Sanaz Fallahkhair

Development of a cross-platform ubiquitous language learning service via mobile phone and interactive television*

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.

Dual device user interface design for ubiquitous language learning: mobile phone and interactive television (iTV)

In this paper we describe the design and development of a system that facilitates language learning from a combination of two devices, interactive television (iTV) and mobile phone. We present a number of requirements for technologies to support informal language learning based on theories of language learning, theories of formal and informal learning, our own studies of adult language learners and the affordances of iTV as a medium to support learning. We describe TAMALLE (television and mobile phone assisted language learning environment), a prototype system based on these requirements and discuss some of the user interface design issues that arise in the context of cross platform dual device systems for ubiquitous learning.

A dual device scenario for informal language learning: interactive television meets the mobile phone

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.

Learning Sentiment from Students’ Feedback for Real-Time Interventions in Classrooms

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.

Predicting students' emotions using machine learning techniques

Detecting students’ real-time emotions has numerous benefits, such as helping lecturers understand their students’ learning behaviour and to address problems like confusion and boredom, which undermine students’ engagement. One way to detect students’ emotions is through their feedback about a lecture. Detecting students’ emotions from their feedback, however, is both demanding and time-consuming. For this purpose, we looked at several models that could be used for detecting emotions from students’ feedback by training seven different machine learning techniques using real students’ feedback. The models with a single emotion performed better than those with multiple emotions. Overall, the best three models were obtained with the CNB classifier for three emotions: amused, bored and excitement.

Guidelines for designing a smart and ubiquitous learning environment with respect to cultural heritage

This paper introduces a list of guidelines for designing mobile location-based learning services with respect to cultural heritage sites. This list was set out based on the results of a user-study in the field. The user study was carried out with adult end-users to evaluate a prototype mobile application that delivered information through mobile phones and smart eye glasses simultaneously regarding cultural heritage sites based on location. Augmented reality and location-based services are utilised in this app.

Detecting Sarcasm from Students’ Feedback in Twitter

Sarcasm is a sophisticated form of act where one says or writes the opposite of what they mean. Sarcasm is a common issue in sentiment analysis and detecting it is a challenge. While models for sarcasm detection have been proposed for general purposes (e.g. Twitter data, Amazon reviews), there is no research addressing this issue in an educational context, despite the increased use of social media in education. In this paper we experiment with several machine learning techniques, features and preprocessing levels to identify sarcasm from students’ feedback collected via Twitter.

Smart Ambient: A Pilot Study to Contextualise a Location-Based Mobile Application to Support Informal Learning from Cultural Heritage Sites

This paper aims to present a pilot study of the development of a Location-Based Mobile application to support informal learning in the cultural heritage domain. In this paper, we introduce a prototype of the Mobile Location Based System. The application is intended for use in outdoor settings and aims to provide the user with automatic notifications of historical information regarding nearby heritage sites. The application presents the information automatically when the device is close to a certain place or statue while people are moving in the vicinity of those places. The study used a scenario-based method to investigate the actors and the suitable context of use. Finally, an evaluation of the prototypes main features was conducted to examine their usefulness.

A survey study to gather requirements for designing a mobile service to enhance learning from cultural heritage

This study was carried out to gather user requirements using a questionnaire survey. The study has investigated how people may use mobile location-aware technologies for learning purposes in cultural heritage contexts. This paper presents the results of this survey study and outlines a number of challenges for further development.

Smart Ambient: Development of Mobile Location Based System to Support Informal Learning in the Cultural Heritage Domain

The purpose of this research is to explore and develop the potential of a location-based mobile application to support informal learning in the cultural heritage domain. Since the notion that learning is interweaved with peoples daily lives, and also that learners get motivated to learn in different contexts and spaces while they are moving, mobile learning has emerged to support learning throughout a lifetime. This research is going to investigate how mobile technology could be developed to support informal learning with respect to cultural heritage sites, and how learners use mobile technology for learning purposes. A major contribution of this research is to develop a task model for mobile location-based learning in order to provide a general framework for informal learning in the cultural heritage domain. Furthermore, this research will use mixed methods (e.g. Interview, questionnaire and focus group) in order to gather the requirements of users and also the officials who have the responsibility for managing and maintaining cultural heritage sites. Finally, a guideline will be provided for researchers who are working in the cultural heritage domain.