Relevant bibliographies by topics / Self-paced

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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 'Self-paced'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Self-paced"

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Yu, Tingzhao, Chaoxu Guo, Lingfeng Wang, Shiming Xiang, and Chunhong Pan. "Self-Paced AutoEncoder." IEEE Signal Processing Letters 25, no. 7 (2018): 1054–58. http://dx.doi.org/10.1109/lsp.2018.2843295.

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Stowers, Michael P., and Martin Tessmer. "Self-paced instruction." New Directions for Higher Education 1986, no. 56 (1986): 17–26. http://dx.doi.org/10.1002/he.36919865604.

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Wessel, Karl, Thomas Zeffiro, Camilo Toro, and Mark Hallett. "Self-Paced Versus Metronome-Paced 7 Finger Movements." Journal of Neuroimaging 7, no. 3 (1997): 145–51. http://dx.doi.org/10.1111/jon199773145.

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Edwards, Clayton M. "Self-Paced Mathematical Instruction." Mathematics Teaching in the Middle School 19, no. 4 (2013): 230–36. http://dx.doi.org/10.5951/mathteacmiddscho.19.4.0230.

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MENG, Deyu, Zongben XU, and Jun SHU. "Meta self-paced learning." SCIENTIA SINICA Informationis 50, no. 6 (2020): 781–93. http://dx.doi.org/10.1360/ssi-2020-0005.

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Holroyd, Tom, Matt Nielsen, S. Miyauchi, and T. Yanagida. "2P219MEG during self-paced and externally paced rhythmic tapping." Seibutsu Butsuri 41, supplement (2001): S150. http://dx.doi.org/10.2142/biophys.41.s150_3.

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Schlegel, Betina, and Margarita Beneke. "A Study of Self-Paced and Machine-Paced Inspection." Proceedings of the Human Factors Society Annual Meeting 30, no. 5 (1986): 471–75. http://dx.doi.org/10.1177/154193128603000514.

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A study was conducted to investigate the performance of inspectors under different conditions using computer generated visual test items. The purpose of the study was to compare performance for self-paced vs. machine-paced inspection tasks. The factors considered were searching for a single type of flaw vs. three types of flaws, the viewing time for machine-paced inspection, and the type of instruction for self-paced inspection. The results showed that performance was better when subjects searched for only one type of flaw. Performance improved with increased viewing time, but was not affected
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Li, Jiangxin, Zhao Kang, Chong Peng, and Wenyu Chen. "Self-Paced Two-dimensional PCA." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (2021): 8392–400. http://dx.doi.org/10.1609/aaai.v35i9.17020.

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Two-dimensional PCA (2DPCA) is an effective approach to reduce dimension and extract features in the image domain. Most recently developed techniques use different error measures to improve their robustness to outliers. When certain data points are overly contaminated, the existing methods are frequently incapable of filtering out and eliminating the excessively polluted ones. Moreover, natural systems have smooth dynamics, an opportunity is lost if an unsupervised objective function remains static. Unlike previous studies, we explicitly differentiate the samples to alleviate the impact of out
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Ren, Yazhou, Xiaofan Que, Dezhong Yao, and Zenglin Xu. "Self-paced multi-task clustering." Neurocomputing 350 (July 2019): 212–20. http://dx.doi.org/10.1016/j.neucom.2019.03.062.

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Klenk, Virginia. "Self-Paced Logic Without Computers." Teaching Philosophy 9, no. 3 (1986): 239–46. http://dx.doi.org/10.5840/teachphil19869338.

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Dissertations / Theses on the topic "Self-paced"

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Bashashati, Hossein. "A user-customized self-paced brain computer interface." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/61248.

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Much attention has been directed towards synchronous Brain Computer Interfaces (BCIs). For these BCIs, the user can only operate the system during specific system-defined periods. Self-paced BCIs, however, allow users to operate the system at any time he/she wishes. The classification of Electroencephalography (EEG) signals in self-paced BCIs is extremely challenging, as the BCI system does not have any clue about the start time of a control task. Also, the data contains a large number of periods during which the user has no intention to control the BCI. For sensory motor self-paced BCIs (fo
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Braet, David Christopher. "Principles for designing online self-paced corporate training." [Ames, Iowa : Iowa State University], 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1473185.

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Jackson, Robin C. "A behavioural analysis of 'choking' in self-paced skills." Thesis, University of St Andrews, 1998. http://hdl.handle.net/10023/3714.

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This thesis is about 'choking' in self-paced skills. Choking refers to 'the occurrence of inferior performance despite striving and incentives for superior performance' (Baumeister and Steinhilber, 1986, p. 361). Self-paced skills are skills in which performance is initiated by the athlete. This research set out to investigate the cause of choking in self-paced skills within the theoretical framework of behaviour analysis. The main focus of the research relates to the distinction between behaviour under the control of verbal antecedents (rule-governed behaviour) and behaviour that is shaped by
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Thomas, Kevin. "Regulation of power output during self-paced cycling exercise." Thesis, Northumbria University, 2013. http://nrl.northumbria.ac.uk/15122/.

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Fatigue is a universal phenomenon with functional and perceptual consequences. The study of fatigue in the exercise sciences has historically focussed on factors that limit performance during exercise. More recent proposals have shifted the focus of this understanding to examine how intensity is regulated during exercise through the study of the pacing strategy, which has both physiological and practical consequences. The aim of this thesis was to investigate the biological basis of self-pacing and the optimum pacing strategy for endurance time-trial events. Study 1 assessed the reproducibilit
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Tsui, Chun Sing Louis. "Adaptive Self-paced Brain-Actuated Control of Mobility Devices." Thesis, University of Essex, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.504838.

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Flaherty, Michelle A. "The effects of Sopite Syndrome on self-paced airsickness desensitization program." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1998. http://handle.dtic.mil/100.2/ADA354296.

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Thesis (M.S. in Operations Research) Naval Postgraduate School, September 1998.<br>"September 1998." Thesis advisor(s): John K. Schmidt, Robert R. Read. Includes bibliographical references (p. 57-59). Also available online.
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Katomeri, Magdalena. "Acute effects of self-paced walking on smoking withdrawal and cravings." Thesis, University of Plymouth, 2007. http://hdl.handle.net/10026.1/2537.

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High relapse rates among smokers attempting to quit have been linked with failure to cope with cigarette cravings and withdrawal symptoms. Smoking cravings and withdrawal behavior is known to be influenced by stress and exposure to smoking cues. Exercise appears to reduce cigarette cravings and withdrawal symptoms. However no study has investigated the effects of exercise on cue-elicited cravings and withdrawal symptoms, or on ad libitum smoking behavior. Aim: Four experimental studies were designed to examine the effects of exercise on smoking behavior. Initial studies focused on the effects
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Bashashati, Ali. "Towards development of a 3-state self-paced brain computer interface system." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/31213.

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Brain computer interface (BCI) systems aim at helping individuals with motor disabilities by providing them the ability to control devices such as wheelchairs and computers, using their brain activity only. The majority of BCI research to-date has focused on developing technology for "synchronous" BCIs. These systems allow the user to affect control during specified time periods only. Self-paced (asynchronous) BCIs on the other hand, are designed to respond whenever the user intends to control the system, otherwise they remain in the so called "inactive output state". This dissertation pursues
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Faradji, Farhad. "Design of a self-paced brain-computer interface based on mental tasks." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/42330.

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People with very severe motor-disabilities have to accept a much-reduced quality of life. Unfortunately, these people cannot use assistive devices as present devices require motor activities. Brain-computer interfaces (BCIs) provide an alternative means of communication between the brain and assistive devices. There are two types of BCIs, synchronous and self-paced. Self-paced BCIs are more practical as they can be used at any time. The vast majority of existing self-paced BCIs are activated by real, attempted, or imagined movements. Few are activated by mental tasks. The high false positive r
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Satti, Abdul Rehman. "Fast adaptive signal processing for intelligent multistate self-paced brain-computer interfaces." Thesis, University of Ulster, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.530506.

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Books on the topic "Self-paced"

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C, Gossage Loyce, ed. Self-paced business mathematics. South-Western Publishing, 1988.

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Matz, Leonard M. Self-paced bank investment training. Sheshunoff Information Services, 1998.

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Anthony, Northrup, and Microsoft Corporation, eds. MCPD self-paced training kit . Microsoft Press, 2007.

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Corporation, Microsoft, ed. MCSA self-paced training kit. 2nd ed. Microsoft Press, 2003.

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Corporation, Microsoft, ed. MCSA self-paced training kit. Microsoft, 2004.

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Anthony, Northrup, and Microsoft Corporation, eds. MCPD self-paced training kit . Microsoft Press, 2007.

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1973-, Thomas Orin, ed. MCSA/MCSE self-paced training kit. Microsoft Press, 2004.

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Corporation, Microsoft, ed. MCSA/MCSE self-paced training kit. 2nd ed. Microsoft Press, 2003.

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Press, Nancy Ottman. Interlibrary loans: A self-paced manual. Pacific Northwest Regional Health Sciences Library Service, 1985.

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Matthew, Strebe, and Microsoft Corporation, eds. MCSA/MCSE self-paced training kit. Microsoft Press, 2003.

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Book chapters on the topic "Self-paced"

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Zięba, Maciej, Jakub M. Tomczak, and Jerzy Świątek. "Self-paced Learning for Imbalanced Data." In Intelligent Information and Database Systems. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49381-6_54.

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Nakamura, Chie, and Suzanne Flynn. "Eye-tracking and self-paced reading." In Routledge Handbook of Technological Advances in Researching Language Learning. Routledge, 2024. http://dx.doi.org/10.4324/9781003459088-23.

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Kitajima, Munéo. "A CCE Study: Slow Self-Paced Navigation." In Memory and Action Selection in Human-Machine Interaction. John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119268628.ch6.

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Min, Fan, Yu Li, and Liyan Liu. "Self-paced Safe Co-training for Regression." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05936-0_6.

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Mital, Anil, Ashok Chalaka, and Waldemar Karwowski. "The Demands and Responses of Machine-Paced and Self-Paced Material Handling Tasks." In Toward the Factory of the Future. Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-82580-4_88.

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Zhang, Xiao-Yu, Shupeng Wang, Yanfei Lv, Peng Li, and Haiping Wang. "Augmented Self-paced Learning with Generative Adversarial Networks." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93713-7_39.

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Lyu, Gengyu, Songhe Feng, Yi Jin, and Yidong Li. "Partial Label Learning via Self-Paced Curriculum Strategy." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67661-2_29.

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Zhang, Xianchao, Heng Shi, Yuangang Li, and Wenxin Liang. "SPGLAD: A Self-paced Learning-Based Crowdsourcing Classification Model." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67274-8_17.

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Zhu, Pengfei, Wenya Ma, and Qinghua Hu. "Self-paced Robust Deep Face Recognition with Label Noise." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16142-2_33.

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Ren, Yazhou, Xin Yan, Zechuan Hu, and Zenglin Xu. "Self-Paced Multi-Task Multi-View Capped-norm Clustering." In Neural Information Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04212-7_18.

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Conference papers on the topic "Self-paced"

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Zhao, Helin, Wei Chen, and Peng Zhou. "Deep Self-paced Active Learning for Image Clustering." In 2024 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2024. http://dx.doi.org/10.1109/icme57554.2024.10687884.

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Shi, Xudong, and Weili Xiong. "A Self-Paced Robust Regression-based Industrial Virtual Sensor." In 2025 IEEE 14th Data Driven Control and Learning Systems (DDCLS). IEEE, 2025. https://doi.org/10.1109/ddcls66240.2025.11065623.

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Zhou, Ying, Xuefeng Liang, Yue Xu, and Bowen Gao. "Sample-level Self-paced Learning to Tackle Multimodal Imbalance Problem." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888208.

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Zhang, Yinghao, Hao Jiang, Youpeng Bao, Shaowei Liao, and Quan Xue. "Array Antenna Diagnosis Using Self-Paced Learning Convolutional Neural Networks." In 2025 19th European Conference on Antennas and Propagation (EuCAP). IEEE, 2025. https://doi.org/10.23919/eucap63536.2025.10999758.

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Liu, Youfa, Bo Du, and Lefei Zhang. "Self-Paced Subspace Clustering." In 2019 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2019. http://dx.doi.org/10.1109/icme.2019.00068.

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Gao, Hongchang, and Heng Huang. "Self-Paced Network Embedding." In KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2018. http://dx.doi.org/10.1145/3219819.3220041.

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Campbell, Jennifer, Andrew Petersen, and Jacqueline Smith. "Self-paced Mastery Learning CS1." In SIGCSE '19: The 50th ACM Technical Symposium on Computer Science Education. ACM, 2019. http://dx.doi.org/10.1145/3287324.3287481.

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Han, Longfei, Dingwen Zhang, Dong Huang, et al. "Self-paced Mixture of Regressions." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/252.

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Mixture of regressions (MoR) is the well-established and effective approach to model discontinuous and heterogeneous data in regression problems. Existing MoR approaches assume smooth joint distribution for its good anlaytic properties. However, such assumption makes existing MoR very sensitive to intra-component outliers (the noisy training data residing in certain components) and the inter-component imbalance (the different amounts of training data in different components). In this paper, we make the earliest effort on Self-paced Learning (SPL) in MoR, i.e., Self-paced mixture of regressions
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Li, Hao, and Maoguo Gong. "Self-paced Convolutional Neural Networks." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/293.

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Convolutional neural networks (CNNs) have achieved breakthrough performance in many pattern recognition tasks. In order to distinguish the reliable data from the noisy and confusing data, we improve CNNs with self-paced learning (SPL) for enhancing the learning robustness of CNNs. In the proposed self-paced convolutional network (SPCN), each sample is assigned to a weight to reflect the easiness of the sample. Then a dynamic self-paced function is incorporated into the leaning objective of CNN to jointly learn the parameters of CNN and the latent weight variable. SPCN learns the samples from e
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Kang, Xiangping, Guoxian Yu, Carlotta Domeniconi, et al. "Crowdsourcing with Self-paced Workers." In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 2021. http://dx.doi.org/10.1109/icdm51629.2021.00038.

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Reports on the topic "Self-paced"

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Moral, Rafael. Advancing in R: Statistical Modeling and Data Visualization. Instats Inc., 2024. http://dx.doi.org/10.61700/6kwxq9jjgpsyx1478.

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This self-paced workshop provides a comprehensive exploration of statistical modeling and data visualization using R, tailored for PhD students, professors, and professional researchers. Participants will gain hands-on experience with data wrangling, visualization, and advanced statistical models, equipping them with essential skills for high-quality research and publications.
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Buitrago García, Hilda Clarena. Making Managing Data Easy-Peasy. Ediciones Universidad Cooperativa de Colombia, 2022. http://dx.doi.org/10.16925/gcnc.42.

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This class note, made up of three sequenced units, aims at helping both Universidad Cooperativa de Colombia (UCC) teachers and their students to use a variety of tools to find, curate, and manage different kinds of data and references effectively. This will, undoubtedly affect their academic work in a positive way since they can save time and have easier access to more organized and clear information. Whether they teach or take an undergraduate or postgraduate program, this module will increase their efficiency and overall performance. Teachers will be able to help their students navigate data
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Chea, Sathya, Sopheak Song, and Seyhakunthy Hun. Competency-Based TVET in Cambodia: Promise and Reality. Cambodia Development Resource Institute, 2020. https://doi.org/10.64202/wp.124.202008.

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Modernisation brings new economic and social challenges around the globe. In this era of the knowledge economy, knowledge and skills have become valuable assets for national development. Many countries have been working out how best to enhance their knowledge and skills pool. Focus has recently turned to competency-based training (CBT), which is believed to enrich students with practical competencies relevant to labour market needs and thus enhance their productivity and, ultimately, national economic growth. The CBT is characterised by a student-centred approach and module-based course with a
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What Sample Size do I Need? Instats Inc., 2024. http://dx.doi.org/10.61700/nqdisjcybqls6935.

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The 'What sample size do I need? with R' seminar is a self-paced workshop presented by the Statistical Services Centre Ltd, aimed at equipping researchers with the knowledge and skills to determine the appropriate sample size for their studies using RStudio. The seminar covers a range of topics including the principles of sample size determination, its impact on research validity, and practical application in real-world research scenarios, with an official Instats certificate of completion provided at the end.
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