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Dissertations / Theses on the topic 'And Personalized Learning'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Ghali, Fawaz. "Social personalized e-learning framework." Thesis, University of Warwick, 2010. http://wrap.warwick.ac.uk/35247/.

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This thesis discusses the topic of how to improve adaptive and personalized e-learning in order to provide novel learning experiences. A recent literature review revealed that adaptive and personalized e-learning systems are not widely used. There is a lack of interoperability between adaptive systems and learning management systems, in addition to limited collaborative and social features. First of all, this thesis investigates the interoperability issue via two case studies. The first case study focuses on how to achieve interoperability between adaptive systems and learning management syste

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Galozy, Alexander. "Data-driven personalized healthcare : Towards personalized interventions via reinforcement learning for Mobile Health." Licentiate thesis, Högskolan i Halmstad, CAISR Centrum för tillämpade intelligenta system (IS-lab), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44091.

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Medical and technological advancement in the last century has led to the unprecedented increase of the populace's quality of life and lifespan. As a result, an ever-increasing number of people live with chronic health conditions that require long-term treatment, resulting in increased healthcare costs and managerial burden to the healthcare provider. This increase in complexity can lead to ineffective decision-making and reduce care quality for the individual while increasing costs. One promising direction to tackle these issues is the active involvement of the patient in managing their care.

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Feffer, Michael A. (Michael Anthony). "Personalized machine learning for facial expression analysis." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119763.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 35-36).<br>For this MEng Thesis Project, I investigated the personalization of deep convolutional networks for facial expression analysis. While prior work focused on population-based ("one-size-fits-all") models for predict

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Sheth, Beerud Dilip. "A learning approach to personalized information filtering." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/37998.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.<br>Includes bibliographical references (leaves 96-100).<br>by Beerud Dilip Sheth.<br>M.S.

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Dinh, The Canh. "Distributed Algorithms for Fast and Personalized Federated Learning." Thesis, The University of Sydney, 2023. https://hdl.handle.net/2123/30019.

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The significant increase in the number of cutting-edge user equipment (UE) results in the phenomenal growth of the data volume generated at the edge. This shift fuels the booming trend of an emerging technique named Federated Learning. In contrast to traditional methods in which data is collected and processed centrally, FL builds a global model from contributions of UE's model without sending private data then effectively ensures data privacy. However, FL faces challenges in non-identically distributed (non-IID) data, communication cost, and convergence rate. Firstly, we propose first-order

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Guelman, Leo. "Optimal personalized treatment learning models with insurance applications." Doctoral thesis, Universitat de Barcelona, 2015. http://hdl.handle.net/10803/289500.

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In many important settings, subjects can show significant heterogeneity in response to a stimulus or “treatment". For instance, a treatment that works for the overall population might be highly inefiective, or even harmful, for a subgroup of subjects with specific characteristics. Similarly, a new treatment may not be better than an existing treatment in the overall population, but there is likely a subgroup of subjects who would benefit from it. The notion that “one size may not fit all" is becoming increasingly recognized in a wide variety of fields, ranging from economics to medicine. This

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Ozpolat, Ebru. "A Framework For A Personalized E-learning System." Phd thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610612/index.pdf.

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This thesis focuses on three of the main components of an e-learning system: Infrastructure model, data integration and personalization. For the infrastructure model, our aim is to get best use of heterogeneously structured, geographically distributed data resources. Therefore, a detailed analysis of the available infrastructure models is carried out and an open source reference implementation based on grid technology is implemented. Furthermore, a simple data integration mechanism is proposed for the suggested reference implementation. For personalization, a statistical algorithm is propose

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Nori, Nozomi. "Machine Learning Approaches for Personalized Clinical Risk Modeling." 京都大学 (Kyoto University), 2017. http://hdl.handle.net/2433/225729.

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Metcalf, Shelia Elaine. "Redesigning High School through a Personalized Learning Model." Thesis, William Woods University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10742972.

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<p> Personalized learning is an evolving method for tailoring the goals of K-12 education to meet the needs of each student. This study supports education leaders in learning how to tailor education plans that maximize success for each student. This study extends learning about a better course of action for schools that might prevent students from being in credit lag and at risk of dropping out. This qualitative research study aims to outline themes about time, place and pace in earning high school credits, as well as combat the reasons why students drop out. Through a qualitative content anal

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Shen, Yan. "Personalized ontology learning for enhancing text mining effectiveness." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/63699/1/Yan_Shen_Thesis.pdf.

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Over the last decade, the majority of existing search techniques is either keyword- based or category-based, resulting in unsatisfactory effectiveness. Meanwhile, studies have illustrated that more than 80% of users preferred personalized search results. As a result, many studies paid a great deal of efforts (referred to as col- laborative filtering) investigating on personalized notions for enhancing retrieval performance. One of the fundamental yet most challenging steps is to capture precise user information needs. Most Web users are inexperienced or lack the capability to express their nee

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BASSANI, ELIAS. "Neural Approaches to Personalized Search." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2023. https://hdl.handle.net/10281/404515.

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I recenti progressi nella ricerca sulle Artificial Neural Networks (reti neurali) hanno fatto avanzare lo stato dell'arte in molti task legati al linguaggio, tra cui l'Information Retrieval, offrendo nuove opportunità per rappresentare e sfruttare le informazioni relative all'utente durante la personalizzazione. Tuttavia, la loro applicazione nel contesto della Personalized Search è ancora un'area di ricerca aperta, con molte questioni e sfide da affrontare. In questa tesi, ci concentriamo sulla rappresentazione delle preferenze dell'utente da più prospettive, sulla gestione e sulla selezione

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Mandorah, Obai. "Stress-Aware Personalized Road Navigation System." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39958.

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Driving can be a stressful task, especially under congestion conditions. Several studies have shown a positive correlation between stress and aggressive behaviour behind the wheel, leading to accidents. One common way to minimize stress while driving is to avoid highly congested roads. However, not all drivers show the same response towards high traffic situations or other road conditions. For instance, some drivers may prefer congested routes to longer ones to minimize travel time. Increasingly, drivers are employing Advanced Traveller Information Systems while commuting to both familiar and

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Mosier, Angela Dawn. "Teachers' Challenges in Implementing Personalized Learning in Content Areas." ScholarWorks, 2018. https://scholarworks.waldenu.edu/dissertations/5908.

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Despite the national trend of assessments for gauging student mastery of prescribed curriculum standards which has placed assessment preparation at the forefront of classroom practices, teachers at a midwestern school promoting personalized learning for students, demonstrated inconsistency in implementation among content areas. An explanatory sequential mixed-methods study based on expectancy-value theory was used to define the challenges that arise as teachers implemented personalized learning in their content area. The research questions addressed the implementation of 5 personalized learnin

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Xu, Jacqueline L. "Active one-shot learning for personalized human affect estimation." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119771.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 43-46).<br>Building models that can classify human affect leads to the challenge of learning on data that is complex in features and limited in size and labels. How can these models balance being general and personalized, capturing both the commonalities and the individual quirks of people? While previous research has explored the intersection of deep learning, active learning, and one-sho

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Trincado, Alonso Juan Luis 1987. "Characterization of clinically relevant RNA alterations for personalized cancer medicine." Doctoral thesis, Universitat Pompeu Fabra, 2019. http://hdl.handle.net/10803/665991.

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The following thesis tries to answer the question of wheter RNA processing alterations are informative for the clinical management of cancer patients. For this, the work is focused on solve two main issues: the development of computational tools to study RNA profiles from multiple tumors and the identification of RNA-related signatures that may be predictive of prognosis and therapy. The thesis is divided in three chapters: First, the development a new tool for fast quantification of differential splicing: SUPPA2. Second, a new methodology for elucidating the prognostic potential of alternati

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Wang, Lei. "Personalized Dynamic Hand Gesture Recognition." Thesis, KTH, Medieteknik och interaktionsdesign, MID, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231345.

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Human gestures, with the spatial-temporal variability, are difficult to be recognized by a generic model or classifier that are applicable for everyone. To address the problem, in this thesis, personalized dynamic gesture recognition approaches are proposed. Specifically, based on Dynamic Time Warping(DTW), a novel concept of Subject Relation Network is introduced to describe the similarity of subjects in performing dynamic gestures, which offers a brand new view for gesture recognition. By clustering or arranging training subjects based on the network, two personalization algorithms are propo

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Crosslin, Matthew B. "Customizable Modality Pathway Learning Design: Exploring Personalized Learning Choices through a Lens of Self-Regulated Learning." Thesis, University of North Texas, 2016. https://digital.library.unt.edu/ark:/67531/metadc849703/.

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Open online courses provide a unique opportunity to examine learner preferences in an environment that removes several pressures associated with traditional learning. This mixed methods study sought to examine the pathways that learners will create for themselves when given the choice between an instructor-directed modality and learner-directed modality. Study participants were first examined based on their levels of self-regulated learning. Follow-up qualitative interviews were conducted to examine the choices that participants made, the impact of the course design on those choices, and what

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Valencia, Arboleda Carlos Felipe. "Contributions to statistical learning and its applications in personalized medicine." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49143.

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This dissertation, in general, is about finding stable solutions to statistical models with very large number of parameters and to analyze their asymptotic statistical properties. In particular, it is centered in the study of regularization methods based on penalized estimation. Those procedures find an estimator that is the result of an optimization problem balancing out the fitting to the data with the plausability of the estimation. The first chapter studies a smoothness regularization estimator for an infinite dimensional parameter in an exponential family model with functional predictors.

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Zhao, Siyuan. "Towards Personalized Learning using Counterfactual Inference for Randomized Controlled Trials." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-dissertations/189.

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Personalized learning considers that the causal effects of a studied learning intervention may differ for the individual student (e.g., maybe girls do better with video hints while boys do better with text hints). To evaluate a learning intervention inside ASSISTments, we run a randomized control trial (RCT) by randomly assigning students into either a control condition or a treatment condition. Making the inference about causal effects of studies interventions is a central problem. Counterfactual inference answers â€œWhat ifâ€� questions, such as "Would this particular student be

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Zhuo, Ying Daisy. "New algorithms in machine learning with applications in personalized medicine." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119284.

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Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2018.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 165-173).<br>Recent advances in machine learning and optimization hold much promise for influencing real-world decision making, especially in areas such as health care where abundant data are increasingly being collected. Howeve

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Barnes, Rachel J. "The Use of Personalized Learning Environments in Corporate Training Programs." Bowling Green State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1302192806.

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Bhosale, Devdutta. "AlcoZone: An Adaptive Hypermedia based Personalized Alcohol Education." Thesis, Virginia Tech, 2006. http://hdl.handle.net/10919/32913.

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In our knowledge based economy, demand for better and effective learning has led to innovative instructional technologies. However, the one-size-fit-all approach taken by many e-Learning systems is not adequate to the different requirements of people who have different goals, preferences, and previous knowledge about a subject. Many e-Learning systems have approached this problem with personalized and customized content. However, many of these systems are closely tied to one particular subject that they are trying to teach; authoring of courses on different subjects using the same framework i

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Alabdulrahman, Rabaa. "Towards Personalized Recommendation Systems: Domain-Driven Machine Learning Techniques and Frameworks." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41012.

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Recommendation systems have been widely utilized in e-commerce settings to aid users through their shopping experiences. The principal advantage of these systems is their ability to narrow down the purchase options in addition to marketing items to customers. However, a number of challenges remain, notably those related to obtaining a clearer understanding of users, their profiles, and their preferences in terms of purchased items. Specifically, recommender systems based on collaborative filtering recommend items that have been rated by other users with preferences similar to those of the targ

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Netcoh, Steven. "Students' Experiences with Personalized Learning: An Examination Using Self-Determination Theory." ScholarWorks @ UVM, 2017. http://scholarworks.uvm.edu/graddis/738.

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In recent years, personalized learning has become a buzzword in the field of education and an approach that schools across the United States have adopted in their attempts to account for the diverse interests, aspirations, and needs of their students. Despite the growing interest in personalized learning, there is a dearth of empirical research on this educational approach, particularly as it relates to the student experience. Given the paucity of research in this area, little is known about the extent to which personalized learning can offer students a more beneficial quality of experience th

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Petrucci, Edoardo. "A Personalized Privacy Management Framework for Android Applications." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016.

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Ogni giorno, l'utente di smartphon e tablet, spesso senza rendersene conto, condivide, tramite varie applicazioni, un'enorme quantità di informazioni. Negli attuali sistemi operativi, l'assenza di meccanismi utili a garantire adeguatamente l'utente, ha spinto questo lavoro di ricerca verso lo sviluppo di un inedito framework.È stato necessario uno studio approfondito dello stato dell'arte di soluzioni con gli stessi obiettivi. Sono stati esaminati sia modelli teorici che pratici, con l'analisi accurata del relativo codice. Il lavoro, in stretto contatto con i colleghi dell'Università Centrale

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Shaw, Cole J. (Cole Jim). "System design and architecture of an online, adaptive, and personalized learning platform." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/81128.

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Thesis (S.M. in Technology and Policy)--Massachusetts Institute of Technology, Engineering Systems Division, 2013.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 76-82).<br>The author proposes that personalized learning can be brought to traditional and nontraditional learners through a new type of asynchronous learning platform called Guided Learning Pathways (GLP). The GLP platform allows learners to intelligently traverse a vast field of learning resources, emphasizing content only of direct relevance to the learner and presenting it in a way that matche

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Aleksandra, Klašnja-Milićević. "Personalized Recommendation Based on Collaborative Tagging Techniques for an e‐Learning System." Phd thesis, Univerzitet u Novom Sadu, Prirodno-matematički fakultet u Novom Sadu, 2013. https://www.cris.uns.ac.rs/record.jsf?recordId=83535&source=NDLTD&language=en.

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The  research  topic  involves  personalization  of  an  e‐learning  system  based  oncollaborative  tagging  techniques  integrated  in  a  recommender  system.  Collaborative  tagging systems allow users to upload their resources, and to label them with arbitrary words, so‐called tags.  The  systems  can  be  distinguished  according  to  what  kind  of  resources  are  supported. Besides helping user to organize his or her personal c

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Peterson, Kelly(Kelly Nicole). "Personalized Gaussian process-based machine learning models for forecasting Alzheimer's Disease progression." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121678.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 81-90).<br>In this thesis, I address the problem of predicting behavioral and cognitive metrics from highly heterogeneous datasets (e.g. genetic, clinical/patient history, neuropsychological, biohumoral, molecular) with missin

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Rico-Fontalvo, Florentino Antonio. "A Decision Support Model for Personalized Cancer Treatment." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5621.

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This work is motivated by the need of providing patients with a decision support system that facilitates the selection of the most appropriate treatment strategy in cancer treatment. Treatment options are currently subject to predetermined clinical pathways and medical expertise, but generally, do not consider the individual patient characteristics or preferences. Although genomic patient data are available, this information is rarely used in the clinical setting for real-life patient care. In the area of personalized medicine, the advancement in the fundamental understanding of cancer biology

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Frunza, Oana Magdalena. "Personalized Medicine through Automatic Extraction of Information from Medical Texts." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/22724.

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The wealth of medical-related information available today gives rise to a multidimensional source of knowledge. Research discoveries published in prestigious venues, electronic-health records data, discharge summaries, clinical notes, etc., all represent important medical information that can assist in the medical decision-making process. The challenge that comes with accessing and using such vast and diverse sources of data stands in the ability to distil and extract reliable and relevant information. Computer-based tools that use natural language processing and machine learning

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Khoshkangini, Reza. "Personalized Game Content Generation and Recommendation for Gamified Systems." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3424854.

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Gamification, that is, the usage of game content in non-game contexts, has been successfully employed in several application domains to foster engagement, as well as to influence the behavior of end users. Although gamification is often effective in inducing behavioral changes in citizens, the difficulty in retaining players and sustaining the acquired behavior over time, shows some limitations of this technology. That is especially unfortunate, because changing players’ demeanor (which have been shaped for a long time), cannot be immediately internalized; rather, the gamification incentive mu

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Sizemore, Mary L. "Personalized Online Learning Labs and Face-to-Face Teaching in First-Year College English Courses." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc984240/.

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The purpose of this two-phase, explanatory mixed methods study was to understand the benefits of teaching grammar from three different learning methods: face-to-face, online personalized learning lab and a blended learning method. The study obtained quantitative results from a pre and post-tests, a general survey and writing assignment rubrics from three English 1301 classes and then follow-up interviews with focus groups from each class to explore those results in more depth. In the first phase, quantitative research questions addressed the relationship of grammar teaching methods and gramma

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Youssef, Éliane. "A design model to personalize learning using mobile technology and devices." Thesis, Strasbourg, 2018. http://www.theses.fr/2018STRAG018.

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Cette recherche définit la personnalisation comme une approche centrée sur l'apprenant où la personne est prise dans son ensemble (émotions, cognition et socialisation). L’apprenant est actif, prend des initiatives et collabore avec les autres. Elle détaille les attributs de l'apprentissage personnalisé, ses perspectives et ses théories et propose un modèle personnalisé testé via le développement d’une application mobile pour les cours de base en informatique pour les étudiants en première année universitaire. Les résultats portent principalement sur l'impact de la personnalisation sur la perf

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Gallagher, Rodney P. "Implementations of technology enhanced personalized learning| Exploration of success criteria, concerns, and characteristics." Thesis, Pepperdine University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3628787.

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<p> Many school districts across the United States are spending large amounts of financial and employee resources on the implementation of technology enhanced personalized learning (TEPL) tools. There is little empirical understanding of the success, concerns, and characteristics of TEPL implementations and the learning environments they enable. This exploratory study used a qualitative descriptive methodology to survey and interview TEPL administrators in an effort to understand their perspectives on TEPL characteristics, definitions of success, concerns, and a general description of how TEPL

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Qiu, Xin. "Statistical Learning Methods for Personalized Medicine." Thesis, 2018. https://doi.org/10.7916/D85X3SCG.

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The theme of this dissertation is to develop simple and interpretable individualized treatment rules (ITRs) using statistical learning methods to assist personalized decision making in clinical practice. Considerable heterogeneity in treatment response is observed among individuals with mental disorders. Administering an individualized treatment rule according to patient-specific characteristics offers an opportunity to tailor treatment strategies to improve response. Black-box machine learning methods for estimating ITRs may produce treatment rules that have optimal benefit but lack transpare

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Su, Shih-Chieh, and 蘇世傑. "Design of Personalized E-Learning System." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/axf2nr.

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碩士<br>朝陽科技大學<br>資訊工程系碩士班<br>94<br>This research is basic on General Model of Instruction (GMI), uses information technology theory and learning theory to build this e-learning system. This research divides into four parts; The first part, to set up the Instructional Objectives, uses the Grey Relational Analysis to evaluate the materials then chooses the right materials. The second part, Placement Assessment, uses the Item Response Theory and Computerized Adaptive Testing system to help finding out the students are prerequisites by assessment. The third part, Instructional Procedures, uses Kell

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Gu, Qianyi. "Personalized information seeking to support intentional learning." Thesis, 2009. 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:3354586.

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Wu, Bo-Han, and 吳柏漢. "Personalized Vocabulary Learning System with Web Content." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/42687332570406385559.

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碩士<br>國立暨南國際大學<br>資訊工程學系<br>94<br>There are many materials for English learning on the Internet, such as text, video, audio and image. Sometimes if we meet some words that we don’t understand while reading an English page, we may consult an online dictionary. Nowadays online dictionary provides not only the meaning of the word, but also the pronunciation, example sentences, synonyms, antonyms, collocation, translation, and etc. But after consulting the online dictionary and know what the word means, we may easily forget the word if we don’t use them often. How to use these data and make users

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Chen, Ching-Shium, and 陳慶瑄. "Building learning communities for personalized digital library." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/04051002154189730679.

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碩士<br>國立中正大學<br>資訊管理學系<br>88<br>The concept of learning community has been applied to some internet-based learning systems. By sharing and interchanging information with others on internet, users are able to learn from peers and thus enhance the reusability and potential value of information. To exploit the benefits of information sharing, we apply the concept of learning community to electronic library. In so doing, we help users in electronic library learn by exchanging information with peers in the same community. A personalized digital library system is developed. Based on users

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Chiou, Yan-Shiang, and 邱彥翔. "A Personalized Movies System for English Learning." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/76588381945631333489.

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碩士<br>淡江大學<br>資訊工程學系碩士班<br>96<br>With the swift expansion of information technology and of the Internet, using multimedia in English learning has quickly become ubiquitous. The application of visual media has also progressed, improved, and become more common. One type of visual media that has played a large role in this expansion is movies, as it is one of the most common tools in English learning. This paper employs the use of“A Personalized Movie System for English Learning”to provide the user with an ideal learning environment, and effectively improve the results of learning English. The s

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Hu, Xinyu. "Personalized Policy Learning with Longitudinal mHealth Data." Thesis, 2019. https://doi.org/10.7916/d8-94k8-1490.

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Mobile devices, such as smartphones and wearable devices, have become a popular platform to deliver recommendations and interact with users. To learn the decision rule of assigning recommendations, i.e. policy, neither one homogeneous policy for all users nor completely heterogeneous policy for each user is appropriate. Many attempts have been made to learn a policy for making recommendations using observational mobile health (mHealth) data. The majority of them focuses on a homogeneous policy, that is a one-fit-to-all policy for all users. It is a fair starting point for mHealth study, but it

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Chien, Peir-Yuan, and 簡培元. "Building an Personalized Adaptive Learning System Driven by Learning Performance." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/41558334406712031141.

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碩士<br>輔仁大學<br>資訊管理學系<br>92<br>Because of the information technology progressing, the electronic environment has become a hot topic. The e-learning is becoming one of the most important applications. Not like before, people can choose the courses they want, and learn in anytime and anywhere through digital materials with out teachers any more. The E-learning makes fashionable both in schools and enterprises. The technology of Object Oriented has become very popular and people are starting to focus on the system design. It includes Unified Modeling Language (UML) and the methods of Ob

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"Effect of Personalized Learning Paths on Learning Quadratics in Algebra." Doctoral diss., 2015. http://hdl.handle.net/2286/R.I.36407.

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abstract: This study was conducted to assess the performance of 176 students who received algebra instruction through an online platform presented in one of two experimental conditions to explore the effect of personalized learning paths by comparing it with linearly flowing instruction. The study was designed around eight research questions investigating the effect of personalized learning paths on students’ learning, intrinsic motivation and satisfaction with their experience. Quantitative results were analyzed using Analysis of Variance (ANOVA), Analysis of Covariance (ANCOVA) and split-plo

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Jiang, Tian. "Personalized Defect Prediction." Thesis, 2013. http://hdl.handle.net/10012/7786.

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Academia and industry expend much effort to predict software defects. Researchers proposed many defect prediction algorithms and metrics. While previous defect prediction techniques often take the author of the code into consideration, none of these techniques build a separate prediction model for each developer. Different developers have different coding styles, commit frequencies, and experience levels, which would result in different defect patterns. When the defects of different developers are combined, such differences are obscured, hurting the prediction performance. This thesis proposes

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Du, Ming-zhang, and 杜明璋. "Personalized Annotation Management for Web Based Learning Service." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/29464948477408787074.

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碩士<br>國立中央大學<br>網路學習科技研究所<br>92<br>Annotation is used to provide interpretation of content. Existed learning standard or annotation tools only provide systematic definition. However, people always have their opinion when he read the content. This opinion is valuable for other people. In this thesis, we describe the design and implementation of a Personalized Annotation Management that enable people to manage, share and reuse their annotation in an efficient way. We develop an anchoring method which associated annotation to E-document in a precisely position. It also provides an adaptive annota

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Liu, Ying. "Statistical Learning Methods for Personalized Medical Decision Making." Thesis, 2016. https://doi.org/10.7916/D8HH6K22.

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The theme of my dissertation is on merging statistical modeling with medical domain knowledge and machine learning algorithms to assist in making personalized medical decisions. In its simplest form, making personalized medical decisions for treatment choices and disease diagnosis modality choices can be transformed into classification or prediction problems in machine learning, where the optimal decision for an individual is a decision rule that yields the best future clinical outcome or maximizes diagnosis accuracy. However, challenges emerge when analyzing complex medical data. On one hand,

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Chen, Yin-Shiue, and 陳胤斈. "A Personalized Recommendation Mechanism for E-learning Courses." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/67964752321069601379.

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碩士<br>華梵大學<br>資訊管理學系碩士班<br>94<br>The pervasiveness of the Internet does not only lead to the popularity of E-learning but also change people’s learning habits. Besides, due to the differences between E-learning and real-space learning, it is worth doing research on how the pedagogic system recommends curricula based on the learner’s interest and competence in order to increase educative results and registering efficiency. Previous researches rely on stratifying the learners, which needs to accumulate numerous learning records in order to recommend courses to the users, while this research defi

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Tsai, TzongHan, and 蔡宗翰. "Machine Learning Classification Techniques for Personalized Email Agents." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/40405262299049145325.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>87<br>This thesis discusses the problem of email classification and prioritization. An email agent system is implemented. It can learn a user''s customs for email classification. Based on this system, experiments of classification correctness and importance prediction are done. The kernal of this system is mail classification module. The naive Bayes classifier, which performs well on text classification is applied. Besides, we represent emails as several different forms to test which form is the best for improving correctness of

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Chou, Yu-Sheng, and 周佑昇. "Personalized Face Retrieval based on Multi-Kernel Learning." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/14222127089871615899.

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碩士<br>中原大學<br>資訊工程研究所<br>102<br>In recent years, attribute-based face image retrieval has become a hot research topic due to the explosive growth of social media. Semantic visual attributes are pre-trained and combined to retrieve specific face images. However, just like an image cannot be described by keywords completely, it is impossible to describe a face image by limited attributes. Therefore, we propose a personalized face image retrieval scheme based on Generalized Multiple Kernel Learning (GMKL) in this paper. Each face image is first aligned by Constrained Local Model (CLM) and landmar

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Jiang, Hsing-Yun, and 江幸耘. "Knowledge Map Construction on Personalized e-Learning Platform." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/32188959861076658799.

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碩士<br>國立中正大學<br>資訊管理學系<br>93<br>The concept of e-Learning particularly emphasizes how to establish training scenario and integrate on platforms with heterogeneity manners. It is obvious that e-Learning is considered as an important issue for governments, enterprises, and education organization. However, there is still neglect for such issues. Without a systematical approach for construction of personalized e-Learning platform, the effect of such platform will be decreased since the learning path might not be adequate for various learners and educators. In this study, we proposed a systematic a

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