Tesi: "Online learning methods"

1

Qin, Lei. "Online machine learning methods for visual tracking". Thesis, Troyes, 2014. http://www.theses.fr/2014TROY0017/document.

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Nous étudions le problème de suivi de cible dans une séquence vidéo sans aucune connaissance préalable autre qu'une référence annotée dans la première image. Pour résoudre ce problème, nous proposons une nouvelle méthode de suivi temps-réel se basant sur à la fois une représentation originale de l’objet à suivre (descripteur) et sur un algorithme adaptatif capable de suivre la cible même dans les conditions les plus difficiles comme le cas où la cible disparaît et réapparait dans le scène (ré-identification). Tout d'abord, pour la représentation d’une région de l’image à suivre dans le temps, nous proposons des améliorations au descripteur de covariance. Ce nouveau descripteur est capable d’extraire des caractéristiques spécifiques à la cible, tout en ayant la capacité à s’adapter aux variations de l’apparence de la cible. Ensuite, l’étape algorithmique consiste à mettre en cascade des modèles génératifs et des modèles discriminatoires afin d’exploiter conjointement leurs capacités à distinguer la cible des autres objets présents dans la scène. Les modèles génératifs sont déployés dans les premières couches afin d’éliminer les candidats les plus faciles alors que les modèles discriminatoires sont déployés dans les couches suivantes afin de distinguer la cibles des autres objets qui lui sont très similaires. L’analyse discriminante des moindres carrés partiels (AD-MCP) est employée pour la construction des modèles discriminatoires. Enfin, un nouvel algorithme d'apprentissage en ligne AD-MCP a été proposé pour la mise à jour incrémentale des modèles discriminatoires
We 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

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Kovanovic, Vitomir. "Assessing cognitive presence using automated learning analytics methods". Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28759.

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With the increasing pace of technological changes in the modern society, there has been a growing interest from educators, business leaders, and policymakers in teaching important higher-order skills which were identified as necessary for thriving in the present-day globalized economy. In this regard, one of the most widely discussed higher order skills is critical thinking, whose importance in shaping problem solving, decision making, and logical thinking has been recognized. Within the domain of distance and online education, the Community of Inquiry (CoI) model provides a pedagogical framework for understanding the critical dimensions of student learning and factors which impact the development of student critical thinking. The CoI model follows the social-constructivist perspective on learning in which learning is seen as happening in both individual minds of learners and through the discourse within the group of learners. Central to the CoI model is the construct of cognitive presence, which captures the student cognitive engagement and the development of critical thinking and deep thinking skills. However, the assessment of cognitive presence is challenging task, particularly given its latent nature and the inherent physical and time separation between students and instructors in distance education settings. One way to address this problem is to make use of the vast amounts of learning data being collected by learning systems. This thesis presents novel methods for understanding and assessing the levels of cognitive presence based on learning analytics techniques and the data collected by learning environments. We first outline a comprehensive model for cognitive presence assessment which builds on the well-established evidence-cantered design (ECD) assessment framework. The proposed assessment model provides a foundation of the thesis, showing how the developed analytical models and their components fit together and how they can be adjusted for new learning contexts. The thesis shows two distinct and complementary analytical methods for assessing students’ cognitive presence and its development. The first method is based on the automated classification of student discussion messages and captures learning as it is observed in the student dialogue. The second analytics method relies on the analysis of log data of students’ use of the learning platform and captures the individual dimension of the learning process. The developed analytics also extend current theoretical understanding of the cognitive presence construct through data-informed operationalization of cognitive presence with different quantitative measures extracted from the student use of online discussions. We also examine methodological challenges of assessing cognitive presence and other forms of cognitive engagement through the analysis of trace data. Finally, with the intent of enabling for the wider adoption of the CoI model for new online learning modalities, the last two chapters examine the use of developed analytics within the context of Massive Open Online Courses (MOOCs). Given the substantial differences between traditional online and MOOC contexts, we first evaluate the suitability of the CoI model for MOOC settings and then assess students’ cognitive presence using the data collected by the MOOC platform. We conclude the thesis with the discussion of practical application and impact of the present work and the directions for the future research.

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3

Johnson, Alicia Leinaala. "Exploration of Factors Affecting the Self-Efficacy of Asynchronous Online Learners: a Mixed Methods Study". Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/77518.

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This study explored former and current graduate and undergraduate online students' self-efficacy perceptions for asynchronous online coursework. Self-efficacy is described as a person's judgment of his or her ability to successfully complete a learning or performance task (Bandura, 1997). Using an exploratory sequential mixed methods approach, this study explored the research question in three phases: 1) Conducted interviews and analyzed transcripts of 11 current and previous asynchronous online learners; 2) Created and enlisted the expert review of a survey instrument developed from the interview data analysis; and 3) Collected and analyzed survey responses from current and previous asynchronous online learners. The findings from this research show, based on 215 participant responses, several factors present or absent in asynchronous online learning experiences have positive, negative or no effects on perceived self-efficacy to complete online course requirements. Findings, limitations, practical implications and future research ideas are discussed in Chapter Six of this document.
Ph. D.

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Conesa, Gago Agustin. "Methods to combine predictions from ensemble learning in multivariate forecasting". Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-103600.

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Making predictions nowadays is of high importance for any company, whether small or large, as thanks to the possibility to analyze the data available, new market opportunities can be found, risks and costs can be reduced, among others. Machine learning algorithms for time series can be used for predicting future values of interest. However, choosing the appropriate algorithm and tuning its metaparameters require a great level of expertise. This creates an adoption barrier for small and medium enterprises which could not afford hiring a machine learning expert to their IT team. For these reasons, this project studies different possibilities to make good predictions based on machine learning algorithms, but without requiring great theoretical knowledge from the users. Moreover, a software package that implements the prediction process has been developed. The software is an ensemble method that first predicts a value taking into account different algorithms at the same time, and then it combines their results considering also the previous performance of each algorithm to obtain a final prediction of the value. Moreover, the solution proposed and implemented in this project can also predict according to a concrete objective (e.g., optimize the prediction, or do not exceed the real value) because not every prediction problem is subject to the same constraints. We have experimented and validated the implementation with three different cases. In all of them, a better performance has been obtained in comparison with each of the algorithms involved, reaching improvements of 45 to 95%.

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Häglund, Emil. "Estimating Prediction Intervals with Machine Learning and Monte Carlo Methods in Online Advertising". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-282826.

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Online advertising presents a complex environment. The vast amount of available websites, platforms and formats as well as the trend of programmatic adpurchasing makes assessing a proposed advertisement in terms of cost and expected return challenging. This paper uses machine learning to predict cost per thousand impressions (CPM), a measure of advertising efficiency, for a planned purchase. Feedforward neural networks and random forest models were compared on their ability to produce CPM point estimates and prediction intervals. To estimate prediction intervals, Monte Carlo dropout and noise estimation was employed for the neural network models. For random forest, a Monte Carlo approach where a large number of models were parameterized using bootstrap sampling was employed. Implemented algorithms were compared using the 5x2cv test. Random forest and neural network models produced similar point estimation accuracy. To obtain valid prediction intervals in terms of coverage probability for the random forest algorithm, hyperparameters had to be adjusted to increase the tree-level variance. This negatively affected the accuracy of point estimates and the random forest prediction intervals were less optimal than those produced by the neural network algorithm. This difference in performance was statistically supported by the 5x2cv test. It is concluded that both evaluated random forest and neural network algorithms for prediction intervals produced valid intervals although neural network estimates were more optimal.
Reklam på nätet är en komplex miljö. Mängden hemsidor, plattformar och format såväl som trenden med programmatiska reklamköp gör det svårt att utvärdera planerad reklam beträffande förväntad kostnad och värde. Den här rapporten använder maskininlärning för att prediktera kostnaden för tusen visningar (CPM), ett mått på annonseringseffektivitet, för ett planerat reklamköp. Random forest och neurala nätverksmodeller jämfördes med avseende på deras förmåga att producera punktskattningar och prediktionsintervall. För att skatta prediktionsintervall för neurala nätverk användes Monte Carlo dropout och skattning av datamängdens brusnivå. För random forest användes en Monte Carlo metod där ett stort antal modeller parametriseras med bootstrapping. Implementerade algoritmer jämfördes med 5x2cv test. Random forest och neural nätverksmodellerna producerade liknande precision för punktskattningar. För att erhålla giltiga prediktionsintervall avseende täckningssannolikhet för random forest krävdes det att parametrar justerades för att öka de enskilda beslutsträdens varians. Detta påverkade precisionen för punktskattningar negativt och prediktionsintervallen för random forest var mindre optimala än de som skattades av neurala nätverksalgoritmen. Denna skillnad i förmåga att skatta prediktionsintervall bekräftades statistiskt av 5x2cv testet.

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Wright, Robert Demmon. "Students' Attitudes Towards Rapport-building Traits and Practices in Online Learning Environments". Thesis, University of North Texas, 2012. https://digital.library.unt.edu/ark:/67531/metadc177265/.

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This research was a triangulated study of student attitudes towards instructors' rapport-building traits and their preferences amongst instructors' rapport-building practices in online learning environments. Participants were undergraduate and graduate students enrolled in courses within an educational technology program at a central Texas university. The study employed a mixed-methods approach involving the Likert-item assessment of learners' attitudes, the identification and prioritization of learner preferences through pairwise comparisons, and semi-structured interviews that provided richer, more detailed information. Findings indicated a strong preference for instructor-based traits and practices over pedagogically-based ones. These traits and practices loaded into the components of social presence, enjoyable interaction, and personal connection.

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Luca, Joseph. "Developing generic skills for tertiary students in an online learning environment". Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2002. https://ro.ecu.edu.au/theses/713.

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Increasingly, higher education institutions are being asked by industry, government and funding bodies to produce graduates with versatile generic skills as well as subject knowledge and expertise. This is causing a major reappraisal of higher education institutions purpose, learning outcomes and research activities. Many institutions are experiencing problems in implementing effective teaching and learning strategies to promote the development of these skills. The study sought to investigate ways of developing students' generic skills through the design and implementation of a learning environment that incorporated three key learning principles - authenticity, self-regulation and reflection. These were integrated into a course design methodology that focused on creating appropriate learning tasks for the given course objectives. The development of learning resources and supports was considered only after key learning tasks had been established for the given context. Using this instructional strategy, a learning environment was created using both face-to- face and online delivery, and tested with a class of final year higher education students. Eleven generic skills were identified as being important for these students. These included - time management, learning-to-learn, self-assessment, leadership, collaboration, communication, peer-assessment, research, analysis/ synthesis, problem solving and task management. A range of data was collected and analysed by triangulating various qualitative and quantitative research methodologies that provided a rich representation of how the students engaged with the learning activities. Results showed that the design of the learning environment was effective in promoting the development of these generic skills, and that the authentic activities were instrumental in motivating students. Once motivated, students then actively engaged with self-directed and reflective activities, which helped construct knowledge and promote generic skill development. The major implication of the study is that generic skill development and deep approaches to learning can be achieved without having to take extra time to specifically teach these skills. Developing generic skills becomes a natural consequence of students actively engaging with learning tasks that are authentic, student-centred and reflective. Using this approach to teaching and learning, course coordinators need to consider which generic skills should be targeted at different year levels to cover the necessary skills.

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Cunningham, E. Ann. "Comparison of Student Success by Course Delivery Methods at an Eastern Tennessee Community College". Digital Commons @ East Tennessee State University, 2015. https://dc.etsu.edu/etd/2585.

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The purpose of this study was to compare academic success based on methods of course delivery for students in a computer applications course at an East Tennessee community college. Additionally, the researcher examined demographic relationships of age, gender, and race to student academic performance in the different delivery methods. The researcher used final course grades as a determinant of academic success. The study was focused on students who took the INFS 1010 Computer Applications course during the academic years, 2011-12, 2012-13, and 2013-14 at a southeast Tennessee community college. The population consisted of 1,177 students who took the INFS 1010 Computer Applications course over a 3-year period. The independent variable method of course delivery is generally defined as traditional, online, or blended. The dependent variable academic success is generally defined as final course grade. A student was considered an academically successful completer of the course by attaining a final course grade of A, B, C, or D. It should be noted that if a student is transferring to another institution, the receiving institution may or may not accept the course credit of a student who received a D grade in this course. However, at the studied institution students receiving final course grades of A, B, C, or D in INFS 1010 are considered successful course completers. The research questions in this study were addressed through data analysis with Chi-Square 2-way contingency table analysis testing procedures. When areas of significance were identified, follow-up pairwise comparisons were conducted to evaluate relationships between the proportions. The quantitative findings revealed no significant overall relationships in final course grades among the 3 delivery methods. However, some relationships were noted within delivery methods by demographic characteristics. The findings of the online delivery method indicated significant relationships among all 3 demographic categories (gender, age, and race) studied. Significant grade relationships were identified in the gender and race categories within the blended delivery method. However, within the traditionally delivered sections of this course the only demographic area with significant findings was the age category.

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Joshi, Apoorva. "Trajectory-based methods to predict user churn in online health communities". Thesis, University of Iowa, 2018. https://ir.uiowa.edu/etd/6152.

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Online Health Communities (OHCs) have positively disrupted the modern global healthcare system as patients and caregivers are interacting online with similar peers to improve quality of their life. Social support is the pillar of OHCs and, hence, analyzing the different types of social support activities contributes to a better understanding and prediction of future user engagement in OHCs. This thesis used data from a popular OHC, called Breastcancer.org, to first classify user posts in the community into the different categories of social support using Word2Vec for language processing and six different classifiers were explored, resulting in the conclusion that Random Forest was the best approach for classification of the user posts. This exercise helped identify the different types of social support activities that users participate in and also detect the most common type of social support activity among users in the community. Thereafter, three trajectory-based methods were proposed and implemented to predict user churn (attrition) from the OHC. Comparison of the proposed trajectory-based methods with two non-trajectory-based benchmark methods helped establish that user trajectories, which represent the month-to-month change in the type of social support activity of users are effective pointers for user churn from the community. The results and findings from this thesis could help OHC managers better understand the needs of users in the community and take necessary steps to improve user retention and community management.

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Tunningley, Joan M. "Self-Regulated Learning and Reflective Journaling in an Online Interprofessional Course: A Mixed Methods Study". University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1511799445626182.

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Quintal, Kyle. "Context-Awareness for Adversarial and Defensive Machine Learning Methods in Cybersecurity". Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/40835.

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Machine Learning has shown great promise when combined with large volumes of historical data and produces great results when combined with contextual properties. In the world of the Internet of Things, the extraction of information regarding context, or contextual information, is increasingly prominent with scientific advances. Combining such advancements with artificial intelligence is one of the themes in this thesis. Particularly, there are two major areas of interest: context-aware attacker modelling and context-aware defensive methods. Both areas use authentication methods to either infiltrate or protect digital systems. After a brief introduction in chapter 1, chapter 2 discusses the current extracted contextual information within cybersecurity studies, and how machine learning accomplishes a variety of cybersecurity goals. Chapter 3 introduces an attacker injection model, championing the adversarial methods. Then, chapter 4 extracts contextual data and provides an intelligent machine learning technique to mitigate anomalous behaviours. Chapter 5 explores the feasibility of adopting a similar defensive methodology in the cyber-physical domain, and future directions are presented in chapter 6. Particularly, we begin this thesis by explaining the need for further improvements in cybersecurity using contextual information and discuss its feasibility, now that ubiquitous sensors exist in our everyday lives. These sensors often show a high correlation with user identity in surprising combinations. Our first contribution lay within the domain of Mobile CrowdSensing (MCS). Despite its benefits, MCS requires proper security solutions to prevent various attacks, notably injection attacks. Our smart-injection model, SINAM, monitors data traffic in an online-learning manner, simulating an injection model with undetection rates of 99%. SINAM leverages contextual similarities within a given sensing campaign to mimic anomalous injections. On the flip-side, we investigate how contextual features can be utilized to improve authentication methods in an enterprise context. Also motivated by the emergence of omnipresent mobile devices, we expand the Spatio-temporal features of unfolding contexts by introducing three contextual metrics: document shareability, document valuation, and user cooperation. These metrics are vetted against modern machine learning techniques and achieved an average of 87% successful authentication attempts. Our third contribution aims to further improve such results but introducing a Smart Enterprise Access Control (SEAC) technique. Combining the new contextual metrics with SEAC achieved an authenticity precision of 99% and a recall of 97%. Finally, the last contribution is an introductory study on risk analysis and mitigation using context. Here, cyber-physical coupling metrics are created to extract a precise representation of unfolding contexts in the medical field. The presented consensus algorithm achieves initial system conveniences and security ratings of 88% and 97% with these news metrics. Even as a feasibility study, physical context extraction shows good promise in improving cybersecurity decisions. In short, machine learning is a powerful tool when coupled with contextual data and is applicable across many industries. Our contributions show how the engineering of contextual features, adversarial and defensive methods can produce applicable solutions in cybersecurity, despite minor shortcomings.

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Jaber, Ghazal. "An approach for online learning in the presence of concept changes". Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00907486.

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Learning from data streams is emerging as an important application area. When the environment changes, it is necessary to rely on on-line learning with the capability to adapt to changing conditions a.k.a. concept drifts. Adapting to concept drifts entails forgetting some or all of the old acquired knowledge when the concept changes while accumulating knowledge regarding the supposedly stationary underlying concept. This tradeoff is called the stability-plasticity dilemma. Ensemble methods have been among the most successful approaches. However, the management of the ensemble which ultimately controls how past data is forgotten has not been thoroughly investigated so far. Our work shows the importance of the forgetting strategy by comparing several approaches. The results thus obtained lead us to propose a new ensemble method with an enhanced forgetting strategy to adapt to concept drifts. Experimental comparisons show that our method compares favorably with the well-known state-of-the-art systems. The majority of previous works focused only on means to detect changes and to adapt to them. In our work, we go one step further by introducing a meta-learning mechanism that is able to detect relevant states of the environment, to recognize recurring contexts and to anticipate likely concepts changes. Hence, the method we suggest, deals with both the challenge of optimizing the stability-plasticity dilemma and with the anticipation and recognition of incoming concepts. This is accomplished through an ensemble method that controls a ensemble of incremental learners. The management of the ensemble of learners enables one to naturally adapt to the dynamics of the concept changes with very few parameters to set, while a learning mechanism managing the changes in the ensemble provides means for the anticipation of, and the quick adaptation to, the underlying modification of the context.

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Payne, Alina R. "Student satisfaction with online learning effectiveness at a Connecticut community college". ScholarWorks, 2008. https://scholarworks.waldenu.edu/dissertations/639.

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In recent years, institutions of higher education have dramatically increased the number of online courses and degree programs offered to students, and yet it remains unclear what factors determine student satisfaction with online course and instructor effectiveness. Accordingly, the purpose of this exploratory correlation study was to examine how factors related to courses and instructors contributed to student perceptions of course quality (CQ) and instructor effectiveness (IE). Six stepwise regression procedures assessed the effects of specific course and instructor characteristics on perceptions of CQ and IE across one year of course evaluation data collected in a northeastern 2-year college. Results revealed that IE, quality of readings and assignments, and quality of threaded discussions had significant effects on CQ. In turn, student ratings of CQ, instructor-inspired interest in course material, and instructor availability and helpfulness had significant effects on IE. The results of the study can allow higher education administrators to make more effective decisions regarding online instruction and course structure, thus leading to increases in student persistence and success within online courses and programs.

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Haidar, Noha Adib. "Perceptions of Higher Education Online Learning Faculty in Lebanon". ScholarWorks, 2014. https://scholarworks.waldenu.edu/dissertations/104.

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The purpose of this case study was to explore faculty attitudes toward online learning in a Lebanese Higher Education Institution (HEI). The research problem addressed the disinterest among faculty at the Arts, Sciences, and Technology University of Lebanon (AUL) in enhancing learning using online technology. The research questions for this study explored the attitudes of the faculty toward applying online learning and the extent of the faculty readiness to adopt this technological change. A qualitative case study design was used that employed multiple sources of information including semi-structured interviews and existing literature. The target population was AUL faculty including full-time instructors and administration (n = 25). Data analysis was guided by the lens of Kanter's theoretical approach, which focused on the ADKAR model for adopting change in faculty's awareness, desire, knowledge, ability, and reinforcement. Key findings indicated negative faculty impressions concerning online learning authority over technology use, culture, and changes such as increased enrollment and different teaching styles. Despite these findings, opportunity for the adoption of online learning was identified. Faculty indicated positive elements, such as the competitive advantage to be the first online institution in the Lebanese Market and higher student enrollment. These results may encourage AUL's faculty to adopt online learning and to follow the educational development steps taken worldwide. This study contributes to social change by expanding the ability of AUL and Lebanese students to compete globally.

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Tonks, DeLaina Cales. "A Mixed Methods Study of Special Education Families' Experiences at an Online Charter School". BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7371.

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Student well-being (as opposed to an overemphasis on learning outcomes or technologies) should serve as the central component of a successful online model for students with disabilities. Historically, research on online schools for students with disabilities has focused on outcomes. One online charter schools growth of the students with disabilities population has outpaced the growth of the general education student population over the past eight years, which is an unusual trend that warrants additional scrutiny. Using anonymous parent and student surveys coupled with in-depth phenomenological interviews, this explanatory mixed-methods study investigates the reasons families of students with disabilities chose online learning at this particular school and what their experiences have been. The findings suggest that parents and students value the learning environment in terms of choosing when, where, and how to learn, and the student experience in terms of safety, support, academics, and teachers. Further analysis suggests the importance of mattering, social safety and connection, open educational resource-enabled pedagogy, and self-determination in providing supportive online learning environments for students with disabilities and their parents. This dissertation can be downloaded at www.delainatonks.com.

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Lishon-Savarino, Nova A. "Systematic Review of Online Developmental Mathematics Adaptive Learning Technology Intervention Investigation". Thesis, NSUWorks, 2016. https://nsuworks.nova.edu/fse_etd/80.

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This systematic review paper reviews instructional strategies for online developmental mathematics courses offered at post-secondary institutions. Adaptive learning technology was thoroughly reviewed as an intervention strategy. Success rates and other data from studies are compared to draw conclusions about the most effective instructional strategies for teaching online developmental mathematics. The following databases were used to examine literature in the subject area: Elton B. Stephens Co. (EBSCOhost), Educational Resources Information Center (ERIC), ProQuest, Wiley - Blackwell Publishers, Journal Storage (JSTOR), and Gale Cengage Learning. These databases were selected from the NovaCat Online Library system via the Nova Southeastern University website. The documents reviewed in this research paper were used as data to give a diverse, concise quantitative and qualitative discussion of results for the comparison of the studies. The implications of this study may be useful to students, faculty, administrators, researchers, etc. The results of this systematic review showed adaptive learning technology’s assistive nature in course design and implementation. Strategies and course design were critical to the outcome of each study reviewed that incorporated adaptive learning technology. Further research is suggested to identify specific combinations of methodologies for most success completion rates of developmental mathematics courses offered at post-secondary institutions.

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Baltz, Diana Lynn-Maria. "Effect of Delivery Method on Nursing Students' Math Competency and Learning Perceptions". ScholarWorks, 2017. https://scholarworks.waldenu.edu/dissertations/3973.

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The delivery method of a math course may affect the math scores of nursing students, which relates to rates of medication errors that could be fatal. The purpose of this study was to discover the relative effectiveness of a delivery method of a math course. Benner's novice-to-expert theory guided the study. A sequential explanatory, mixed-methods, nonexperimental pre-/posttest alternative treatment design was used. Phase 1 answered which delivery methods-online self-directed, face-to-face, or a mix of online self-directed with instructor lead-were associated with the best Medication Administration Competency exam results. Phase 2 included students' assessment of each learning method. The sample size was 148 students who were admitted to 1 nursing school between 2011 and 2013. The data were collected from 4 sources: (a) archival standardized entrance exam math scores, (b) archival standardized exit exam math scores, (c) a qualitative survey regarding student perceptions of the delivery method, and (d) a qualitative section of the same survey with math questions. The ANCOVA analysis showed no statistically significant difference in the delivery method used. Students with lower pretest exam scores took the posttest exam more times and also had lower posttest grades. The content analysis showed that students from all 3 groups did not see an advantage in the delivery method, but in certain teaching strategies that support learning. Therefore, the nursing school should continue to allow students to select their preferred delivery method, or offer fewer methods as they were equivalent. Positive change could come from using teaching strategies that students valued, improving their ability to provide correct dosages and increasing patient safety in the healthcare environment.

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Abu, Dawood Sumayah Mohammadlutfi. "Students' Attitudes toward Educational Gamification in Online Learning Environments". Thesis, University of North Texas, 2019. https://digital.library.unt.edu/ark:/67531/metadc1505265/.

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This study explored undergraduate and graduate students' attitudes toward the pleasurability of educational gamification in online learning environments. The study is a sequential explanatory mixed-methods research that investigated students' attitudes quantitatively, then qualitatively. In the quantitative phase, an online survey, the Pleasurable Learning Experiences scale (PLLEXs), was administrated at one of the largest public southwestern universities in the U.S. (N = 119). The qualitative phase involved conducting eight semi-structured interviews with selected participants. The PLLEXs uses a 4-point Likert scale that encompasses 4 subscales: (a) Preferences for Instructions, (b) Preferences for Instructors' Teaching Styles, (c) Preferences for Activities, and (d) Preferences for Learning Effectiveness. A series of analyses of variances (ANOVAs) were used to identify predictors of students' overall attitudes toward educational gamification. The main findings were: (a) students had strong preferences toward educational gamification with Preferences of Instructions rated the highest subscale and Preferences for Activities rated the lowest subscale, (b) major was a statistically significant predictor of students' attitudes toward educational gamification, (c) international students had statistically significant lower preferences toward educational gamification compared with U.S. domestic students, (c) online learning experiences measured by the number of previous online courses and the number of hours spent weekly on computers for academic-related work were statistically significant predictors of students' attitudes toward educational gamification, (d) instructor's feedback was the most important aspect and online collaboration was the most challenging aspect in online learning environments, and (e) the use of multimedia in LMSs can support or hinder teaching and learning activities.

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Lillberg, Oliver, e Erik Kongpachith. "Online teaching methods effectiveness in students’ focus retention and lecture enjoyment". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302796.

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Online teaching has become more common during the past decades as technological advancements provide new education possibilities. New technology also gives teachers new ways to conduct teaching. Today, there are many different teaching methods which can be utilized. Which teaching methods should be used in order to retain students’ focus during lectures? Which ones should be used to improve students’ enjoyment of lectures? This thesis studies these questions through an eye tracking experiment. Test subjects watched short lecture clips, each using a different teaching while being recorded using eye tracking. In addition, the test subjects answered a questionnaire regarding lecture enjoyment. Based from the results, it is found that focus during and enjoyment of online lectures seems to be mostly dictated by cognitive load. Teaching methods which use some sort of technique to reduce the amount of cognitive load are better at keeping students focused. Furthermore, students seem to enjoy teaching methods which uses a chalk board more.
Onlineundervisning har blivit mycket vanligare under det senaste decenniet tack vare teknologiska framsteg. Nya teknologier har också gett lärare nya sätt att undervisa. Idag finns det många olika undervisningsmetoder som kan användas. Men vilka undervisningsmetoder borde lärare använda för att hålla studenter koncentrerade under föreläsningar? Vilka metoder skall användas för att öka studenters föreläsningsglädje? Denna studie svarar på dessa frågor med ett ögonspårningsexperiment. Försökspersoner tittade på korta föreläsningsklipp som använde olika undervisningsmetoder medans deras ögonrörelser var inspelade med ögonspårning. Försökspersonerna svarade även på en enkät om föreläsningsglädje. Från resultatet framgick det att fokus och hur mycket de gillade att kolla på online föreläsningen påverkades mest av kognitiv belastning. Undervisningsmetoder som använder sig av tekniker som minskar den kognitiva belastningen var bättre i att hålla studenter fokuserade. Studenter verkade även visa större föreläsningsglädje för undervisningsmetoder som anväder svarta tavlan.

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Wong, On-wing, e 黃安穎. "Computational methods for identifying and classifying questions in online collaborative learning discourse of Hong Kong students". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hub.hku.hk/bib/B50605859.

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This study aims to investigate the automated question detection and classification methods to support teachers in monitoring the progression of discussion in Computer-Supported Collaborative Learning (CSCL) discourse of Hong Kong students. Questioning is an important component of CSCL. Through the analysis of question types in CSCL discourse, teachers may probably get a general idea of how an inquiry is constructed. This study is an attempt to take up this time-consuming task of question classification with the techniques developed from machine learning. In general, the performance of machine learning algorithms will improve by increasing the amount of empirical data for training. The amount of training data is a determining factor for the performance of machine learning algorithms. The machine learning based question classification algorithms may not able to detect those question types with a small amount of training data. In order not to miss out those questions, an extra step to detect the occurrence of all question types might be needed. One Chinese and one English datasets are collected from an online discussion platform. These datasets are selected for comparing the performance of question detection and classification in the two languages, and a sentence is defined as the unit of analysis. Question detection is a process to distinguish questions from other types of discourse act. A hybrid method is proposed to combine the rule-based question mark method and machine-learning-based syntax method for question detection. This method achieves 94.8% f1-score and 98.9% accuracy in English question detection and 94.8% f1-score and 93.9% accuracy in Chinese question detection. While question detection focuses mainly on the identification of questions, question classification concentrates on the categorization of questions. The literature showed that the tree kernel method is almost a standardized method for question classification. The classification of English verification and reason questions using tree kernel method can both attained f1-score above 80%. Though the precision of Chinese question classification using the same settings remains at a similar level, the recall drops greatly. This result indicates that the syntax-based tree kernel method may not be appropriate for classifying questions in Chinese languages. In order to improve on the Chinese question classification result, Case-Based Reasoning (CBR) is introduced. CBR is a method to retrieve example case(s) which shares the maximum percentage of similarity with the test case from a database. In this study, the similarity is measured by the lexemes that composed a question. Although the implementation of the CBR method can improve the recall, it also causes the great drop of precision. Considering the high precision of tree kernel method and wide coverage of CBR method, a hybrid method is proposed to combine the two methods. The experiment result shows that f1-score of the hybrid method for multi-class classification surpasses the tree kernel and CBR methods. This indicates that the implementation of hybrid method can generally improve the result of Chinese question classification.
published_or_final_version
Education
Master
Master of Philosophy

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21

Archibald, Douglas. "Fostering Cognitive Presence in Higher Education through the Authentic Design, Delivery, and Evaluation of an Online Learning Resource: A Mixed Methods Study". Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/19911.

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The impact of Internet technology on critical thinking is of growing interest among researchers. However, there still remains much to explore in terms of how critical thinking can be fostered through online environments for higher education. Ten years ago, Garrison, Anderson, and Archer (2000) published an article describing the Community of Inquiry (CoI) framework which provided an outline of three core elements that were able to describe and measure a collaborative and positive educational experience in an online learning environment, namely teaching presence (design, facilitation, and direct instruction), social presence (the ability of learners to project themselves socially and emotionally), and cognitive presence (the extent to which learners are able to construct and confirm meaning through sustained reflection and discourse). This dissertation extends the body of research surrounding the CoI framework and also the literature on developing critical thinking in online environments by examining and exploring the extent to which teaching and social presence contribute to cognitive presence. The researcher was able to do this by offering 189 learners enrolled in 10 research methods courses and educational research courses an opportunity to use an innovative online resource (Research Design Learning Resource – RDLR) to assist them in learning about educational research and developing research proposals. By exploring how participants used this resource the researcher was able to gain insight into what factors contributed to a successful online learning experience and fostered cognitive presence. Quantitative and qualitative research approaches (mixed methods) were used in this study. The quantitative results indicated that both social and teaching presence had a strong positive relationship with cognitive presence and that learners generally perceived to have a positive learning experience using the RDLR. The qualitative findings helped elaborate the significant quantitative results and were organised into the following themes: making connections, multiple perspectives, resource design, being a self-directed learner, learning strategies, learning preferences, and barriers to cognitive presence. Future directions for critical thinking in online environments are discussed.

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Brook, Christopher. "Exploring community development in online settings". Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2004. https://ro.ecu.edu.au/theses/835.

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As educators and training providers embrace online technologies, some researchers posit that the development of a learning community is perhaps the most fundamental goal of online instructors (Hiltz, 1997). The process for developing and maintaining learning communities, however, remains unclear (Bonk & Wisher, 2000; PaJloff & Pratt, 1999). This study sought to provide insight into factors that influence community development through an exploration of the community experience in online settings. To do this, it was necessary to establish an understanding of the community construct through an extensive review of contemporary literature. A review of the online learning community literature was conducted to provide a broader perspective on the process for developing a learning community and to ensure that current knowledge informed the study. As a consequence of the expansive literature review a framework to guide the exploration of the community experience in online settings was designed. This framework identified pre-existing factors as well as instructor actions that influence community development in a chain if events that concludes with the community experience. The research took the form of a multi case study methodology based on the qualitative research paradigm conducted over a one:-semester period. Data gathering processes were based on Grounded Theory (Strauss, 1987) utilising course related discourse, instructor interviews, observations and the , Sense of Community Index (Chavis, Hogge, McMillan, & Wandersman, 1986). Data analysis utilised a constant comparative approach in the data coding and management processes. Data was categorised according to factors that demonstrate community development, the elements of SOC and emergent themes. Findings were presented as an aggregation of all courses to provide an expansive view of factors that influence community development. Findings suggest that the Model developed to guide the study provides a robust framework that is useful in investigating the sense of community experienced in online settings. Numerous pre-existing factors that limit community development were identified. However, instructor actions that promote community development, and in some instances overcome limitations presented by pre-existing factors, were also identified. The interrelationship between these factors was seen to influence in various ways the sense of community experienced by students in the each of the settings. The major implications of the study are that instructors will inevitably encounter pre-existing conditions that will limit community development. Given the context specific nature of the community experience it is difficult for researchers to provide a discrete set of design principles that will account for all considerations in the process of community development. It is the instructor who is in the position to ascertain the most effective strategies to overcome factors that limit community development. The Model developed in this study, provides a robust framework for identifying pre-existing factors that are likely to influence community development. The Model also provides a strong framework for guiding instructors in the selection of instructional strategies that promote community development. At the conclusion of the thesis factors that serve to limit the generalisability of findings are described and suggestions 'for future research are provided.

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Hamann, Dick T. "The Construction and Validation of an M-Learning Framework for Online and Blended Learning Environments". NSUWorks, 2015. http://nsuworks.nova.edu/gscis_etd/38.

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With the wide adoption of mobile technologies, new opportunities exist with regard to how these technologies can be used to support teaching and learning. However, there is limited empirical evidence on the use of mobile learning (m-learning) frameworks that support adult students in online and blended learning environments and consider ways to support administrators, faculty, and students in the adoption of mobile technologies for teaching and learning. The goal was to develop and validate an m-learning framework capturing the administrative, communication, and instructional elements that must be considered when integrating m-learning technologies to support adult community college students. Using design and development research methods, an m-learning framework was constructed and validated. Based on the literature review and the results of the data analysis, the framework was developed and included three sections: major categories; needs within categories; and attributes of the needs. Each section is composed of at least one of those major categories: section 1 composed of Access and Security; section 2 composed of Applications and Instructional Materials; and section 3 composed of Control and Monitoring Systems. Combined, all three sections account for five major categories. The final m-learning framework was design to include specific guidelines to help administrators and faculty make decisions about the adoption of m-learning technologies to support teaching and learning in online and blended learning environments.

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Pawling, Kimberly. "INTEGRATING UNIVERSAL DESIGN FOR LEARNING CONCEPTS INTO SECONDARY GENERAL EDUCATION INSTRUCTIONAL METHODS COURSES". Doctoral diss., University of Central Florida, 2010. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3302.

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Because many general education teachers feel unprepared to provide students with disabilities with appropriate instruction, changes to teacher education programs are needed (Burdette, 2007; Smith et al., 2010). Teacher education programs need to integrate content regarding instructional methods for teaching and accommodating students with disabilities in secondary, general education classrooms (Burdette, 2007; Smith et al., 2010). The results of the research should provide insight (1) to determine if integrating instruction on UDL into preservice SGE students' instructional methods courses will increase students' knowledge of UDL and (2) to determine if integrating instruction on Universal Design for Learning (UDL) will affect how secondary social studies (SS) students design lesson plans, particularly, content delivery and student assessment, in regards to the three principles of UDL. The research design was a quantitative, quasi-experimental design. The participants in the research study were students enrolled in four content specific SGE instructional methods courses: Social Studies (SS), Language Arts (LA), Mathematics (M) and Science (S). Research question oneÂ s data were analyzed both within content area, Wilcxon test for matched pairs, and between content areas, Mann-Whitney U test for independent samples. The results from research question one indicated a significant difference (p < .05). between pre and post UDL Knowledge test scores within the SGE SS participants. Within the other three SGE content areas, M, LA, and S, subjectsÂ UDL Knowledge pre to posttest scores did not significantly change. When each content areaÂ s difference score for the UDL knowledge pre and posttest were calculated and compared between content areas, only the SS and S pairing demonstrated a statistically significant difference score (p < .05). Data from research question two indicated no statistically significant difference (p > .05) between pre and post intervention UDL lesson plan rubric scores. The study provides impetus for future research regarding effective delivery of UDL content in teacher preparation programs. The study also provides suggestions for future researchers who may be interested in designing a similar research study. Finally, the study provides teacher education leadership with questions regarding how the three principles of UDL planning, instruction, and assessment align with the current teacher and student educational evaluation practice of standardized assessments.
Ph.D.
Department of Child, Family and Community Sciences
Education
Education EdD

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Bryant, Martha Guidry. "The Development of the Massive Open Online Course Virtual Learning Environment Scale (MVLE) and Model to Measure Satisfaction of MOOC Online Learning Courses in Higher Education| A Mixed Methods Study". Thesis, University of Louisiana at Lafayette, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10617545.

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Information technology is not new to academia. Numerous educators and research scholars studying the usefulness of digital technologies in the classroom seek to determine whether the underlying pedagogies effectively bridge the gap between quality of instruction, student engagement, and eventual acceptance of the technology tool in use. With the increase and sophistication in the delivery of online courses in colleges across the country, we now see the recent emergence of an online course structure called Massive Open Online Courses, MOOCs (Sandeen, 2014). MOOCs have intensified the interest and desire for students and universities to become engaged in educational opportunities by thinking of new and advanced systems for content delivery. The purpose of this study was (1) to explore student perceptions of the MOOC Virtual Learning Environment Scale or MVLE, characteristics including general systems, pedagogy, social presence, individual characteristics and satisfaction, (2) to examine the interrelationships between theorized MOOC virtual learning environment technology constructs, (3) to develop a conceptual model of the MVLE to create and validate a MOOC Virtual Learning scale, and (4) to understand administrator’s perceptions of the characteristics of MOOCs and their futuristic views of MOOCs. The major findings for this study are: (1) the MVLE model is an appropriate scale to measure MOOC design, implementation and evaluation, (2) the creation of a more “learner focused” approach to MOOCs that includes a definition of the MOOC learner is important to understanding MOOC course designs and their impact within a virtual learning environment, and (3) the characteristics of higher education leaders as it relates to MOOC development must be transformational with a shared vision among university stakeholders, staff, faculty, and the community that must be sustained through ongoing support both academically and financially.

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Smith, Patrick Steven. "Learning to Adapt: Online Social Science Instruction in Higher Education". PDXScholar, 2013. https://pdxscholar.library.pdx.edu/open_access_etds/1089.

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Online learning is a rapidly growing phenomenon in post-secondary education. Institutions of higher learning have embraced online learning for its perceived merits, but without the consideration of how instructors deal with this different learning medium. Little is known of the extent to which different disciplines are suited to the online medium; this is pertinent to disciplines that rely on spontaneous in-person discussion. Furthermore, as colleges continue to invest heavily in online learning, instructors who only possess face-to-face teaching experience may begin teaching online. This poses a pedagogical challenge for instructors who are unfamiliar with the medium. This qualitative, in-depth interview study with ten social science instructors elucidates the process of transition from face-to-face teaching to online teaching. Through grounded analysis, a few key themes emerged. Respondents explain that teaching in the online classroom is qualitatively different from teaching in-person. The asynchronisity of the online classroom - which means students do not "meet", discuss, or learn at the same time - is a subtle yet significant difference between the two mediums. The asynchronous classroom means instructors relinquish control of when and where students will engage in study and discussion, and this requires students to have especially high self-regulatory skills. Respondents also explained that their online courses were several times larger than their in-person ones, with some courses allowing over twice as many students as an in-person course. Consequently, instructors must find new ways to approach teaching in the online medium. This pitfall of relying on old, obsolete methods in the online medium can be avoided if instructors are provided with the peer and pedagogical support of their professional peers, and access to teaching assistants to manage the greater time commitment of teaching online. In order to have a positive experience, online teachers must be willing to take on an intellectual challenge that may defy how they perceive themselves and their role in higher education. If instructors are open to a new intellectual challenge and possess the proper resources, they will become committed to teaching online and perceive the advantages of the medium to outweigh the disadvantages.

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Faidley, Joel. "Comparison of Learning Outcomes from Online and Face-to-Face Accounting Courses". Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etd/3434.

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Online education continues to evolve and grow dramatically at colleges and universities across the globe. Today’s society is comprised of people who are increasingly busy with work and family obligations and who are looking for more flexible and expedited avenues for higher education. Institutions seek to meet these new demands by offering online distance educational opportunities while increasing cash flow for their college. Unfortunately the pitfalls to this rush to meet online demand results in what some researchers assert are inadequate quality content and curriculum. Others indicate there are not significant differences in the outcomes from online learning compared with traditional face-to-face classes. Much of the research has been conducted on nonquantitative courses, quantitative courses with small sample sizes, or large sample sizes that are not controlled for quality of online content, delivery, or verification of learning. The purpose of this quasi-experimental ex-post-facto study was to compare student outcomes from two Principles of Accounting courses both delivered in two methods of instruction: traditional face-to-face (F2F) and an on-line asynchronous format. The online content for both courses was developed with assistance of academic technology professionals at the participating university. Student learning was measured as final course grade where all exams were administered by a testing center in a proctored environment. The sample size included 124 students from the online sections and 433 students from the traditional face-to-face sections. Eight research questions were examined using independent samples t-test for 6 of the analyses, ANOVA for 1 question, and multiple regression for predictors of mean final course grade. The results indicated students performed significantly better in the face-to-face classes than the online sections. Female students scored significantly higher than male students in both methods of instruction. ACT composite score, ACT math score, GPA, gender, and method of instruction all were significantly related to final course grade. Age was not a significant predictor of final course grade but in the online sections nontraditional students (age 25 and older) scored significantly higher than students under the age of 25.

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Garver-Daniels, Tessa Marie. "An Action Research Study of a Secondary Art Classroom in Appalachia Utilizing Flipped Classroom Hybridization Methods". Kent State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=kent1627049030752619.

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Chartree, Jedsada. "Monitoring Dengue Outbreaks Using Online Data". Thesis, University of North Texas, 2014. https://digital.library.unt.edu/ark:/67531/metadc500167/.

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Internet technology has affected humans' lives in many disciplines. The search engine is one of the most important Internet tools in that it allows people to search for what they want. Search queries entered in a web search engine can be used to predict dengue incidence. This vector borne disease causes severe illness and kills a large number of people every year. This dissertation utilizes the capabilities of search queries related to dengue and climate to forecast the number of dengue cases. Several machine learning techniques are applied for data analysis, including Multiple Linear Regression, Artificial Neural Networks, and the Seasonal Autoregressive Integrated Moving Average. Predictive models produced from these machine learning methods are measured for their performance to find which technique generates the best model for dengue prediction. The results of experiments presented in this dissertation indicate that search query data related to dengue and climate can be used to forecast the number of dengue cases. The performance measurement of predictive models shows that Artificial Neural Networks outperform the others. These results will help public health officials in planning to deal with the outbreaks.

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Walmsley-Smith, Helen. "Creating the eDesign Assessment Tool (eDAT) to represent and evaluate online distance learning designs : a mixed methods study". Thesis, Staffordshire University, 2018. http://eprints.staffs.ac.uk/4746/.

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There are concerns that retention is lower in ODL (online distance learning) than in face-to-face courses and that this has a negative impact on both student and institution. Learning design has been shown to have an impact on retention, but a tool for a variety of developers, designers and tutors to consistently describe and compare learning designs of ODL courses remains elusive. This study argues that the lack of a common terminology and different tutor perspectives about learning hamper both development and representation of ODL designs. Existing research suggests that a complex mixture of student demographics, characteristics and skills, as well as course and institution features including level, subject and amount and type of tutor support may impact on ODL student retention. In particular, research suggests that activities that include interaction and feedback in ODL have a positive impact on retention. However, much of this research has been conducted using a range of student surveys that do not allow for comparison to retention data, or across courses or institutions due to their subjective nature. There has been little research on the impact of tutor perspectives on developing, representing and sharing learning designs. This study examines the creation of the e-Design Assessment Tool (eDAT) that represents and quantifies interaction and feedback activities so they can be compared to retention and other learning data for a course. A mixed methods approach was used to: a) test the effectiveness of existing terminology for categorising learning activities using a content analysis methodology by trialling sets of terms with a sample set of ODL course activities. b) identify tutor perspectives about learning and teaching using repertory grids based on personal construct psychology, and exploring the impact of these perspectives on the different meanings and uses of learning activity terminology. The content analysis testing of learning activity terminology was challenging. The pilot studies had low inter-rater reliability, suggesting difficulties in independent rating of existing learning design terminology. However, the final eDAT tool created through data collected for this study used terms that did lead to a greater level of inter-rater reliability. A significant contribution of this study is the use of repertory grids to gain insight into the issues relating to the development of a quantitative tool. The repertory grid interviews indicated that there were significant differences in the ways tutors and raters understood and used key educational concepts including interaction and feedback, and that there was a variety of vocabulary used when describing learning activities. This study argues that tutor perspectives impact on designing, representing and evaluating ODL courses in a way that has implications for learning design and for professional development of tutors. The final eDAT builds on and develops existing learning design representation and evaluation tools, but utilises more consistent terminology. Thus it offers a simplified approach to pedagogic guidance in the form of quantification of interaction and feedback activities in a course, and embeds reflection on tutor perspectives underpinning the design to support sharing and reuse. This combination will lead to better ways to represent learning designs, as well as providing a method for gathering learning analytics data useful for the comparison of learning designs to student retention data and thereby improve practice in ODL.

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Lebec, Michael T. "An analysis of learning in an online biology course for teachers and teacher candidates: A mixed methods approach". Diss., The University of Arizona, 2003. http://hdl.handle.net/10150/298792.

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Due to discipline specific shortages, web-based learning has been proposed as a convenient way to upgrade the content knowledge of instructors interested in learning to teach science. Despite quantitative evidence that web-based instruction is equivalent to traditional methods, questions remain regarding its use. The efficiency and practicality of this approach with teachers in particular has not been extensively studied. This investigation examines learning in an online biology course designed to help teachers prepare for science certification exams. Research questions concern flow teachers learn biology in the online environment and how this setting influences the learning process. Quantitative and qualitative methodologies are employed in an attempt to provide a more complete perspective than typical studies of online learning. Concept maps, tests, and online discussion transcripts are compared as measures of assimilated knowledge, while interviews reflect participants' views on the course. Findings indicate that participants experienced gains in declarative knowledge, but little improvement with respect to conditional knowledge. Qualitative examination of concept maps demonstrates gaps in participants' understandings of key course ideas. Engagement in the use of online resources varied according to participants' attitudes towards online learning. Subjects also reported a lack of motivation to fully engage in the course due to busy teaching schedules and the absence of accountability.

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Abufouda, Mohammed [Verfasser], e Katharina [Akademischer Betreuer] Zweig. "Learning From Networked-data: Methods and Models for Understanding Online Social Networks Dynamics / Mohammed Abufouda ; Betreuer: Katharina Zweig". Kaiserslautern : Technische Universität Kaiserslautern, 2020. http://d-nb.info/1221599747/34.

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Pesaranghader, Ali. "A Reservoir of Adaptive Algorithms for Online Learning from Evolving Data Streams". Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38190.

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Continuous change and development are essential aspects of evolving environments and applications, including, but not limited to, smart cities, military, medicine, nuclear reactors, self-driving cars, aviation, and aerospace. That is, the fundamental characteristics of such environments may evolve, and so cause dangerous consequences, e.g., putting people lives at stake, if no reaction is adopted. Therefore, learning systems need to apply intelligent algorithms to monitor evolvement in their environments and update themselves effectively. Further, we may experience fluctuations regarding the performance of learning algorithms due to the nature of incoming data as it continuously evolves. That is, the current efficient learning approach may become deprecated after a change in data or environment. Hence, the question 'how to have an efficient learning algorithm over time against evolving data?' has to be addressed. In this thesis, we have made two contributions to settle the challenges described above. In the machine learning literature, the phenomenon of (distributional) change in data is known as concept drift. Concept drift may shift decision boundaries, and cause a decline in accuracy. Learning algorithms, indeed, have to detect concept drift in evolving data streams and replace their predictive models accordingly. To address this challenge, adaptive learners have been devised which may utilize drift detection methods to locate the drift points in dynamic and changing data streams. A drift detection method able to discover the drift points quickly, with the lowest false positive and false negative rates, is preferred. False positive refers to incorrectly alarming for concept drift, and false negative refers to not alarming for concept drift. In this thesis, we introduce three algorithms, called as the Fast Hoeffding Drift Detection Method (FHDDM), the Stacking Fast Hoeffding Drift Detection Method (FHDDMS), and the McDiarmid Drift Detection Methods (MDDMs), for detecting drift points with the minimum delay, false positive, and false negative rates. FHDDM is a sliding window-based algorithm and applies Hoeffding’s inequality (Hoeffding, 1963) to detect concept drift. FHDDM slides its window over the prediction results, which are either 1 (for a correct prediction) or 0 (for a wrong prediction). Meanwhile, it compares the mean of elements inside the window with the maximum mean observed so far; subsequently, a significant difference between the two means, upper-bounded by the Hoeffding inequality, indicates the occurrence of concept drift. The FHDDMS extends the FHDDM algorithm by sliding multiple windows over its entries for a better drift detection regarding the detection delay and false negative rate. In contrast to FHDDM/S, the MDDM variants assign weights to their entries, i.e., higher weights are associated with the most recent entries in the sliding window, for faster detection of concept drift. The rationale is that recent examples reflect the ongoing situation adequately. Then, by putting higher weights on the latest entries, we may detect concept drift quickly. An MDDM algorithm bounds the difference between the weighted mean of elements in the sliding window and the maximum weighted mean seen so far, using McDiarmid’s inequality (McDiarmid, 1989). Eventually, it alarms for concept drift once a significant difference is experienced. We experimentally show that FHDDM/S and MDDMs outperform the state-of-the-art by representing promising results in terms of the adaptation and classification measures. Due to the evolving nature of data streams, the performance of an adaptive learner, which is defined by the classification, adaptation, and resource consumption measures, may fluctuate over time. In fact, a learning algorithm, in the form of a (classifier, detector) pair, may present a significant performance before a concept drift point, but not after. We define this problem by the question 'how can we ensure that an efficient classifier-detector pair is present at any time in an evolving environment?' To answer this, we have developed the Tornado framework which runs various kinds of learning algorithms simultaneously against evolving data streams. Each algorithm incrementally and independently trains a predictive model and updates the statistics of its drift detector. Meanwhile, our framework monitors the (classifier, detector) pairs, and recommends the efficient one, concerning the classification, adaptation, and resource consumption performance, to the user. We further define the holistic CAR measure that integrates the classification, adaptation, and resource consumption measures for evaluating the performance of adaptive learning algorithms. Our experiments confirm that the most efficient algorithm may differ over time because of the developing and evolving nature of data streams.

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Sanders, Tammy S. "A Value-Driven Exploration of Online & In-Person Learning for Professionals". FIU Digital Commons, 2016. http://digitalcommons.fiu.edu/etd/3053.

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The purpose of this dissertation is to undertake a learner-centered exploration of delivery mode relevance in professional learning. Given the increasing pervasiveness of technology-mediated online delivery in nondegree professional learning at the individual and enterprise levels, this study has a particular focus on learning value ascribed by learners to online and in-person delivery in relation to their professional development. Qualitative and quantitative methods were used in this study to collect and examine data from adult professionals in an effort to determine how value is ascribed to learning. With this aim in mind, this study focused on the following research questions: Are there differences in preferred learning format between men and women? Are there differences in preferred learning format between professionals in varied age groups? Are there differences in preferred learning format between professionals who have engaged in learning online and people who have not? For participants who indicate learning format preferences, how do they define and describe their preferences and on what aspects of the learning experience do they base their preferences? For participants who indicate learning format preferences, how are these participants describing and interpreting meaningful relevance for learning in their day-to-day professional lives? A key driver of this exploration was a scarcity of representative understanding in wider research about the relationship between varied modes of professional learning delivery and professional learning transfer, which has long typified learning value in the adult professional context. By exploring professionals’ learning experiences and delivery mode preferences, this study arrived at several explanatory concepts, to include: learning preference premiums as impactful value-drivers for learners; ubiquitous blend as a comprehensive value-based approach to professional learning design and delivery; and absolute proximity as a deliberate technology-mediated merging of work and learning contexts that supports professionals in achieving full applicability of their learning.

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Leite, Maria Teresa Meirelles [UNIFESP]. "Cursos em educação médica continuada on-line na América Latina: um olhar pedagógico". Universidade Federal de São Paulo (UNIFESP), 2008. http://repositorio.unifesp.br/handle/11600/9286.

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Made available in DSpace on 2015-07-22T20:49:49Z (GMT). No. of bitstreams: 0 Previous issue date: 2008-11-28
Leite, Maria Teresa Meirelles. Cursos em educação médica continuada on-line na América Latina: um olhar pedagógico. [Online Continuing Medical Education Courses in Latina America: a pedagogical approach]. Orientador: Daniel Sigulem. São Paulo: s.n., 2008. [146]. Dissertação(Mestrado em Ciências)-Universidade Federal de São Paulo. Escola Paulista de Medicina. Resumo: Introdução: A educação médica continuada on-line oferecida por universidades parece priorizar os conteúdos transmitidos, a qualidade estética e a sofisticação tecnológica, dedicando pouca atenção aos aspectos pedagógicos. Os objetivos desta pesquisa são: sistematizar aspectos teóricos de educação, de educação a distância e de educação médica continuada (EMC) e identificar as abordagens pedagógicas prevalentes nos cursos em EMC on-line no Brasil e América Latina, por meio de artigos publicados. Metodologia: Após a elaboração do quadro teórico e do estabelecimento do escopo da pesquisa foram conduzidas buscas bibliográficas em bases de dados eletrônicas internacionais em saúde, medicina e humanidades. A seguir, foram realizadas pesquisas complementares, em websites de universidades da América do Sul. Resultados: Apenas quatro relatos obedeciam plenamente aos critérios estabelecidos para a investigação, sendo que três (3) parecem obedecer à abordagem cognitivista e um (1) à abordagem tradicional. Após a busca complementar, constatou-se que de um total de duzentas e vinte e seis (226) escolas médicas presentes em treze (13) países da América do Sul, estão disponíveis para consulta na Internet treze (13) experiências de EMC on-line. Entre elas, houve referência a concepções de educação em apenas oito (8). Discussão: Foram discriminados os limites impostos a esta pesquisa, observaram-se as características de uma busca bibliográfica envolvendo a área de humanas, estabeleceu-se que os resultados não constituem um referencial para avaliar os cursos quanto à concepção de educação, considerou-se a legitimidade dos resultados como amostragem significativa para ilustrar o volume de artigos disponíveis sobre o tema. Foram analisados os artigos incluídos, assim como diversos achados relevantes. Foram discutidos outros aspectos relacionados ao quadro teórico inicial, como perfil do docente e pós-modernidade. Conclusões: 1. Praticamente inexiste, na comunidade acadêmica latino-americana que desenvolve estudos pedagógicos nesta área, um envolvimento com a publicação científica nas bases de dados pesquisadas. 2. A falta de informações sobre aspectos pedagógicos pode revelar a ausência de um especialista em educação no planejamento dos cursos e pode apontar para a necessidade dos cursos de Pedagogia brasileiros investirem na formação do profissional capaz de atender essa demanda. 3. Há grande tendência das instituições privadas oferecerem educação continuada a distância, o que ficou evidente a partir da pesquisa complementar nos sites das universidades. 4. A partir dos resultados, foi possível constatar também que vários autores concordam que, com as recentes mudanças sociais, muitas transformações são esperadas da Educação. 5. O quadro teórico desenvolvido pode auxiliar a estabelecer fundamentos para reconhecer onde o conhecimento pedagógico se insere no contexto da EaD. 6. Além da pesquisa sobre o número de cursos oferecidos na América Latina e suas características, pode ser relevante utilizar o mesmo referencial teórico para estabelecer critérios de análise qualitativa dos cursos oferecidos por uma instituição específica, indicando possibilidade de novas pesquisas..
Introduction: Continuing medical education online offered by universities in Brazil seems to prioritize aesthetic quality and technological sophistication, paying little attention to pedagogical aspects. The objectives of this research are: to systematize theoretical aspects to the concepts of education, distance learning and continuing medical education (CME) and to identify the pedagogical approaches adopted in CME online courses in Brazil and Latin America by means of published articles. Method: After the construction of the theoretical basis and the establishment of the research limits, bibliographical queries in international electronic databases in health, medicine and humanities were carried through. Following, complementary research was performed in South American universities websites. Results: Only four articles obeyed the established criteria, from which three (3) seem to hold a cognitivist pedagogical approach and one (1) held a traditional approach of education. After the complementary search, it was found that, from a total of two hundred and twenty and six (226) medical schools in thirteen (13) South American countries, there were thirteen (13) online CME courses available for consultation in the Internet. Among them, only eight (8) referred to the education conceptions. Discussion: The limits for the query were discriminated, the characteristics of a bibliographical query involving the humanities was observed, it was established that the present results cannot constitute a referential tool to evaluate the courses, the legitimacy of the results as a significant sampling to illustrate the volume of available articles on the subject was discussed, the four articles were deeply analyzed, as well as diverse other findings that were not included. Besides, other aspects were discussed, as teacher profile and post modernity. Conclusions: 1. There is practically no involvement with scientific publication in the searched databases by the Latin American researchers in educational approaches for online continuing medical education. 2. The lack of information on pedagogical aspects may reveal the absence of an education specialist in the courses design and may suggest for the necessity for Brazilian Pedagogy courses to form a professional who is capable of answering this demand. 3. The trend of private institutions to offer online CME was evidenced from the complementary research. 4. From the results, it was possible to see that some authors agree that with the recent social changes, many transformations are expected from Education. 5. The developed theoretical basis may be able to contribute to establish connections to understand how the pedagogical knowledge inserts in the context of distance learning. 6. Beyond the research on the number of courses offered in Latin America and its characteristics, it is desirable to use the same theoretical referential to establish criteria for qualitative analysis for the courses offered by a specific institution, indicating a possibility of continuing this research.
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BV UNIFESP: Teses e dissertações

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Orcutt, Janice Marie. "Teaching Presence and Intellectual Climate in a Structured Online Learning Environment". NSUWorks, 2016. http://nsuworks.nova.edu/gscis_etd/975.

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Teaching presence and its implications for the intellectual climate of an online classroom cannot be fully understood unless explored from the perspective of the instructors who experience it. Framed in the theoretical perspective of the Community of Inquiry (CoI) model, this collective case study investigated the actions, intentions and perceptions of instructors with the intent of developing an in-depth understanding of the phenomenon of teaching presence as it was established in a structured online learning environment. The experiences of selected successful instructors in this specific online context were explored to gain insight on how pedagogical choices influenced the establishment of an intellectual climate appropriate to the courses taught. Using semi-structured interviews as the main source of data, the study utilized the Interpretative Phenomenological Analysis (IPA) method as an analytical tool to address concerns of rigor in the qualitative interpretation of experiential data. It was the goal of this study to gain an understanding of how teaching presence is established and the decision processes employed in doing so in order to make a contribution to the body of knowledge from a practical pedagogical perspective. Findings of the study provided insight into the following: Practices in Establishing Teaching Presence. Intentions of Instructors. Influence on Intellectual Climate. Nature of Teaching Presence. Overall, the collective case revealed that an active interest and passion for teaching and an understanding of relevance to the student encouraged student engagement, and inspired intellectual curiosity and a shared responsibility for the learning process. The findings show that the common goal of learning shared by instructor and student had its foundations in the creation of authentic relationships between instructor and students that extend beyond stated learning objectives and expected outcomes. The results of this study contribute to knowledge related to the nature of teaching presence and its role in setting an academic climate, addressing the overarching question of the study about how instructors establish teaching presence and inspire intellectual curiosity within the courses they teach. In addition, the experiences of the selected instructors helped provide a vocabulary with which to describe the shared pedagogies of instructors and served to catalog commonalities in actions and intent associated with setting an intellectual climate that met the requirements of academic rigor appropriate to the courses they taught.

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Tsai, Pei-Chen. "Online storybook as a facilitator for english as a second language learning". CSUSB ScholarWorks, 2003. https://scholarworks.lib.csusb.edu/etd-project/2398.

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Lawanto, Kevin N. "Exploring Trends in Middle School Students' Computational Thinking in the Online Scratch Community: a Pilot Study". DigitalCommons@USU, 2016. https://digitalcommons.usu.edu/etd/5072.

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Teaching computational thinking has been a focus of recent efforts to broaden the reach of computer science (CS) education for today’s students who live and work in a world that is heavily influenced by computing principles. Computational thinking (CT) essentially means thinking like a computer scientist by using principles and concepts learned in CS as part of our daily lives. Not only is CT essential for the development of computer applications, but it can also be used to support problem solving across all disciplines. Computational thinking involves solving problems by drawing from skills fundamental to CS such as decomposition, pattern recognition, abstraction, and algorithm design. The present study examined how Dr. Scratch, a CT assessment tool, functions as an assessment for computational thinking. This study compared strengths and weaknesses of the CT skills of 360 seventh- and eighth-grade students who were engaged in a Scratch programming environment through the use of Dr. Scratch. The data were collected from a publicly available dataset available on the Scratch website. The Mann-Whitney U analysis revealed that there were specific similarities and differences between the seventh- and eighth-grade CT skills. The findings also highlight affordances and constraints of Dr. Scratch as a CT tool and address the challenges of analyzing Scratch projects from young Scratch learners. Recommendations are offered to researchers and educators about how they might use Scratch data to help improve students’ CT skills.

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Cheng, Sheng-Lun. "The Relationships Between Perceived Course Structures, Conscientiousness, Motivational Beliefs, and Academic Procrastination in Online Undergraduate Courses". The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1565872460207536.

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Selent, Douglas A. "Creating Systems and Applying Large-Scale Methods to Improve Student Remediation in Online Tutoring Systems in Real-time and at Scale". Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-dissertations/308.

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"A common problem shared amongst online tutoring systems is the time-consuming nature of content creation. It has been estimated that an hour of online instruction can take up to 100-300 hours to create. Several systems have created tools to expedite content creation, such as the Cognitive Tutors Authoring Tool (CTAT) and the ASSISTments builder. Although these tools make content creation more efficient, they all still depend on the efforts of a content creator and/or past historical. These tools do not take full advantage of the power of the crowd. These issues and challenges faced by online tutoring systems provide an ideal environment to implement a solution using crowdsourcing. I created the PeerASSIST system to provide a solution to the challenges faced with tutoring content creation. PeerASSIST crowdsources the work students have done on problems inside the ASSISTments online tutoring system and redistributes that work as a form of tutoring to their peers, who are in need of assistance. Multi-objective multi-armed bandit algorithms are used to distribute student work, which balance exploring which work is good and exploiting the best currently known work. These policies are customized to run in a real-world environment with multiple asynchronous reward functions and an infinite number of actions. Inspired by major companies such as Google, Facebook, and Bing, PeerASSIST is also designed as a platform for simultaneous online experimentation in real-time and at scale. Currently over 600 teachers (grades K-12) are requiring students to show their work. Over 300,000 instances of student work have been collected from over 18,000 students across 28,000 problems. From the student work collected, 2,000 instances have been redistributed to over 550 students who needed help over the past few months. I conducted a randomized controlled experiment to evaluate the effectiveness of PeerASSIST on student performance. Other contributions include representing learning maps as Bayesian networks to model student performance, creating a machine-learning algorithm to derive student incorrect processes from their incorrect answer and the inputs of the problem, and applying Bayesian hypothesis testing to A/B experiments. We showed that learning maps can be simplified without practical loss of accuracy and that time series data is necessary to simplify learning maps if the static data is highly correlated. I also created several interventions to evaluate the effectiveness of the buggy messages generated from the machine-learned incorrect processes. The null results of these experiments demonstrate the difficulty of creating a successful tutoring and suggest that other methods of tutoring content creation (i.e. PeerASSIST) should be explored."

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Lian, Hongri. "The Design and Development of an Online Database-Driven Peer Assessment Tool Using Division Rule Theory". Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/51803.

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Peer assessment has been adopted as a means of fair and equitable measurement of individual contributions to group work (Cheng and Warren, 2000; Conway and Kember, 1993; Gatfield, 1999; Goldfinch and Raeside, 1990; Lejk and Wyvill, 2001; Lejk, Wyvill, and Farrow, 1996) and it usually requires a certain mechanism or formula to quantify peer assessment criteria. The problem, however, is that it leads to circumstances where a student can be strategic and be easily able to obtain a higher score by simply giving lower scores to other members within a group. The need is to find a new mechanism and the purpose of this study is to develop an Online Database-Driven Peer Assessment Tool (ODDPAT) using the Division Rule mechanism as its core computational algorithm. This developmental study used modified Collaborative Create-Adapt-Generalize (CAG) model (Hicks, Potter, Snider, and Holmes, 2004) as its design and developmental framework. The process of design, development, and evaluation of the entire project was documented. Three experts were interviewed and detailed analysis of data was discussed. Finally, recommendations were made for its implementation and future research.
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Koptur, Evren. "TRANSFERABLE LEARNING SKILLS OF AN MMORPG: A WORLD OF WARCRAFT QUEST". Kent State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=kent1459175932.

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Arpin, Rachel Ann. "The Effectiveness of Digital Escape Rooms to Deliver Leadership Training: A Mixed-Methods Study". Franklin University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=frank1620315747289854.

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Sykes, Richard. "The Impact on Student Learning Outcomes of Video When Used as a Primary Teaching Tool in the Internet Hybrid Classroom". Digital Commons @ East Tennessee State University, 2012. https://dc.etsu.edu/etd/1517.

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With increasing concern over the state of the education system in the United States, more and more emphasis is being placed on teaching methods. Internet related infrastructure has become cheaper and more powerful, and online learning environments are taking a stronger presence in most higher education institutions with more video content being sought for these environments. However, the effectiveness of using video as a teaching tool is still uncertain. Without additional research, video remains an expensive gamble for an already struggling system. The purpose of this study is to evaluate the impact on student learning outcomes of video content as a primary teaching method compared to more traditional lecture based classes.

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Lai, Yang. "Learning Copyright in Chinese Fandom: A Study of Informal Learning in Cyberspace". Ohio University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1605279450637024.

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Bähr, Steffen [Verfasser], e J. [Akademischer Betreuer] Becker. "Real-Time Trigger and online Data Reduction based on Machine Learning Methods for Particle Detector Technology / Steffen Bähr ; Betreuer: J. Becker". Karlsruhe : KIT-Bibliothek, 2021. http://d-nb.info/1238147771/34.

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Mondeik, Shelly. "An analysis of student learning comparing traditional, interactive television, and online delivery methods in cross sectional anatomy at Northcentral Technical College". Online version, 2008. http://www.uwstout.edu/lib/thesis/2008/2008mondeiks.pdf.

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Кварамба, Рувімбо Рона, e Ruvimbo Ronah Kwaramba. "Methods of Big Data Analysis and Process in Creating a System of Recommendation for an online store". Master's thesis, Тернопільський національний технічний університет імені Івана Пулюя, 2021. http://elartu.tntu.edu.ua/handle/lib/36744.

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Метою дослідження є обґрунтування математичного підходу та відповідного програмного забезпечення для рекомендаційної системи для рекомендації житла для клієнтів. Для досягнення цієї мети необхідно: проаналізувати характеристики вхідних даних та завдання, яке необхідно вирішити. Проаналізувати та обґрунтувати математичний підхід до побудови системи рекомендацій. .Аналіз та обґрунтування програмних технологій для впровадження системи. Вибір та обґрунтування середовища виконання рекомендаційної системи та впровадження прототипу рекомендаційної системи
The purpose of the work is to develop a software and hardware module for monitoring of the parameters of the microclimate of the building and the environment. Theoretical and practical aspects of software and hardware module of development for monitoring of building microclimate parameters were considered in the work. The choice of means of software development of the hardware and software complex is substantiated. In the course of this work, a software and hardware module for monitoring of the parameters of the microclimate was developed and implemented, which measures the ambient temperature, relative humidity and atmospheric pressure without human intervention.
CHAPTER 1 1.1 Recommendation System...8 1.2 Big Data Concept...11 1.3 Machine Learning...14 1.4 Cloud Technology...17 1.5 The structure of the big data framework...18 CHAPTER 2 2.2 Methods to make recommendation...22 2.2 Software Recommendations System...23 2.3 Machine Learning...29 2.4 Google Cloud Platform...30 CHAPTER 3 3.1 Deployment and processing costs...33 3.2 Data analysis...34 3.3 Code Analysis...38 3.4 Big Data Frameworks...44 3.5Using GCP DataProc...48 CHAPTER 4 Occupational Health and Safety...49 CONCLUSION...51 REFERENCES...52 APPENDICES...53

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Elahi, Haroon. "A Boosted-Window Ensemble". Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5658.

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Context. The problem of obtaining predictions from stream data involves training on the labeled instances and suggesting the class values for the unseen stream instances. The nature of the data-stream environments makes this task complicated. The large number of instances, the possibility of changes in the data distribution, presence of noise and drifting concepts are just some of the factors that add complexity to the problem. Various supervised-learning algorithms have been designed by putting together efficient data-sampling, ensemble-learning, and incremental-learning methods. The performance of the algorithm is dependent on the chosen methods. This leaves an opportunity to design new supervised-learning algorithms by using different combinations of constructing methods. Objectives. This thesis work proposes a fast and accurate supervised-learning algorithm for performing predictions on the data-streams. This algorithm is called as Boosted-Window Ensemble (BWE), which is invented using the mixture-of-experts technique. BWE uses Sliding Window, Online Boosting and incremental-learning for data-sampling, ensemble-learning, and maintaining a consistent state with the current stream data, respectively. In this regard, a sliding window method is introduced. This method uses partial-updates for sliding the window on the data-stream and is called Partially-Updating Sliding Window (PUSW). The investigation is carried out to compare two variants of sliding window and three different ensemble-learning methods for choosing the superior methods. Methods. The thesis uses experimentation approach for evaluating the Boosted-Window Ensemble (BWE). CPU-time and the Prediction accuracy are used as performance indicators, where CPU-time is the execution time in seconds. The benchmark algorithms include: Accuracy-Updated Ensemble1 (AUE1), Accuracy-Updated Ensemble2 (AUE2), and Accuracy-Weighted Ensemble (AWE). The experiments use nine synthetic and five real-world datasets for generating performance estimates. The Asymptotic Friedman test and the Wilcoxon Signed-Rank test are used for hypothesis testing. The Wilcoxon-Nemenyi-McDonald-Thompson test is used for performing post-hoc analysis. Results. The hypothesis testing suggests that: 1) both for the synthetic and real-wrold datasets, the Boosted Window Ensemble (BWE) has significantly lower CPU-time values than two benchmark algorithms (Accuracy-updated Ensemble1 (AUE1) and Accuracy-weighted Ensemble (AWE). 2) BWE returns similar prediction accuracy as AUE1 and AWE for synthetic datasets. 3) BWE returns similar prediction accuracy as the three benchmark algorithms for the real-world datasets. Conclusions. Experimental results demonstrate that the proposed algorithm can be as accurate as the state-of-the-art benchmark algorithms, while obtaining predictions from the stream data. The results further show that the use of Partially-Updating Sliding Window has resulted in lower CPU-time for BWE as compared with the chunk-based sliding window method used in AUE1, AUE2, and AWE.

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Copley, Stacey. "Assessment of Instructor Information Technology Self-efficacy on Online Course Delivery Modes and Design Methods in the Radiologic Sciences". The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1343745085.

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Tesi sul tema "Online learning methods"

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