Academic literature on the topic 'Learning artifact'
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Journal articles on the topic "Learning artifact"
Fahrenbach, Florian, Kate Revoredo, and Flavia Maria Santoro. "Valuing prior learning." European Journal of Training and Development 44, no. 2/3 (December 12, 2019): 209–35. http://dx.doi.org/10.1108/ejtd-05-2019-0070.
Full textKromrey, M. L., D. Tamada, H. Johno, S. Funayama, N. Nagata, S. Ichikawa, J. P. Kühn, H. Onishi, and U. Motosugi. "Reduction of respiratory motion artifacts in gadoxetate-enhanced MR with a deep learning–based filter using convolutional neural network." European Radiology 30, no. 11 (June 17, 2020): 5923–32. http://dx.doi.org/10.1007/s00330-020-07006-1.
Full textHasasneh, Ahmad, Nikolas Kampel, Praveen Sripad, N. Jon Shah, and Jürgen Dammers. "Deep Learning Approach for Automatic Classification of Ocular and Cardiac Artifacts in MEG Data." Journal of Engineering 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/1350692.
Full textDeepika, J., T. Senthil, C. Rajan, and A. Surendar. "Machine learning algorithms: a background artifact." International Journal of Engineering & Technology 7, no. 1.1 (December 21, 2017): 143. http://dx.doi.org/10.14419/ijet.v7i1.1.9214.
Full textGraffieti, Gabriele, and Davide Maltoni. "Artifact-Free Single Image Defogging." Atmosphere 12, no. 5 (April 29, 2021): 577. http://dx.doi.org/10.3390/atmos12050577.
Full textLee, Seung-Bo, Hakseung Kim, Young-Tak Kim, Frederick A. Zeiler, Peter Smielewski, Marek Czosnyka, and Dong-Joo Kim. "Artifact removal from neurophysiological signals: impact on intracranial and arterial pressure monitoring in traumatic brain injury." Journal of Neurosurgery 132, no. 6 (June 2020): 1952–60. http://dx.doi.org/10.3171/2019.2.jns182260.
Full textWu, Chao, Xiaonan Zhao, Mark Welsh, Kellianne Costello, Kajia Cao, Ahmad Abou Tayoun, Marilyn Li, and Mahdi Sarmady. "Using Machine Learning to Identify True Somatic Variants from Next-Generation Sequencing." Clinical Chemistry 66, no. 1 (December 30, 2019): 239–46. http://dx.doi.org/10.1373/clinchem.2019.308213.
Full textWeiss, Dennis M. "Learning to be human with sociable robots." Paladyn, Journal of Behavioral Robotics 11, no. 1 (February 18, 2020): 19–30. http://dx.doi.org/10.1515/pjbr-2020-0002.
Full textBedi, Pradeep, S. B. Goyal, Dileep Kumar Yadav, Sunil Kumar, and Monika Sharma. "Hybrid Learning Model for Metal Artifact Reduction." Journal of Physics: Conference Series 1714 (January 2021): 012021. http://dx.doi.org/10.1088/1742-6596/1714/1/012021.
Full textParmaxi, Antigoni, and Panayiotis Zaphiris. "Emerging Technologies for Artifact Construction in Learning." Computers in Human Behavior 99 (October 2019): 366–67. http://dx.doi.org/10.1016/j.chb.2019.05.034.
Full textDissertations / Theses on the topic "Learning artifact"
Eireflet, Johan, and Buhtoo Helen Petersson. "Det nya verktyget : En undersökning av förskollärares upplevelser med surfplattan." Thesis, Högskolan i Halmstad, Akademin för lärande, humaniora och samhälle, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-28141.
Full textDumas, Kuchling Janine. "1:1 Digital devices and preparatory school teachers’ classroom practices." Diss., University of Pretoria, 2020. http://hdl.handle.net/2263/80435.
Full textDissertation (MEd)--University of Pretoria 2020.
pt2021
Humanities Education
MEd
Unrestricted
Riblett, Matthew J. "Motion-Induced Artifact Mitigation and Image Enhancement Strategies for Four-Dimensional Fan-Beam and Cone-Beam Computed Tomography." VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5542.
Full textGibbs, Béatrice. "Wii lär oss dansa? : Om dansspel, rörelsekvaliteter och lärande i idrott och hälsa." Licentiate thesis, Gymnastik- och idrottshögskolan, GIH, Forskningsgruppen för pedagogik, idrott och fritidskultur, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:gih:diva-3345.
Full textForskningslinjen Utbildning
Thorén, Mia. ""Och väggarna förvandlades till världen runtomkring" : pedagogers röster och praktiker kring att främja elevers fantasi och kreativitet i och genom fritidshemmets inomhusmiljö." Thesis, Högskolan Kristianstad, Fakulteten för lärarutbildning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-20270.
Full textThe emphasis of Swedish leisure-time teachers assignment lies in the unpredictable characteristics of play, explorative and creative ways of working and encouraging child fantasy and creativity. The leisure time centre is also a physical space where a complex mix of expectations of informal learning and care takes place. The physical learning environment of Swedish leisure-time centres, and the teacher’s assignment concerning it, is neither in detail regulated in steering documents nor well documented in academic research but seems to be an upcoming subject of attention. This study, which has been executed as walking interviews with teachers in four Swedish leisure-time centres, aims to – through a sociocultural and multimodal theoretical perspective – illustrate the intention and applied practices concerning the encouraging of child fantasy and creativity in and through physical learning environment. It also aims to explore the use and range of artifacts and how they are presented to children. The results of this study present a unanimous picture of the range of artifacts used and an assignment highly vivid to the teachers but still unspecified as well as marginalized.
Ghazi, Shabo Andira, and Amal Audish Basa Sarok. "Vilka beskrivningar avteknikämnet framkommer hosniondeklassare i grundskolan?" Thesis, KTH, Lärande, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280878.
Full textThe purpose of this study has been to understand how high school students year 9 in six Swedish schools describe the subject of technology and state what they have learned and what they lacked about content in the subject of technology after 9 years in primary school. The study is based on a questionnaire completed using paper and pen. Respondents had to answer questions about what technology is, and what they have learned and possibly missed in technology teaching during primary school. 252 questionnaires were distributed and 250 were answered. DiGirnonimo's (2011) framework has been used to categorize statements regarding the nature of technology that appear in students' questionnaire responses. The results show that technology described as an artifact and technology as a creative process as well as descriptions related to the role of technology in society are the most recurring dimensions in the respondents' answers. However, not many students describe technology related to the history of technology or technology as human activity. The results show that students seem to have learned the most about construction technology and drawing technology. Another content that is emphasized by the students as something they have learned a lot about is programming. Themes such as electrical engineering, ways of thinking to solve problems, etc. (as an engineer), technical systems, environmental issues related to technology also emerge, but with less frequency. The results show that students generally mention technology teaching from different angles. Many students can express several of DiGironimo's dimensions when asked what technology is. However, as some areas, such as the historical dimension, do not really emerge, clarity and more well-planned technical teaching are still required to cover the entire content of the syllabus for the technical subject. A relatively large proportion of the students show uncertainty about what technology content they lacked in their technology teaching. This may of course be because they do not know what to expect from the teaching. What emerges in the students' answers is that they lack programming, technical content, construction, resources, practical work, which also coincides with what they think they have learned. One interpretation is that these areas are the students'description of what the subject of technology includes and that this is what they also considerneeding more of.
Bigenho, Christopher William. "Student reflections as artifacts of self-regulatory behaviors for learning: A tale of two courses." Thesis, University of North Texas, 2011. https://digital.library.unt.edu/ark:/67531/metadc103291/.
Full textWetterlund, Simon. "Samiska politikers lärande : Rätten att få vara exkluderad och fortfarande vara inkluderad." Thesis, Uppsala universitet, Institutionen för pedagogik, didaktik och utbildningsstudier, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-254563.
Full textSteeples, Christine. "Networked learning environments : continuing professional development and the creation and use of multimedia artifacts." Thesis, Lancaster University, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.418857.
Full textSutter, Berthel. "Instruction at heart. Activity-theoretical studies of learning and development in coronary clinical work." Doctoral thesis, Ronneby : Blekinge Institute of Technology, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-00185.
Full textStudier av läkares co-coaching av varandra som ett led i deras samarbete rörande kranskärlsdiagnostiskt arbete. Artefaktanvändning, lärande och versamhetsutveckling.
Books on the topic "Learning artifact"
Dillenbourg, Pierre, Jeffrey Huang, and Mauro Cherubini, eds. Interactive Artifacts and Furniture Supporting Collaborative Work and Learning. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-77234-9.
Full textDillenbourg, Pierre. Interactive Artifacts and Furniture Supporting Collaborative Work and Learning. Boston, MA: Springer Science+Business Media, LLC, 2009.
Find full textIvarsson, Jonas. Renderings & reasoning: Studying artifacts in human knowing. Göteborg, Sweden: Acta Universitatis Gothoburgensis, 2004.
Find full textArtifact Case Studies: Interpreting Children's Work and Teachers' Classroom Strategies (Merrill Education Student Enrichment). Prentice Hall, 2003.
Find full textOrmrod, Jeanne Ellis. Artifact Case Studies: Interpreting Children's Work and Teachers' Classroom Strategies (Merrill Education Student Enrichment). Prentice Hall, 2003.
Find full text1963-, Nehaniv Chrystopher L., and Dautenhahn Kerstin, eds. Imitation in animals and artifacts. Cambridge, Mass: MIT Press, 2002.
Find full textNehaniv, Chrystopher L., and Kerstin Dautenhahn. Imitation in Animals and Artifacts. MIT Press, 2002.
Find full textNehaniv, Chrystopher L., and Kerstin Dautenhahn. Imitation in Animals and Artifacts. MIT Press, 2019.
Find full text(Editor), Kerstin Dautenhahn, and Chrystopher L. Nehaniv (Editor), eds. Imitation in Animals and Artifacts (Complex Adaptive Systems). The MIT Press, 2002.
Find full textDillenbourg, Pierre, Jeffrey Huang, and Mauro Cherubini. Interactive Artifacts and Furniture Supporting Collaborative Work and Learning. Springer, 2009.
Find full textBook chapters on the topic "Learning artifact"
Cole, Mike. "Re-covering the Idea of a Tertiary Artifact." In Cultural-Historical Approaches to Studying Learning and Development, 303–21. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6826-4_20.
Full textHadaya, Pierre, Abderrahmane Leshob, and Julien Nicolas de Verteuil. "An Artifact for Learning the TOGAF Architecture Development Method." In Advances in E-Business Engineering for Ubiquitous Computing, 435–49. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34986-8_31.
Full textHuang, Yixing, Alexander Preuhs, Günter Lauritsch, Michael Manhart, Xiaolin Huang, and Andreas Maier. "Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior." In Machine Learning for Medical Image Reconstruction, 101–12. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33843-5_10.
Full textKim, Dong Kyu, and Sam Keene. "Fast Automatic Artifact Annotator for EEG Signals Using Deep Learning." In Biomedical Signal Processing, 195–221. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67494-6_7.
Full textCunnington, J. P. W., G. R. Norman, J. M. Blake, W. D. Dauphinee, and D. E. Blackmore. "Applying Learning Taxonomies to Test Items: Is a Fact an Artifact?" In Advances in Medical Education, 139–42. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-4886-3_40.
Full textSawaragi, Tetsuo. "Reproductive Process-Oriented Data Mining from Interactions between Human and Complex Artifact System." In Machine Learning and Data Mining in Pattern Recognition, 180–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48097-8_15.
Full textDora, Chinmayee, and Pradyut Kumar Biswal. "An ELM Based Regression Model for ECG Artifact Minimization from Single Channel EEG." In Intelligent Data Engineering and Automated Learning – IDEAL 2018, 269–76. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03493-1_29.
Full textBordiés, Osmel, Andreas Papasalouros, and Yannis Dimitriadis. "Estimating the Gap between Informal Descriptions and Formal Models of Artifact Flows in CSCL." In Open Learning and Teaching in Educational Communities, 554–55. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11200-8_66.
Full textHuang, Yixing, Yanye Lu, Oliver Taubmann, Guenter Lauritsch, and Andreas Maier. "Traditional Machine Learning Techniques for Streak Artifact Reduction in Limited Angle Tomography." In Bildverarbeitung für die Medizin 2018, 222–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-56537-7_62.
Full textMoyes, Andrew, Kun Zhang, Ming Ji, Huiyu Zhou, and Danny Crookes. "Unsupervised Deep Learning for Stain Separation and Artifact Detection in Histopathology Images." In Communications in Computer and Information Science, 221–34. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52791-4_18.
Full textConference papers on the topic "Learning artifact"
Vasiliou, Christina. "Collaborative Learning In An Artifact Ecology." In the 2015 International Conference. New York, New York, USA: ACM Press, 2015. http://dx.doi.org/10.1145/2817721.2820987.
Full textNedelcu, Elena, Raluca Portase, Ramona Tolas, Raul Muresan, Mihaela Dinsoreanu, and Rodica Potolea. "Artifact detection in EEG using machine learning." In 2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2017. http://dx.doi.org/10.1109/iccp.2017.8116986.
Full textNiu, Chuang, Mengzhou Li, and Ge Wang. "Multi-window learning for metal artifact reduction." In Developments in X-Ray Tomography XIII, edited by Bert Müller and Ge Wang. SPIE, 2021. http://dx.doi.org/10.1117/12.2596239.
Full textMolina, Facundo, Pablo Ponzio, Nazareno Aguirre, and Marcelo Frias. "EvoSpex: An Evolutionary Algorithm for Learning Postconditions (artifact)." In 2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion). IEEE, 2021. http://dx.doi.org/10.1109/icse-companion52605.2021.00080.
Full textNjomou, Aquilas Tchanjou, Alexandra Johanne Bifona Africa, Bram Adams, and Marios Fokaefs. "MSR4ML: Reconstructing Artifact Traceability in Machine Learning Repositories." In 2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 2021. http://dx.doi.org/10.1109/saner50967.2021.00061.
Full textPrakash, Prakhar, and Sandeep Dutta. "Deep learning-based artifact detection for diagnostic CT images." In Physics of Medical Imaging, edited by Hilde Bosmans, Guang-Hong Chen, and Taly Gilat Schmidt. SPIE, 2019. http://dx.doi.org/10.1117/12.2511766.
Full textXu, Shiyu, and Hao Dang. "Deep residual learning enabled metal artifact reduction in CT." In Physics of Medical Imaging, edited by Guang-Hong Chen, Joseph Y. Lo, and Taly Gilat Schmidt. SPIE, 2018. http://dx.doi.org/10.1117/12.2293945.
Full textLee, Sangmin S., Kiwon Lee, and Guiyeom Kang. "EEG Artifact Removal by Bayesian Deep Learning & ICA." In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society. IEEE, 2020. http://dx.doi.org/10.1109/embc44109.2020.9175785.
Full textHu, Yueyu, Haichuan Ma, Dong Liu, and Jiaying Liu. "Compression Artifact Removal with Ensemble Learning of Neural Networks." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2020. http://dx.doi.org/10.1109/cvprw50498.2020.00074.
Full textErkens, Melanie, Oliver Daems, and H. Ulrich Hoppe. "Artifact Analysis around Video Creation in Collaborative STEM Learning Scenarios." In 2014 IEEE 14th International Conference on Advanced Learning Technologies (ICALT). IEEE, 2014. http://dx.doi.org/10.1109/icalt.2014.116.
Full textReports on the topic "Learning artifact"
Chang, Chihway, Alex Drlica-Wagner, Brian Nord, Donah, Michelle Wang, and Michael H. L. S. Wang. A Machine Learning Approach to the Detection of Ghosting Artifacts in Dark Energy Survey Images. Office of Scientific and Technical Information (OSTI), December 2019. http://dx.doi.org/10.2172/1594126.
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