13,822 research outputs found
A Q-sorting methodology for Computer-Adaptive Surveys - Style "Research"
Computer-Adaptive Surveys (CAS) are multi-dimensional instruments where questions asked of respondents depend on the previous questions asked. Due to the complexity of CAS, little work has been done on developing methods for validating their construct validity. This paper describes the process of using a variant of Q-sorting to validate a CAS item bank. The method and preliminary results are presented. In addition, lessons learned from this study are discussed
PPI Questionnaire on Adaptive Wearable Appropriateness as an Autistic Intervention
Autism Spectrum Condition (ASC) is a life-long diagnosis, which has a subset of features including hyper-, seeking- and/or hypo-reactivity to sensory inputs or unusual interests (APA, 2013). These qualities are evident across environmental (e.g. response to specific sounds, visual fascination with lights or movements) and physiological domains (e.g. anxiety, respiration or euthermia). Scholars report that ninety (90%) of autistic adults experience sensory issues causing significant barriers at school/work (Leekam et al., 2007). As part of a larger PhD Research Project, this pilot study establishes designs, processes and measures that may establish if autistic individuals find value utilising adaptive/wearable interventions that possibly alter, redirect and/or attenuate disruptive stimuli. This study incorporates benign information not yet containing practical data, other than to provision and trial space where real data is nominally present. This pilot loads systems functionality for future use (e.g. consent, demographic collection, measures, post-mortem/survey feedback, storage, sorting, query, statistical analyses and reporting). Finally, this pilot provisions a follow-on and full-fledge Participant Public Involvement (PPI) designed to exploit data from focus group and co-produced surveys/designs. In turn, these may be used to inform an as-yet-to-be developed interventional prototype. Hence, the forthcoming PPIâby leveraging this pilotâaims to describe what degree sensory distractions occur among adolescent and adult ASC participants. Both pilot and PPI aspire to whether focus, anxiety and attentional concerns are perceived as negative issues and if individuals prefer assistance (vis Ă vis assistive wearables) to reduce anxiety, distractions and increase focus at school and at work (Bagley et al., 2016). This study results yield promise; in that, a subsequent PPI can be leveraged to obtain co-designed autistic data leading to a randomised clinical trial
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ReSCon '09, Research Student Conference: Book of Abstracts
The second SED Research Student Conference (ReSCon2009) was hosted over three days, 22-24 June 2009, in the Lecture Centre at Brunel University. The conference consisted of technical presentations, a poster session and social events. The abstracts and presentations were the result of ongoing research by postgraduate research students from the School of Engineering and Design at Brunel University. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School
Using Organizational, Coordination, and Contingency Theories to Examine Project Manager Insights on Agile and Traditional Success Factors for Information Technology Projects
Two dominant research views addressing disappointing success rates for information technology (IT) projects suggest project success may depend on the presence of a large number of critical success factors or advocate for agile project management as an alternative to traditional practice. However, after two decades of research, success rates remain low, and the role of critical success factors or project management approach remains unclear. The purpose of this study was to use views of experienced project managers to explore the contribution of success factors and management approach to project success. Applying organizational, coordination, and contingency theories, the research questions examined IT project manager perceptions about success factors, how those success factors interrelate, and the role of management approach in project success. A Q methodology mixed method design was used to analyze subjective insights of project managers about the important critical success factors for IT projects. Two critical success factors emerged as important: a sustained commitment from upper management to the project and clear, measurable project goals and objectives. Three composite factors also surfaced representing the importance of people-project interactions, user/client involvement, and traditional project management tasks. The analyses found no broad support for agile project management and could not confirm principles of organizational or coordination theories as critical for project success. However, a contingent relationship might exist between some critical success factors and merits further investigation. Helping the project management community understand IT project success factors could improve project execution and reduce failure rates leading to sizeable savings for project clients
A survey and taxonomy of self-aware and self-adaptive cloud autoscaling systems
Autoscaling system can reconfigure cloud-based services and applications, through various configurations of cloud sofware and provisions of hardware resources, to adapt to the changing environment at runtime. Such a behavior offers the foundation for achieving elasticity in modern cloud computing paradigm. Given the dynamic and uncertain nature of the shared cloud infrastructure, cloud autoscaling system has been engineered as one of the most complex, sophisticated and intelligent artifacts created by human, aiming to achieve self-aware, self-adaptive and dependable runtime scaling. Yet, existing Self-aware and Self-adaptive Cloud Autoscaling System (SSCAS) is not mature to a state that it can be reliably exploited in the cloud. In this article, we survey the state-of-the-art research studies on SSCAS and provide a comprehensive taxonomy for this feld. We present detailed analysis of the results and provide insights on open challenges, as well as the promising directions that are worth investigated in the future work of this area of research. Our survey and taxonomy contribute to the fundamentals of engineering more intelligent autoscaling systems in the cloud
An adaptive educational system that caters for combination of two models of learning styles
This thesis aimed to explore the affect of combining two models of learning styles (VARK, and Honey and Mumford) in terms of studentsâ learning gains and satisfaction. VARK focuses on how the students perceive learning, while Honey and Mumford examines how an individual would like to learn. A web-based educational system was built to test the combination of the two models of learning styles. A study to examine the feasibility of the system was carried out on 129 participants to explore whether the system presented tutorials according to their individual learning styles. A second study to investigate learning gains and user satisfaction was carried out on 149 participants. Satisfaction was divided into three main concepts: usability, preference and perception of learning. Learning gains were tested by giving participants a pre-test, a post-test and a confirmatory test. Participants were divided into four groups and had the lesson presented according to one learning style of either the VARK or Honey & Mumford model, both of the participantsâ learning styles or with no personal customization. The results found that participants who used the two models of learning styles showed higher learning gains and had higher levels of satisfaction across all three factors; compared to those using only one or no learning style. Furthermore, those using only one learning style showed higher learning gains and had higher levels of satisfaction than those with no learning style. The application of these findings would be of benefit to educational institutionsâ decision makers, educators, students and e-learning designers.
Adaptation is a key feature of the system of research. It is intended for future work; preliminary research has shown that the users profile and learning item will change over time. This important finding is worth exploring in future research
Evidence Based Design of Heuristics: Usability and Computer Assisted Assessment
The research reported here examines the usability of Computer Assisted Assessment(CAA) and the development of domain specific heuristics. CAA is being adopted within educational institutions and the pedagogical implications are widely investigated, but little research has been conducted into the usability of CAA applications.
The thesis is: severe usability problems exist in GAA applications causing unacceptable consequences, and that using an evidence based design approach GAA heuristics can be devised The thesis reports a series of evaluations that show severe usability problems do occur in three CAA applications. The process of creating domain specific heuristics is analysed, critiqued and a novel evidence based design approach for the design of domain specific heuristics is proposed. Gathering evidence from evaluations and the literature, a set of heuristics for CAA are presented. There are four main contributions to knowledge in the thesis: the heuristics; the corpus of usability problems; the Damage Index for prioritising usability problems from multiple evaluations and the evidence based design approach to synthesise heuristics.
The focus of the research evolves with the first objective being to determine If severe usability problems exist that can cause users d?ffIculties and dissatisfaction with unacceptable consequences whitct using existing commercial CAA software applications? Using a survey methodology, students' report a level of satisfaction but due to low inter-group consistency surveys are judged to be ineffective at eliciting usability problems. Alternative methods are analysed and the heuristic evaluation method is judged to be suitable. A study is designed to evaluate Nielsen's heuristic set within the CAA domain and they are deemed to be ineffective based on the formula proposed by Hanson et al. (2003). Domain specific heuristics are therefore necessary and further studies are designed to build a corpus of usability problems to facilitate
the evidence based design approach to synthesise a set of heuristics, in order to aggregate the corpus and prioritise the severity of the problems a Damage Index formula is devised.
The work concludes with a discussion of the heuristic design methodology and potential for future work; this includes the application of the CAA heuristics and applying the heuristic design methodology to other specific domains
A Self-Regulated Learning Approach to Educational Recommender Design
Recommender systems, or recommenders, are information filtering systems prevalent today in many fields. One type of recommender found in the field of education, the educational recommender, is a key component of adaptive learning solutions as these systems avoid âone-size-fits-allâ approaches by tailoring the learning process to the needs of individual learners. To function, these systems utilize learning analytics in a student-facing manner.
While existing research has shown promise and explores a variety of types of educational recommenders, there is currently a lack of research that ties educational theory to the design and implementation of these systems. The theory considered here, self-regulated learning, is underexplored in educational recommender research. Self-regulated learning advocates a cyclical feedback loop that focuses on putting students in control of their learning with consideration for activities such as goal setting, selection of learning strategies, and monitoring of oneâs performance.
The goal of this research is to explore how best to build a self-regulated learning guided educational recommender and discover its influence on academic success. This research applies a design science methodology in the creation of a novel educational recommender framework with a theoretical base in self-regulated learning. Guided by existing research, it advocates for a hybrid recommender approach consisting of knowledge-based and collaborative filtering, made possible by supporting ontologies that represent the learner, learning objects, and learner actions. This research also incorporates existing Information Systems (IS) theory in the evaluation, drawing further connections between these systems and the field of IS. The self-regulated learning-based recommender framework is evaluated in a higher education environment via a web-based demonstration in several case study instances using mixed-method analysis to determine this approachâs fit and perceived impact on academic success. Results indicate that the self-regulated learning-based approach demonstrated a technology fit that was positively related to student academic performance while student comments illuminated many advantages to this approach, such as its ability to focus and support various studying efforts. In addition to
contributing to the field of IS research by delivering an innovative framework and demonstration, this research also results in self-regulated learning-based educational recommender design principles that serve to guide both future researchers and practitioners in IS
and education
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