Gotowa bibliografia na temat „Online learning methods”
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Artykuły w czasopismach na temat "Online learning methods"
Tarasenko, M. "ONLINE LEARNING: INTERACTIVE METHODS". Pedagogy of the formation of a creative person in higher and secondary schools 2, nr 77 (2021): 49–53. http://dx.doi.org/10.32840/1992-5786.2021.77-2.9.
Pełny tekst źródłaTekin, Cem, i Mingyan Liu. "Online Learning Methods for Networking". Foundations and Trends® in Networking 8, nr 4 (2013): 281–409. http://dx.doi.org/10.1561/1300000050.
Pełny tekst źródłaSalmons, Janet. "Case methods for online learning". eLearn 2003, nr 6 (czerwiec 2003): 2. http://dx.doi.org/10.1145/863928.863932.
Pełny tekst źródłaPRASETYA, Prita, i Sekar Wulan PRASETYANINGTYAS. "LEARNING STATISTICAL METHODES WITH ONLINE ONLINE COURSE". ICCD 3, nr 1 (27.10.2021): 312–15. http://dx.doi.org/10.33068/iccd.vol3.iss1.368.
Pełny tekst źródłaVilkhovchenko, Nadiia P. "ESP distance learning methods At technical universities". Bulletin of Alfred Nobel University Series "Pedagogy and Psychology» 1, nr 23 (czerwiec 2022): 116–23. http://dx.doi.org/10.32342/2522-4115-2022-1-23-14.
Pełny tekst źródłaRini, Hesty Prima, i Dewi Khrisna Sawitri. "Effectiveness of Online Learning: The Learning Methods and Media". Ilomata International Journal of Social Science 3, nr 1 (10.02.2022): 330–39. http://dx.doi.org/10.52728/ijss.v3i1.389.
Pełny tekst źródłaTîrziu, Andreea-Maria, i Cătălin I. Vrabie. "NET Generation. Thinking outside the box by using online learning methods". New Trends and Issues Proceedings on Humanities and Social Sciences 2, nr 7 (27.01.2016): 41–47. http://dx.doi.org/10.18844/gjhss.v2i7.1178.
Pełny tekst źródłaSampe, Maria Zefanya, i Syafrudi Syafrudi. "ONLINE MATHEMATICS LEARNING STRATEGY APPROACH: TEACHING METHODS AND LEARNING ASSESSMENT". Jurnal Pendidikan Matematika (JUPITEK) 7, nr 1 (7.07.2024): 42–55. http://dx.doi.org/10.30598/jupitekvol7iss1pp42-55.
Pełny tekst źródłaLoi, Chek Kim, Jason Miin Hwa Lim, Norazah Mohd Suki i Hock Ann Lee. "Exploring University Students’ Online Learning Readiness: A Mixed Methods Study of Forced Online Learning". Journal of Language and Education 10, nr 1 (30.03.2024): 49–67. http://dx.doi.org/10.17323/jle.2024.16016.
Pełny tekst źródłaNF, Jhoanita, i Siti Khadijah. "PARENTS’ PERCEPTIONS OF ONLINE LEARNING METHODS: A QUANTITATIVE STUDY". Makna: Jurnal Kajian Komunikasi, Bahasa, dan Budaya 10, nr 1 (9.03.2022): 21–30. http://dx.doi.org/10.33558/makna.v10i1.3242.
Pełny tekst źródłaRozprawy doktorskie na temat "Online learning methods"
Qin, Lei. "Online machine learning methods for visual tracking". Thesis, Troyes, 2014. http://www.theses.fr/2014TROY0017/document.
Pełny tekst źródłaWe study the challenging problem of tracking an arbitrary object in video sequences with no prior knowledge other than a template annotated in the first frame. To tackle this problem, we build a robust tracking system consisting of the following components. First, for image region representation, we propose some improvements to the region covariance descriptor. Characteristics of a specific object are taken into consideration, before constructing the covariance descriptor. Second, for building the object appearance model, we propose to combine the merits of both generative models and discriminative models by organizing them in a detection cascade. Specifically, generative models are deployed in the early layers for eliminating most easy candidates whereas discriminative models are in the later layers for distinguishing the object from a few similar "distracters". The Partial Least Squares Discriminant Analysis (PLS-DA) is employed for building the discriminative object appearance models. Third, for updating the generative models, we propose a weakly-supervised model updating method, which is based on cluster analysis using the mean-shift gradient density estimation procedure. Fourth, a novel online PLS-DA learning algorithm is developed for incrementally updating the discriminative models. The final tracking system that integrates all these building blocks exhibits good robustness for most challenges in visual tracking. Comparing results conducted in challenging video sequences showed that the proposed tracking system performs favorably with respect to a number of state-of-the-art methods
Kovanovic, Vitomir. "Assessing cognitive presence using automated learning analytics methods". Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28759.
Pełny tekst źródłaJohnson, Alicia Leinaala. "Exploration of Factors Affecting the Self-Efficacy of Asynchronous Online Learners: a Mixed Methods Study". Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/77518.
Pełny tekst źródłaPh. D.
Conesa, Gago Agustin. "Methods to combine predictions from ensemble learning in multivariate forecasting". Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-103600.
Pełny tekst źródłaHäglund, Emil. "Estimating Prediction Intervals with Machine Learning and Monte Carlo Methods in Online Advertising". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-282826.
Pełny tekst źródłaReklam på nätet är en komplex miljö. Mängden hemsidor, plattformar och format såväl som trenden med programmatiska reklamköp gör det svårt att utvärdera planerad reklam beträffande förväntad kostnad och värde. Den här rapporten använder maskininlärning för att prediktera kostnaden för tusen visningar (CPM), ett mått på annonseringseffektivitet, för ett planerat reklamköp. Random forest och neurala nätverksmodeller jämfördes med avseende på deras förmåga att producera punktskattningar och prediktionsintervall. För att skatta prediktionsintervall för neurala nätverk användes Monte Carlo dropout och skattning av datamängdens brusnivå. För random forest användes en Monte Carlo metod där ett stort antal modeller parametriseras med bootstrapping. Implementerade algoritmer jämfördes med 5x2cv test. Random forest och neural nätverksmodellerna producerade liknande precision för punktskattningar. För att erhålla giltiga prediktionsintervall avseende täckningssannolikhet för random forest krävdes det att parametrar justerades för att öka de enskilda beslutsträdens varians. Detta påverkade precisionen för punktskattningar negativt och prediktionsintervallen för random forest var mindre optimala än de som skattades av neurala nätverksalgoritmen. Denna skillnad i förmåga att skatta prediktionsintervall bekräftades statistiskt av 5x2cv testet.
Wright, Robert Demmon. "Students' Attitudes Towards Rapport-building Traits and Practices in Online Learning Environments". Thesis, University of North Texas, 2012. https://digital.library.unt.edu/ark:/67531/metadc177265/.
Pełny tekst źródłaLuca, Joseph. "Developing generic skills for tertiary students in an online learning environment". Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2002. https://ro.ecu.edu.au/theses/713.
Pełny tekst źródłaCunningham, E. Ann. "Comparison of Student Success by Course Delivery Methods at an Eastern Tennessee Community College". Digital Commons @ East Tennessee State University, 2015. https://dc.etsu.edu/etd/2585.
Pełny tekst źródłaJoshi, Apoorva. "Trajectory-based methods to predict user churn in online health communities". Thesis, University of Iowa, 2018. https://ir.uiowa.edu/etd/6152.
Pełny tekst źródłaTunningley, Joan M. "Self-Regulated Learning and Reflective Journaling in an Online Interprofessional Course: A Mixed Methods Study". University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1511799445626182.
Pełny tekst źródłaKsiążki na temat "Online learning methods"
plc, Epic Group, red. Methods: A practical guide to the methods used in online learning. Brighton: Epic Group, 1999.
Znajdź pełny tekst źródłaA, Fisher Cheryl, i Rietschel Matthew J, red. Developing online learning environments in nursing education. Wyd. 3. New York, NY: Springer, 2014.
Znajdź pełny tekst źródła1966-, Lindberg J. Ola, i Olofsson Anders D. 1973-, red. Online learning communities and teacher professional development: Methods for improved education delivery. Hershey, PA: Information Science Reference, 2010.
Znajdź pełny tekst źródła1966-, Lambropoulos Niki, i Romero Margarido 1980-, red. Educational social software for context-aware learning: Collaborative methods and human interaction. Hershey, PA: Information Science Reference, 2010.
Znajdź pełny tekst źródła1966-, Lambropoulos Niki, i Romero Margarido 1980-, red. Educational social software for context-aware learning: Collaborative methods and human interaction. Hershey, PA: Information Science Reference, 2010.
Znajdź pełny tekst źródła1966-, Lambropoulos Niki, i Romero Margarido 1980-, red. Educational social software for context-aware learning: Collaborative methods and human interaction. Hershey, PA: Information Science Reference, 2010.
Znajdź pełny tekst źródłaLoke, Jennifer C. F. Critical discourse analysis of interprofessional online learning in health care education. Hauppauge, N.Y: Nova Science Publishers, 2011.
Znajdź pełny tekst źródłaConceição, Simone C. O., 1963-, red. Motivating and retaining online students: Research-based strategies that work. San Francisco, CA: Jossey-Bass, a Wiley brand, 2014.
Znajdź pełny tekst źródłaTekin i Mingyan Liu. Online Learning Methods for Networking. Now Publishers, 2015.
Znajdź pełny tekst źródłaLOK, Johnny Ch. Advantages Comparision Between Classroom and Online Learning Methods. Independently Published, 2020.
Znajdź pełny tekst źródłaCzęści książek na temat "Online learning methods"
Bartz-Beielstein, Thomas. "Special Requirements for Online Machine Learning Methods". W Online Machine Learning, 63–69. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-7007-0_6.
Pełny tekst źródłaConceição, Simone C. O., i Susan M. Yelich Biniecki. "Closed and Open Online Discussion Forums". W Methods for Facilitating Adult Learning, 316–32. New York: Routledge, 2024. http://dx.doi.org/10.4324/9781003446019-25.
Pełny tekst źródłaMittelman, Rachel J. "Overcoming Resistance to Teaching History Online and Methods of Engagement". W Teaching and Learning History Online, 7–14. New York: Routledge, 2023. http://dx.doi.org/10.4324/9781003258414-3.
Pełny tekst źródłaGunawardena, Charlotte Nirmalani, Nick V. Flor i Damien M. Sánchez. "Social Construction of Knowledge (SCK) Platforms, Scraping, and Methods". W Knowledge Co-Construction in Online Learning, 70–77. New York: Routledge, 2025. https://doi.org/10.4324/9781003324461-7.
Pełny tekst źródłaRay, Asmita, Vishal Goyal i Samir Kumar Bandyopadhyay. "Student Stress Detection in Online Learning During Outbreak". W Computational Methods in Psychiatry, 259–81. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6637-0_13.
Pełny tekst źródłaGunawardena, Charlotte Nirmalani, Nick V. Flor i Damien M. Sánchez. "Social Learning Analytic Methods (SLAM) for Examining Online Social Dynamics". W Knowledge Co-Construction in Online Learning, 57–69. New York: Routledge, 2025. https://doi.org/10.4324/9781003324461-6.
Pełny tekst źródłaPark, Sunyoung, Boreum (Jenny) Ju i Shinhee Jeong. "Adopting Massive Open Online Courses (MOOCs) in Adult Learning Contexts". W Methods for Facilitating Adult Learning, 333–49. New York: Routledge, 2024. http://dx.doi.org/10.4324/9781003446019-26.
Pełny tekst źródłaSiska, Jumiati, Meydia Afrina, Sudarwan Danim i Agus Susanta. "The Effectiveness of Demonstrative Learning Methods in Improving Students’ Learning Outcomes". W Online Conference of Education Research International (OCERI 2023), 319–25. Paris: Atlantis Press SARL, 2023. http://dx.doi.org/10.2991/978-2-38476-108-1_31.
Pełny tekst źródłaKatiyar, Kalpana, Hera Fatma i Simran Singh. "Student’s Stress Detection in Online Learning During the Outbreak". W Computational Methods in Psychiatry, 335–48. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6637-0_16.
Pełny tekst źródłaOiwa, Hidekazu, Shin Matsushima i Hiroshi Nakagawa. "Frequency-Aware Truncated Methods for Sparse Online Learning". W Machine Learning and Knowledge Discovery in Databases, 533–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23783-6_34.
Pełny tekst źródłaStreszczenia konferencji na temat "Online learning methods"
Atia, Hend Abdelbakey, Magdy Aboul-Ela, Christina Albert Reyad i Nancy Awadallah Awad. "Online Payments Fraud Detection Using Machine Learning Techniques". W 2024 Intelligent Methods, Systems, and Applications (IMSA), 402–9. IEEE, 2024. http://dx.doi.org/10.1109/imsa61967.2024.10652834.
Pełny tekst źródłaZhang, Hao, Liang Huang, Kai Zhao i Ryan McDonald. "Online Learning for Inexact Hypergraph Search". W Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, 908–13. Stroudsburg, PA, USA: Association for Computational Linguistics, 2013. http://dx.doi.org/10.18653/v1/d13-1093.
Pełny tekst źródłaJung, Hoin, i Xiaoqian Wang. "Fairness-Aware Online Positive-Unlabeled Learning". W Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, 170–85. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-industry.14.
Pełny tekst źródłaShdefat, Ahmed Younes, Mennatallah Mohamed, Shahd Khaled, Farrah Hany, Hanaa Fathi i Diaa Salama AbdElminaam. "Comparative Analysis of Machine Learning Models in Online Payment Fraud Prediction". W 2024 Intelligent Methods, Systems, and Applications (IMSA), 243–50. IEEE, 2024. http://dx.doi.org/10.1109/imsa61967.2024.10652861.
Pełny tekst źródłaDe Silva, D. I., i K. S. N. Athukorala. "Advancing Online Education: An Interactive Framework for Aligning Teaching Methods with Learning Styles". W 2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), 1–7. IEEE, 2024. https://doi.org/10.1109/icses63760.2024.10910891.
Pełny tekst źródłaHillier, Michael, Florian Wellmann, Boyan Brodaric, Eric de Kemp i Ernst Schetselaar. "MACHINE LEARNING METHODS FOR 3D GEOLOGICAL MODEL CONSTRUCTION". W GSA 2020 Connects Online. Geological Society of America, 2020. http://dx.doi.org/10.1130/abs/2020am-355922.
Pełny tekst źródłaSaenal, Selfiana, Syakhruni Syakhruni i Muh Kurniawan Adi Kusuma Wiharja. "Online Learning Methods for Learning Dance at School". W 1st World Conference on Social and Humanities Research (W-SHARE 2021). Paris, France: Atlantis Press, 2022. http://dx.doi.org/10.2991/assehr.k.220402.056.
Pełny tekst źródłaLu, Lyujian, Hua Wang, Hua Wang, Yaoguo Li, Yaoguo Li, Thomas Monecke, Thomas Monecke, Hoon Seo i Hoon Seo. "PREDICTING 3D GEOSPATIAL DATA USING MACHINE LEARNING-BASED IMPUTATION METHODS". W GSA 2020 Connects Online. Geological Society of America, 2020. http://dx.doi.org/10.1130/abs/2020am-356905.
Pełny tekst źródłaLui, Timothy, Daniel Gregory, Sharon A. Cowling i Well-Shen Lee. "APPLYING MACHINE LEARNING METHODS TO PREDICT GEOLOGY USING SOIL SAMPLE GEOCHEMISTRY". W GSA 2020 Connects Online. Geological Society of America, 2020. http://dx.doi.org/10.1130/abs/2020am-359454.
Pełny tekst źródłaLuo, Hongyin, Zhiyuan Liu, Huanbo Luan i Maosong Sun. "Online Learning of Interpretable Word Embeddings". W Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2015. http://dx.doi.org/10.18653/v1/d15-1196.
Pełny tekst źródłaRaporty organizacyjne na temat "Online learning methods"
Stanley, April Elisha, i Arienne McCracken. Methods for increasing student learning in an online undergraduate analysis of apparel and production course. Ames: Iowa State University, Digital Repository, 2017. http://dx.doi.org/10.31274/itaa_proceedings-180814-353.
Pełny tekst źródłaYatsenko, Halyna, i Andriy Yatsenko. Використання креативних методів навчання під час викладання дисциплін «Історія української журналістики» і «Креативний текст». Ivan Franko National University of Lviv, marzec 2023. http://dx.doi.org/10.30970/vjo.2023.52-53.11736.
Pełny tekst źródłaTusiime, Hilary Mukwenda, i Nahom Eyasu Alemu. Embracing E-Learning in Public Universities in Ethiopia and Uganda. Mary Lou Fulton Teachers College, grudzień 2023. http://dx.doi.org/10.14507/mcf-eli.j2.
Pełny tekst źródłaClement, Timothy, i Brett Vaughan. Evaluation of a mobile learning platform for clinical supervision. University of Melbourne, 2021. http://dx.doi.org/10.46580/124369.
Pełny tekst źródłaAkhmedjanova, Diana, i Komiljon Karimov. Covid-19’s Effects on Higher Education in Uzbekistan: The Case of Westminster International University in Tashkent. TOSHKENT SHAHRIDAGI XALQARO VESTMINSTER UNIVERSITETI, listopad 2020. https://doi.org/10.70735/azco9450.
Pełny tekst źródłaNahorniak, Maya. Occupation of profession: Methodology of laboratory classes from practically-oriented courses under distance learning (on an example of discipline «Radioproduction»). Ivan Franko National University of Lviv, luty 2022. http://dx.doi.org/10.30970/vjo.2022.51.11412.
Pełny tekst źródłaSchmidt-Sane, Megan, Tabitha Hrynick, Erica Nelson i Tom Barker. Mutual Learning for Policy Impact: Insights from CORE. Adapting research methods in the context of Covid-19. Institute of Development Studies (IDS), grudzień 2021. http://dx.doi.org/10.19088/core.2021.008.
Pełny tekst źródłaFalfushynska, Halina I., Bogdan B. Buyak, Hryhorii V. Tereshchuk, Grygoriy M. Torbin i Mykhailo M. Kasianchuk. Strengthening of e-learning at the leading Ukrainian pedagogical universities in the time of COVID-19 pandemic. [б. в.], czerwiec 2021. http://dx.doi.org/10.31812/123456789/4442.
Pełny tekst źródłaДирда, Ірина Анатоліївна, Марина Вікторівна Малоіван i Анна Олександрівна Томіліна. Innovative online teaching tools for students who major in english philology: challenges and opportinutuies. Видавнича група «Наукові перспективи», 2023. http://dx.doi.org/10.31812/123456789/7078.
Pełny tekst źródłaDanylchuk, Hanna B., i Serhiy O. Semerikov. Advances in machine learning for the innovation economy: in the shadow of war. Криворізький державний педагогічний університет, sierpień 2023. http://dx.doi.org/10.31812/123456789/7732.
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