Relevant bibliographies by topics / Online learning

Academic literature on the topic 'Online learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 August 2024

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Journal articles on the topic "Online learning"

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Van Wart, Montgomery. "Online Learning." International Journal of Adult Education and Technology 13, no. 2 (April 1, 2022): 1–18. http://dx.doi.org/10.4018/ijaet.312581.

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The COVID-19 pandemic accelerated the trend to the use of online learning more extensively as well as our experience with many new, and often unusual, situational variations. This article reviews what we know about the important factors affecting effective online learning, summarizes the extensive research findings about online learning, reviews some of the overall gaps in the research in terms of moving the state-of-the-art forward, and provides a summary of types of initiatives that departments, colleges, and universities can take to upgrade their online teaching/learning offerings. It also provides a summary of complementary opportunities/strengths and challenges provided by online teaching. Challenges include additional skills to be learned by faculty, various challenges in implementing integrity tools, mode selection issues, lowered perceived learning experience, increased faculty workload, student choice “traps,” and faculty satisfaction conflicts.
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Elliott, Brenda, and Cynthia A. Dillon. "Online Learning." Journal for Nurses in Staff Development 28, no. 6 (2012): 285–87. http://dx.doi.org/10.1097/nnd.0b013e31827259de.

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Cowan, John. "Learning online." British Journal of Educational Technology 36, no. 1 (December 2, 2004): 115–16. http://dx.doi.org/10.1111/j.1467-8535.2005.00445_5.x.

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Deeson, Eric. "Online learning." British Journal of Educational Technology 36, no. 4 (July 2005): 699–700. http://dx.doi.org/10.1111/j.1467-8535.2005.00547_7.x.

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Guerry, S. "Online learning." BMJ 327, no. 7426 (November 29, 2003): 174s —a—174. http://dx.doi.org/10.1136/bmj.327.7426.s174-a.

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Hall, Susan W. "Online learning." Nursing Made Incredibly Easy! 14, no. 1 (2016): 8–9. http://dx.doi.org/10.1097/01.nme.0000475170.85231.0f.

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Seng, Lau, and Fitri Suraya Mohamad. "Online learning." Internet and Higher Education 5, no. 2 (June 2002): 109–18. http://dx.doi.org/10.1016/s1096-7516(02)00087-8.

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Stanton, Marietta. "Online Learning." Online Journal of Rural Nursing and Health Care 7, no. 2 (December 2007): 2. http://dx.doi.org/10.14574/ojrnhc.v7i2.132.

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Smith, Tara, and Rowena Barrett. "Online learning." Journal of Small Business and Enterprise Development 23, no. 1 (February 15, 2016): 207–23. http://dx.doi.org/10.1108/jsbed-05-2014-0083.

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Purpose – If owner-managers engage in management development activities then chances of success may be improved for small businesses. But small business owner-managers (SBOMs) are a difficult group to engage in management development activities. While practitioners worry about timing, content and location of development activities, the purpose of this paper is to examine what drives SBOMs to participate in an online discussion forum (ODF) as a form of management development. An ODF was run with SBOMs and the factors affecting their participation are reported from this exploratory study. Design/methodology/approach – A qualitative methodology was used where data gathered from three sources, the ODF posts, in-depth interviews with participants and a focus group with non-participants. These were analysed to evaluate factors affecting participation of SBOMs in an ODF. Findings – The findings point to the importance of owner-managers’ attitudes. Attitudes that positively affected SBOMs participation in the ODF included; appreciating that learning leads to business success; positive self-efficacy developed through prior online experience; and an occupational identity as a business manager. Research limitations/implications – Few SBOMs participated in the ODF, which is consistent with research finding that they are a difficult group to engage in management development learning activities. Three forms of data were analysed to strengthen results. Practical implications – Caution should be exercised when considering investment in e-learning to develop the managerial capabilities of SBOMs. Originality/value – Evidence of the factors important for participation in an informal voluntary ODF. The findings suggest greater emphasis should be placed on changing attitudes if SBOMs are to be encouraged to participate in management development activities.
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Cameron, Heather, and Jacqui Limberger. "Online learning." Journal of European Industrial Training 28, no. 5 (June 2004): 429–39. http://dx.doi.org/10.1108/03090590410533107.

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Dissertations / Theses on the topic "Online learning"

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Adamskiy, Dmitry. "Adaptive online learning." Thesis, Royal Holloway, University of London, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.591060.

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The research that constitutes this thesis was driven by the two related goals in mind. The first one was to develop new efficient online learning algorithms and to study their properties and theoretical guarantees. The second one was to study real-world data and find algorithms appropriate for the particular real-world problems. This thesis studies online prediction with few assumptions about the nature of the data. This is important for real-world applications of machine learning as complex assumptions about the data are rarely justified. We consider two frameworks: conformal prediction, which is based on the randomness assumption, and prediction with expert advice, where no assumptions about the data are made at all. Conformal predictors are set predictors, that is a set of possible labels is issued by Learner at each trial. After the prediction is made the real label is revealed and Learner's prediction is evaluated. 10 case of classification the label space is finite so Learner makes an error if the true label is not in the set produced by Learner. Conformal prediction was originally developed for the supervised learning task and was proved to be valid in the sense of making errors with a prespecified probability. We will study possible ways of extending this approach to the semi-supervised case and build a valid algorithm for this t ask. Also, we will apply conformal prediction technique to the problem of diagnosing tuberculosis in cattle. Whereas conformal prediction relies on just the randomness assumption, prediction with expert advice drops this one as well. One may wonder whether it is possible to make good predictions under these circumstances. However Learner is provided with predictions of a certain class of experts (or prediction strategies) and may base his prediction on them. The goal then is to perform not much worse than the best strategy in the class. This is achieved by carefully mixing (aggregating) predictions of the base experts. However, often the nature of data changes over time, such that there is a region where one expert is good, followed by a region where another is good and so on. This leads to the algorithms which we call adaptive: they take into account this structure of the data. We explore the possibilities offered by the framework of specialist experts to build adaptive algorithms. This line of thought allows us then to provide an intuitive explanation for the mysterious Mixing Past Posteriors algorithm and build a new algorithm with sharp bounds for Online Multitask Learning.
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Ford, William. "Online Learning in Biology: An Investigation into Designing Online Learning Resources." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etd/3330.

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As technology continues to advance, many instructors are incorporating online activities into their courses. While online learning has several benefits, there is still debate on how instructors can best develop and utilize these resources in their classroom. This study is split into two smaller projects that both aim to provide further insights on how to develop online activities that target undergraduate biology students. The first project revealed that elaborative feedback in a phylogenetic activity was more useful for students who had some exposure to phylogenetics prior to completing the activity. The results of the second project revealed that the appearance of two simulations’ user interfaces does not have a significant effect on learning outcomes. However, many students responded that these simulations did increase their understanding of the concepts, indicating simulations can play an important role in the biology classroom.
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Gallagher, Debra. "Learning styles, self-efficacy, and satisfaction with online learning is online learning for everyone? /." Bowling Green, Ohio : Bowling Green State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=bgsu1171920981.

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Gallagher, Debra K. "LEARNING STYLES, SELF-EFFICACY, AND SATISFACTION WITH ONLINE LEARNING: IS ONLINE LEARNING FOR EVERYONE?" Bowling Green State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1171920981.

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Harrington, Edward, and edwardharrington@homemail com au. "Aspects of Online Learning." The Australian National University. Research School of Information Sciences and Engineering, 2004. http://thesis.anu.edu.au./public/adt-ANU20060328.160810.

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Online learning algorithms have several key advantages compared to their batch learning algorithm counterparts: they are generally more memory efficient, and computationally mor efficient; they are simpler to implement; and they are able to adapt to changes where the learning model is time varying. Online algorithms because of their simplicity are very appealing to practitioners. his thesis investigates several online learning algorithms and their application. The thesis has an underlying theme of the idea of combining several simple algorithms to give better performance. In this thesis we investigate: combining weights, combining hypothesis, and (sort of) hierarchical combining.¶ Firstly, we propose a new online variant of the Bayes point machine (BPM), called the online Bayes point machine (OBPM). We study the theoretical and empirical performance of the OBPm algorithm. We show that the empirical performance of the OBPM algorithm is comparable with other large margin classifier methods such as the approximately large margin algorithm (ALMA) and methods which maximise the margin explicitly, like the support vector machine (SVM). The OBPM algorithm when used with a parallel architecture offers potential computational savings compared to ALMA. We compare the test error performance of the OBPM algorithm with other online algorithms: the Perceptron, the voted-Perceptron, and Bagging. We demonstrate that the combinationof the voted-Perceptron algorithm and the OBPM algorithm, called voted-OBPM algorithm has better test error performance than the voted-Perceptron and Bagging algorithms. We investigate the use of various online voting methods against the problem of ranking, and the problem of collaborative filtering of instances. We look at the application of online Bagging and OBPM algorithms to the telecommunications problem of channel equalization. We show that both online methods were successful at reducing the effect on the test error of label flipping and additive noise.¶ Secondly, we introduce a new mixture of experts algorithm, the fixed-share hierarchy (FSH) algorithm. The FSH algorithm is able to track the mixture of experts when the switching rate between the best experts may not be constant. We study the theoretical aspects of the FSH and the practical application of it to adaptive equalization. Using simulations we show that the FSH algorithm is able to track the best expert, or mixture of experts, in both the case where the switching rate is constant and the case where the switching rate is time varying.
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Liwicki, Stephan. "Robust online subspace learning." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/23234.

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In this thesis, I aim to advance the theories of online non-linear subspace learning through the development of strategies which are both efficient and robust. The use of subspace learning methods is very popular in computer vision and they have been employed to numerous tasks. With the increasing need for real-time applications, the formulation of online (i.e. incremental and real-time) learning methods is a vibrant research field and has received much attention from the research community. A major advantage of incremental systems is that they update the hypothesis during execution, thus allowing for the incorporation of the real data seen in the testing phase. Tracking acts as an attractive and popular evaluation tool for incremental systems, and thus, the connection between online learning and adaptive tracking is seen commonly in the literature. The proposed system in this thesis facilitates learning from noisy input data, e.g. caused by occlusions, casted shadows and pose variations, that are challenging problems in general tracking frameworks. First, a fast and robust alternative to standard L2-norm principal component analysis (PCA) is introduced, which I coin Euler PCA (e-PCA). The formulation of e-PCA is based on robust, non-linear kernel PCA (KPCA) with a cosine-based kernel function that is expressed via an explicit feature space. When applied to tracking, face reconstruction and background modeling, promising results are achieved. In the second part, the problem of matching vectors of 3D rotations is explicitly targeted. A novel distance which is robust for 3D rotations is introduced, and formulated as a kernel function. The kernel leads to a new representation of 3D rotations, the full-angle quaternion (FAQ) representation. Finally, I propose 3D object recognition from point clouds, and object tracking with color values using FAQs. A domain-specific kernel function designed for visual data is then presented. KPCA with Krein space kernels is introduced, as this kernel is indefinite, and an exact incremental learning framework for the new kernel is developed. In a tracker framework, the presented online learning outperforms the competitors in nine popular and challenging video sequences. In the final part, the generalized eigenvalue problem is studied. Specifically, incremental slow feature analysis (SFA) with indefinite kernels is proposed, and applied to temporal video segmentation and tracking with change detection. As online SFA allows for drift detection, further improvements are achieved in the evaluation of the tracking task.
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Harrington, Edward Francis. "Aspects of online learning /." View thesis entry in Australian Digital Theses Program, 2004. http://thesis.anu.edu.au/public/adt-ANU20060328.160810/index.html.

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Boyer, Naomi Rose. "Building Online Learning: System Insights into Group Learning in an International Online Environment." Scholar Commons, 2001. http://purl.fcla.edu/fcla/etd/SFE0000026.

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Fernando, Champika. "Online learning webs : designing support structures for online communities." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/95602.

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Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 71-72).
This thesis explores how we can design online learning communities to better support connections to the people and resources beginners need when learning to program. I describe and analyze the design and implementation of the Scripts Workshop, a learning environment that supports members of the Scratch online community who are stuck on a programming problem in a Scratch project. The Scripts Workshop considers the People, Activities and Spaces needed to support these users in getting un-stuck. I conclude by describing a set of design principles for building learning webs within online communities, derived from the Scripts Workshop experiment.
by Champika Fernando.
S.M.
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Rottmann, Axel [Verfasser], and Wolfram [Akademischer Betreuer] Burgard. "Approaches to online reinforcement learning for miniature airships = Online Reinforcement Learning Verfahren für Miniaturluftschiffe." Freiburg : Universität, 2012. http://d-nb.info/1123473560/34.

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Books on the topic "Online learning"

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Thomas, Michael. Online Learning. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications Ltd, 2011. http://dx.doi.org/10.4135/9781446262719.

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Martin, Florence, and Helen Coleman. Online Learning. 2455 Teller Road, Thousand Oaks California 91320: SAGE Publications, Inc., 2022. http://dx.doi.org/10.4135/9781071898338.

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Anderson, David. Learning online. Knutsford: Matrix, 1990.

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Stub, Sara Toth. Online Learning. 2455 Teller Road, Thousand Oaks California 91320 United States: CQ Press, 2022. http://dx.doi.org/10.4135/cqresrre20220401.

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Liebowitz, Jay. Online Learning Analytics. Boca Raton: Auerbach Publications, 2021. http://dx.doi.org/10.1201/9781003194620.

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Mann, Laurence, Jieun Kiaer, and Emine Çakır. Online Language Learning. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-91418-9.

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Cornelius, Sarah, Carole Gordon, and Jan Schyma. Live Online Learning. London: Macmillan Education UK, 2014. http://dx.doi.org/10.1007/978-1-137-32876-2.

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Dabbagh, Nada, Rose M. Marra, and Jane L. Howland. Meaningful Online Learning. First edition. | New York: Routledge, 2018. |: Routledge, 2018. http://dx.doi.org/10.4324/9781315528458.

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Polkowski, Zdzislaw, Samarjeet Borah, Sambit Kumar Mishra, and Darshana Desai. Online Learning Systems. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003272823.

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Bonk, Curtis Jay. Empowering Online Learning. New York: John Wiley & Sons, Ltd., 2009.

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Book chapters on the topic "Online learning"

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Ubell, Robert. "Adaptive Learning." In Staying Online, 42–48. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003036326-6.

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Martin, Florence, and Anthony Karl Betrus. "Online Learning." In Digital Media for Learning, 111–27. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33120-7_6.

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Gallego, Guillermo, and Huseyin Topaloglu. "Online Learning." In International Series in Operations Research & Management Science, 275–89. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9606-3_10.

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Kao, Ming-Yang. "Online Learning." In Encyclopedia of Algorithms, 598. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-30162-4_265.

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Sunal, Cynthia S., and Vivian H. Wright. "Online Learning." In Encyclopedia of the Sciences of Learning, 2499–502. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_388.

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Blockeel, Hendrik, Geoffrey I. Webb, Peter Auer, and Geoffrey I. Webb. "Online Learning." In Encyclopedia of Machine Learning, 736–43. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_613.

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Scanlon, Margaret, and David Buckingham. "Learning online." In IFIP Advances in Information and Communication Technology, 137–47. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-0-387-35668-6_14.

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Hasan, Imam. "Online Learning." In Proceedings of the Tegal International Conference on Applied Social Science & Humanities (TICASSH 2022), 217–23. Paris: Atlantis Press SARL, 2022. http://dx.doi.org/10.2991/978-2-494069-09-1_28.

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Harmon, Keeley C., Joe Ann Clark, Jeffery M. Dyck, and Vicki Moran. "Online Learning." In Nurse Educator's Guide to Best Teaching Practice, 81–110. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42539-9_6.

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Rafatirad, Setareh, Houman Homayoun, Zhiqian Chen, and Sai Manoj Pudukotai Dinakarrao. "Online Learning." In Machine Learning for Computer Scientists and Data Analysts, 235–56. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96756-7_6.

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Conference papers on the topic "Online learning"

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Wei, Yujie, Jiaxin Ye, Zhizhong Huang, Junping Zhang, and Hongming Shan. "Online Prototype Learning for Online Continual Learning." In 2023 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2023. http://dx.doi.org/10.1109/iccv51070.2023.01720.

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Panigrahi, Ritanjali. "Online Learning." In SIGMIS-CPR '17: Computers and People Research Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3084381.3084434.

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DOWNING, KEVIN, HOKLING CHEUNG, CRUSHER WONG, and KRISTINA SHIN. "THINKING ABOUT THINKING ONLINE." In Enhancing Learning Through Technology. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812772725_0003.

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Krichen, Jack P. "Evolving online learning." In the 10th ACM conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1631728.1631733.

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Talaghzi, Jallal, Abdellah Bennane, Mohammed Majid Himmi, Mostafa Bellafkih, and Aziza Benomar. "Online Adaptive Learning." In SITA'20: Theories and Applications. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3419604.3419759.

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Watjatrakul, Boonlert. "Online Learning Adoption." In ICDEL 2019: 2019 the 4th International Conference on Distance Education and Learning. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3338147.3338176.

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Widiyanti, Indri Ayu, Jajat S. Ardiwinata, and Laksmi Dewi. "Online Learning Motivation." In First Transnational Webinar on Adult and Continuing Education (TRACED 2020). Paris, France: Atlantis Press, 2021. http://dx.doi.org/10.2991/assehr.k.210508.013.

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Kintonova, Aliya, I. F. Povkhan, A. Sabitov, Aizhan Tokkuliyeva, and Natalya Demidchik. "Online Learning Technologies." In 2022 IEEE 7th International Energy Conference (ENERGYCON). IEEE, 2022. http://dx.doi.org/10.1109/energycon53164.2022.9830387.

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Zimakova, E. "LEARNING GERMAN ONLINE." In GERMAN IN BASHKORTOSTAN: PROBLEMS AND PROSPECTS. Baskir State University, 2022. http://dx.doi.org/10.33184/nyvb2022-2022-04-29.21.

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Partanen, Tiina, Linda Mannila, and Timo Poranen. "Learning programming online." In Koli Calling 2016: 16th Koli Calling International Conference on Computing Education Research. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2999541.2999567.

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Reports on the topic "Online learning"

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Wu, D. Derek. Online Learning in Postsecondary Education. New York: Ithaka S+R, August 2015. http://dx.doi.org/10.18665/sr.221027.

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Chingos, Matthew, Christine Mulhern, Rebecca Griffiths, and Richard Spies. Interactive Online Learning on Campus. New York: Ithaka S+R, August 2015. http://dx.doi.org/10.18665/sr.22522.

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Bowen, William, Kelly Lack, Matthew Chingos, and Thomas Nygren. Interactive Learning Online at Public Universities. New York: Ithaka S+R, August 2015. http://dx.doi.org/10.18665/sr.22464.

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Cellini, Stephanie Riegg, and Hernando Grueso. Student Learning in Online College Programs. Cambridge, MA: National Bureau of Economic Research, March 2021. http://dx.doi.org/10.3386/w28552.

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Phillips, Sara, ed. Classzoom: how online learning can work. Monash University, December 2021. http://dx.doi.org/10.54377/e562-f750.

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Einav, Liran, Theresa Kuchler, Jonathan Levin, and Neel Sundaresan. Learning from Seller Experiments in Online Markets. Cambridge, MA: National Bureau of Economic Research, September 2011. http://dx.doi.org/10.3386/w17385.

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Griffiths, Rebecca, Matthew Chingos, and Christine Mulhern. Can Online Learning Improve College Math Readiness? New York: Ithaka S+R, December 2015. http://dx.doi.org/10.18665/sr.275477.

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Freeman, Jade, and Mark Dennison. Online Learning Approach to Predict Value of Information. DEVCOM Army Research Laboratory, October 2023. http://dx.doi.org/10.21236/ad1213064.

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anis, sehab. The Best Online Learning Sites in the World. ResearchHub Technologies, Inc., August 2023. http://dx.doi.org/10.55277/researchhub.v30ni5qk.

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Wong, Janis, and Dr Mahlet (Milly) Zimeta. ODI Fellow Report: Data governance for online learning. Open Data Institute, September 2021. http://dx.doi.org/10.61557/prjc8161.

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