742 resultados para raccomandazione e-learning privacy tecnica rule-based recommender suggerimento
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Social media tools are increasingly popular in Computer Supported Collaborative Learning and the analysis of students' contributions on these tools is an emerging research direction. Previous studies have mainly focused on examining quantitative behavior indicators on social media tools. In contrast, the approach proposed in this paper relies on the actual content analysis of each student's contributions in a learning environment. More specifically, in this study, textual complexity analysis is applied to investigate how student's writing style on social media tools can be used to predict their academic performance and their learning style. Multiple textual complexity indices are used for analyzing the blog and microblog posts of 27 students engaged in a project-based learning activity. The preliminary results of this pilot study are encouraging, with several indexes predictive of student grades and/or learning styles.
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Different types of serious games have been used in elucidating computer science areas such as computer games, mobile games, Lego-based games, virtual worlds and webbased games. Different evaluation techniques have been conducted like questionnaires, interviews, discussions and tests. Simulation have been widely used in computer science as a motivational and interactive learning tool. This paper aims to evaluate the possibility of successful implementation of simulation in computer programming modules. A framework is proposed to measure the impact of serious games on enhancing students understanding of key computer science concepts. Experiments will be held on the EEECS of Queens University Belfast students to test the framework and attain results.
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For a structural engineer, effective communication and interaction with architects cannot be underestimated as a key skill to success throughout their professional career. Structural engineers and architects have to share a common language and understanding of each other in order to achieve the most desirable architectural and structural designs. This interaction and engagement develops during their professional career but needs to be nurtured during their undergraduate studies. The objective of this paper is to present the strategies employed to engage higher order thinking in structural engineering students in order to help them solve complex problem-based learning (PBL) design scenarios presented by architecture students. The strategies employed were applied in the experimental setting of an undergraduate module in structural engineering at Queens University Belfast in the UK. The strategies employed were active learning to engage with content knowledge, the use of physical conceptual structural models to reinforce key concepts and finally, reinforcing the need for hand sketching of ideas to promote higher order problem-solving. The strategies employed were evaluated through student survey, student feedback and module facilitator (this author) reflection. The strategies were qualitatively perceived by the tutor and quantitatively evaluated by students in a cross-sectional study to help interaction with the architecture students, aid interdisciplinary learning and help students creatively solve problems (through higher order thinking). The students clearly enjoyed this module and in particular interacting with structural engineering tutors and students from another discipline
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Algorithms for concept drift handling are important for various applications including video analysis and smart grids. In this paper we present decision tree ensemble classication method based on the Random Forest algorithm for concept drift. The weighted majority voting ensemble aggregation rule is employed based on the ideas of Accuracy Weighted Ensemble (AWE) method. Base learner weight in our case is computed for each sample evaluation using base learners accuracy and intrinsic proximity measure of Random Forest. Our algorithm exploits both temporal weighting of samples and ensemble pruning as a forgetting strategy. We present results of empirical comparison of our method with riginal random forest with incorporated replace-the-looser forgetting andother state-of-the-art concept-drift classiers like AWE2.
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Introduction A computer-based simulation game (CSG) was used for the first time in a final-year undergraduate module. A change management simulation game was used in the seminar classes as a formative exercise that was linked to parts of the students summative assessment. The module evaluation suggests that most students learned from using the CSG.
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Computer-based simulation games (CSG) are a form of innovation in learning and teaching. CGS are used more pervasively in various ways such as a class activity (formative exercises) and as part of summative assessments (Leemkuil and De Jong, 2012; Zantow et al., 2005). This study investigates the current and potential use of CGS in Worcester Business Schools (WBS) Business Management undergraduate programmes. The initial survey of off-the-shelf simulation reveals that there are various categories of simulations, with each offering varying levels of complexity and learning opportunities depending on the field of study. The findings suggest that whilst there is marginal adoption of the use CSG in learning and teaching, there is significant opportunity to increase the use of CSG in enhancing learning and learner achievement, especially in Level 5 modules. The use of CSG is situational and its adoption should be undertaken on a case-by-case basis. WBS can play a major role by creating an environment that encourages and supports the use of CSG as well as other forms of innovative learning and teaching methods. Thus the key recommendation involves providing module teams further support in embedding and integrating CSG into their modules.
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Empirical evidence has demonstrated the benefits of using simulation games in enhancing learning especially in terms of cognitive gains. This is to be expected as the dynamism and non-linearity of simulation games are more cognitively demanding. However, the other effects of simulation games, specifically in terms of learners emotions, have not been given much attention and are under-investigated. This study aims to demonstrate that simulation games stimulate positive emotions from learners that help to enhance learning. The study finds that the affect-based constructs of interest, engagement and appreciation are positively correlated to learning. A stepwise multiple regression analysis shows that a model involving interest and engagement are significantly associated with learning. The emotions of learners should be considered in the development of curriculum, and the delivery of learning and teaching as positive emotions enhances learning.
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Although business simulations are widely used in management education, there is no consensus about how to optimise their application. Our research explores the use of business simulations as a dimension of a blended learning pedagogic approach for undergraduate business education. Accepting that few best-practice prescriptive models for the design and implementation of simulations in this context have been presented, and that there is little empirical evidence for the claims made by proponents of such models, we address the lacuna by considering business student perspectives on the use of simulations. We then intersect available data with espoused positive outcomes made by the authors of a prescriptive model. We find the model to be essentially robust and offer evidence to support this position. In so doing we provide one of the few empirically based studies to support claims made by proponents of simulations in business education. The research should prove valuable for those with an academic interest in the use of simulations, either as a blended learning dimension or as a stand-alone business education activity. Further, the findings contribute to the academic debate surrounding the use and efficacy of simulation-based training [SBT] within business and management education.
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Introduction For a long time, language learning research focusing on young learners was a neglected field of research. Most empirical studies within the broad area of second/foreign language acquisition were instead carried out among adults in tertiary education and it was not until in the 1990s that the scope of research broadened to include also young learners, then loosely defined as children in primary and/or secondary education (see, for example, Hasselgreen & Drew, 2012; McKay, 2006; Nikolov, 2009a). In fact, some agreement upon how to define young learners was not properly discussed until in 2013, when Gail Ellis (2013) provided some useful clarifications as regards how to label learners within the broad age-span that encompasses both primary and secondary school. In short, based on a literature overview, she concludes that the term young learners is most often used for children between the ages of five and eleven/twelve, which in most countries would be equivalent to learners in primary school. Thus, since young learners did not catch much scholarly attention until fairly recently, research volumes on the topic have been scarce. However, with a rapidly growing interest in examining how small children learn foreign languages, there has been a sudden increase in terms of the number of books available targeting young language learners. A first, major contribution was Nikolovs (2009b) Early learning of modern foreign languages, in which 16 studies of young language learners from different countries are accounted for. Another important contribution is the edited book that will be reviewed here, which specifically targets studies about various aspects of second/foreign language learning among young (mainly Norwegian) learners. Bearing in mind that Norway and Sweden are very similar countries in terms of schooling, language background, and demographics only to give three examples of similarities between these two nations it is particularly relevant for Swedish scholars within the fields of education and second language acquisition to become familiar with research findings from the neighboring country. In this review, the editors and the outline of the book are first described, then brief summaries of each chapter are provided, before the text closes with an evaluation of the volume.
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This thesis addresses the Batch Reinforcement Learning methods in Robotics. This sub-class of Reinforcement Learning has shown promising results and has been the focus of recent research. Three contributions are proposed that aim to extend the state-of-art methods allowing for a faster and more stable learning process, such as required for learning in Robotics. The Q-learning update-rule is widely applied, since it allows to learn without the presence of a model of the environment. However, this update-rule is transition-based and does not take advantage of the underlying episodic structure of collected batch of interactions. The Q-Batch update-rule is proposed in this thesis, to process experiencies along the trajectories collected in the interaction phase. This allows a faster propagation of obtained rewards and penalties, resulting in faster and more robust learning. Non-parametric function approximations are explored, such as Gaussian Processes. This type of approximators allows to encode prior knowledge about the latent function, in the form of kernels, providing a higher level of exibility and accuracy. The application of Gaussian Processes in Batch Reinforcement Learning presented a higher performance in learning tasks than other function approximations used in the literature. Lastly, in order to extract more information from the experiences collected by the agent, model-learning techniques are incorporated to learn the system dynamics. In this way, it is possible to augment the set of collected experiences with experiences generated through planning using the learned models. Experiments were carried out mainly in simulation, with some tests carried out in a physical robotic platform. The obtained results show that the proposed approaches are able to outperform the classical Fitted Q Iteration.
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A primary goal of context-aware systems is delivering the right information at the right place and right time to users in order to enable them to make effective decisions and improve their quality of life. There are three key requirements for achieving this goal: determining what information is relevant, personalizing it based on the users context (location, preferences, behavioral history etc.), and delivering it to them in a timely manner without an explicit request from them. These requirements create a paradigm that we term as Proactive Context-aware Computing. Most of the existing context-aware systems fulfill only a subset of these requirements. Many of these systems focus only on personalization of the requested information based on users current context. Moreover, they are often designed for specific domains. In addition, most of the existing systems are reactive - the users request for some information and the system delivers it to them. These systems are not proactive i.e. they cannot anticipate users intent and behavior and act proactively without an explicit request from them. In order to overcome these limitations, we need to conduct a deeper analysis and enhance our understanding of context-aware systems that are generic, universal, proactive and applicable to a wide variety of domains. To support this dissertation, we explore several directions. Clearly the most significant sources of information about users today are smartphones. A large amount of users context can be acquired through them and they can be used as an effective means to deliver information to users. In addition, social media such as Facebook, Flickr and Foursquare provide a rich and powerful platform to mine users interests, preferences and behavioral history. We employ the ubiquity of smartphones and the wealth of information available from social media to address the challenge of building proactive context-aware systems. We have implemented and evaluated a few approaches, including some as part of the Rover framework, to achieve the paradigm of Proactive Context-aware Computing. Rover is a context-aware research platform which has been evolving for the last 6 years. Since location is one of the most important context for users, we have developed Locus, an indoor localization, tracking and navigation system for multi-story buildings. Other important dimensions of users context include the activities that they are engaged in. To this end, we have developed SenseMe, a system that leverages the smartphone and its multiple sensors in order to perform multidimensional context and activity recognition for users. As part of the SenseMe project, we also conducted an exploratory study of privacy, trust, risks and other concerns of users with smart phone based personal sensing systems and applications. To determine what information would be relevant to users situations, we have developed TellMe - a system that employs a new, flexible and scalable approach based on Natural Language Processing techniques to perform bootstrapped discovery and ranking of relevant information in context-aware systems. In order to personalize the relevant information, we have also developed an algorithm and system for mining a broad range of users preferences from their social network profiles and activities. For recommending new information to the users based on their past behavior and context history (such as visited locations, activities and time), we have developed a recommender system and approach for performing multi-dimensional collaborative recommendations using tensor factorization. For timely delivery of personalized and relevant information, it is essential to anticipate and predict users behavior. To this end, we have developed a unified infrastructure, within the Rover framework, and implemented several novel approaches and algorithms that employ various contextual features and state of the art machine learning techniques for building diverse behavioral models of users. Examples of generated models include classifying users semantic places and mobility states, predicting their availability for accepting calls on smartphones and inferring their device charging behavior. Finally, to enable proactivity in context-aware systems, we have also developed a planning framework based on HTN planning. Together, these works provide a major push in the direction of proactive context-aware computing.
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Barnsley Colleges level 3 and 4 diplomas in digital learning design are delivered in one year, enabling apprentices to be employed alongside their studies in the colleges innovative learning design company, Elephant Learning Designs. The limited time this allows for delivery and assessment has prompted course leaders to rethink their approach to course structure, assessment and feedback design, and the role of technology in evidence collection.
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The resource-based view identifies a number of factors that may influence employees informal learning. In a cross-sectional survey of 113 German employees in the energy sector, we examined a number of potential predictors of informal learning and a more positive informal learning attitude. The results showed that proactive help-seeking and professional self-efficacy were positive predictors of informal learning. Employees who were older, who enjoyed learning, sought help and were self-efficacious learners had a more positive attitude towards formal learning. Employees who had a more positive attitude about informal learning rated organisational learning provisions as less important, potentially due to being proactive help-seekers. Managers rated organisational learning resources as less important than non-managerial employees. However, managers also reported higher professional self-efficacy. These circumstances may also influence their decision-making regarding the need to provide learning resources to others in the workplace.
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Abstract-The immune system is a complex biological system with a highly distributed, adaptive and self-organising nature. This paper presents an artificial immune system (AIS) that exploits some of these characteristics and is applied to the task of film recommendation by collaborative filtering (CF). Natural evolution and in particular the immune system have not been designed for classical optimisation. However, for this problem, we are not interested in finding a single optimum. Rather we intend to identify a sub-set of good matches on which recommendations can be based. It is our hypothesis that an AIS built on two central aspects of the biological immune system will be an ideal candidate to achieve this: Antigen - antibody interaction for matching and antibody - antibody interaction for diversity. Computational results are presented in support of this conjecture and compared to those found by other CF techniques.