223 research outputs found
Contextual Sensitivity in Grounded Theory: The Role of Pilot Studies
Grounded Theory is an established methodological approach for context specific inductive theory building. The grounded nature of the methodology refers to these specific contexts from which emergent propositions are drawn. Thus, any grounded theory study requires not only theoretical sensitivity, but also a good insight on how to design the research in the human activity systems to be studied. The lack of this insight may result in inefficient theoretical sampling or even erroneous purposeful sampling. These problems would not necessarily be critical, as it could be argued that through the elliptical process that characterizes grounded theory, remedial loops would always bring the researcher to the core of the theory. However, these elliptical remedial processes can take very long periods of time and result in catastrophic delays in research projects. As a strategy, this paper discusses, contrasts and compares the use of pilot studies in four different grounded theory projects. Each pilot brought different insights about the context, resulting in changes of focus, guidance to improve data collection instruments and informing theoretical sampling. Additionally, as all four projects were undertaken by researchers with little experience of inductive approaches in general and grounded theory in particular, the pilot studies also served the purpose of training in interviewing, relating to interviewees, memoing, constant comparison and coding. This last outcome of the pilot study was actually not planned initially, but revealed itself to be a crucial success factor in the running of the projects. The paper concludes with a theoretical proposition for the concept of contextual sensitivity and for the inclusion of the pilot study in grounded theory research designs
An Examination of Online Learning Security Requirements Within a Virtual Learning Environment of an Irish University
As the adoption of e-learning and need for lifelong learning increases, it is vital the administrator of a virtual learning environment continually ensures reliable and secure data. This case study engaged in the initial steps of analyzing the use and security needs of a virtual learning service within a university of Ireland. The university provided two virtual learning services which were comparatively analyzed, from a security and data protection perspective. In addition, survey results obtained from the university user community for one of the e-learning services were examined. Findings from the study were presented as user security requirements and recommendations, when planning future security initiatives of the e-learning services within the university
requirements and use cases
In this report, we introduce our initial vision of the Corporate Semantic Web
as the next step in the broad field of Semantic Web research. We identify
requirements of the corporate environment and gaps between current approaches
to tackle problems facing ontology engineering, semantic collaboration, and
semantic search. Each of these pillars will yield innovative methods and tools
during the project runtime until 2013. Corporate ontology engineering will
improve the facilitation of agile ontology engineering to lessen the costs of
ontology development and, especially, maintenance. Corporate semantic
collaboration focuses the human-centered aspects of knowledge management in
corporate contexts. Corporate semantic search is settled on the highest
application level of the three research areas and at that point it is a
representative for applications working on and with the appropriately
represented and delivered background knowledge. We propose an initial layout
for an integrative architecture of a Corporate Semantic Web provided by these
three core pillars
A Prediction-Based Framework to Reduce Procrastination in Adaptive Learning Systems
Procrastination and other types of dilatory behaviour are common in online learning, especially in higher education. While procrastination is associated with worse performance and discomfort, positive forms of delay can be used as a deliberate strategy without any such consequences. Although dilatory behaviour has received attention in research, it has to my knowledge never been included as an integral part of an adaptive learning system. Differentiating between different types of delay within such a system would allow for tailored interventions to be provided in the future without alienating students who use delay as a successful strategy. In this thesis, I present four studies that provide the basis for such an endeavour. I first discuss the results of two studies that focussed on the prediction of the extent of dilatory behaviour in online assignments. The results of both studies revealed an advantage of objective predictors based on log data over subjective variables based on questionnaires. The predictive performance slightly improved when both sets of predictors were combined. In one of these studies, we implemented Bayesian multilevel models while the other aimed at comparing various machine learning algorithms to determine the best candidates for a future inclusion in real-time predictive models. The results reveal that the most suitable algorithm depended on the type of predictor, implying that multiple models should be implemented in the field, rather than selecting just one. I then present a framework for an adaptive learning system based on the other two studies, where I highlight how dilatory behaviour can be incorporated into such a system, in light of the previously discussed results. I conclude this thesis by providing an outlook into the necessary next steps before an adaptive learning system focussing on delay can be established
A hybrid e-learning framework: Process-based, semantically-enriched and service-oriented
Despite the recent innovations in e-Learning, much development is needed to ensure better learning experience for everyone and bridge the research gap in the current state of the art e-Learning artefacts. Contemporary e-learning artefacts possess various limitations as follows. First, they offer inadequate variations of adaptivity, since their recommendations are limited to e-learning resources, peers or communities. Second, they are often overwhelmed with technology at the expense of proper pedagogy and learning theories underpinning e-learning practices. Third, they do not comprehensively capture the e-learning experiences as their focus shifts to e-learning activities instead of e-learning processes. In reality, learning is a complex process that includes various activities and interactions between different roles to achieve certain gaols in a continuously evolving environment. Fourth, they tend more towards legacy systems and lack the agility and flexibility in their structure and design. To respond to the above limitations, this research aims at investigating the effectiveness of combining three advanced technologies (i.e., Business Process Modelling and Enactment, Semantics and Service Oriented Computing – SOC–) with learning pedagogy in order to enhance the e-learner experience. The key design artefact of this research is the development of the HeLPS e-Learning Framework – Hybrid e-Learning Framework that is Process-based, Semantically-enriched and Service Oriented-enabled. In this framework, a generic e-learning process has been developed bottom-up based on surveying a wide range of e-learning models (i.e., practical artefacts) and their underpinning pedagogies/concepts (i.e., theories); and then forming a generic e-learning process. Furthermore, an e-Learning Meta-Model has been developed in order to capture the semantics of e-learning domain and its processes. Such processes have been formally modelled and dynamically enacted using a service-oriented enabled architecture. This framework has been evaluated using a concern-based evaluation employing both static and dynamic approaches. The HeLPS e-Learning Framework along with its components have been evaluated by applying a data-driven approach and artificially-constructed case study to check its effectiveness in capturing the semantics, enriching e-learning processes and deriving services that can enhance the e-learner experience. Results revealed the effectiveness of combining the above-mentioned technologies in order to enhance the e-learner experience. Also, further research directions have been suggested.This research contributes to enhancing the e-learner experience by making the e-learning artefacts driven by pedagogy and informed by the latest technologies. One major novel contribution of this research is the introduction of a layered architectural framework (i.e., HeLPS) that combines business process modelling and enactment, semantics and SOC together. Another novel contribution is adopting the process-based approach in e-learning domain through: identifying these processes and developing a generic business process model from a set of related e-learning business process models that have the same goals and associated objectives. A third key contribution is the development of the e-Learning Meta-Model, which captures a high-abstract view of learning domain and encapsulates various domain rules using the Semantic Web Rule Language. Additional contribution is promoting the utilisation of Service-Orientation in e-learning through developing a semantically-enriched approach to identify and discover web services from e-learning business process models. Fifth, e-Learner Experience Model (eLEM) and e-Learning Capability Maturity Model (eLCMM) have been developed, where the former aims at identifying and quantifying the e-learner experience and the latter represents a well-defined evolutionary plateau towards achieving a mature e-learning process from a technological perspective. Both models have been combined with a new developed data-driven Validation and Verification Model to develop a Concern-based Evaluation Approach for e-Learning artefacts, which is considered as another contribution
Simple identification tools in FishBase
Simple identification tools for fish species were included in the FishBase information system from its inception. Early tools made use of the relational model and characters like fin ray meristics. Soon pictures and drawings were added as a further help, similar to a field guide. Later came the computerization of existing dichotomous keys, again in combination with pictures and other information, and the ability to restrict possible species by country, area, or taxonomic group. Today, www.FishBase.org offers four different ways to identify species. This paper describes these tools with their advantages and disadvantages, and suggests various options for further
development. It explores the possibility of a holistic and integrated computeraided strategy
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