Unraveling networked learning initiatives: an analytic framework

Networked learning happens naturally within the social systems of which we are all part. However, in certain circumstances individuals may want to actively take initiative to initiate interaction with others they are not yet regularly in exchange with. This may be the case when external influences and societal changes require innovation of existing practices. This paper proposes a framework with relevant dimensions providing insight into precipitated characteristics of designed as well as ‘fostered or grown’ networked learning initiatives. Networked learning initiatives are characterized as “goal-directed, interest-, or needs based activities of a group of (at least three) individuals that initiate interaction across the boundaries of their regular social systems”. The proposed framework is based on two existing research traditions, namely 'networked learning' and 'learning networks', comparing, integrating and building upon knowledge from both perspectives. We uncover some interesting differences between definitions, but also similarities in the way they describe what ‘networked’ means and how learning is conceptualized. We think it is productive to combine both research perspectives, since they both study the process of learning in networks extensively, albeit from different points of view, and their combination can provide valuable insights in networked learning initiatives. We uncover important features of networked learning initiatives, characterize actors and connections of which they are comprised and conditions which facilitate and support them. The resulting framework could be used both for analytic purposes and (partly) as a design framework. In this framework it is acknowledged that not all successful networks have the same characteristics: there is no standard ‘constellation’ of people, roles, rules, tools and artefacts, although there are indications that some network structures work better than others. Interactions of individuals can only be designed and fostered till a certain degree: the type of network and its ‘growth’ (e.g. in terms of the quantity of people involved, or the quality and relevance of co-created concepts, ideas, artefacts and solutions to its ‘inhabitants’) is in the hand of the people involved. Therefore, the framework consists of dimensions on a sliding scale. It introduces a structured and analytic way to look at the precipitation of networked learning initiatives: learning networks. Successive research on the application of this framework and feedback from the networked learning community is needed to further validate it’s usability and value to both research as well as practice.

Evaluation Framework EFI for Measuring the Impact of Learning, Education and Training

This article introduces the Evaluation Framework EFI for the Impact Measurement of learning, education and training: The Evaluation Framework for Impact Measurement was developed for specifying the evaluation phase and its objectives and tasks within the IDEAL Reference Model for the introduction and optimization of quality development within learning, education and training. First, a description of the Evaluation Framework for Impact Measurement will be provided, followed by a brief overview of the IDEAL Reference Model. Finally, an example for the implementation of the Evaluation Framework for Impact Measurement within the ARISTOTELE project is presented.

Designing networked learning for innovation: A dimension perspective

In her presentation, Emmy discusses a case study where a teacher group is followed in its innovative attempt to start up a new curriculum. Searching for guidelines to facilitate knowledge-creating teacher groups, the ‘Dimensions of Social Learning (DSL) Framework’ is applied as a starting point to assess the group's social configuration. To supplement the DSL Framework towards knowledge creation, social capital and value creation theories were studied. This has resulted in an extended framework that can bring the knowledge creation of teacher groups into view.

Efficient Learning of Bayesian Networks with Bounded Tree-width

Learning Bayesian networks with bounded tree-width has attracted much attention recently, because low tree-width allows exact inference to be performed efficiently. Some existing methods \cite{korhonen2exact, nie2014advances} tackle the problem by using $k$-trees to learn the optimal Bayesian network with tree-width up to $k$. Finding the best $k$-tree, however, is computationally intractable. In this paper, we propose a sampling method to efficiently find representative $k$-trees by introducing an informative score function to characterize the quality of a $k$-tree. To further improve the quality of the $k$-trees, we propose a probabilistic hill climbing approach that locally refines the sampled $k$-trees. The proposed algorithm can efficiently learn a quality Bayesian network with tree-width at most $k$. Experimental results demonstrate that our approach is more computationally efficient than the exact methods with comparable accuracy, and outperforms most existing approximate methods.

How using Technology Enhanced Learning could help modernise traditional large group teaching or Lecturing.

The efficiency of lecturing or large group teaching has been called into question for many years. An abundance of literature details the components of effective teaching which are not provided in the traditional lecture setting, with many alternative methods of teaching recommended. However, with continued constraints on resources large group teaching is here to stay and student’s expect and are familiar with this method.

Technology Enhanced Learning may be the way forward, to prevent educators from “throwing out the baby with the bath water”. TEL could help Educator’s especially in the area of life sciences which is often taught by lectures to engage and involve students in their learning, provide feedback and incorporate the “quality” of small group teaching, case studies and Enquiry Based Learning into the large group setting thus promoting effective and deep learning.

A Machine Learning Analysis of Twitter Sentiment to the Sandy Hook Shootings

Gun related violence is a complex issue and accounts for a large proportion of violent incidents. In the research reported in this paper, we set out to investigate the pro-gun and anti-gun sentiments expressed on a social media platform, namely Twitter, in response to the 2012 Sandy Hook Elementary School shooting in Connecticut, USA. Machine learning techniques are applied to classify a data corpus of over 700,000 tweets. The sentiments are captured using a public sentiment score that considers the volume of tweets as well as population. A web-based interactive tool is developed to visualise the sentiments and is available at this http://www.gunsontwitter.com. The key findings from this research are: (i) There are elevated rates of both pro-gun and anti-gun sentiments on the day of the shooting. Surprisingly, the pro-gun sentiment remains high for a number of days following the event but the anti-gun sentiment quickly falls to pre-event levels. (ii) There is a different public response from each state, with the highest pro-gun sentiment not coming from those with highest gun ownership levels but rather from California, Texas and New York.

Music Learning Networks: Supporting the Music Learning of Adolescents

Thesis (Ph.D.)--University of Washington, 2016-08

Evaluation of Community-led Complementary Feeding and Learning Sessions

Thesis (Master's)--University of Washington, 2016-08

A satisfação e a intenção de continuidade de uso em servços de e-learning:validação empírica de um modelo aplicado no serviço público

The present study aims to investigate the constructs of Technological Readiness Index (TRI) and the Expectancy Disconfirmation Theory (EDT) as determinants of satisfaction and continuance intention use in e-learning services. Is proposed a theoretical model that seeks to measure the phenomenon suited to the needs of public organizations that offer distance learning course with the use of virtual platforms for employees. The research was conducted from a quantitative analytical approach, via online survey in a sample of 343 employees of 2 public organizations in RN who have had e-learning experience. The strategy of data analysis used multivariate analysis techniques, including structural equation modeling (SEM), operationalized by AMOS© software. The results showed that quality, quality disconfirmation, value and value disconfirmation positively impact on satisfaction, as well as disconfirmation usability, innovativeness and optimism. Likewise, satisfaction proved to be decisive for the purpose of continuance intention use. In addition, technological readiness and performance are strongly related. Based on the structural model found by the study, public organizations can implement e-learning services for employees focusing on improving learning and improving skills practiced in the organizational environment

Evaluation of online higher education: learning, interaction and technology

The work presents a theoretical framework for the evaluation of e-Teaching that aims at positioning the online activities designed and developed by the teacher as to the Learning, Interaction and Technology Dimensions. The theoretical research that underlies the study was developed reflecting current thinking on the promotion of quality of teaching and of the integration of information and communication tools into the curriculum in Higher Education (HE), i.e., bearing in mind some European guidelines and policies on this subject. This way, an answer was sought to be given to one of the aims put forward in this study, namely to contribute towards the development of a conceptual framework to support research on evaluation of e-teaching in the context of HE. Based on the theoretical research carried out, an evaluation tool (SCAI) was designed, which integrates the two questionnaires developed to collect the teachers' and the students' perceptions regarding the development of e-activities. Consequently, an empirical study was structured and carried out, allowing SCAI tool to be tested and validated in real cases. From the comparison of the theoretical framework established and the analysis of the data obtained, we found that the differences in teaching should be valued and seen as assets by HE institutions rather than annihilated in a globalizing perspective.

Promoting oral health in adults with learning disability

Introduction: It is complex to define learning disabilities, there is no single universal definition used; there are different interpretations and definitions used for learning disabilities in different countries and communities. Primarily, the term “learning disability” sometimes used as “learning difficulties” is a term widely used in UK. There are various types and degree of severity of learning disabilities depending upon the extent of disorder. Though different definitions used all over the world, its types and classification coupled with their health and oral health needs are discussed in this review. Objectives:  To review the background literature on definitions of learning disabilities and health needs of this population.  To review literature on individual clinical preventive intervention to determine the effectiveness in promoting oral health amongst adults in learning disabilities.  To review literature in relation to community based preventive dental measures.  To determine the interventions in this areas are appropriate to support policy and practice and if these interventions establish good evidence to suggest that the oral health needs of adults with learning disabilities are met or not.  To make recommendations in implementing future preventive oral health interventions for adults with learning disabilities. Methodology: It was develop a comprehensive narrative synthesis of previously published literature from different sources and summarizes the whole research in a particular area identifying gap of knowledge. It provides a broad perspective of a subject and supports continuing education. It also is directed to inform policy and further research. It is a qualitative type of research with a broad question and critical analysis of literature published in books, article and journals. The research question evaluated on PICOS criteria is: Effectiveness of preventive dental interventions in adults with learning disabilities. The research question clearly defines the PICOS i.e. participants, interventions, comparison, outcome and study design. The Cochrane database of systematic reviews (CDSR), Database of Abstracts of Reviews of effects (DARE) through York University and National institute of Health and Clinical Excellence (NICE) was searched to identify need of this review. There was no literature review found on the preventive dental interventions found hence, justifying this review. The guidance used in this review is from York University and methods opted for search of literature is based on the following: Type of participants, interventions, outcome measure, studies and search. The review of literature; author search; systematic and narrative reviews, through the following electronic databases via UFP library services: Pub-Med, Medline, EMBASE, CINHAL, Google scholar; Science Direct; Social and Medicine. A comprehensive search of all available literature from 1990-2015, including systematic reviews, policy documents and some guideline documents was done. Internet resource used to access; Department of Health, World Health Organization, Disability World, Disability Rights Commission, the Stationery office, MENCAP, Australian Learning Disability Association. The literature search was carried out with single word, combined words and phrases, authors' names and the title of literature search. Results: It is primarily looking at the oral health interventions available for adults with learning disabilities in clinical settings and the community measures observed over a period of 25 years 1990-2015. There were 7of the clinical intervention studies and one community based intervention study was added in this review. Conclusion: There is a gap of knowledge identified in not having ample research in the area of preventive dental interventions in adults with learning or intellectual disabilities and there is a need of more research, studies need to be of a better quality and a special consideration is required in the community settings where maintenance of oral hygiene for this vulnerable group of society is hugely dependent on their caregivers. Though, the policy and guideline directs on the preventive dental interventions of adults with LD there still a gap evident in understanding and implication of the guidance in practice by the dental and care support team. Understanding learning disabilities and to identify their behavior, compliance and oral health needs is paramount for all professionals working with or for them at each level.

Participatory Patterns in an International Air Quality Monitoring Initiative

The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution.

'Explicit Learning: an Effort towards Human Scheduling Algorithms'

Abstract Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem. In our previous indirect GAs, learning is implicit and is restricted to the efficient adjustment of weights for a set of rules that are used to construct schedules. The major limitation of those approaches is that they learn in a non-human way: like most existing construction algorithms, once the best weight combination is found, the rules used in the construction process are fixed at each iteration. However, normally a long sequence of moves is needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning processes. When a human scheduler is working, he normally builds a schedule step by step following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not completed yet, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this research we intend to design more human-like scheduling algorithms, by using ideas derived from Bayesian Optimization Algorithms (BOA) and Learning Classifier Systems (LCS) to implement explicit learning from past solutions. BOA can be applied to learn to identify good partial solutions and to complete them by building a Bayesian network of the joint distribution of solutions [3]. A Bayesian network is a directed acyclic graph with each node corresponding to one variable, and each variable corresponding to individual rule by which a schedule will be constructed step by step. The conditional probabilities are computed according to an initial set of promising solutions. Subsequently, each new instance for each node is generated by using the corresponding conditional probabilities, until values for all nodes have been generated. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the Bayesian network is updated again using the current set of good rule strings. The algorithm thereby tries to explicitly identify and mix promising building blocks. It should be noted that for most scheduling problems the structure of the network model is known and all the variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus learning can amount to 'counting' in the case of multinomial distributions. In the LCS approach, each rule has its strength showing its current usefulness in the system, and this strength is constantly assessed [4]. To implement sophisticated learning based on previous solutions, an improved LCS-based algorithm is designed, which consists of the following three steps. The initialization step is to assign each rule at each stage a constant initial strength. Then rules are selected by using the Roulette Wheel strategy. The next step is to reinforce the strengths of the rules used in the previous solution, keeping the strength of unused rules unchanged. The selection step is to select fitter rules for the next generation. It is envisaged that the LCS part of the algorithm will be used as a hill climber to the BOA algorithm. This is exciting and ambitious research, which might provide the stepping-stone for a new class of scheduling algorithms. Data sets from nurse scheduling and mall problems will be used as test-beds. It is envisaged that once the concept has been proven successful, it will be implemented into general scheduling algorithms. It is also hoped that this research will give some preliminary answers about how to include human-like learning into scheduling algorithms and may therefore be of interest to researchers and practitioners in areas of scheduling and evolutionary computation. References 1. Aickelin, U. and Dowsland, K. (2003) 'Indirect Genetic Algorithm for a Nurse Scheduling Problem', Computer & Operational Research (in print). 2. Li, J. and Kwan, R.S.K. (2003), 'Fuzzy Genetic Algorithm for Driver Scheduling', European Journal of Operational Research 147(2): 334-344. 3. Pelikan, M., Goldberg, D. and Cantu-Paz, E. (1999) 'BOA: The Bayesian Optimization Algorithm', IlliGAL Report No 99003, University of Illinois. 4. Wilson, S. (1994) 'ZCS: A Zeroth-level Classifier System', Evolutionary Computation 2(1), pp 1-18.

The introduction of e-learning in European universities: models and strategies

The aim of this paper is to present the models and the strategies of adoption of e-learning in a group of European universities, most of them located in the regions called “the four motors of Europe” (Baden-Württenberg, Catalunya, Lombardy and Rhône-Alpes) and in Switzerland. Our analysis focuses on four dimensions: the rationale behind the introduction of e-learning, the organisation of the activities and, in particular, the existence of a university centre for e-learning, the type of activities, and, finally, the type of public reached by e-learning. The majority of campus universities in our sample introduced e-learning to improve the quality of education of their students and, for the most part, as a support for existing courses. Some of the campus universities went even further insofar as they have introduced some online courses into their curricula. This has led to forms of cooperation where different universities share some of their courses. Finally, a small number of campus universities have included as part of their educational offer full distance degree programs which can be attended also by non residential students. The above cases show that there is no general move from campus universities towards distance education, but rather a more selective behaviour. Thus we conclude that e-learning, although it is undoubtedly spreading in both distance and presence universities, is not yet bringing fundamental changes in the institutions themselves. E-learning is at the moment integrated into the existing organization and educational offer. (DIPF/Orig.)

Datasets, features, learning, and models in visual recognition

Visual recognition is a fundamental research topic in computer vision. This dissertation explores datasets, features, learning, and models used for visual recognition. In order to train visual models and evaluate different recognition algorithms, this dissertation develops an approach to collect object image datasets on web pages using an analysis of text around the image and of image appearance. This method exploits established online knowledge resources (Wikipedia pages for text; Flickr and Caltech data sets for images). The resources provide rich text and object appearance information. This dissertation describes results on two datasets. The first is Berg’s collection of 10 animal categories; on this dataset, we significantly outperform previous approaches. On an additional set of 5 categories, experimental results show the effectiveness of the method. Images are represented as features for visual recognition. This dissertation introduces a text-based image feature and demonstrates that it consistently improves performance on hard object classification problems. The feature is built using an auxiliary dataset of images annotated with tags, downloaded from the Internet. Image tags are noisy. The method obtains the text features of an unannotated image from the tags of its k-nearest neighbors in this auxiliary collection. A visual classifier presented with an object viewed under novel circumstances (say, a new viewing direction) must rely on its visual examples. This text feature may not change, because the auxiliary dataset likely contains a similar picture. While the tags associated with images are noisy, they are more stable when appearance changes. The performance of this feature is tested using PASCAL VOC 2006 and 2007 datasets. This feature performs well; it consistently improves the performance of visual object classifiers, and is particularly effective when the training dataset is small. With more and more collected training data, computational cost becomes a bottleneck, especially when training sophisticated classifiers such as kernelized SVM. This dissertation proposes a fast training algorithm called Stochastic Intersection Kernel Machine (SIKMA). This proposed training method will be useful for many vision problems, as it can produce a kernel classifier that is more accurate than a linear classifier, and can be trained on tens of thousands of examples in two minutes. It processes training examples one by one in a sequence, so memory cost is no longer the bottleneck to process large scale datasets. This dissertation applies this approach to train classifiers of Flickr groups with many group training examples. The resulting Flickr group prediction scores can be used to measure image similarity between two images. Experimental results on the Corel dataset and a PASCAL VOC dataset show the learned Flickr features perform better on image matching, retrieval, and classification than conventional visual features. Visual models are usually trained to best separate positive and negative training examples. However, when recognizing a large number of object categories, there may not be enough training examples for most objects, due to the intrinsic long-tailed distribution of objects in the real world. This dissertation proposes an approach to use comparative object similarity. The key insight is that, given a set of object categories which are similar and a set of categories which are dissimilar, a good object model should respond more strongly to examples from similar categories than to examples from dissimilar categories. This dissertation develops a regularized kernel machine algorithm to use this category dependent similarity regularization. Experiments on hundreds of categories show that our method can make significant improvement for categories with few or even no positive examples.