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.
Full textFord, 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.
Full textGallagher, 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.
Full textGallagher, 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.
Full textHarrington, 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.
Full textLiwicki, Stephan. "Robust online subspace learning." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/23234.
Full textHarrington, 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.
Full textBoyer, 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.
Full textFernando, Champika. "Online learning webs : designing support structures for online communities." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/95602.
Full textCataloged 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.
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.
Full textKuzmin, Dima. "Online learning with matrix parameters /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2009. http://uclibs.org/PID/11984.
Full textÖfjäll, Kristoffer. "Online Learning for Robot Vision." Licentiate thesis, Linköpings universitet, Datorseende, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-110892.
Full textEdakunni, Narayanan U. "Bayesian locally weighted online learning." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/3844.
Full textAbu, Ziden Azidah. "Personal Learning in Online Discussions." Thesis, University of Canterbury. University Centre for Teaching and Learning, 2007. http://hdl.handle.net/10092/1063.
Full textBarbaro, Billy. "Tuning Hyperparameters for Online Learning." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1522419008006144.
Full textGreener, Susan Linda. "Exploring readiness for online learning." Thesis, University of Brighton, 2008. https://research.brighton.ac.uk/en/studentTheses/4b53eb72-2f1e-4588-9d5e-86e570b3a74f.
Full textCAMPOLONGO, NICOLO'. "ADAPTIVE AND IMPLICIT ONLINE LEARNING." Doctoral thesis, Università degli Studi di Milano, 2021. http://hdl.handle.net/2434/823932.
Full textWargo, Katalin. "Online Faculty Development: Disorienting Dilemmas In Learning To Teach Online." W&M ScholarWorks, 2021. https://scholarworks.wm.edu/etd/1627407585.
Full textFitzgerald, Clifford Thomas. "Self-directed and collaborative online learning: learning style and performance." Thesis, Boston University, 2003. https://hdl.handle.net/2144/33470.
Full textPLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.
The purpose of this study was to determine whether a match between a participant's learning style and type of online instruction improved learner performance on tests measuring comprehension and retention. Learning style was measured by the Self-Directed Leamer Readiness Scale (SDLRS) and the Grasha-Riechmann Student Learning Style Scale (GRSLSS) and online instruction varied among online courses, recorded online courses, and computer-based tutorials. The setting for the study was a high tech machine vision company in Massachusetts and online users of its products were the participants. Three groups of learners participated in the study: employees, high school students, and customers. All three groups were comprised of engineers or engineering students. All 106 participants completed a survey that measured their preference for self-directed and collaborative learning style with the standard instruments SDLRS and GRSLSS. Participants completed 323 pre- and post-tests for 46 live online courses, recorded online courses, and computer-based tutorials during the data collection phase of the study. Those participants learning in their preferred learning style had the highest mean improvement from pre- to post-tests. Those participants with average or below average scores for self-directed and collaborative learning style showed the least improvement. The results of this study supported the hypothesis that matching the type of activity, collaborative or self-directed, to the learner's preferred learning style improved performance. The study included ten research questions.
2031-01-01
Harrison, Michelle. "Developing spaces for learning in online open learning environments." Thesis, Lancaster University, 2016. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.719806.
Full textWang, Dawei. "Enhancing individualised learning and interaction in online learning environments." Thesis, Robert Gordon University, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.491201.
Full textMonteleoni, Claire E. (Claire Elizabeth) 1975. "Learning with online constraints : shifting concepts and active learning." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/38308.
Full textIncludes bibliographical references (p. 99-102).
Many practical problems such as forecasting, real-time decision making, streaming data applications, and resource-constrained learning, can be modeled as learning with online constraints. This thesis is concerned with analyzing and designing algorithms for learning under the following online constraints: i) The algorithm has only sequential, or one-at-time, access to data. ii) The time and space complexity of the algorithm must not scale with the number of observations. We analyze learning with online constraints in a variety of settings, including active learning. The active learning model is applicable to any domain in which unlabeled data is easy to come by and there exists a (potentially difficult or expensive) mechanism by which to attain labels. First, we analyze a supervised learning framework in which no statistical assumptions are made about the sequence of observations, and algorithms are evaluated based on their regret, i.e. their relative prediction loss with respect to the hindsight-optimal algorithm in a comparator class. We derive a, lower bound on regret for a class of online learning algorithms designed to track shifting concepts in this framework. We apply an algorithm we provided in previous work, that avoids this lower bound, to an energy-management problem in wireless networks, and demonstrate this application in a network simulation.
(cont.) Second, we analyze a supervised learning framework in which the observations are assumed to be iid, and algorithms are compared by the number of prediction mistakes made in reaching a target generalization error. We provide a lower bound on mistakes for Perceptron, a standard online learning algorithm, for this framework. We introduce a modification to Perceptron and show that it avoids this lower bound, and in fact attains the optimal mistake-complexity for this setting. Third, we motivate and analyze an online active learning framework. The observations are assumed to be iid, and algorithms are judged by the number of label queries to reach a target generalization error. Our lower bound applies to the active learning setting as well, as a lower bound on labels for Perceptron paired with any active learning rule. We provide a new online active learning algorithm that avoids the lower bound, and we upper bound its label-complexity. The upper bound is optimal and also bounds the algorithm's total errors (labeled and unlabeled). We analyze the algorithm further, yielding a label-complexity bound under relaxed assumptions. Using optical character recognition data, we empirically compare the new algorithm to an online active learning algorithm with data-dependent performance guarantees, as well as to the combined variants of these two algorithms.
by Claire E. Monteleoni.
Ph.D.
Besich, Marilyn Ann. "Learning tactics of successful online learners." Diss., Montana State University, 2005. http://etd.lib.montana.edu/etd/2005/besich/BesichM0505.pdf.
Full textLuscinski, Autumn. "Best Practices in Adult Online Learning." Thesis, Pepperdine University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10608529.
Full textStudents in the United States are obtaining more college degrees than ever before. In 1975, 21.9% of Americans held bachelor’s degrees, and in 2012, 33.5% of Americans held bachelor’s degrees (Rampell, 2013). A study in 2011 indicated that Americans possessing a bachelor’s degree earn approximately $2.27 million, those with master’s degrees earn $2.67 million and those with doctoral degrees earn $3.65 million over their adult lifetime, dwarfing those with some college, who earn $1.55 million, or no college, who earn $1.30 million (Burnsed, 2011).
Unfortunately, the increase in college degree attainment in the United States does not include all Americans. Among low-income students, degree attainment has been fairly flat for several decades (Mortenson, 2016). Although education can be a great equalizer and opportunity generator, among lower income students it is often times an insurmountable challenge to obtain a bachelor’s or post baccalaureate degree. College students can have challenges in obtaining learning opportunities due to factors beyond their control, such as geography and access to quality instruction.
In order to provide equity and opportunity for nontraditional students who either working, have family responsibilities, or are low income or first generation college attenders, it is important to make every effort to connect these students with meaningful and attainable opportunities to obtain a college degree. One such delivery model of curriculum is online learning. Online learning in higher education—in which students are obtaining bachelors, masters, or doctoral degrees—takes place either partially or fully in a virtual environment accessible from e-learning devices such as laptops, tablets, or smartphones.
The goal of this study was a greater understanding the best practices in adult online education. The participants in the study were asked to help identify both the challenges and successes experienced in their online learning environments. While success in both teaching and learning is subjective, the data revealed a number of common themes, which indicated similar elements that lead to success in an online environment in areas of curriculum design, classroom management, and use of technology.
Meyer, Maxime. "Machine learning to detect online grooming." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-260390.
Full textGulati, Shalni. "Learning during online and blended courses." Thesis, City University London, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.433652.
Full textGhosh, Shaona. "Online machine learning for combinatorial data." Thesis, University of Southampton, 2016. https://eprints.soton.ac.uk/420649/.
Full textMonteleoni, Claire E. (Claire Elizabeth) 1975. "Online learning of non-stationary sequences." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/87360.
Full textIncludes bibliographical references (p. 47-48).
by Claire E. Monteleoni.
S.M.
Fourie, Aidan. ""Online Platform for Deep Learning Education"." Master's thesis, Faculty of Commerce, 2019. http://hdl.handle.net/11427/31381.
Full textLiu, Fang. "Efficient Online Learning with Bandit Feedback." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587680990430268.
Full textWang, Li Ph D. Massachusetts Institute of Technology. "Online and offline learning in operations." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/129080.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (pages 213-219).
With the rapid advancement of information technology and accelerated development of data science, the importance of integrating data into decision-making has never been stronger. In this thesis, we propose data-driven algorithms to incorporate learning from data in three operations problems, concerning both online learning and offline learning settings. First, we study a single product pricing problem with demand censoring in an offline data-driven setting. In this problem, a retailer is given a finite level of inventory, and faces a random demand that is price sensitive in a linear fashion with unknown parameters and distribution. Any unsatisfied demand is lost and unobservable. The retailer's objective is to use offline censored demand data to find an optimal price, maximizing her expected revenue with finite inventories.
We characterize an exact condition for the identifiability of near-optimal algorithms, and propose a data-driven algorithm that guarantees near-optimality in the identifiable case and approaches best-achievable optimality gap in the unidentifiable case. Next, we study the classic multi-period joint pricing and inventory control problem in an offline data-driven setting. We assume the demand functions and noise distributions are unknown, and propose a data-driven approximation algorithm, which uses offline demand data to solve the joint pricing and inventory control problem. We establish a polynomial sample complexity bound, the number of data samples needed to guarantee a near-optimal profit. A simulation study suggests that the data-driven algorithm solves the dynamic program effectively. Finally, we study an online learning problem for product selection in urban warehouses managed by fast-delivery retailers. We distill the problem into a semi-bandit model with linear generalization.
There are n products, each with a feature vector of dimension T. In each of the T periods, a retailer selects K products to offer, where T is much greater than T or b. We propose an online learning algorithm that iteratively shrinks the upper confidence bounds within each period. Compared to the standard UCB algorithm, we prove the new algorithm reduces the most dominant regret term by a factor of d, and experiments on datasets from Alibaba Group suggest it lowers the total regret by at least 10%..
by Li Wang.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center
Wainer, L. J. "Online graph-based learning for classification." Thesis, University College London (University of London), 2008. http://discovery.ucl.ac.uk/1446151/.
Full textBanks, Johnetta P. "Student Retention at Online Learning Institutions." ScholarWorks, 2019. https://scholarworks.waldenu.edu/dissertations/7593.
Full textSentenac, Flore. "Learning and Algorithms for Online Matching." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAG005.
Full textThis thesis focuses mainly on online matching problems, where sets of resources are sequentially allocated to demand streams. We treat them both from an online learning and a competitive analysis perspective, always in the case when the input is stochastic.On the online learning side, we study how the specific matching structure influences learning in the first part, then how carry-over effects in the system affect its performance.On the competitive analysis side, we study the online matching problem in specific classes of random graphs, in an effort to move away from worst-case analysis.Finally, we explore how learning can be leveraged in the scheduling problem
Drysdale, Jeffery S. "Online Facilitators and Sense of Community in K-12 Online Learning." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/3838.
Full textDel, Valle Rodrigo. "Online learning learner characteristics and their approaches to managing learning /." [Bloomington, Ind.] : Indiana University, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:3204535.
Full textSource: Dissertation Abstracts International, Volume: 67-01, Section: A, page: 0152. Adviser: Thomas M. Duffy. "Title from dissertation home page (viewed Jan. 8, 2007)."
Nguyen, Thi Thu Thuy. "Advanced machine learning techniques for online and data stream learning." Thesis, Griffith University, 2019. http://hdl.handle.net/10072/386536.
Full textThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
Full Text
Squillace, Diana Marie. "Distance education: The development of online learning environments for the online student." CSUSB ScholarWorks, 2003. https://scholarworks.lib.csusb.edu/etd-project/2394.
Full textNotholt, Jochen. "Online-Lernen für Juristen Verbesserungschancen in der Informationsverarbeitung durch den Einsatz aktueller Online-Technik." Saarbrücken Verl. Alma Mater, 2006. http://d-nb.info/99023133X/04.
Full textLiljeström, Monica. "Learning text talk online : Collaborative learning in asynchronous text based discussion forums." Doctoral thesis, Umeå universitet, Pedagogiska institutionen, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-34199.
Full textSancho, Asensio Andreu. "Facing online challenges using learning classifier systems." Doctoral thesis, Universitat Ramon Llull, 2014. http://hdl.handle.net/10803/144508.
Full textLos grandes avances en el campo del aprendizaje automático han resultado en el diseño de máquinas capaces de aprender y de extraer información útil y original de la experiencia. Recientemente alguna de estas técnicas de aprendizaje se han aplicado con éxito para resolver problemas del mundo real en ámbitos tecnológicos, médicos, científicos e industriales, los cuales no se podían tratar con técnicas convencionales de análisis ya sea por su complejidad o por el gran volumen de datos a procesar. Dado este éxito inicial, los sistemas de aprendizaje automático se enfrentan actualmente a problemas de complejidad cada vez m ́as elevada, lo que ha resultado en un aumento de la actividad investigadora en sistemas capaces de afrontar nuevos problemas del mundo real de manera eficiente y escalable. Una de las familias más prometedoras dentro del aprendizaje automático son los sistemas clasificadores basados en algoritmos genéticos (LCSs), el funcionamiento de los cuales se inspira en la naturaleza. Los LCSs intentan representar las políticas de actuación de expertos humanos usando conjuntos de reglas que se emplean para escoger las mejores acciones a realizar en todo momento. Así pues estos sistemas aprenden políticas de actuación de manera incremental mientras van adquiriendo experiencia a través de la nueva información que se les va presentando. Los LCSs se han aplicado con éxito en campos tan diversos como en la predicción de cáncer de próstata o en sistemas de soporte de bolsa, entre otros. Además en algunos casos se ha demostrado que los LCSs realizan tareas superando la precisión de expertos humanos. El propósito de la presente tesis es explorar la naturaleza online del aprendizaje empleado por los LCSs de estilo Michigan para la minería de grandes cantidades de datos en forma de flujos continuos de información a alta velocidad y cambiantes en el tiempo. La extracción del conocimiento a partir de estas fuentes de datos es clave para obtener una mejor comprensión de los procesos que se describen. Así, aprender de estos datos plantea nuevos retos a las técnicas tradicionales, las cuales no están diseñadas para tratar flujos de datos continuos y donde los conceptos y los niveles de ruido pueden variar en el tiempo de forma arbitraria. La contribución del la presente tesis toma el eXtended Classifier System (XCS), el LCS de tipo Michigan más estudiado y uno de los sistemas de aprendizaje automático más competentes, como punto de partida. De esta forma los retos abordados en esta tesis son dos: el primer desafío es la construcción de un sistema supervisado competente sobre el framework de los LCSs de estilo Michigan que aprende de flujos de datos con una capacidad de reacción rápida a los cambios de concepto y al ruido. Como muchas aplicaciones científicas e industriales generan grandes volúmenes de datos sin etiquetar, el segundo reto es aplicar las lecciones aprendidas para continuar con el diseño de nuevos LCSs de tipo Michigan capaces de solucionar problemas online sin asumir una estructura a priori en los datos de entrada.
Last advances in machine learning have fostered the design of competent algorithms that are able to learn and extract novel and useful information from data. Recently, some of these techniques have been successfully applied to solve real-‐world problems in distinct technological, scientific and industrial areas; problems that were not possible to handle by the traditional engineering methodology of analysis either for their inherent complexity or by the huge volumes of data involved. Due to the initial success of these pioneers, current machine learning systems are facing problems with higher difficulties that hamper the learning process of such algorithms, promoting the interest of practitioners for designing systems that are able to scalably and efficiently tackle real-‐world problems. One of the most appealing machine learning paradigms are Learning Classifier Systems (LCSs), and more specifically Michigan-‐style LCSs, an open framework that combines an apportionment of credit mechanism with a knowledge discovery technique inspired by biological processes to evolve their internal knowledge. In this regard, LCSs mimic human experts by making use of rule lists to choose the best action to a given problem situation, acquiring their knowledge through the experience. LCSs have been applied with relative success to a wide set of real-‐ world problems such as cancer prediction or business support systems, among many others. Furthermore, on some of these areas LCSs have demonstrated learning capacities that exceed those of human experts for that particular task. The purpose of this thesis is to explore the online learning nature of Michigan-‐style LCSs for mining large amounts of data in the form of continuous, high speed and time-‐changing streams of information. Most often, extracting knowledge from these data is key, in order to gain a better understanding of the processes that the data are describing. Learning from these data poses new challenges to traditional machine learning techniques, which are not typically designed to deal with data in which concepts and noise levels may vary over time. The contribution of this thesis takes the extended classifier system (XCS), the most studied Michigan-‐style LCS and one of the most competent machine learning algorithms, as the starting point. Thus, the challenges addressed in this thesis are twofold: the first challenge is building a competent supervised system based on the guidance of Michigan-‐style LCSs that learns from data streams with a fast reaction capacity to changes in concept and noisy inputs. As many scientific and industrial applications generate vast amounts of unlabelled data, the second challenge is to apply the lessons learned in the previous issue to continue with the design of unsupervised Michigan-‐style LCSs that handle online problems without assuming any a priori structure in input data.
Qin, Lei. "Online machine learning methods for visual tracking." Thesis, Troyes, 2014. http://www.theses.fr/2014TROY0017/document.
Full textWe 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
Schroeder, Barbara A. "Multimedia-enhanced instruction in online learning environments /." ProQuest subscription required:, 2006. http://proquest.umi.com/pqdweb?did=1179968651&sid=3&Fmt=2&clientId=8813&RQT=309&VName=PQD.
Full textAn, Yun-Jo. "Collaborative problem-based learning in online environments." [Bloomington, Ind.] : Indiana University, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3219913.
Full text"Title from dissertation home page (viewed June 26, 2007)." Source: Dissertation Abstracts International, Volume: 67-06, Section: A, page: 2121. Adviser: Charles Reigeluth.
Pralle, Mandi Jo. "Visual design in the online learning environment." [Ames, Iowa : Iowa State University], 2007.
Find full textChan, Wai-man. "Exploring collaborative learning online in history classes." Click to view the E-thesis via HKUTO, 2003. http://sunzi.lib.hku.hk/hkuto/record/B39848656.
Full textJahng, Namsook. "Examining collaborative learning in an online course." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/30495.
Full textChan, Wai-man, and 陳偉民. "Exploring collaborative learning online in history classes." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B39848656.
Full textPasteris, S. U. "Efficient algorithms for online learning over graphs." Thesis, University College London (University of London), 2016. http://discovery.ucl.ac.uk/1516210/.
Full textSteils, Nadia. "Antecedents and consequences of online consumer learning." Thesis, Lille 1, 2016. http://www.theses.fr/2016LIL12007.
Full textLearning is a fundamental process underlying consumer behavior. This is especially true when (new) products or services are purchased and used for the first time. Existing research in psychology and marketing has focused on pedagogical principles to explain how consumers learn to use products in offline settings. This research aims to broaden this scope by exploring learning processes in online contexts and by drawing on an andragogical, i.e. adult learning, and cognitive perspective. Using a multi-method approach based on a qualitative study including semi-structured interviews and non-participant observations, and a quantitative part involving a survey and experiments, our results contribute to the understanding of consumer e-learning. First, we identify how and by which processes adult consumers learn in an online environment. Second, we determine andragogical and online factors that help reducing consumers’ cognitive effort in new product learning, and consequently improve their appropriation of the product usage. In a context in which the ineffectiveness of traditional step-by-step instructions leads to reduced insight-based learning and product usage intention, this research contributes theoretically to the field of consumer learning by investigating consumer learning from an andragogical and cognitive perspective, and addressing critical issues such as product unlearning