms to scale. In contemporary research on the political economy of education and learning, the notion of an education system’s coherence for learning refers to the extent to which an education system develops relations of accountability that support improved learning outcomes across a range of relationships that define an education system and an array of policy design elements that education policies contain (Pritchett 2015, Kaffenberger and Spivack 2022). In the development literature, the notion of iterative adaptation speaks to a process wherein the performance of policies can improve rapidly through experimentation rather than mechanical transplantation of “best practices” (Andrews et al. 2013, Le 2018). From the standpoint of research on education systems and major reform efforts aimed at enhancing learning, the case of VNEN represents a particularly interesting instance of the innovation of pedagogical and curricular reforms that were, at their most successful moments, deeply coherent for learning, but which encountered problems at scale owing to a range of factors highlighted in this analysis. More broadly and however problematic at times, Vietnam’s VNEN experience contributed to the broad uptake and diffusion of new curricula and teaching practices. This raises questions about what we can learn from VNEN, including its successes and problems, that may have value for promoting continued improvement in Education systems performance around learning in Vietnam and other settings.
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Robinson, Andy. Monitoring and Evaluation for Rural Sanitation and Hygiene: Framework. Institute of Development Studies (IDS), diciembre de 2021. http://dx.doi.org/10.19088/slh.2021.027.

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The monitoring and evaluation (M&E) Guidelines and Framework presented in this document (and in the accompanying M&E Indicator Framework) aim to encourage stakeholders in the rural sanitation and hygiene sector to take a more comprehensive, comparable and people focused approach to monitoring and evaluation. Many M&E frameworks currently reflect the interests and ambitions of particular implementing agencies – that is, community-led total sanitation (CLTS) interventions focused on open-defecation free (ODF) outcomes in triggered communities; market-based sanitation interventions focused on the number of products sold and whether sanitation businesses were profitable; and sanitation finance interventions reporting the number of facilities built using financial support. Few M&E frameworks have been designed to examine the overall sanitation and hygiene situation – to assess how interventions have affected sanitation and hygiene outcomes across an entire area (rather than just in specific target communities); to look at who (from the overall population) benefitted from the intervention, and who did not; to report on the level and quality of service used; or examine whether public health has improved. Since 2015, the Sustainable Development Goals (SDGs) have extended and deepened the international monitoring requirements for sanitation and hygiene. The 2030 SDG sanitation target 6.2 includes requirements to: • Achieve access to adequate sanitation and hygiene for all • Achieve access to equitable sanitation and hygiene for all • End open defecation • Pay special attention to the needs of women and girls • Pay special attention to those in vulnerable situations The 2030 SDG sanitation target calls for universal use of basic sanitation services, and for the elimination of open defecation, both of which require M&E systems that cover entire administration areas (i.e. every person and community within a district) and which are able to identify people and groups that lack services, or continue unsafe practices. Fortunately, the SDG requirements are well aligned with the sector trend towards system strengthening, in recognition that governments are responsible both for the provision of sustainable services and for monitoring the achievement of sustained outcomes. This document provides guidelines on the monitoring and evaluation of rural sanitation and hygiene, and presents an M&E framework that outlines core elements and features for reporting on progress towards the 2030 SDG sanitation target (and related national goals and targets for rural sanitation and hygiene), while also encouraging learning and accountability. Given wide variations in the ambition, capacity and resources available for monitoring and evaluation, it is apparent that not all of the M&E processes and indicators described will be appropriate for all stakeholders. The intention is to provide guidelines and details on useful and progressive approaches to monitoring rural sanitation and hygiene, from which a range of rural sanitation and hygiene duty bearers and practitioners – including governments, implementation agencies, development partners and service providers – can select and use those most appropriate to their needs. Eventually, it is hoped that all of the more progressive M&E elements and features will become standard, and be incorporated in all sector monitoring systems.
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