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Journal articles on the topic 'Self-paced'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 June 2025

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1

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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2

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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3

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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4

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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5

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 by the type of instruction. There was no difference between self-paced and machine-paced inspection provided the viewing time was sufficient. Overall, performance in self-paced inspection was better.

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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 outliers and propose a novel method called Self-Paced 2DPCA (SP2DPCA)algorithm, which progresses from `easy’ to `complex’ samples. By using an alternative optimization strategy, SP2DPCA looks for optimal projection matrix and filters out outliers iteratively. Theoretical analysis demonstrates the robustness nature of our method. Extensive experiments on image reconstruction and clustering verify the superiority of our approach.

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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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11

Mauger, Alexis R., Alan J. Metcalfe, Lee Taylor, and Paul C. Castle. "The efficacy of the self-paced V̇O2max test to measure maximal oxygen uptake in treadmill running." Applied Physiology, Nutrition, and Metabolism 38, no. 12 (2013): 1211–16. http://dx.doi.org/10.1139/apnm-2012-0384.

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The novel self-paced, cycle-based maximal oxygen uptake (V̇O2max) test (SPV) has been shown to produce higher V̇O2max values than standard graded exercise test (GXT) protocols. This study sought to ascertain whether these observations would also be apparent in a self-paced, treadmill-based test design. Fourteen trained male runners performed a standard GXT on a motorised treadmill and a self-paced V̇O2max test on a nonmotorised treadmill in a counter-balanced design. The GXT included a plateau verification and was designed to last between 8 and 12 min. The self-paced test included 5 × 2 min stages and allowed participants to set their own running speed based on fixed increments in rating of perceived exertion. Significantly higher V̇O2max values (t[13] = 3.71, p = 0.003) were achieved in the self-paced test (64.4 ± 7.3 mL·kg−1·min−1) compared with the GXT (61.3 ± 7.3 mL·kg−1·min−1), and 13 of the 14 participants achieved the same or higher V̇O2max values in the self-paced test. Higher (p = 0.01) maximum heart rates were observed in the GXT (191 ± 10 beats·min−1 vs. 187 ± 7 beats·min−1), but no differences were observed in any other recorded variables. The self-paced V̇O2max test may provide a more valid means of measuring V̇O2max than the GXT and suggests that a V̇O2 plateau during a GXT does not always signify achievement of a definitive V̇O2max. These results provide further support that self-paced V̇O2max testing produces higher values for maximal oxygen uptake.

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Gu, Bin, Chenkang Zhang, Huan Xiong, and Heng Huang. "Balanced Self-Paced Learning for AUC Maximization." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6765–73. http://dx.doi.org/10.1609/aaai.v36i6.20632.

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Learning to improve AUC performance is an important topic in machine learning. However, AUC maximization algorithms may decrease generalization performance due to the noisy data. Self-paced learning is an effective method for handling noisy data. However, existing self-paced learning methods are limited to pointwise learning, while AUC maximization is a pairwise learning problem. To solve this challenging problem, we innovatively propose a balanced self-paced AUC maximization algorithm (BSPAUC). Specifically, we first provide a statistical objective for self-paced AUC. Based on this, we propose our self-paced AUC maximization formulation, where a novel balanced self-paced regularization term is embedded to ensure that the selected positive and negative samples have proper proportions. Specially, the sub-problem with respect to all weight variables may be non-convex in our formulation, while the one is normally convex in existing self-paced problems. To address this, we propose a doubly cyclic block coordinate descent method. More importantly, we prove that the sub-problem with respect to all weight variables converges to a stationary point on the basis of closed-form solutions, and our BSPAUC converges to a stationary point of our fixed optimization objective under a mild assumption. Considering both the deep learning and kernel-based implementations, experimental results on several large-scale datasets demonstrate that our BSPAUC has a better generalization performance than existing state-of-the-art AUC maximization methods.

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Gill, Grandon, and Carolyn F. Holton. "A Self-Paced Introductory Programming Course." Journal of Information Technology Education: Research 5 (2006): 095–105. http://dx.doi.org/10.28945/236.

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Zhang, Meng, Zi Xu Gu, Adan Amer, Gaganpreet Sidhu, and Seshasai Srinivasan. "Software Suite for Self-Paced Learning." International Journal of Emerging Technologies in Learning (iJET) 17, no. 19 (2022): 20–32. http://dx.doi.org/10.3991/ijet.v17i19.34575.

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 The faculty of engineering at XYZ University is very diverse with students coming from a variety of socio-economic backgrounds and nationalities. There is an expected level of prerequisite knowledge for courses in the program, especially for subjects in mathematics and physics. Yet, a significant number of students struggle to reach the same level of prior knowledge as their peers due to a lack of educational resources or due to language barriers. Entering lectures without the expected baseline understanding of the concepts increases the risk of cognitive overload because these students exert additional mental effort in retaining the pre-lecture information along with the lecture content. As a solution to this problem, we present a suite of web-based interactive programs that are accessible to the students outside the class. The Software Suite will provide supplementary material to assist with self-paced learning in a 3rd year undergraduate engineering course, i.e., Finite Element Analysis, at XYZ University’s ABC Department. It will enable students design and analyze various engineering applications, namely, Spring System, Trusses, Beams, Frames, and Heat Transfer along one or two dimensions. The purpose of the interactive platform with several programs is to actively engage the students with this cognitive tool to cement their understanding of the concepts instead of passively perceiving it as a tutor or repository of information.

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Al-taezi, Mohammed, Pengfei Zhu, Qinghua Hu, Yu Wang, and Abdulrahman Al-badwi. "Self-paced hierarchical metric learning (SPHML)." International Journal of Machine Learning and Cybernetics 12, no. 9 (2021): 2529–41. http://dx.doi.org/10.1007/s13042-021-01336-2.

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Huang, Zongmo, Yazhou Ren, Xiaorong Pu, Lili Pan, Dezhong Yao, and Guoxian Yu. "Dual self-paced multi-view clustering." Neural Networks 140 (August 2021): 184–92. http://dx.doi.org/10.1016/j.neunet.2021.02.022.

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17

Nixon, Philip D., and Richard E. Passingham. "The striatum and self-paced movements." Behavioral Neuroscience 112, no. 3 (1998): 719–24. http://dx.doi.org/10.1037/0735-7044.112.3.719.

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18

Wallot, Sebastian, and Guy Van Orden. "Nonlinear analyses of self-paced reading." Mental Lexicon 6, no. 2 (2011): 245–74. http://dx.doi.org/10.1075/ml.6.2.02wal.

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Nonlinear methods of fractal analysis and recurrence quantification analysis are becoming more commonplace in the cognitive and behavioral sciences. These methods are illustrated here in a tutorial style using self-paced reading data. Self-paced reading was performed in which each spacebar press revealed a story word-by-word or else sentence-by-sentence. Participant readers were either Ph.D. candidates in English literature or undergraduates from an introductory psychology course and the same story was read by all, either one time only or reread another time on another occasion. The nonlinear analyses revealed crucial differences between the word unit and sentence unit conditions. Performance in the word unit condition was dominated by a task specific strategy, yielding data patterns more like those observed in tapping tasks. Nonlinear analyses of the sentence unit condition, however, discriminated between graduate and undergraduate readers, and first readings of the story from re-reading. From these analyses, the repeated reading of the same story reveals a kind of über-fluency, in a manner of speaking, of the Ph.D. candidates in English literature, whose performance stayed at or closer to a performance ceiling in both readings.

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19

Xu, Wei, Wei Liu, Haoyuan Chi, Song Qiu, and Yu Jin. "Self-paced learning with privileged information." Neurocomputing 362 (October 2019): 147–55. http://dx.doi.org/10.1016/j.neucom.2019.06.072.

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Huh, Dongsung, and Terrence J. Sejnowski. "Conservation law for self-paced movements." Proceedings of the National Academy of Sciences 113, no. 31 (2016): 8831–36. http://dx.doi.org/10.1073/pnas.1608724113.

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Optimal control models of biological movements introduce external task factors to specify the pace of movements. Here, we present the dual to the principle of optimality based on a conserved quantity, called “drive,” that represents the influence of internal motivation level on movement pace. Optimal control and drive conservation provide equivalent descriptions for the regularities observed within individual movements. For regularities across movements, drive conservation predicts a previously unidentified scaling law between the overall size and speed of various self-paced hand movements in the absence of any external tasks, which we confirmed with psychophysical experiments. Drive can be interpreted as a high-level control variable that sets the overall pace of movements and may be represented in the brain as the tonic levels of neuromodulators that control the level of internal motivation, thus providing insights into how internal states affect biological motor control.

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21

Inkson, Donna, and Erica Smith. "Self paced learning: A student perspective." Australian Educational Researcher 28, no. 1 (2001): 107–28. http://dx.doi.org/10.1007/bf03219746.

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Inkson, Donna, and Erica Smith. "Self paced learning: A student perspective." Australian Educational Researcher 28, no. 2 (2001): 107–28. http://dx.doi.org/10.1007/bf03219755.

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23

Wehlander, Terri. "Performance Improvement Through Self-Paced Learning." Journal For Healthcare Quality 19, no. 4 (1997): 26–28. http://dx.doi.org/10.1111/j.1945-1474.1997.tb00897.x.

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24

Weng, Pangyen. "Developmental Math, Flipped and Self-Paced." PRIMUS 25, no. 9-10 (2015): 768–81. http://dx.doi.org/10.1080/10511970.2015.1031297.

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Gong, Yanlu, Quanwang Wu, Mengchu Zhou, and Junhao Wen. "Self-paced multi-label co-training." Information Sciences 622 (April 2023): 269–81. http://dx.doi.org/10.1016/j.ins.2022.11.153.

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Sun, Yuan, Jian Dai, Zhenwen Ren, Yingke Chen, Dezhong Peng, and Peng Hu. "Dual Self-Paced Cross-Modal Hashing." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 14 (2024): 15184–92. http://dx.doi.org/10.1609/aaai.v38i14.29441.

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Cross-modal hashing~(CMH) is an efficient technique to retrieve relevant data across different modalities, such as images, texts, and videos, which has attracted more and more attention due to its low storage cost and fast query speed. Although existing CMH methods achieve remarkable processes, almost all of them treat all samples of varying difficulty levels without discrimination, thus leaving them vulnerable to noise or outliers. Based on this observation, we reveal and study dual difficulty levels implied in cross-modal hashing learning, \ie instance-level and feature-level difficulty. To address this problem, we propose a novel Dual Self-Paced Cross-Modal Hashing (DSCMH) that mimics human cognitive learning to learn hashing from ``easy'' to ``hard'' in both instance and feature levels, thereby embracing robustness against noise/outliers. Specifically, our DSCMH assigns weights to each instance and feature to measure their difficulty or reliability, and then uses these weights to automatically filter out the noisy and irrelevant data points in the original space. By gradually increasing the weights during training, our method can focus on more instances and features from ``easy'' to ``hard'' in training, thus mitigating the adverse effects of noise or outliers. Extensive experiments are conducted on three widely-used benchmark datasets to demonstrate the effectiveness and robustness of the proposed DSCMH over 12 state-of-the-art CMH methods.

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Freeman, Mark J., and Duane I. Miller. "Effects of Locus of Control and Pacing on Performance of and Satisfaction with a Simulated Inspection Task." Perceptual and Motor Skills 69, no. 3-1 (1989): 779–85. http://dx.doi.org/10.1177/00315125890693-113.

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Subjects performed a simulated-inspection task where they searched 200 printed circuits for defects. Self-paced subjects were allowed to control the amount of time spent on each circuit, while the machine-paced subjects were allowed 10 sec. per circuit. Each subject completed Rotter's (1966) Locus of Control Scale (I-E). Variance was maximized by excluding the middle scores of the I-E scale. Self-paced subjects performed significantly better on correct decisions and on the number of misses. Internal scorers performed significantly better than external scorers on the number of misses. The personality variable interacted with the pacing variable when false alarms were considered. in the self-paced condition, external subjects had fewer false alarms than internal scorers. There were no differences in locus of control, pacing, or the interaction for these variables when satisfaction was the dependent variable. Evidence suggests locus of control may be useful in selecting inspectors for self-paced and machine-paced tasks.

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Scheiber, Peter, John G. Seifert, and Erich Müller. "Instructor-Paced vs. Self-Paced Skiing Modes in Older Recreational Alpine Skiers." Journal of Strength and Conditioning Research 25, no. 4 (2011): 988–96. http://dx.doi.org/10.1519/jsc.0b013e3181d4eb2d.

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Rooks, Daniel S., Bernard J. Ransil, and Wilson C. Hayes. "Self-Paced Exercise and Neuromotor Performance in Community-Dwelling Older Adults." Journal of Aging and Physical Activity 5, no. 2 (1997): 135–49. http://dx.doi.org/10.1123/japa.5.2.135.

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The aim of this study was to determine the efficacy and safety of 16 weeks of self-paced resistance training or walking protocols on neuromotor and functional parameters in active, community-dwelling older adults. Twenty-two sequentially recruited older adults were randomly assigned to one of two exercise groups: self-paced resistance training and self-paced walking. Static and dynamic balance, upper and lower extremity reaction times, muscle strength, and stairclimbing speed were measured before and immediately after 16 weeks of exercise. Preliminary data showed that 16 weeks of self-paced. progressive, lower body resistance training improved balance (one-legged stance with eyes open, 68%). reaction time (10%), muscle strength (160%), and stair climbing speed (28%), while a self-paced walking program improved balance (one-legged stance with eyes open, 51%), stair climbing speed (16%), and in certain circumstances muscle strength (25%), in active, community-dwelling older adults.

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Rhode, Jason, and Murali Krishnamurthi. "Preparing Faculty to Teach Online: Recommendations for Developing Self-Paced Training." International Journal of Information and Education Technology 6, no. 5 (2016): 376–82. http://dx.doi.org/10.7763/ijiet.2016.v6.717.

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Astorino, Todd Anthony, David William McMillan, Ross Montgomery Edmunds, and Eduardo Sanchez. "Increased cardiac output elicits higher V̇O2max in response to self-paced exercise." Applied Physiology, Nutrition, and Metabolism 40, no. 3 (2015): 223–29. http://dx.doi.org/10.1139/apnm-2014-0305.

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Recently, a self-paced protocol demonstrated higher maximal oxygen uptake versus the traditional ramp protocol. The primary aim of the current study was to further explore potential differences in maximal oxygen uptake between the ramp and self-paced protocols using simultaneous measurement of cardiac output. Active men and women of various fitness levels (N = 30, mean age = 26.0 ± 5.0 years) completed 3 graded exercise tests separated by a minimum of 48 h. Participants initially completed progressive ramp exercise to exhaustion to determine maximal oxygen uptake followed by a verification test to confirm maximal oxygen uptake attainment. Over the next 2 sessions, they performed a self-paced and an additional ramp protocol. During exercise, gas exchange data were obtained using indirect calorimetry, and thoracic impedance was utilized to estimate hemodynamic function (stroke volume and cardiac output). One-way ANOVA with repeated measures was used to determine differences in maximal oxygen uptake and cardiac output between ramp and self-paced testing. Results demonstrated lower (p < 0.001) maximal oxygen uptake via the ramp (47.2 ± 10.2 mL·kg–1·min–1) versus the self-paced (50.2 ± 9.6 mL·kg–1·min–1) protocol, with no interaction (p = 0.06) seen for fitness level. Maximal heart rate and cardiac output (p = 0.02) were higher in the self-paced protocol versus ramp exercise. In conclusion, data show that the traditional ramp protocol may underestimate maximal oxygen uptake compared with a newly developed self-paced protocol, with a greater cardiac output potentially responsible for this outcome.

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Koć-Januchta, Marta M., Tim N. Höffler, Helmut Prechtl, and Detlev Leutner. "Is too much help an obstacle? Effects of interactivity and cognitive style on learning with dynamic versus non-dynamic visualizations with narrative explanations." Educational Technology Research and Development 68, no. 6 (2020): 2971–90. http://dx.doi.org/10.1007/s11423-020-09822-0.

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AbstractThe aim of this study was to investigate the role of visual/verbal cognitive style and interactivity level in dynamic and non-dynamic multimedia learning environments. A group of 235 biology students learned about photosynthesis either from a computer-based animation or a series of static pictures with spoken explanatory text. Participants were randomly assigned to one of two conditions: with or without the possibility to pause, to play, or to fast-forward/rewind the learning environment (self-paced versus system-paced condition). Participants obtained better results when learning with the system-paced environment than with the self-paced one. A significant triple interaction between cognitive style, type of pacing, and type of visualization showed that highly developed visualizers learned poorer with self-paced static pictures than with system-paced static pictures. There were no significant effects regarding verbal cognitive style. Results shed more light on the relation between different levels of interactivity and visual cognitive style, when learning from static pictures.

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Lee, Harold H., Shira Dunsiger, Lauren Connell Bohlen, Holly K. Boyle, Jessica A. Emerson, and David M. Williams. "Age Moderates the Effect of Self-Paced Exercise on Exercise Adherence among Overweight Adults." Journal of Aging and Health 32, no. 3-4 (2018): 154–61. http://dx.doi.org/10.1177/0898264318812139.

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Objective: The present study tested the hypothesis that the effect of self-paced exercise on adherence to exercise programs is more pronounced with increasing age. Method: Fifty-nine low-active overweight adults (18-65 years) were encouraged to walk 30 to 60 min/day and randomized to either self-paced ( n = 30) or prescribed moderate-intensity ( n = 29) conditions. Results: The effect of study condition was moderated by age (main effect: b = 6.14, SE = 2.54, p = .02; Condition × Age: b = −11.55, SE = 3.77, p < .01), such that among participants >50 years, those in the self-paced condition exercised 6 more min/day than participants in the prescribed moderate-intensity condition ( p = .02), whereas among participants <50 years, those in the self-paced condition exercised 5.4 fewer min/day compared with those in the moderate-intensity condition ( p = .05). Affective response to physical activity did not mediate the moderating effect of age. Discussion: As age increases, adults may be more likely to adhere to self-paced versus prescribed moderate-intensity exercise.

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Faulkner, James, Johannes Gerhard, Lee Stoner, and Danielle Lambrick. "Self-Paced Walking within a Diverse Topographical Environment Elicits an Appropriate Training Stimulus for Cardiac Rehabilitation Patients." Rehabilitation Research and Practice 2012 (2012): 1–5. http://dx.doi.org/10.1155/2012/140871.

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Purpose. To assess the effect of a self-paced walking intervention within a topographically varied outdoor environment on physiological and perceptual markers in cardiac rehabilitation (CR) patients.Methods. Sixteen phase II CR patients completed twelve self-paced one-mile walking sessions over a four-week period within a community-based CR programme. Walking velocity, heart rate (HR), and ratings of perceived exertion (RPE) were reported at eight stages throughout the self-paced walks.Results. The study showed a significant increase in walking velocity from week 1 (~4.5 km/h) to week 4 (~5.1 km/h) of the self-paced walking programme (P<.05). A significantly higher HR was also observed in week 4 (111±13 b·min−1;~69% of maximal HR) compared to week 1 (106±14 b·min−1;~65% of maximal HR,P<.001). There were no changes in the average RPE across the course of the 4-week self-paced walking programme (P>.05).Conclusion. A self-paced walking programme may elicit an appropriate training stimulus for CR patients when exercising within a diverse topographical environment. Participants completed a one-mile walk within a shorter period of time and at a higher physiological intensity than that elicited at the onset of the programme, despite no observed changes in participants' subjective perception of exertion.

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Holroyd, T., M. Nielsen, S. Miyauchi, T. Yanagida, R. Turner, and Q. Deeley. "An MEG investigation of rhythmic tapping comparing self-paced and externally paced modes." NeuroImage 13, no. 6 (2001): 1188. http://dx.doi.org/10.1016/s1053-8119(01)92506-6.

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Hildebrandt, Gary H., and Michael A. Belmont. "Self-Paced Versus Instructor-Paced Preclinical Training in Operative Dentistry: A Case Study." Journal of Dental Education 82, no. 11 (2018): 1178–84. http://dx.doi.org/10.21815/jde.018.122.

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Flynn, Catherine, Joel Olson, and Michelle Reinhardt. "Self-Regulated Learning in Online Graduate Business Communication Courses: A Qualitative Inquiry." Business and Professional Communication Quarterly 83, no. 1 (2019): 80–95. http://dx.doi.org/10.1177/2329490619885904.

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This qualitative study reviewed student application of self-regulated learning (SRL) processes in self-paced graduate business communication courses. It was preceded by a quantitative analysis of the same courses. In both studies, researchers sought to understand student experience in a self-paced learning environment, and how this experience demonstrated SRL and increased student performance. Neither study established a clear connection between a self-paced learning environment, SRL, and student performance. However, both studies confirmed the importance of student predisposition for the cyclical phases of preparation, performance, and appraisal and highlighted the critical role of support in readying students for learning strategy changes.

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Adeoye, Moses Adeleke, Entika Fani Prastikawati, Rasheedat Modupe Oladimeji, and Adeseko Sunday Olaifa. "From Overwhelm to Success: Empowering Educational Personnel with Microlearning and Self-Paced Training to Maximize Performance and Avoid Burnout." International Journal of Educational Review 6, no. 1 (2024): 46–58. http://dx.doi.org/10.33369/ijer.v6i1.31228.

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This study investigates how self-paced training and microlearning can empower teachers, improve their output, and keep them from burning out. The heavy workloads and high levels of stress that teachers and administrators, commonly experience can negatively impact their general well-being and capacity to perform their professions efficiently. This study explores the possibility of self-paced training and microlearning as tools to empower educational personnel to solve these issues. The distribution of brief, bite-sized learning modules that concentrate on particular subjects or abilities is referred to as microlearning. Due to the modules' quick completion times and ease of access, educators can accommodate learning into their already hectic schedules. Self-paced training gives people the freedom to learn at their own pace and customizes and adapts their learning process. Through the use of self-paced training and microlearning, educational staff members can acquire critical knowledge and skills in a convenient and customized way. Furthermore, by enabling educators to prioritize their professional growth and manage their workload, these training methods can assist in avoiding burnout in the classroom. The study will also investigate the possible obstacles and difficulties that educators may encounter while introducing microlearning and self-paced training. To sum up, self-paced training and microlearning have the power to completely change how educators acquire knowledge and grow as professionals. Teachers can enhance their performance, knowledge, and skills by implementing these tactics, which will help students and the educational system as a whole.

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39

Protter, Murray H. "The Self-Paced Calculus Program at Berkeley." American Mathematical Monthly 98, no. 3 (1991): 245. http://dx.doi.org/10.2307/2325030.

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GUO, Lihua. "Self-Paced Learning with Statistics Uncertainty Prior." IEICE Transactions on Information and Systems E101.D, no. 3 (2018): 812–16. http://dx.doi.org/10.1587/transinf.2017edl8169.

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Travlos, Antonios K. "Kr-Withdrawal and Self-Paced Motor Performance." Perceptual and Motor Skills 89, no. 3_suppl (1999): 1111–26. http://dx.doi.org/10.2466/pms.1999.89.3f.1111.

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Terboven, Christian, Julian Miller, Sandra Wienke, and Matthias S. Müller. "Self-paced Learning in HPC Lab Courses." Journal of Computational Science Education 11, no. 1 (2020): 61–67. http://dx.doi.org/10.22369/issn.2153-4136/11/1/10.

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Sun, Lijian, and Yun Zhou. "FSPMTL: Flexible Self-Paced Multi-Task Learning." IEEE Access 8 (2020): 132012–20. http://dx.doi.org/10.1109/access.2020.3009988.

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MYLES, W. S., and T. T. ROMET. "Self-paced work in sleep deprived subjects." Ergonomics 30, no. 8 (1987): 1175–84. http://dx.doi.org/10.1080/00140138708966006.

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Zhang, Shixing, Deqiang Han, Jean Dezert, and Yi Yang. "Weighted Self-Paced Learning with Belief Functions." Expert Systems with Applications 255 (December 2024): 124535. http://dx.doi.org/10.1016/j.eswa.2024.124535.

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Trojbicz, Lucas Rosiello, Mayara Vieira Damasceno, Leonardo Alves Pasqua, Arthur Fernandes Gáspari, Adriano Eduardo Lima-Silva, and Rômulo Bertuzzi. "Parasympathetic activity delayed after self-paced exercise." European Journal of Sport Science 18, no. 6 (2018): 842–50. http://dx.doi.org/10.1080/17461391.2018.1462855.

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Low, David, Martha Gramlich, and Barbara Wright Engram. "Self-Paced Exercise Program for Office Workers." AAOHN Journal 55, no. 3 (2007): 99–105. http://dx.doi.org/10.1177/216507990705500302.

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Larsson, Jonas, Balázs Gulyás, and Per E. Roland. "Cortical representation of self-paced finger movement." NeuroReport 7, no. 2 (1996): 463–68. http://dx.doi.org/10.1097/00001756-199601310-00021.

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Maccotta, Luigi, Jeffrey M. Zacks, and Randy L. Buckner. "Rapid self-paced event-related functional MRI." NeuroImage 13, no. 6 (2001): 192. http://dx.doi.org/10.1016/s1053-8119(01)91535-6.

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Tullis, Jonathan G., and Aaron S. Benjamin. "On the effectiveness of self-paced learning." Journal of Memory and Language 64, no. 2 (2011): 109–18. http://dx.doi.org/10.1016/j.jml.2010.11.002.

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