Academic literature on the topic 'Conventional learning'

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Journal articles on the topic "Conventional learning"

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Voiklis, John, and James E. Corter. "Conventional Wisdom: Negotiating Conventions of Reference Enhances Category Learning." Cognitive Science 36, no. 4 (2012): 607–34. http://dx.doi.org/10.1111/j.1551-6709.2011.01230.x.

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Mukherjee, Sanjoy. "Non-conventional Entrepreneurial Learning." Journal of Human Values 13, no. 1 (2007): 23–34. http://dx.doi.org/10.1177/097168580601300104.

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Weinschenk, Aaron C. "Polls and Elections: Conventional Wisdom: Political Learning During Presidential Nominating Conventions." Presidential Studies Quarterly 47, no. 1 (2017): 186–203. http://dx.doi.org/10.1111/psq.12352.

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Wongsri. "Learning outcomes between Socioscientific Issues-Based Learning and Conventional Learning Activities." Journal of Social Sciences 6, no. 2 (2010): 240–43. http://dx.doi.org/10.3844/jssp.2010.240.243.

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Sri, Mardani, . Riyadi, and Slamet Budiarti Indah. "Application of Gross Motor Skills-Based and Conventional Learning Models to the Understanding of Mathematics Concepts in Grade 1 Elementary Schools in Karanganyar District." International Journal of Current Science Research and Review 08, no. 01 (2025): 200–207. https://doi.org/10.5281/zenodo.14644350.

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Abstract : This research focuses on the application of gross motor skills-based and conventional learning models to the understanding of mathematical concepts in grade 1 elementary school (SD). The purpose of the research is to describe which is better between gross motor skills-based learning and conventional learning on understanding the concept of mathematics in grade 1 elementary school. The study population was grade 1 elementary school students in Karanganyar district. Research sampling using randam sampling. Data retrieval techniques using tests. Data analysis using two-way Anava with u
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Weng, Ziqiao. "From Conventional Machine Learning to AutoML." Journal of Physics: Conference Series 1207 (April 2019): 012015. http://dx.doi.org/10.1088/1742-6596/1207/1/012015.

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Sengupta, Partho P., and Y. Chandrashekhar. "From Conventional Deep Learning to GPT." JACC: Cardiovascular Imaging 16, no. 8 (2023): 1129–31. http://dx.doi.org/10.1016/j.jcmg.2023.07.001.

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Hadi Saputro, Suyatno. "The Impact of Problem Based Learning on Learning Outcomes in Nursing Students." International Journal of Multidisciplinary Research and Analysis 05, no. 10 (2022): 2784–88. http://dx.doi.org/10.47191/ijmra/v5-i10-29.

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This study aims to determine the impact of problem-based learning on nursing student learning outcomes. The research method used is quasi-experimental by comparing the two groups, namely the group treated with problem-based learning and conventional learning. The research subjects were 7th semester nursing students, totaling 68 students who were divided into two groups, experiment with problem-based learning and control with conventional learning. Analysis of the data in this study by comparing learning outcomes in the two groups, experimental and control. The results of data analysis showed t
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Anggraini, R. D., A. Murni, and Sakur. "Differences in students’ learning outcomes between discovery learning and conventional learning models." Journal of Physics: Conference Series 1088 (September 2018): 012070. http://dx.doi.org/10.1088/1742-6596/1088/1/012070.

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Maharani, Indah, Nabila Febriyanti Dabutar, Fatma Tresno Ingtyas, and Laurena Ginting. "The Correlation of Learning Motivation and Conventional Learning Methods with Civic Education Learning Outcomes." Edunesia : Jurnal Ilmiah Pendidikan 6, no. 1 (2025): 382–95. https://doi.org/10.51276/edu.v6i1.1096.

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This study examines the relationship between learning Motivation and conventional teaching methods on students' learning outcomes in Civic Education. The research employed a quantitative approach with a correlational design. Data were collected through questionnaires designed to measure learning motivation, applying conventional teaching methods, and students' learning outcomes. Data analysis was conducted using simple linear regression and correlation analysis. The results revealed a significant positive relationship between learning Motivation and students' learning outcomes and a significan
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Dissertations / Theses on the topic "Conventional learning"

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Kiernan, Leo Andrew. "Learning systems for power transmission networks." Thesis, University of Reading, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.267908.

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Rideout, Elizabeth. "The experience of learning and teaching in a non-conventional nursing curriculum." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0005/NQ41495.pdf.

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Miramontes, Hercog Luis. "Evolutionary and conventional reinforcement learning in multi agent systems for social simulation." Thesis, London South Bank University, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.288112.

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Wong, Wing-kei Vicky, and 黃穎琪. "A comparison between alternative and conventional assessments to assess young children's learning: teachers'perspectives." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B35535611.

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Pizarchik, Mary. "The effects of experiential learning: An examination of three styles of experiential education programs and their implications for conventional classrooms." CSUSB ScholarWorks, 2007. https://scholarworks.lib.csusb.edu/etd-project/3305.

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Using methodologies of interviews and observation, this study focuses on three distinctive and successful kinds of experiential education: a summer arts program, an outdoor science program and a wilderness education program. The project applies insights from the programs to the central question of this thesis: How can experiential learning be utilized within the traditional classroom given the constraints of the No Child Left Behind Law and standardized teaching?
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Wong, Wing-kei Vicky. "A comparison between alternative and conventional assessments to assess young children's learning teachers' perspectives /." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B35535611.

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Reed, Philip A. "The Relationship Between Learning Style and Conventional or Modular Laboratory Preference Among Technology Education Teachers in Virginia." Diss., Virginia Tech, 2000. http://hdl.handle.net/10919/27144.

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This study investigated the relationship between the laboratory environments and the learning styles of middle school technology education teachers in the Commonwealth of Virginia. Based on the assumption that a strong relationship between teaching and learning styles exists, it was hypothesized that teacher preference for one type of laboratory over another (conventional or modular) may be an issue of learning style. A random sample (n=195) was drawn from the entire population (as identified by the Virginia Department of Education in 1998) of public middle school technology education teach
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Murangi, Heroldt Vekaama. "Managing student transition from conventional to open schooling: a case study of Namibia." Thesis, University of Pretoria, 2017. http://hdl.handle.net/2263/62894.

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The provision of school equivalency programme through Open and Distance Learning (ODL) has gained momentum in the 21st century through the establishment of open schools. Open schools has now become a model of choice for many young people and adults who do not want to pursue their secondary education journey through the conventional delivery system. Additionally, shrinking financial resources have made it impossible for governments especially in the developing world to expand education through the conventional system. Pityana (2007) claims that ODL is key in advancing the development agenda of
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Feest, Kathy. "Learning from the pre registration house officer experience : lessons from conventional interpretation and the creative narrative approach." Thesis, University of Bristol, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.398782.

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Zhang, Wuming. "Towards non-conventional face recognition : shadow removal and heterogeneous scenario." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEC030/document.

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Ces dernières années, la biométrie a fait l’objet d’une grande attention en raison du besoin sans cesse croissant d’authentification d’identité, notamment pour sécuriser de plus en plus d’applications enlignes. Parmi divers traits biométriques, le visage offre des avantages compétitifs sur les autres, e.g., les empreintes digitales ou l’iris, car il est naturel, non-intrusif et facilement acceptable par les humains. Aujourd’hui, les techniques conventionnelles de reconnaissance faciale ont atteint une performance quasi-parfaite dans un environnement fortement contraint où la pose, l’éclairage,
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Books on the topic "Conventional learning"

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Brozowski, Carol A. Evaluation of and reasoning about social conventional and moral transgressions: A comparison of learning disabled and control children. s.n.], 1987.

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Ellen, McIntyre, and Educational Resources Information Center (U.S.), eds. From emergent to conventional reading: Similarities and differences in children's learning in skills-based and whole language classrooms. U.S. Dept. of Education, Office of Educational Research and Improvement, Educational Resources Information Center, 1997.

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Salleh, Zaleha. Integrating Islamic economics into a conventional economics programme: A case study of business distance learners' learning experiences at University Technology MARA, Malaysia. University of Birmingham, 2000.

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R, Wasserman Steven, O'Brien Jacqueline Wasserman, and Applebaum Edmond L, eds. Learning independently: A directory of approximately 4,000 self-instruction resources, including correspondence courses, programmed learning products, audio and videocassettes, films and filmstrips, games and simulations, multi-media kits, and conventional learning materials such as books intended for non-formal education. 3rd ed. Gale Research Co., 1987.

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R, Wasserman Steven, O'Brien Jacqueline Wasserman, and Applebaum Edmond L, eds. Learning independently: A directory of approximately 4,000 self-instruction resources, including correspondence courses, programmed learning products, audio and videocassettes, films and filmstrips, games and simulations, multi-media kits, and conventional learning materials such as books intended for non-formal education. 3rd ed. Gale Research Co., 1987.

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International Conference on Machine Learning and Cybernetics (3rd 2004 Shanghai, China). Proceedings of 2004 International Conference on Machine Learning and Cybernetics: August 6-29, 2004, Worldfield Convention Hotel, Shanghai, China. IEEE, 2004.

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Partington, Gitika. A study of the musical pathways of members of the Natural Voice Practitioners Network who use music from oral traditions and body work as their primary learning strategies.: How has the conventional musical education system helped or hindered their musical/vocal development and affected their work with others?. University of Surrey Roehampton, 2001.

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Viteli, Jarmo. Interactive technology and education: A comparison of call taught and conventionally taught adult foreign students in learning English idioms. Tampereen yliopiston Hämeenlinnan opettajankoulutuslaitos, 1989.

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R, Himes James, ed. Implementing the Convention on the Rights of the Child: Resource mobilization in low-income countries. M. Nijhoff, 1995.

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Ham, Laurie. Learning about biodiversity: A first look at the theory and practice of biodiversity education, awareness, and training in Canada / [researched and written by Laurie Ham and Elin Kelsey for the Biodiversity Convention Office, Environment Canada]. The Office, 1998.

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Book chapters on the topic "Conventional learning"

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Lee, Sangkyu, and Issam El Naqa. "Conventional Machine Learning Methods." In Machine and Deep Learning in Oncology, Medical Physics and Radiology. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-83047-2_3.

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Albakri, Mohammed, and A. Trevor Wood-Harper. "Beyond Conventional Digital Learning Platforms." In Innovation Strategy for the Future of Teaching and Learning. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87604-2_4.

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Butt, Adeel Masood, Nabiha Abdullah, Amna Sattar, et al. "Advantages of Nanomedicine Over Conventional Therapeutics." In Learning Materials in Biosciences. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7626-3_2.

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Kumar, Ashish. "Conventional framework for visual tracking." In Visual Object Tracking using Deep Learning. CRC Press, 2023. http://dx.doi.org/10.1201/9781003456322-6.

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Venkat Raman, Srikanth. "Conventional Methods for Fault Diagnosis." In Machine Learning Support for Fault Diagnosis of System-on-Chip. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19639-3_2.

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Daoudi, Nadia, Kevin Allix, Tegawendé François Bissyandé, and Jacques Klein. "Conventional Machine Learning-Based Android Malware Detectors." In Advances in Information Security. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-66245-4_7.

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de Lope, Javier, Enrique Hernández, Vanessa Vargas, and Manuel Graña. "Speech Emotion Recognition by Conventional Machine Learning and Deep Learning." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86271-8_27.

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Müller, Claude. "Learning Activities." In SpringerBriefs in Education. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-89045-1_8.

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Abstract Learner engagement is crucial for effective learning, making it the teacher’s responsibility to design activities that actively involve learners. In digital learning environments, key activities include practice, case work, content discussions, and assessment of learning progress. Digital tools also introduce new possibilities, such as simulations and VR, which foster innovative learning approaches. It is important that learning activities not only replicate conventional teaching but also expand and transform teaching practices (Kimmons et al. 2020).
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Erickson, Karen A., Charna D’Ardenne, Nitasha M. Clark, David A. Koppenhaver, and George W. Noblit. "Engaging in Dialogue without Conventional Language." In Social and Dialogic Thinking and Learning in Special Education. Routledge, 2021. http://dx.doi.org/10.4324/9781003175285-16.

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Dubey, Parul, and Arvind Kumar Tiwari. "Study of conventional & non-conventional SEPIC converter in solar photovoltaic system using Proteus." In Machine Learning and the Internet of Things in Solar Power Generation. CRC Press, 2023. http://dx.doi.org/10.1201/9781003302964-2.

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Conference papers on the topic "Conventional learning"

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Hong, Liang, Xingguo Guo, Zeyar Aung, and Wei Hu. "Robust Hardware Trojan Detection: Conventional Machine Learning vs. Graph Learning Approaches." In 2024 IEEE 23rd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 2024. https://doi.org/10.1109/trustcom63139.2024.00217.

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K S, Chandraprabha, Maya B. S, and Asha T. "Breast Carcinoma Detection Using Conventional Sophisticated Learning Algorithms." In 2024 Fourth International Conference on Multimedia Processing, Communication & Information Technology (MPCIT). IEEE, 2024. https://doi.org/10.1109/mpcit62449.2024.10892701.

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Kaur, Manvi, Kriti Jain, Ananya Singla, and Karuna Kadian. "Quantum Exploration in Ransomware Detection with Conventional Machine Learning Approaches." In 2024 IEEE International Conference on Contemporary Computing and Communications (InC4). IEEE, 2024. http://dx.doi.org/10.1109/inc460750.2024.10649082.

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Senthilselvi, A., Ballela Avinash Reddy, Charan Deepesh Kristam, Rachaputi Yeswanth Vardhan, and Senthil Pandi S. "Deep Learning for Sign Language Interpretation with Conventional Neural Network." In 2024 International Conference on Smart Technologies for Sustainable Development Goals (ICSTSDG). IEEE, 2024. https://doi.org/10.1109/icstsdg61998.2024.11026679.

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Wang, Bin, Wenqiang Liang, and Guojun Ma. "LiDAR Filtering Algorithms: Comparison between Conventional and Deep Learning Approaches." In 2025 6th International Conference on Geology, Mapping and Remote Sensing (ICGMRS). IEEE, 2025. https://doi.org/10.1109/icgmrs66001.2025.11065041.

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Yokogawa, Kaoru, Masashi Hiroishi, Sousuke Arnasaki, Hirohisa Aman, and Tomoyuki Yokogawa. "A Comparative Study on COSMIC FP Approximation with Deep Learning and Conventional Machine Learning." In 2024 IEEE/ACIS 22nd International Conference on Software Engineering Research, Management and Applications (SERA). IEEE, 2024. http://dx.doi.org/10.1109/sera61261.2024.10685591.

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Hao Thng, Guo, and Said Mikki. "Investigating The Use Of Machine Learning in Conventional Heterodyned-Based Receivers." In 2024 Asia Communications and Photonics Conference (ACP) and International Conference on Information Photonics and Optical Communications (IPOC). IEEE, 2024. https://doi.org/10.1109/acp/ipoc63121.2024.10809666.

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Testolina, Michela, and Touradj Ebrahimi. "Benchmarking conventional and learning-based objective image quality metrics on compression artifacts." In Applications of Digital Image Processing XLVII, edited by Andrew G. Tescher and Touradj Ebrahimi. SPIE, 2024. http://dx.doi.org/10.1117/12.3030906.

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K, Vidhya, Vamsi Krishna B, Haritha M, Arun Sekar Rajasekaran, Roopa Chandrika R, and Jayaraj Velusamy. "Multiclass Brain Tumor Detection using Convolutional Neural Network and Conventional Machine Learning." In 2024 International Conference on Emerging Research in Computational Science (ICERCS). IEEE, 2024. https://doi.org/10.1109/icercs63125.2024.10894882.

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Sacrey, D., and R. Roden. "How Machine Learning Is Replacing Conventional Interpretation." In First EAGE/PESGB Workshop Machine Learning. EAGE Publications BV, 2018. http://dx.doi.org/10.3997/2214-4609.201803011.

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Reports on the topic "Conventional learning"

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Hedyehzadeh, Mohammadreza, Shadi Yoosefian, Dezfuli Nezhad, and Naser Safdarian. Evaluation of Conventional Machine Learning Methods for Brain Tumour Type Classification. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2020. http://dx.doi.org/10.7546/crabs.2020.06.14.

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Grogger, Jeffrey, Sean Gupta, Ria Ivandic, and Tom Kirchmaier. Comparing Conventional and Machine-Learning Approaches to Risk Assessment in Domestic Abuse Cases. National Bureau of Economic Research, 2020. http://dx.doi.org/10.3386/w28293.

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Wezeman, Siemon T., Mathew George, and Pieter D. Wezeman. Transparency in Armaments in South East Asia: Learning from Three Decades of the United Nations Register of Conventional Arms. Stockholm International Peace Research Institute, 2024. http://dx.doi.org/10.55163/dcco3611.

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The United Nations Register of Conventional Arms (UNROCA) was established in 1991 as a transparency mechanism with the main goal of preventing potentially destabilizing build-ups of armaments. UNROCA reporting is particularly relevant to South East Asian states, and they are willing to participate. However, after high reporting rates in UNROCA’s first two decades, these states’ reporting rates have been low in recent years. When they report, they give all the required information on their arms imports and much additional, and useful, detail. Moreover, they do not consider UNROCA’s discriminato
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Porto, Stella C., Jacqueline Pinto Mota, and Andrea Attis Beltran. An Integrated Approach to Impact Evaluation and Recognition of Learning. Inter-American Development Bank, 2024. http://dx.doi.org/10.18235/0013075.

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MEiRA is a novel method for evaluating learning effectiveness, emphasizing practical knowledge application and learner achievement. It transcends traditional metrics by valuing the learning journey and its outcomes equally. Applicable in organizational training and broader learning contexts, its designed for scenarios where learners may not be part of a known organization. MEiRA follows the learning journey through five pillars: Engagement, Perception and Appreciation, Cognition and Knowledge Acquisition, Intention and Commitment, and Transfer and Impact Stories. It uses open badges as an intr
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Hart, Carl R., D. Keith Wilson, Chris L. Pettit, and Edward T. Nykaza. Machine-Learning of Long-Range Sound Propagation Through Simulated Atmospheric Turbulence. U.S. Army Engineer Research and Development Center, 2021. http://dx.doi.org/10.21079/11681/41182.

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Conventional numerical methods can capture the inherent variability of long-range outdoor sound propagation. However, computational memory and time requirements are high. In contrast, machine-learning models provide very fast predictions. This comes by learning from experimental observations or surrogate data. Yet, it is unknown what type of surrogate data is most suitable for machine-learning. This study used a Crank-Nicholson parabolic equation (CNPE) for generating the surrogate data. The CNPE input data were sampled by the Latin hypercube technique. Two separate datasets comprised 5000 sam
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Kaffenberger, Michelle, Jason Silberstein, and Marla Spivack. Evaluating Systems: Three Approaches for Analyzing Education Systems and Informing Action. Research on Improving Systems of Education (RISE), 2022. http://dx.doi.org/10.35489/bsg-rise-wp_2022/093.

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While conventional interventions and evaluations address the symptoms of the learning crisis, there is growing acknowledgement that widespread and sustained learning improvements will require systems approaches that diagnose and address the root causes of low learning. This paper presents and applies three methods to evaluate education systems and inform how to improve system coherence for learning. First, we use learning trajectories to evaluate the dynamics of children’s learning in 22 low- and middle-income countries. Second, we present a set of principles called the ALIGNS principles and s
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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted feature
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Kramarenko, T. H., O. S. Pylypenko, and O. Yu Serdiuk. Digital technologies in specialized mathematics education: application of GeoGebra in Stereometry teaching. [б. в.], 2021. http://dx.doi.org/10.31812/123456789/4534.

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The purpose of the paper is to improve methodology of teaching Mathematics via the use of digital technologies. The task of the paper is to identify the issues that require a theoretical and experimental solution. The objective of the paper is the educational process in the higher education institution, the subject of the paper is modern ICT. The result of the study is the learning tools of pedagogically considered and adequate bending of conventional and modern learning environment implemented into the educational process. The possibilities of using cloud technologies and Dynamic Mathematics
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Pylypenko, Olha S., Tetiana H. Kramarenko, and Ivan O. Muzyka. Application of GeoGebra in Stereometry teaching. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3898.

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The purpose of the paper is to improve methodology of teaching Mathematics via the use of cloud technology. The task of the paper is to identify the issues that require a theoretical and experimental solution. The objective of the paper is the educational process in the higher education institution, the subject of the paper is modern ICT. The result of the study is the learning tools of pedagogically considered and adequate bending of conventional and modern learning environment implemented into the educational process. The possibilities of using cloud technologies and Dynamic Mathematics syst
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Prykhodko, Alona M., Oksana O. Rezvan, Nataliia P. Volkova, and Stanislav T. Tolmachev. Use of Web 2.0 technology tool – educational blog – in the system of foreign language teaching. [б. в.], 2019. http://dx.doi.org/10.31812/123456789/3252.

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This paper discusses the use of a Web 2.0 technology tool – educational blog – in the system of teaching foreign languages for enhancement of teaching effectiveness and optimization of students’ performance. The authors describe the content, characteristics and didactic properties of an educational blog as an alternative or auxiliary educational environment, define its methodological objectives and list a number of advantages of this approach versus conventional teaching model. The effectiveness of the above-mentioned Web 2.0 technology tool was confirmed by the experiment which showed that an
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