MOOCs, Open educational resources and social networking: bridging the gap between informal and formal learning

MOOCs and open educational resources (OER) provide a wealth of learning opportunities for people around the globe, many of whom have no access to formal higher education. OER are often difficult to locate and are accessed on their own without support from or dialogue with subject experts and peers. This paper looks at whether it is possible to develop effective learning communities around OER and whether these communities can emerge spontaneously and in a self-organised way without moderation. It examines the complex interplay between formal and informal learning, and examines whether MOOCs are the answer to providing effective interaction and dialogue for those wishing to study at university level for free on the Internet.

Toward Never-Ending Object Learning for Robots

Thesis (Ph.D.)--University of Washington, 2016-06

Teaching College Writing in a High School Setting: The Impact of Concurrent Enrollment on Teacher Learning and Practice

Thesis (Ph.D.)--University of Washington, 2016-06

Socio-Historical Factors Mediating Collaborative Teaching and Learning: A Design-Based Investigation and Intervention

Thesis (Ph.D.)--University of Washington, 2016-06

Costs of learning: the dynamics of mixed-prey exploitation by silver perch, Bidyanus bidyanus (Mitchell, 1838)

Although generalist predators have been reported to forage less efficiently than specialists, there is little information on the extent to which learning can improve the efficiency of mixed-prey foraging. Repeated exposure of silver perch to mixed prey (pelagic Artemia and benthic Chironomus larvae) led to substantial fluctuations in reward rate over relatively long (20-day) timescales. When perch that were familiar with a single prey type were offered two prey types simultaneously, the rate at which they captured both familiar and unfamiliar prey dropped progressively over succeeding trials. This result was not predicted by simple learning paradigms, but could be explained in terms of an interaction between learning and attention. Between-trial patterns in overall intake were complex and differed between the two prey types, but were unaffected by previous prey specialization. However, patterns of prey priority (i.e. the prey type that was preferred at the start of a trial) did vary with previous prey training. All groups of fish converged on the most profitable prey type (chironomids), but this process took 15-20 trials. In contrast, fish offered a single prey type reached asymptotic intake rates within five trials and retained high capture abilities for at least 5 weeks. Learning and memory allow fish to maximize foraging efficiency on patches of a single prey type. However, when foragers are faced with mixed prey populations, cognitive constraints associated with divided attention may impair efficiency, and this impairment can be exacerbated by experience. (c) 2005 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.

SQLator: An online SQL learning workbench

SQL (Structured Query Language) is one of the essential topics in foundation databases courses in higher education. Due to its apparent simple syntax, learning to use the full power of SQL can be a very difficult activity. In this paper, we introduce SQLator, which is a web-based interactive tool for learning SQL. SQLator's key function is the evaluate function, which allows a user to evaluate the correctness of his/her query formulation. The evaluate engine is based on complex heuristic algorithms. The tool also provides instructors the facility to create and populate database schemas with an associated pool of SQL queries. Currently it hosts two databases with a query pool of 300+ across the two databases. The pool is divided into 3 categories according to query complexity. The SQLator user can perform unlimited executions and evaluations on query formulations and/or view the solutions. The SQLator evaluate function has a high rate of success in evaluating the user's statement as correct (or incorrect) corresponding to the question. We will present in this paper, the basic architecture and functions of SQLator. We will further discuss the value of SQLator as an educational technology and report on educational outcomes based on studies conducted at the School of Information Technology and Electrical Engineering, The University of Queensland.

Patterns in complex systems modeling

The design, development, and use of complex systems models raises a unique class of challenges and potential pitfalls, many of which are commonly recurring problems. Over time, researchers gain experience in this form of modeling, choosing algorithms, techniques, and frameworks that improve the quality, confidence level, and speed of development of their models. This increasing collective experience of complex systems modellers is a resource that should be captured. Fields such as software engineering and architecture have benefited from the development of generic solutions to recurring problems, called patterns. Using pattern development techniques from these fields, insights from communities such as learning and information processing, data mining, bioinformatics, and agent-based modeling can be identified and captured. Collections of such 'pattern languages' would allow knowledge gained through experience to be readily accessible to less-experienced practitioners and to other domains. This paper proposes a methodology for capturing the wisdom of computational modelers by introducing example visualization patterns, and a pattern classification system for analyzing the relationship between micro and macro behaviour in complex systems models. We anticipate that a new field of complex systems patterns will provide an invaluable resource for both practicing and future generations of modelers.

Representation and learning of visual information for pose recognition

Recovering position from sensor information is an important problem in mobile robotics, known as localisation. Localisation requires a map or some other description of the environment to provide the robot with a context to interpret sensor data. The mobile robot system under discussion is using an artificial neural representation of position. Building a geometrical map of the environment with a single camera and artificial neural networks is difficult. Instead it would be simpler to learn position as a function of the visual input. Usually when learning images, an intermediate representation is employed. An appropriate starting point for biologically plausible image representation is the complex cells of the visual cortex, which have invariance properties that appear useful for localisation. The effectiveness for localisation of two different complex cell models are evaluated. Finally the ability of a simple neural network with single shot learning to recognise these representations and localise a robot is examined.

Semi-supervised learning of hierarchical latent trait models for data visualisation

An interactive hierarchical Generative Topographic Mapping (HGTM) ¸iteH_GTM has been developed to visualise complex data sets. In this paper, we build a more general visualisation system by extending the HGTM visualisation system in 3 directions: bf (1) We generalize HGTM to noise models from the exponential family of distributions. The basic building block is the Latent Trait Model (LTM) developed in ¸iteKaban_pami. bf (2) We give the user a choice of initializing the child plots of the current plot in either em interactive, or em automatic mode. In the interactive mode the user interactively selects ``regions of interest'' as in ¸iteH_GTM, whereas in the automatic mode an unsupervised minimum message length (MML)-driven construction of a mixture of LTMs is employed. bf (3) We derive general formulas for magnification factors in latent trait models. Magnification factors are a useful tool to improve our understanding of the visualisation plots, since they can highlight the boundaries between data clusters. The unsupervised construction is particularly useful when high-level plots are covered with dense clusters of highly overlapping data projections, making it difficult to use the interactive mode. Such a situation often arises when visualizing large data sets. We illustrate our approach on a toy example and apply our system to three more complex real data sets.

Embedding e-portfolios for effective lifelong learning:a case study

Lifelong learning is a ‘keystone’ of educational policies (Faure, 1972) where the emphasis on learning shifts from teacher to learner. Higher Education (HE) institutions should be committed to developing lifelong learning, that is promoting learning that is flexible, diverse and relevant at different times, and in different places, and is pursued throughout life. Therefore the HE sector needs to develop effective strategies to encourage engagement in meaningful learning for diverse student populations. The use of e-portfolios, as a ‘purposeful aggregation of digital items’ (Sutherland & Powell, 2007), can meet the needs of the student community by encouraging reflection, the recording of experiences and achievements, and personal development planning (PDP). The use of e-portfolios also promotes inclusivity in learning as it provides students with the opportunity to articulate their aspirations and take the first steps along the pathway of lifelong learning. However, ensuring the uptake of opportunities within their learning is more complex than the students simply having access to the software. Therefore it is argued here that crucial to the effective uptake and engagement of the e-portfolio is embedding it purposefully within the curriculum. In order to investigate effective implementation of e-portfolios an explanatory case study on their use was carried out, initially focusing on 3 groups of students engaged in work-based learning and professional practice. The 3 groups had e-Portfolios embedded and assessed at different levels. Group 1 did not have the e-Portfolio embedded into their curriculum nor was the e-Portfolio assessed. Group 2 had the e-Portfolio embedded into the curriculum and formatively assessed. Group 3 also had the e-Portfolio embedded into the curriculum and were summatively assessed. Results suggest that the use of e-Portfolios needs to be integral to curriculum design in modules rather than used as an additional tool. In addition to this more user engagement was found in group 2 where the e-Portfolio was formatively assessed only. The implications of this case study are further discussed in terms of curriculum development.

Causality and synchronisation in complex systems with applications to neuroscience

This thesis presents an investigation, of synchronisation and causality, motivated by problems in computational neuroscience. The thesis addresses both theoretical and practical signal processing issues regarding the estimation of interdependence from a set of multivariate data generated by a complex underlying dynamical system. This topic is driven by a series of problems in neuroscience, which represents the principal background motive behind the material in this work. The underlying system is the human brain and the generative process of the data is based on modern electromagnetic neuroimaging methods . In this thesis, the underlying functional of the brain mechanisms are derived from the recent mathematical formalism of dynamical systems in complex networks. This is justified principally on the grounds of the complex hierarchical and multiscale nature of the brain and it offers new methods of analysis to model its emergent phenomena. A fundamental approach to study the neural activity is to investigate the connectivity pattern developed by the brain’s complex network. Three types of connectivity are important to study: 1) anatomical connectivity refering to the physical links forming the topology of the brain network; 2) effective connectivity concerning with the way the neural elements communicate with each other using the brain’s anatomical structure, through phenomena of synchronisation and information transfer; 3) functional connectivity, presenting an epistemic concept which alludes to the interdependence between data measured from the brain network. The main contribution of this thesis is to present, apply and discuss novel algorithms of functional connectivities, which are designed to extract different specific aspects of interaction between the underlying generators of the data. Firstly, a univariate statistic is developed to allow for indirect assessment of synchronisation in the local network from a single time series. This approach is useful in inferring the coupling as in a local cortical area as observed by a single measurement electrode. Secondly, different existing methods of phase synchronisation are considered from the perspective of experimental data analysis and inference of coupling from observed data. These methods are designed to address the estimation of medium to long range connectivity and their differences are particularly relevant in the context of volume conduction, that is known to produce spurious detections of connectivity. Finally, an asymmetric temporal metric is introduced in order to detect the direction of the coupling between different regions of the brain. The method developed in this thesis is based on a machine learning extensions of the well known concept of Granger causality. The thesis discussion is developed alongside examples of synthetic and experimental real data. The synthetic data are simulations of complex dynamical systems with the intention to mimic the behaviour of simple cortical neural assemblies. They are helpful to test the techniques developed in this thesis. The real datasets are provided to illustrate the problem of brain connectivity in the case of important neurological disorders such as Epilepsy and Parkinson’s disease. The methods of functional connectivity in this thesis are applied to intracranial EEG recordings in order to extract features, which characterize underlying spatiotemporal dynamics before during and after an epileptic seizure and predict seizure location and onset prior to conventional electrographic signs. The methodology is also applied to a MEG dataset containing healthy, Parkinson’s and dementia subjects with the scope of distinguishing patterns of pathological from physiological connectivity.

Visual data mining: integrating machine learning with information visualization

Today, the data available to tackle many scientific challenges is vast in quantity and diverse in nature. The exploration of heterogeneous information spaces requires suitable mining algorithms as well as effective visual interfaces. Most existing systems concentrate either on mining algorithms or on visualization techniques. Though visual methods developed in information visualization have been helpful, for improved understanding of a complex large high-dimensional dataset, there is a need for an effective projection of such a dataset onto a lower-dimension (2D or 3D) manifold. This paper introduces a flexible visual data mining framework which combines advanced projection algorithms developed in the machine learning domain and visual techniques developed in the information visualization domain. The framework follows Shneiderman’s mantra to provide an effective user interface. The advantage of such an interface is that the user is directly involved in the data mining process. We integrate principled projection methods, such as Generative Topographic Mapping (GTM) and Hierarchical GTM (HGTM), with powerful visual techniques, such as magnification factors, directional curvatures, parallel coordinates, billboarding, and user interaction facilities, to provide an integrated visual data mining framework. Results on a real life high-dimensional dataset from the chemoinformatics domain are also reported and discussed. Projection results of GTM are analytically compared with the projection results from other traditional projection methods, and it is also shown that the HGTM algorithm provides additional value for large datasets. The computational complexity of these algorithms is discussed to demonstrate their suitability for the visual data mining framework.

A bridge too far? Breaching the research/learning and teaching nexus in a previously research-led university

The relationship between research and learning and teaching represents what has been described as ‘amongst the most intellectually tangled, managerially complex and politically contentious issues in mass higher education’ (Scott, 2005, p 53). Despite this, arguments that in order to achieve high quality scholarly outcomes, university teachers need to adopt an approach to teaching similar to that of research (i.e. founded upon academic rigour and evidence), has long been discussed in the literature (see for example, Elton, 2005 & Healey, 2000). However, the practicalities of promoting an empirical and evidence-based approach to teaching within a research-led institution makes dealing with the research/learning and teaching nexus a somewhat challenging proposition. Drawing upon the findings of a mixed methodological study, this paper critically analyses the pedagogical, organisational and practical issues encountered by academics and support staff working within a newly established Centre for Learning Innovation and Professional Practice. Comprising an eclectic group of staff drawn from across the five Schools in the University, the Centre is dedicated to enhancing student learning through the development of evidence based teaching practice. Based upon the premise that the promotion of research-led teaching will act to bring teaching and research together, and in doing so enhance students learning experiences (Simmons & Elen 2007), the paper critically analyses the challenges encountered by staff responsible for developing and introducing a new learning & teaching focused organisational strategy (by reflecting on the previous 12 months work). In doing so it makes a significant contribution to current academic theory and debate in the areas of pedagogic practice and organisational management. Focusing specifically on the impact of the new policy on various aspects of university life including, pedagogic practice, student support, staff training, and organisational management, the paper critically addresses the cultural and attitudinal challenges of change management (Kotter, 1996) within a ‘grey-brick’ university. It concludes by arguing that the move towards becoming a more learning-focused university has started to develop an awareness of the positive impact the change initiative is having on the student experience and wider institution; whilst also drawing attention to the organisational challenges ahead.

Learning by exporting:lessons from high-technology SMEs

We investigate the learning by exporting hypothesis by examining the effect of exporting on the subsequent innovation performance of a sample of high-technology SMEs based in the UK. We find evidence of learning by exporting, but the pattern of this effect is complex. Exporting helps high-tech SMEs innovate subsequently, but does not make them more innovation intensive. There is evidence that consistent exposure to export markets helps firms overcome the innovation hurdle, but that there is a positive scale effect of exposure to export markets which allows innovative firms to sell more of their new-to-market products on entering export markets. Service sector firms are able to reap the benefits of exposure to export markets at an earlier (entry) stage of the internationalization process than are manufacturing firms. Innovation-intensive firms exhibit a different pattern of entry to and exit from export markets from low-intensity innovators, and this is reflected in different effects of exporting. © 2012 Elsevier Ltd.