Abstract-Students' real-time feedback has numerous advantages in education, however, analysing feedback while teaching is both stressful and time consuming. To address this problem, we propose to analyse feedback automatically using sentiment analysis. Sentiment analysis is domain dependent and although it has been applied to the educational domain before, it has not been previously used for real-time feedback. To find the best model for automatic analysis we look at four aspects: preprocessing, features, machine learning techniques and the use of the neutral class. We found that the highest result for the four aspects is Support Vector Machines (SVM) with the highest level of preprocessing, unigrams and no neutral class, which gave a 95 percent accuracy.

Abstract. Knowledge about users sentiments can be used for a variety of adaptation purposes. In the case of teaching, knowledge about students sentiments can be used to address problems like confusion and boredom which affect students engagement. For this purpose, we looked at several methods that could be used for learning sentiment from students feedback. Thus, Naive Bayes, Complement Naive Bayes (CNB), Maximum Entropy and Support Vector Machine (SVM) were trained using real students' feedback. Two classifiers stand out as better at learning sentiment, with SVM resulting in the highest accuracy at 94%, followed by CNB at 84%. We also experimented with the use of the neutral class and the results indicated that, generally, classifiers perform better when the neutral class is excluded.

This paper describes the development processes for a cross-platform ubiquitous language learning service via interactive television (iTV) and mobile phone. Adapting a learner-centred design methodology, a number of requirements were gathered from multiple sources that were subsequently used in TAMALLE (television and mobile phone assisted language learning environment) development. A number of issues that arise in the context of cross-platform user interface design and architecture for ubiquitous language learning were tackled. Finally, we discuss a multi-method evaluation regime to gauge usability, perceived usefulness and desirability of TAMALLE system. The result broadly revealed an overall positive response from language learners. Although, there were some reported difficulties in reading text and on-screen display mainly on the iTV side of the interface, TAMALLE was perceived to be a usable, useful and desirable tool to support informal language learning and also for gaining new contextual and cultural knowledge.

Advances in digital technology have a profound impact on conventional healthcare systems. We examine the trailblazing use of online interventions to enable autonomous psychological care which can greatly enhance individual- and population-level access to services. There is strong evidence supporting online cognitive–behavioural therapy and more engaging programmes are now appearing so as to reduce user ‘attrition’. The next generation of autonomous psychotherapy programmes will implement adaptive and personalised responses, moving beyond impersonalised advice on cognitive and behavioural techniques. This will be a more authentic form of psychotherapy that integrates therapy with the actual relationship experiences of the individual user.

Researchers have investigated the possibilities for supporting language learning through a range of technologies, most recently mobile phones and interactive television (iTV). Drawing on a focus group study, we present a scenario demonstrating an approach that blends the features of these two technologies. Three areas are identified for further exploration: pedagogy, technical feasibility and interaction design issues.