871 resultados para remote learning courses
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This study looked at the reasons why Vanier College students in computer programming are encountering difficulties in their learning process, Factors such as prior academic background, prior computer experience, mother tongue, and learning styles were examined to see how they play a role in students' success in programming courses. The initial research hypotheses were the following : Computer science students using understanding and integrating succeed better than students using following coding, or problem solving. Students using problem solving succeed better than those who use participating and enculturation. Students who use coding perform better than those who prefer participating ans enculturation. In addition, this study hoped to examine whether there is a gender difference in how students learn programming.||Résumé :||La présente étude a examiné les raisons pour lesquelles les étudiants en informatique du Collège Vanier rencontrent des difficultés dans leurs études en programmation. Les facteurs tel que le niveau des études précédentes, l'expérience en informatique, la langue maternelle e les méthodes d'apprentissage ont été considérés pour voir quel rôle ces facteurs jouent pour promouvoir la réussite dans les cours de programmation.Les hypothèses initiales de recherche ont été formulées comme suit : 1. Les étudiants en informatique utilisant la compréhension et l'intégration réussissent mieux que ceux utilisant «suivre», le codage ou la résolution des problèmes. 2, Les étudiants utilisant la résolution des problèmes réussissent mieux que ceux qui utilisent la participation dans la culture informatique. 3, Les étudiants utilisant le codage réussissent mieux que ceux qui utilisent la participation dans la culture informatique.
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Modifications in vegetation cover can have an impact on the climate through changes in biogeochemical and biogeophysical processes. In this paper, the tree canopy cover percentage of a savannah-like ecosystem (montado/dehesa) was estimated at Landsat pixel level for 2011, and the role of different canopy cover percentages on land surface albedo (LSA) and land surface temperature (LST) were analysed. A modelling procedure using a SGB machine-learning algorithm and Landsat 5-TM spectral bands and derived vegetation indices as explanatory variables, showed that the estimation of montado canopy cover was obtained with good agreement (R2 = 78.4%). Overall, montado canopy cover estimations showed that low canopy cover class (MT_1) is the most representative with 50.63% of total montado area. MODIS LSA and LST products were used to investigate the magnitude of differences in mean annual LSA and LST values between contrasting montado canopy cover percentages. As a result, it was found a significant statistical relationship between montado canopy cover percentage and mean annual surface albedo (R2 = 0.866, p < 0.001) and surface temperature (R2 = 0.942, p < 0.001). The comparisons between the four contrasting montado canopy cover classes showed marked differences in LSA (χ2 = 192.17, df = 3, p < 0.001) and LST (χ2 = 318.18, df = 3, p < 0.001). The highest montado canopy cover percentage (MT_4) generally had lower albedo than lowest canopy cover class, presenting a difference of −11.2% in mean annual albedo values. It was also showed that MT_4 and MT_3 are the cooler canopy cover classes, and MT_2 and MT_1 the warmer, where MT_1 class had a difference of 3.42 °C compared with MT_4 class. Overall, this research highlighted the role that potential changes in montado canopy cover may play in local land surface albedo and temperature variations, as an increase in these two biogeophysical parameters may potentially bring about, in the long term, local/regional climatic changes moving towards greater aridity.
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The scientific and technological development, has brought the possibility of people having access to a large volume of information, especially via the Internet. However, this information has to be filtered, due to danger of assimilating wrong and unreliable information. The so-called teaching type "e-learning" adopted by some schools, seems to have solved these problems. This paper focuses on some problems associated with the teaching system of "e-learning" and the difficulty to adapt it to certain areas of knowledge. The advantages of the courses in "bi-learning" are presented as well as some of the drawbacks associated with this type of education.
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Le tecniche di Machine Learning sono molto utili in quanto consento di massimizzare l’utilizzo delle informazioni in tempo reale. Il metodo Random Forests può essere annoverato tra le tecniche di Machine Learning più recenti e performanti. Sfruttando le caratteristiche e le potenzialità di questo metodo, la presente tesi di dottorato affronta due casi di studio differenti; grazie ai quali è stato possibile elaborare due differenti modelli previsionali. Il primo caso di studio si è incentrato sui principali fiumi della regione Emilia-Romagna, caratterizzati da tempi di risposta molto brevi. La scelta di questi fiumi non è stata casuale: negli ultimi anni, infatti, in detti bacini si sono verificati diversi eventi di piena, in gran parte di tipo “flash flood”. Il secondo caso di studio riguarda le sezioni principali del fiume Po, dove il tempo di propagazione dell’onda di piena è maggiore rispetto ai corsi d’acqua del primo caso di studio analizzato. Partendo da una grande quantità di dati, il primo passo è stato selezionare e definire i dati in ingresso in funzione degli obiettivi da raggiungere, per entrambi i casi studio. Per l’elaborazione del modello relativo ai fiumi dell’Emilia-Romagna, sono stati presi in considerazione esclusivamente i dati osservati; a differenza del bacino del fiume Po in cui ai dati osservati sono stati affiancati anche i dati di previsione provenienti dalla catena modellistica Mike11 NAM/HD. Sfruttando una delle principali caratteristiche del metodo Random Forests, è stata stimata una probabilità di accadimento: questo aspetto è fondamentale sia nella fase tecnica che in fase decisionale per qualsiasi attività di intervento di protezione civile. L'elaborazione dei dati e i dati sviluppati sono stati effettuati in ambiente R. Al termine della fase di validazione, gli incoraggianti risultati ottenuti hanno permesso di inserire il modello sviluppato nel primo caso studio all’interno dell’architettura operativa di FEWS.
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Reinforcement Learning is an increasingly popular area of Artificial Intelligence. The applications of this learning paradigm are many, but its application in mobile computing is in its infancy. This study aims to provide an overview of current Reinforcement Learning applications on mobile devices, as well as to introduce a new framework for iOS devices: Swift-RL Lib. This new Swift package allows developers to easily support and integrate two of the most common RL algorithms, Q-Learning and Deep Q-Network, in a fully customizable environment. All processes are performed on the device, without any need for remote computation. The framework was tested in different settings and evaluated through several use cases. Through an in-depth performance analysis, we show that the platform provides effective and efficient support for Reinforcement Learning for mobile applications.
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Student voice data is a key factor as Manchester Metropolitan University strives to continually improve institutional technology enhanced learning (TEL) infrastructure. A bi-annual Institutional Student Survey enables students to communicate their experience of learning, teaching and assessment on programmes and specific units studied. Each cycle of the survey contains approximately 40–50,000 free text comments from students pertaining to what they appreciate and what they would like to see improved. A detailed thematic analysis of this data has identified 18 themes, arranged into six categories relating to the ‘Best’ aspects of courses, and 25 themes, arranged in seven categories in relation to aspects of courses considered to be ‘in need of improvement’. This student data was then used as a basis for semi-structured interviews with staff. Anecdotally, evidence suggested that student expectations and staff expectations around TEL and the virtual learning environment (VLE) differed. On-going evaluation of this work has highlighted a disconnect. In significant instances, academic colleagues seemingly misinterpret the student voice analysis and consequently struggle to respond effectively. In response to the analysis, the learning technologist's role has been to re-interpret the analysis and redevelop TEL staff development and training activities. The changes implemented have focused on: contextualising resources in VLE; making lectures more interactive; enriching the curriculum with audio–visual resources; and setting expectations around communications.
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This article explores the lived experiences of two academics in a UK Higher Education Institution who have embedded digital learning approaches within their curriculum delivery. Achieving student excellence can be impeded by a lack of engagement and sense of identity on large courses. Digital learning strategies can offer opportunities to overcome these challenges by empowering students to engage self-confidently. Through an evaluation of the authors’ own experiences of using social media, polling and web-conferencing software, the article shows how interacting with students via a range of learning technologies can create more inclusive and engaging learning environments. Including feedback from students within this article provides evidence that diversification of communication within teaching and learning practice gives students more choice and opportunity to interact with both their peers and teaching staff. The article concludes with recommendations for embedding technology, whilst acknowledging the well-established value of face-to-face interaction.
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Students perceive online courses differently than traditional courses. Negative perceptions can lead to unfavourable learning outcomes including decreased motivation and persistence. Throughout this review, a broad range of factors that affect performance and satisfaction within the online learning environment for adult learners will be examined including learning outcomes, instructional design and learner characteristics, followed by suggestions for further research, and concluding with implications for online learning pertinent to administrators, instructors, course designers and students. Online learning may not be appropriate for every student. Identifying particular characteristics that contribute to online success versus failure may aid in predicting possible learning outcomes and save students from enrolling in online courses if this type of learning environment is not appropriate for them. Furthermore, knowing these learner attributes may assist faculty in designing quality online courses to meet students’ needs. Adequate instructional methods, support, course structure and design can facilitate student performance and satisfaction.
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Blended Learning Essentials is a free suite of online courses for the Vocational Education and Training sector to promote effective practice and pedagogy in blended learning. The courses were run and supported from 2016 onwards by a consortium of partners funded by Ufi Charitable Trust. The lead partners were the University of Leeds, the UCL Institute of Education, the Association for Learning Technology (ALT), and FutureLearn. The Blended Learning Essentials (BLE) courses are for anyone working in further education, skills training, vocational education, workplace learning, lifelong learning or adult education, who wants to learn about and implement blended learning. The project reports cover engagement and marketing work undertaken during this project phase to reach the courses’ key audiences and work undertaken during this project phase to develop and promote the pathways to accreditation available to course participants. These reports are shared by ALT as a project partner on behalf of the BLE Project under a CC-BY-NC-ND licence. �
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Service-learning in higher education is gaining attention as a reliable tool to support students’ learning and fulfil the mission of higher education institutions (HEIs). This dissertation addresses existing gaps in the literature by examining the effects and perspectives of service-learning in HEIs through three studies. The first study compares the effects of a voluntary semester-long service-learning course with traditional courses. A survey completed by 110 students before and after the lectures found no significant group differences in the psychosocial variables under inspection. Nevertheless, service-learning students showed higher scores concerning the quality of participation. Factors such as students’ perception of competence, duration of service-learning, and self-reported measures may have influenced the results. The second study explores the under-researched perspective of community partners in higher education and European settings. Twelve semi-structured interviews were conducted with community partners from various community organisations across Europe. The results highlight positive effects on community members and organisations, intrinsic motivations, organisational empowerment, different forms of reciprocity, the co-educational role of community partners, and the significant role of a sense of community and belonging. The third study focuses on faculty perspectives on service-learning in the European context. Twenty-two semi-structured interviews were conducted in 14 European countries. The findings confirm the transformative impact of service-learning on the community, students, teachers, and HEIs, emphasising the importance of motivation and institutionalisation processes in sustaining engaged scholarship. The study also identifies the relevance of the community experience, sense of community, and community responsibility with the service-learning experience; relatedness is proposed as the fifth pillar of service-learning. Overall, this dissertation provides new insights into the effects and perspectives of service-learning in higher education. It integrates the 4Rs model with the addition of relatedness, guiding the theoretical and practical implications of the findings. The dissertation also suggests limitations and areas for further research.
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Reasons for the iniquities of caries, globally recognized, may be related to how Cariology has been taught in dental schools. In Brazil, the most important universities, when considering healthcare teaching, are the public ones. The objective of this study was to identify the insertion of the contents of Cariology in the course flowcharts of public dental schools in the country. The survey was conducted in 2013 seeking to identify the realities of different geographical regions, aimed to the census of public dental schools. It was performed a documentary analysis of the menus of disciplines, identifying the following issues: number of dental schools that include content related to Cariology in their curricula; average total workload undergraduate courses and disciplines that contemplate the theme; distribution of disciplines in professional training cycles (basic, clinical and public health); existence of discipline and/or a specific department; verification of bibliographic indication directly related to Cariology. The response rate was 93.6%. All dental schools recommended specific books, and none of them had a Department of Cariology. All dental schools in the country contemplated content related to Cariology in their disciplines, distributed in specific disciplines (except for the Northern region) and disciplines in the three cycles of learning (basic, clinical and public health), with larger workload in the clinical cycle. Although public dental schools in Brazil demonstrated commitment to contemplating the content related to Cariology in their disciplines, the emphasis on the clinical cycle may not be promoting the integrated formation of students, which could be contributing to reflect the inequalities of the disease in the country.
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The text describes a study about the adoption of virtual learning environments and its consequences to the learning process of undergraduate students at the State University of Campinas - Unicamp. These environments can be incorporated in various ways into the academic daily life of students and teachers. One efficient way to promote the adoption of these environments, as observed by the Distance Learning support team, is to train teachers and students in their use. Two training alternatives are described in this text to instruct the academic community in the use of TelEduc, a freeware developed and coordinated by the NIED - Núcleo de Informática Aplicada à Educação (Center for Information Technology Applied to Education), and officially adopted by Unicamp. Training courses are offered in two ways - presence or distance learning - to suit each teacher's preferences. This article compares the two modes of training, showing their strong and weak points. The adoption of TelEduc and its direct consequences to the learning process are described in a study carried out with some engineering undergraduates at Unicamp. The authors' questions and the general views of teachers and students regarding the effectiveness of the use of TelEduc as a supporting tool to presence teaching are presented. This investigation revealed the importance of training teachers in the effective use of these environments.
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PURPOSE: To determine the mean critical fusion frequency and the short-term fluctuation, to analyze the influence of age, gender, and the learning effect in healthy subjects undergoing flicker perimetry. METHODS: Study 1 - 95 healthy subjects underwent flicker perimetry once in one eye. Mean critical fusion frequency values were compared between genders, and the influence of age was evaluated using linear regression analysis. Study 2 - 20 healthy subjects underwent flicker perimetry 5 times in one eye. The first 3 sessions were separated by an interval of 1 to 30 days, whereas the last 3 sessions were performed within the same day. The first 3 sessions were used to investigate the presence of a learning effect, whereas the last 3 tests were used to calculate short-term fluctuation. RESULTS: Study 1 - Linear regression analysis demonstrated that mean global, foveal, central, and critical fusion frequency per quadrant significantly decreased with age (p<0.05).There were no statistically significant differences in mean critical fusion frequency values between males and females (p>0.05), with the exception of the central area and inferonasal quadrant (p=0.049 and p=0.011, respectively), where the values were lower in females. Study 2 - Mean global (p=0.014), central (p=0.008), and peripheral (p=0.03) critical fusion frequency were significantly lower in the first session compared to the second and third sessions. The mean global short-term fluctuation was 5.06±1.13 Hz, the mean interindividual and intraindividual variabilities were 11.2±2.8% and 6.4±1.5%, respectively. CONCLUSION: This study suggests that, in healthy subjects, critical fusion frequency decreases with age, that flicker perimetry is associated with a learning effect, and that a moderately high short-term fluctuation is expected.
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PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.
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Universidade Estadual de Campinas. Faculdade de Educação Física