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Добірка наукової літератури з теми "Attentional learning"

Автор: Grafiati

Опубліковано: 11 березня 2023

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Статті в журналах з теми "Attentional learning"

Anderson, Brian A. "The attention habit: how reward learning shapes attentional selection." Annals of the New York Academy of Sciences 1369, no. 1 (November 23, 2015): 24–39. http://dx.doi.org/10.1111/nyas.12957.

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Vidnyanszky, Z., and W. Sohn. "Attentional learning: learning to bias sensory competition." Journal of Vision 3, no. 9 (March 16, 2010): 174. http://dx.doi.org/10.1167/3.9.174.

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Leber, A. B., and J. i. Kawahara. "Abstract learning of attentional set." Journal of Vision 8, no. 6 (April 2, 2010): 874. http://dx.doi.org/10.1167/8.6.874.

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Wierzchoń, Michał, Vincent Gaillard, Dariusz Asanowicz, and Axel Cleeremans. "Manipulating attentional load in sequence learning through random number generation." Advances in Cognitive Psychology 8, no. 2 (June 28, 2012): 179–95. http://dx.doi.org/10.5709/acp-0114-0.

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Okumura, Yuko, Yasuhiro Kanakogi, Tessei Kobayashi, and Shoji Itakura. "Do attentional cues affect infant learning?" Proceedings of the Annual Convention of the Japanese Psychological Association 82 (September 25, 2018): 1EV—079–1EV—079. http://dx.doi.org/10.4992/pacjpa.82.0_1ev-079.

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Ahissar, M., and S. Hochstein. "Attentional control of early perceptual learning." Proceedings of the National Academy of Sciences 90, no. 12 (June 15, 1993): 5718–22. http://dx.doi.org/10.1073/pnas.90.12.5718.

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Lefebvre, C., A. Seitz, T. Watanabe, and P. Jolicoeur. "Learning blinks during the attentional blink." Journal of Vision 5, no. 8 (September 1, 2005): 1065. http://dx.doi.org/10.1167/5.8.1065.

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Olivers, Christian, and Artem Belopolsky. "Oculomotor Measures of Learning Attentional Templates." Journal of Vision 16, no. 12 (September 1, 2016): 120. http://dx.doi.org/10.1167/16.12.120.

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Livesey, E., I. Harris, and J. Harris. "Implicit learning and the attentional blink." Journal of Vision 9, no. 8 (March 21, 2010): 159. http://dx.doi.org/10.1167/9.8.159.

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Shanks, David R., Lee A. Rowland, and Mandeep S. Ranger. "Attentional load and implicit sequence learning." Psychological Research Psychologische Forschung 69, no. 5-6 (April 23, 2005): 369–82. http://dx.doi.org/10.1007/s00426-004-0211-8.

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Дисертації з теми "Attentional learning"

Vatterott, Daniel Brown. "Learning to overcome distraction." Diss., University of Iowa, 2015. https://ir.uiowa.edu/etd/1784.

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Анотація:

Complex behaviors require selectively attending to task-relevant items, and ignoring conspicuous, irrelevant items. For example, driving requires selectively attending to other cars on the road while ignoring flashing billboards. Dominant models of attentional control posit that we avoid distraction by biasing attention towards task-relevant items, and our ability to avoid distraction depends on the strength and specificity of this bias. I find that a strong, specific bias towards task-relevant items is insufficient for preventing distraction. Instead, preventing distraction also requires past experience ignoring distractors. I also find that long-term memory systems, rather than visual short-term memory or priming memory systems, maintain this experience. Based upon these findings, I propose that effective attentional control not only demands a strong, specific bias towards task-relevant items, but also requires that observers learn to ignore conspicuous, irrelevant items.

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Chih-Ta, Tai. "Some neural bases of attentional learning." Thesis, University of Oxford, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.670291.

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Rowland, L. "Attentional processes in implicit sequence learning." Thesis, University College London (University of London), 2006. http://discovery.ucl.ac.uk/1446102/.

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Анотація:

Recent conceptualisations of human learning and memory have drawn a distinction between conscious (explicit) and unconscious (implicit) processing modes (e.g., Clark & Squire, 1998). In line with this dichotomy, researchers have suggested that implicit learning is accomplished by automatic mechanisms that acquire information incidentally (Jimenez, 2003). Concordant with classical definitions of automaticity (Schneider & Shiffrin, 1977), the present thesis investigates whether implicit learning can be distinguished by its propensity to operate without placing demands on attentional resources and by its independence from selectional control. In contrast to previous studies, it was found that learning in the probabilistic serial reaction time (SRT) task is impaired by the presence of a secondary task designed to consume attentional resources (Experiment 1 cf. Jimenez & Mendez, 1999), and that selective attention during encoding is necessary for learning about an incidental to-be-ignored sequence (Experiments 6, 7 & 9 cf. Cock, Berry, & Buchner, 2002). Thus, these results do not support the existence of an automatic implicit learning system. Additionally, Experiment 1 presents evidence that SRT learning is conscious. However, experimental procedures that interfered with input stages of SRT learning - by introducing irrelevant distractors into the display - revealed that implicit learning is highly resistant to disruption of the selection process (Experiments 2-5 & 9). Moreover, other experiments (Experiments 8 & 9) show that two complex probabilistic sequences can be learned simultaneously, which further indicates that such learning is robust in the presence of noisy input. Collectively, these findings are consistent with the view that implicit learning is subserved by a powerful incidental learning mechanism, yet, like explicit processes, requires attention and awareness to function optimally (St. John & Shanks, 1997).

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Lam, W. K., and 林永佳. "The attentional demands of implicit motor learning." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B42182207.

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Lam, W. K. "The attentional demands of implicit motor learning." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B42182207.

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Cosman, Joshua Daniel. "Task-specific learning supports control over visual distraction." Diss., University of Iowa, 2012. https://ir.uiowa.edu/etd/2845.

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Анотація:

There is more information in the visual environment than we can process at a given time, and as a result selective attention mechanisms have developed that allow us to focus on information that is relevant to us while ignoring information that is not. It is often assumed that our ability to overcome distraction by irrelevant information in the environment requires conscious, effortful processing, and traditional theories of selective attention have emphasized the role of an observer's explicit intentions in driving this control. At the same time, effortful control on the basis of explicit processes may be maladaptive when the behaviors to be executed are complex and dynamic, as is the case with many behaviors that we carry out on a daily basis. One way to increase the efficiency of this process would be to store information regarding past experiences with a distracting stimulus, and use this information to control distraction upon future encounters with that particular stimulus. The focus of the current thesis was to examine such a "learned control" view of distraction, where experience with particular stimuli is the critical factor determining whether or not a salient stimulus will capture attention and distract us in a given situation. In Chapters 2 through 4, I established a role for task-specific learning in the ability of observers to overcome attentional capture, showing that experience with particular attributes of distracting stimuli and the context in which the task was performed led to a predictable decrease in capture. In Chapter 5, I examined the neural basis of these learned control effects, and the results suggest that neocortical and medial temporal lobe learning mechanisms both contribute to the experience-dependent modulation of attentional capture observed in Chapters 2-4. Based on these results, a model of attentional capture was proposed in which experience with particular stimulus attributes and their context critically determine the ability of salient, task-irrelevant information to capture attention and cause distraction. I conclude that although explicit processes may play some role in this process under some conditions, much of our ability to overcome distraction results directly from past experience with the visual world.

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Sheikh, Rohani Saeid. "Acquiring fear and threat related attentional biases through informational learning." Thesis, University of Sussex, 2012. http://sro.sussex.ac.uk/id/eprint/38613/.

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Research has found that threat related attentional biases towards novel animals can be induced in children by giving threat information about the animals. Naturally occurring (i.e. non-induced) threat related attentional biases have also been found in both children and adults in the past research. The naturally occurring threat stimuli mainly include phobia stimuli and the threat stimuli that are assumed to have evolutionary roots (e.g., threatening facial expressions, and poisonous animals). In the present research, induced and naturally occurring threat related attentional biases were investigated and contrasted in children and adults. The participants' manual RTs and eye movements were measured in five experiments using the visual search paradigm to examine the attentional biases. The participating children, regardless of their trait anxiety scores, showed attentional bias toward angry faces as indexed by RT and eye movement measures. In the second and third experiments, children acquired fear of novel animals by listening to threat information about them. They later showed attentional bias to the newly feared stimuli: the presence of the animal's images interfered with detecting an irrelevant target, and the animal's images were detected faster than the control stimuli when presented as hidden targets in naturalistic scenes. In the fourth and fifth experiments, no enhancement of attentional bias towards fear-relevant stimuli due to receiving threat information was evident, as no difference was found between the threat information and the no information snake stimuli in terms of attention deployment measures. Strong evidence of naturally occurring attentional bias toward snake stimuli, however, was found in both RTs and overt attention indices. Overall, the RT data provided more robust evidence than the eye movement data in support of the predicted threat related attentional biases. It was argued that attentional biases to fear stimuli might have different levels which develop over time, with fast threat processing (indexed by faster RTs) appearing soon after the fear is acquired.

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Tharp, Ian James. "The effect of personality on attentional strategy in category learning." Thesis, Goldsmiths College (University of London), 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.499975.

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Анотація:

This thesis explores the mediating effects of personality on attention and performance during the learning of novel categories. Major theories of category learning emphasise the role of dopamine on a variety of processes engaged during such learning. Two core personality domains, namely extraversion and a cluster of traits collectively termed impulsive, anti-social, sensation seeking (ImpASS) were considered These personality traits were of interest because it has been suggested that their biological basis may partly reflect variation in dopaminergic neurotransmission. Schizotypal personality, owing to its association with schizophrenia, may also reflect dopaminergic function and was also considered.

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Micucci, Antonia <1989&gt. "Rejecting emotional distractors: experience-mediated attentional learning and motivational relevance." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amsdottorato.unibo.it/8896/1/tesi%20antonia%20micucci.pdf.

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Анотація:

Survival depends on the ability to rapidly detect emotionally significant stimuli, and adapt one's behavior accordingly. When an emotional stimulus occurs, attention is involuntarily diverted to it, causing a disruption in performance in a concurrent task. Emotional distractors not only capture attention but also engage cortico-limbic motivational systems. In terms of cortical responses, it is well established that emotional pictures elicit a larger late positive potential (LPP) than neutral ones. The behavioral interference and emotional modulation of the LPP have been interpreted as evidence that emotional stimuli are prioritized in terms of perception, and that the engagement of motivational systems occurs automatically. However, few studies have examined whether we can learn to ignore constantly irrelevant emotional stimuli through direct experience. The current thesis examines the extent to which experience with task-irrelevant images modulates attentional capture by emotional pictures, and which stage of emotional processing is affected by distractor experience. In Experiment 1 (Experiments 1a and 1b), the role of distractor experience was examined in terms of distractor frequency, showing that frequent exposure to distracting images reduced the interference of novel (never repeated) emotional stimuli, even when they were rare, and consequently, highly significant. In line with this finding, Experiment 2 (Experiments 2a and 2b) provided evidence that practice with variable distracting images reduced the emotional interference effect. Conversely, the affective modulation of the LPP persisted despite the frequent occurrence of distractors and the prolonged exposure to distractors. Altogether these findings suggest that evaluative processes are mandatory, as suggested by the affective modulation of the LPP. However, observers can adaptively ignore irrelevant emotional stimuli after the evaluation process has occurred, indicating that the ability to overcome emotional attentional capture results directly from experience with distracting events, and depends, therefore, on the possibility to learn that such stimuli are inconsequential.

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Ostrowski, Erik Jon. "THE INFLUENCE OF SELF-REGULATED ATTENTIONAL FOCUS ON MOTOR SKILL LEARNING AND PERFORMANCE." OpenSIUC, 2012. https://opensiuc.lib.siu.edu/theses/901.

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Анотація:

The advantages of an external focus of attention are very consistent within the motor learning literature. That is, focusing on cues external to the body while performing a task will allow for greater motor skill learning and performance benefits compared to focusing internally. Likewise, there is a vast consistency within the self-regulated learning literature. Individuals that are allowed to alter or adjust a certain characteristic of their practice environment have consistently performed significantly better than individuals following a predetermined practice arrangement mirroring the schedule determined by their self-regulated counterpart. The purpose of this study was to investigate the motor skill learning and performance benefits of choice in attentional focus instructions while executing a golf putt. It was hypothesized that in practice: the self-regulated and control groups will perform similar to each other, but both will perform better than the yoked group; all participants will perform better when focusing externally rather than internally; and a majority of participants within all groups would indicate that focusing externally rather than internally elicited greater performance benefits by the end of practice on day 1. It was also hypothesized that the self-regulated and control groups will perform similar to each other on the retention and transfer tests, but both will perform better than the yoked group; and finally, a majority of participants would indicate that focusing externally rather than internally will elicit greater performance benefits by the end of the retention and transfer tests respectively. The results from this study indicated that the self-regulated and control groups performed similar to each other during practice, retention and transfer, but did not differ significantly from the yoked group. Also, approximately only 40% of all participants believed that adopting an external focus of attention would elicit greater performance benefits than an internal focus during practice, retention and transfer. This study provides practitioners with an increased understanding of how a participant-controlled learning environment affects which focus of attention is adopted by the learner. Since the advantages of an external focus of attention are robust, and there appeared to be no clear preference for using one focus over the other when given a choice, it might be appropriate to suggest that practitioners should continue to promote an external focus of attention in their learning environments until further research investigating the interactions of self-regulated practice and attentional focus are conducted.

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Книги з теми "Attentional learning"

John, Everatt, ed. Reading and dyslexia: Visual and attentional processes. London: Routledge, 1999.

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name, No. Attention and implicit learning. Amsterdam, Netherlands: John Benjamins Pub., 2003.

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Jiménez, Luis, ed. Attention and Implicit Learning. Amsterdam: John Benjamins Publishing Company, 2003. http://dx.doi.org/10.1075/aicr.48.

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Gear, Jane. Attention, affect and learning. [Kingston-upon-Hull?]: School of Adult and Continuing Education, University of Hull, 1987.

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1964-, Jiménez Luis, ed. Attention and implicit learning. Amsterdam: John Benjamins Publishing Company, Netherlands, 2002.

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Pollmann, Stefan, ed. Spatial Learning and Attention Guidance. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-4939-9948-4.

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ill, Piazza Gail 1956, ed. Pay attention, Slosh! Morton Grove, Ill: A. Whitman, 1997.

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Liao, Chu-Min. Self-focused attention, analogy learning and implicit motor learning. Birmingham: University of Birmingham, 2001.

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Peng, Lin. Investor attention: Overconfidence and category learning. Cambridge, MA: National Bureau of Economic Research, 2005.

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Learning disabilities: How to recognize and manage learning and behavioral problems in children. Duluth, Minn: Benline Press, 1997.

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Частини книг з теми "Attentional learning"

Scherrmann, Jean-Michel, Kim Wolff, Christine A. Franco, Marc N. Potenza, Tayfun Uzbay, Lisiane Bizarro, David C. S. Roberts, et al. "Attentional Learning." In Encyclopedia of Psychopharmacology, 181. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68706-1_3084.

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Schreter, Zoltan. "Learning by attentional scanning." In Lecture Notes in Computer Science, 492–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-59497-3_214.

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Di Nocera, Dario, Alberto Finzi, Silvia Rossi, and Mariacarla Staffa. "Attentional Action Selection Using Reinforcement Learning." In From Animals to Animats 12, 371–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33093-3_37.

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Davelaar, Eddy J. "Attentional Modulation of Spread of Activation." In Encyclopedia of the Sciences of Learning, 366–69. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_599.

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Hu, Ruonan, Junjie Shang, and Qi Xia. "A Study of Primary School Pupils’ Motivation, Emotional Intelligence and Attentional Control Ability." In Blended Learning: Educational Innovation for Personalized Learning, 327–38. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21562-0_27.

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Abernethy, Bruce, Jonathon P. Maxwell, Richard S. W. Masters, John Van Der Kamp, and Robin C. Jackson. "Attentional Processes in Skill Learning and Expert Performance." In Handbook of Sport Psychology, 245–63. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118270011.ch11.

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Isaev, Peter, and Patrick Hammer. "An Attentional Control Mechanism for Reasoning and Learning." In Artificial General Intelligence, 221–30. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52152-3_23.

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Chen, Xuelei, Shiqing Wei, Chao Yi, Lingwei Quan, and Cunyue Lu. "Progressive Attentional Learning for Underwater Image Super-Resolution." In Intelligent Robotics and Applications, 233–43. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66645-3_20.

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Li, Hui, Liu Yang, and Fei Gao. "More Attentional Local Descriptors for Few-Shot Learning." In Artificial Neural Networks and Machine Learning – ICANN 2020, 419–30. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61609-0_33.

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Dai, Yunshu, Jianwei Fei, Huaming Wang, and Zhihua Xia. "Attentional Local Contrastive Learning for Face Forgery Detection." In Lecture Notes in Computer Science, 709–21. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15919-0_59.

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Тези доповідей конференцій з теми "Attentional learning"

Xiao, Jun, Hao Ye, Xiangnan He, Hanwang Zhang, Fei Wu, and Tat-Seng Chua. "Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/435.

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Factorization Machines (FMs) are a supervised learning approach that enhances the linear regression model by incorporating the second-order feature interactions. Despite effectiveness, FM can be hindered by its modelling of all feature interactions with the same weight, as not all feature interactions are equally useful and predictive. For example, the interactions with useless features may even introduce noises and adversely degrade the performance. In this work, we improve FM by discriminating the importance of different feature interactions. We propose a novel model named Attentional Factorization Machine (AFM), which learns the importance of each feature interaction from data via a neural attention network. Extensive experiments on two real-world datasets demonstrate the effectiveness of AFM. Empirically, it is shown on regression task AFM betters FM with a 8.6% relative improvement, and consistently outperforms the state-of-the-art deep learning methods Wide&Deep [Cheng et al., 2016] and DeepCross [Shan et al., 2016] with a much simpler structure and fewer model parameters. Our implementation of AFM is publicly available at: https://github.com/hexiangnan/attentional_factorization_machine

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Wang, Qiang, Zhu Teng, Junliang Xing, Jin Gao, Weiming Hu, and Stephen Maybank. "Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Tracking." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00510.

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Spencer, Jaime, Richard Bowden, and Simon Hadfield. "Medusa: Universal Feature Learning via Attentional Multitasking." In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2022. http://dx.doi.org/10.1109/cvprw56347.2022.00425.

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Suanda, Sumarga H., Seth B. Foster, Linda B. Smith, and Chen Yu. "Attentional constraints and statistics in toddlers' word learning." In 2013 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL). IEEE, 2013. http://dx.doi.org/10.1109/devlrn.2013.6652542.

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Berto, Leticia M., Leonardo de L. Rossi, Eric Rohmer, Paula D. P. Costa, Alexandre S. Simoes, Ricardo R. Gudwin, and Esther L. Colombini. "Learning over the Attentional Space with Mobile Robots." In 2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). IEEE, 2020. http://dx.doi.org/10.1109/icdl-epirob48136.2020.9278119.

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Wu, Bo, Xiaoya Yang, Chuxiong Sun, Rui Wang, Xiaohui Hu, and Yan Hu. "Learning Effective Value Function Factorization via Attentional Communication." In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2020. http://dx.doi.org/10.1109/smc42975.2020.9283355.

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Guan, Ziqiao, Kevin G. Yager, Dantong Yu, and Hong Qin. "Multi-Label Visual Feature Learning with Attentional Aggregation." In 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2020. http://dx.doi.org/10.1109/wacv45572.2020.9093311.

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Nocera, Dario di, Alberto Finzi, Silvia Rossi, and Mariacarla Staffa. "Attentional Shifting and Curiosity: a Reinforcement Learning Approach." In 11. Congresso Brasileiro de Inteligência Computacional. SBIC, 2016. http://dx.doi.org/10.21528/cbic2013-256.

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Wang, Qiurui, Chun Yuan, and Zhihui Lin. "Learning attentional recurrent neural network for visual tracking." In 2017 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2017. http://dx.doi.org/10.1109/icme.2017.8019422.

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Peng, Zhaoqing, Libo Zhang, and Tiejian Luo. "Learning to Communicate via Supervised Attentional Message Processing." In the 31st International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3205326.3205346.

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Звіти організацій з теми "Attentional learning"

Dew-Becker, Ian, and Charles Nathanson. Directed Attention and Nonparametric Learning. Cambridge, MA: National Bureau of Economic Research, October 2017. http://dx.doi.org/10.3386/w23917.

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Caplin, Andrew, John Leahy, and Filip Matějka. Social Learning and Selective Attention. Cambridge, MA: National Bureau of Economic Research, March 2015. http://dx.doi.org/10.3386/w21001.

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Peng, Lin, and Wei Xiong. Investor Attention: Overconfidence and Category Learning. Cambridge, MA: National Bureau of Economic Research, June 2005. http://dx.doi.org/10.3386/w11400.

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DeWinter, Alun, Arinola Adefila, and Katherine Wimpenny. Jordan Opportunity for Virtual Innovative Teaching and Learning. International Online Teaching and Learning, with Particular Attention to the Jordanian Case. Coventry University, June 2021. http://dx.doi.org/10.18552/jovital/2021/0001.

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Gustafsson, Martin, and Nick Taylor. The Politics of Improving Learning Outcomes in South Africa. Research on Improving Systems of Education (RISE), October 2022. http://dx.doi.org/10.35489/bsg-rise-2022/pe03.

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This paper examines the political economy and the ideology, two important determinants of educational development, in the South African context, using an approach which is in part dialogical, while paying special attention to the acquisition of foundational skills in the early grades.

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Kimrey, Christopher M. Learning to See the Opportunities in Crisis and Catastrophe: A Decision Makers Guide to the Issue-Attention Cycle. Fort Belvoir, VA: Defense Technical Information Center, September 2015. http://dx.doi.org/10.21236/ad1009071.

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Arif, Sirojuddin, Rezanti Putri Pramana, Niken Rarasati, and Destina Wahyu Winarti. Nurturing Learning Culture among Teachers: Demand-Driven Teacher Professional Development and the Development of Teacher Learning Culture in Jakarta, Indonesia. Research on Improving Systems of Education (RISE), November 2022. http://dx.doi.org/10.35489/bsg-risewp_2022/117.

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Despite the growing attention to the importance of learning culture among teachers in enhancing teaching quality, we lack systematic knowledge about how to build such a culture. Can demand-driven teacher professional development (TPD) enhance learning culture among teachers? To answer the question, we assess the implementation of the TPD reform in Jakarta, Indonesia. The province has a prolonged history of a top-down TPD system. The top-down system, where teachers can only participate in training based on assignment, has detached TPD activities from school ecosystems. Principals and teachers have no autonomy to initiate TPD activities based on the need to improve learning outcomes in their schools. This study observes changes in individual teachers related to TPD activities triggered by the reform. However, the magnitude of the changes varies depending on teachers’ skills, motivation, and leadership style. The study suggests that shifting a TPD system from top-down to bottom-up requires differentiated assistance catered to the school leaders’ and teachers’ capabilities.

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Kolgatin, Oleksandr H., Larisa S. Kolgatina, Nadiia S. Ponomareva, and Ekaterina O. Shmeltser. Systematicity of students’ independent work in cloud learning environment. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3247.

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The paper deals with the problem of out-of-class students’ independent work in information and communication learning environment based on cloud technologies. Results of appropriate survey among students of pedagogical university are discussed. The students answered the questions about systematicity of their learning activity and propositions for its improving. It is determined that the leading problems are needs in more careful instruction according to features of the task completing, insufficient experience in self-management, the lack of internal motivation. Most of all, students recommend to provide the tasks with detail instruction (oral or written) and to pay attention to careful planning the time that is necessary for full completion of the task. It is pointed that such complicated requirements can be satisfied only by complex use of information and communication technologies as well as the automated system of pedagogical diagnostics. Some requirements for management of students’ out-of-classroom independent work are formulated as a result of this discussion.

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Osadchyi, Viacheslav V., Hanna Y. Chemerys, Kateryna P. Osadcha, Vladyslav S. Kruhlyk, Serhii L. Koniukhov, and Arnold E. Kiv. Conceptual model of learning based on the combined capabilities of augmented and virtual reality technologies with adaptive learning systems. [б. в.], November 2020. http://dx.doi.org/10.31812/123456789/4417.

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The article is devoted to actual problem of using modern ICT tools to increase the level of efficiency of the educational process. The current state and relevance of the use of augmented reality (AR) and virtual reality (VR) technologies as an appropriate means of improving the educational process are considered. In particular, attention is paid to the potential of the combined capabilities of AR and VR technologies with adaptive learning systems. Insufficient elaboration of cross-use opportunities for achieving of efficiency of the educational process in state-of-the-art research has been identified. Based on analysis of latest publications and experience of using of augmented and virtual reality technologies, as well as the concept of adaptive learning, conceptual model of learning based on the combined capabilities of AR and VR technologies with adaptive learning systems has been designed. The use of VR and AR technologies as a special information environment is justified, which is applied in accordance with the identified dominant type of students' thinking. The prospects of using the proposed model in training process at educational institutions for the implementation and support of new teaching and learning strategies, as well as improving learning outcomes are determined by the example of such courses as “Algorithms and data structures”, “Computer graphics and three-dimensional modeling”, “Circuit Engineering”, “Computer Architecture”.

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Bykova, Tatyana B., Mykola V. Ivashchenko, Darja A. Kassim, and Vasyl I. Kovalchuk. Blended learning in the context of digitalization. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4441.

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The realities of digitalization require changes in strategies for choosing educational technologies. The modern educational process is not possible without the use of digital technologies. Digital technologies have led to the arising and development of blended learning. However, its effectiveness is determined not only by technology. The human factor receives special attention in this direction. Analysis of the World Development Report 2016: Digital Dividends allows us to identify digital competence as a necessary condition for the successful use of digital technologies, and hence blended learning. Learning interactions designing in the process of implementing blended learning requires timely diagnosis of the level of digital competence. A popular tool for this is the Digital Competence Framework for Citizens. To clarify the peculiarities of its use was made an analysis of the experimental implementation results of blended learning in the industrial training in sewing for intended masters. During the research, it was revealed that the most important digital competence areas for the variable learning establishment in the training of future professionals are Information and data literacy, Communication and collaboration and Problem solving. In addition, competence for area Problem solving conduce to increase the level of competence for all other areas. The level of digital competence of the subjects mainly coincide to the characteristics of basic and secondary levels. The obtained data clarified the reasons for the difficulties, decrease motivation and cognitive activity that occur among students using distance courses-resources learning designed for blended learning. Thus, the use of the Digital Competence Framework for Citizens at the initial stage of implementing blended learning can make a rational choice of strategies for combining face-to-face and distance learning technologies.

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