Beyond Turn-taking

The article discusses several epistemological and methodological issues related to the analysis of discourse in general and of educational talk in particular. The theoretical framework provided by conversation analysis (CA) is applied and critically discussed in the analysis of an empirical example of educational talk. Several questions seem pertinent: Can we - as analysts - have direct access to talk "as it actually occurs", independent of any kind of theorizing and predefined categorization? What is the epistemological status of the conversation analytic categories? What are the limitations of applying turn-taking as an analytical category in the study of talk? To what extent can we presume the knowledgeability of the interlocutors as a premise in our analysis? On the background of my own attempts at applying CA in the analysis of educational discourse, I argue for a widening of the perspective from a narrow, empiricist focus on conversational turn-takings and sequential organization of talk, for example in the handling of issues like silences and absences in talk. On the other hand, I also warn against the pitfalls of historicist, abstract social theory; here exemplified with some texts from theorists applying abstract philosophical categories from dialectical and historical materialism like "the law of contradiction" as explanatory tools in the study of situated action. In the study of educational meaning making we should avoid empiricist as well as historicist approaches and explanations

Using Social Robots to Teach Language Skills to Immigrant Children in an Oslo City District

Social robots have been shown to help in language education for children. This can be good aid for immigrant children that need additional help to learn a second language their parents do not understand to attend school. We present the setup for a long-term study that is being carried out in blinded to aid immigrant children with poor skills in the Norwegian language to improve their vocabulary. This includes additional tools to help parents follow along and provide additional help at home.Comment: 3 pages, 1 figur

A Conceptual System Architecture for Motivation-enhanced Learning for Students with Dyslexia

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkIncreased user motivation from interaction process leads to improved interaction, resulting in increased motivation again, which forms a positive self-propagating cycle. Therefore, a system will be more effective if the user is more motivated. Especially for students with dyslexia, it is common for them to experience more learning difficulties that affect their learning motivation. That's why we need to employ techniques to enhance user motivation in the interaction process. In this research, we will present a system architecture for motivation-enhanced learning and the detailed process of the construction of our motivation model using ontological approach for students with dyslexia. The proposed framework of the personalised learning system incorporates our motivation model and corresponding personalisation mechanism aiming to improve learning motivation and performance of students with dyslexia. Additionally, we also provide examples of inference rules and a use scenario for illustration of personalisation to be employed in our system

Mining usage data for adaptive personalisation of smartphone based help-on-demand services

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Mobile computing devices and their applications that encompass context aware components are becoming increasingly more prevalent. The context-awareness of these types of applications typically focuses on the services offered. In this paper we describe a framework that supports the monitoring and analysis of mobile application usage patterns with the goal of updating user models for adaptive services and user interface personalisation. This paper focuses on two aspects of the framework. The first is the modelling and storage of the usage data. The second focuses on the data mining component of the framework, outlining the five different capabilities of the adaptation in addition to the algorithms used. The proposed framework has been evaluated through specific case studies, with the results attained demonstrating the effectiveness of the data mining capabilities and in particular the adaptation of the User Interface. The accuracy and efficiency of the algorithms used are also evaluated with three users. The results of the evaluation show that the aims of the data mining component were achieved with the personalisation and adaptation of content and user interface, respectively

Learning Behaviour for Service Personalisation and Adaptation

Context-aware applications within pervasive environments are increasingly being developed as services and deployed in the cloud. As such these services are increasingly required to be adaptive to individual users to meet their specific needs or to reflect the changes of their behavior. To address this emerging challenge this paper introduces a service-oriented personalisation framework for service personalisation with special emphasis being placed on behavior learning for user model and service function adaptation. The paper describes the system architecture and the underlying methods and technologies including modelling and reasoning, behavior analysis and a personalisation mechanism. The approach has been implemented in a service-oriented prototype system, and evaluated in a typical scenario of providing personalised travel assistance for the elderly using the help-on-demand services deployed on smartphone

Ontological user modelling and semantic rule-based reasoning for personalisation of Help-On-Demand services in pervasive environments

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Existing context-aware applications are limited in their support of user personalisation. Nevertheless, the increase in the use of context-aware technologies has sparked the growth in assistive applications resulting in a need to enable adaptation to reflect the changes in user behaviours. This paper introduces a systematic approach to service personalisation for mobile users in pervasive environments and presents a service-oriented distributed system architecture. The developed approach makes use of semantic technologies for user modelling and personalisation reasoning. In the paper we characterise user behaviours and needs in pervasive environments upon which ontological user models are created with special emphasis being placed on ontological modelling of dynamic and adaptive user profiles. We develop a rule-based personalisation mechanism that exploits semantic web rule mark-up language for rule design and a combination of semantic and rule-based reasoning for personalisation. We use two case studies focusing on providing personalised travel assistance for people using Help-on-Demand services deployed on a smart-phone to contextualise the discussions within the paper. The proposed approach is implemented in a prototype system, which includes Help-on-Demand services, content management services, user models and personalisation mechanisms in addition to application specific rules. Experiments have been designed and conducted to test and evaluate the approach with initial results demonstrating the functionality of the approach

Is intracranial volume a risk factor for IDH-mutant low-grade glioma? A case-control study

Purpose Risk of cancer has been associated with body or organ size in several studies. We sought to investigate the relationship between intracranial volume (ICV) (as a proxy for lifetime maximum brain size) and risk of IDH-mutant low-grade glioma. Methods In a multicenter case–control study based on population-based data, we included 154 patients with IDH-mutant WHO grade 2 glioma and 995 healthy controls. ICV in both groups was calculated from 3D MRI brain scans using an automated reverse brain mask method, and then compared using a binomial logistic regression model. Results We found a non-linear association between ICV and risk of glioma with increasing risk above and below a threshold of 1394 ml (p < 0.001). After adjusting for ICV, sex was not a risk factor for glioma. Conclusion Intracranial volume may be a risk factor for IDH-mutant low-grade glioma, but the relationship seems to be non-linear with increased risk both above and below a threshold in intracranial volume.publishedVersio

Remote magnetic versus manual catheters: evaluation of ablation effect in atrial fibrillation by myocardial marker levels

Background A remote magnetic navigation (MN) system is available for radiofrequency ablation of atrial fibrillation (AF), challenging the conventional manual ablation technique. The myocardial markers were measured to compare the effects of the two types of MN catheters with those of a manual-irrigated catheter in AF ablation. Methods AF patients underwent an ablation procedure using either a conventional manual-irrigated catheter (CIR, n=65) or an MN system utilizing either an irrigated (RMI, n=23) or non-irrigated catheter (RMN, n=26). Levels of troponin T (TnT) and the cardiac isoform of creatin kinase (CKMB) were measured before and after ablation. Results Mean procedure times and total ablation times were longer employing the remote magnetic system. In all groups, there were pronounced increases in markers of myocardial injury after ablation, demonstrating a significant correlation between total ablation time and post-ablation levels of TnT and CKMB (CIR r=0.61 and 0.53, p<0.001; RMI r=0.74 and 0.73, p<0.001; and RMN r=0.51 and 0.59, p<0.01). Time-corrected release of TnT was significantly higher in the CIR group than in the other groups. Of the patients, 59.6% were free from AF at follow-up (12.2± 5.4 months) and there were no differences in success rate between the three groups. Conclusions Remote magnetic catheters may create more discrete and predictable ablation lesions measured by myocardial enzymes and may require longer total ablation time to reach the procedural endpoints. Remote magnetic non-irrigated catheters do not appear to be inferior to magnetic irrigated catheters in terms of myocardial enzyme release and clinical outcome

Preoperative Brain Tumor Imaging:Models and Software for Segmentation and Standardized Reporting

For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports, incorporating a wide range of tumor characteristics, represents a major hurdle. In this study, we investigate the most occurring brain tumor types: glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4,000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Segmentation performances were assessed in-depth using a wide-range of voxel and patient-wise metrics covering volume, distance, and probabilistic aspects. Finally, two software solutions have been developed, enabling an easy use of the trained models and standardized generation of clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances were quite homogeneous across the four different brain tumor types, with an average true positive Dice ranging between 80 and 90%, patient-wise recall between 88 and 98%, and patient-wise precision around 95%. In conjunction to Dice, the identified most relevant other metrics were the relative absolute volume difference, the variation of information, and the Hausdorff, Mahalanobis, and object average symmetric surface distances. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16-54 s depending on the dimensions of the MRI volume. For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5-15 min are necessary. All trained models have been made open-access together with the source code for both software solutions and validation metrics computation. In the future, a method to convert results from a set of metrics into a final single score would be highly desirable for easier ranking across trained models. In addition, an automatic classification of the brain tumor type would be necessary to replace manual user input. Finally, the inclusion of post-operative segmentation in both software solutions will be key for generating complete post-operative standardized clinical reports