Maria Virvou

Mobile educational features in authoring tools for personalised tutoring

One important field where mobile technology can make significant contributions is Education. In the fast pace of modern life, students and instructors would appreciate using constructively some spare time that they may have, in order to work on lessons at any place, even when away from offices, classrooms and labs where computers are usually located. In this paper, we describe a mobile authoring tool that we have developed and is called Mobile Author. Mobile Author can be used by human instructors either from a computer or a mobile phone to create their own Intelligent Tutoring Systems (ITSs) and to distribute them to their students. After the ITSs have been created, students can also use any computer or mobile phone to have access to theory and tests. The tutoring systems can assess the students performance, inform the databases that record the students progress and provide advice adapted to the needs of individual students. Finally, instructors can monitor their students progress and communicate with their students during the course. The mobile features of both the authoring tool itself and the resulting ITSs from it have been evaluated by instructors and students, respectively. The results of the evaluation showed that mobile features are indeed considered useful.

A Framework for the Initialization of Student Models in Web-based Intelligent Tutoring Systems

Initializing a student model for individualized tutoring in educational applications is a difficult task, since very little is known about a new student. On the other hand, fast and efficient initialization of the student model is necessary. Otherwise the tutoring system may lose its credibility in the first interactions with the student. In this paper we describe a framework for the initialization of student models in Web-based educational applications. The framework is called ISM. The basic idea of ISM is to set initial values for all aspects of student models using an innovative combination of stereotypes and the distance weighted k-nearest neighbor algorithm. In particular, a student is first assigned to a stereotype category concerning her/his knowledge level of the domain being taught. Then, the model of the new student is initialized by applying the distance weighted k-nearest neighbor algorithm among the students that belong to the same stereotype category with the new student. ISM has been applied in a language learning system, which has been used as a test-bed. The quality of the student models created using ISM has been evaluated in an experiment involving classroom students and their teachers. The results from this experiment showed that the initialization of student models was improved using the ISM framework.

Personalised adult e-training on computer use based on multiple attribute decision making

Abstract This paper examines the utility of a multiple attribute decision making method, the Simple Additive Weighting (SAW), for the purposes of an Intelligent Learning Environment (ILE) that provides adults with personalised e-learning. The ILE is called Web Intelligent Trainer and is meant to help novice users learn how to manipulate the file store of their personal computer. The generation of advice makes use of adaptive hypermedia techniques and is adapted to each individual learners needs, depending on their knowledge level, age, habits and difficulties. SAW has been applied in the ILE and has been evaluated with respect to the performance of the ILE. As a result, SAW seems particularly appropriate for the ILE.

Analysis and design of a web-based authoring tool generating intelligent tutoring systems

Authoring tools for intelligent tutoring systems (ITSs) are meant to provide environments where instructors may author their own ITSs in varying domains. In this way, painful constructions of ITSs, which are not reusable, may be avoided. However, the construction of an authoring tool is associated with many problems, such as the generality of the techniques incorporated, domain-independence, effectiveness for the prospective authors (instructors), and effectiveness for the students who will use the resulting ITSs. In this paper we will report on an empirical study that we conducted in order to design and develop WEAR, an ITS authoring tool for Algebra-related domains. In the study we investigated several aspects concerning the attitude and behaviour of both students and instructors. The study revealed important issues and was then used for the specification of the design of WEAR. A brief description of the developed system is also included in the paper so that the way that the design specifications were put into practice may be shown. However, a lot of the authoring tools requirements that came to light could be applicable to other authoring tools as well. The most important requirement of this kind was the need for an instructor modelling component so that adaptivity could be provided to human instructors (authors). The provision of such facility is a novelty in the area of ITS authoring tools.

Sentiment analysis of Facebook statuses using Naive Bayes classifier for language learning

The growing expansion of contents, placed on the Web, provides a huge collection of textual resources. People share their experiences, opinions or simply talk just about whatever concerns them online. The large amount of available data attracts system developers, studying on automatic mining and analysis. In this paper, the primary and underlying idea is that the fact of knowing how people feel about certain topics can be considered as a classification task. Peoples feelings can be positive, negative or neutral. A sentiment is often represented in subtle or complex ways in a text. An online user can use a diverse range of other techniques to express his or her emotions. Apart from that, s/he may mix objective and subjective information about a certain topic. On top of that, data gathered from the World Wide Web often contain a lot of noise. Indeed, the task of automatic sentiment recognition in online text becomes more difficult for all the aforementioned reasons. Hence, we present how sentiment analysis can assist language learning, by stimulating the educational process and experimental results on the Naive Bayes Classifier.

Web Passive Voice Tutor: an intelligent computer assisted language learning system over the WWW

We describe Web Passive Voice Tutor (Web PVT), an adaptive teaching Web-based intelligent computer assisted language learning (ICALL) program that is aimed at teaching non-native speakers the passive voice of the English language. The design of the system has been largely based on the results of an empirical study that was conducted at schools with the collaboration of human teachers. Web PVT incorporates techniques from intelligent tutoring systems (ITS) and adaptive hypermedia (AH) technologies to provide students with individualised instruction and feedback. The system uses a combination of stereotypes and the overlay technique for the initialisation of the student model, which is then refined by observing the student while working with the system. The resulting student model is used for the annotation of the links to topics presented to the student. In addition, it is also used in the process of error diagnosis and the adaptation of feedback and advice provided to the student.

Initializing the student model using stereotypes and machine learning

In this paper we describe the method for initializing the student model in a Web-based Algebra Tutor, which is called Web-EasyMath. The system uses an innovative combination of stereotypes and the distance weighted k-nearest neighbor algorithm to initialize the model of a new student. In particular, the student is first assigned to a stereotype category concerning her/his knowledge level based on her/his performance on a preliminary test. The system then initializes all aspects of the student model using the distance weighted k-nearest neighbor algorithm among the students that belong to the same stereotype category with the new student. The basic idea of the application of the algorithm is to weigh the contribution of each of the neighbor students according to their distance from the new student; the distance between students is calculated based on a similarity measure. In the case of Web-EasyMath the similarity measure is estimated taking into account the school class students belong to, their degree of carefulness while solving exercises as well as their proficiency in using simple arithmetic operations.

Automatic reasoning and help about human errors in using an operating system

Abstract Human errors occur frequently in the interaction of a user with an operating system. However, current user interfaces of operating systems lack some reasoning ability about users intentions and beliefs. Intelligent Help Systems (IHS) can provide additional reasoning and help. This paper presents a discussion of the features of IHSs and a review of a few IHSs for users of operating systems. Then it describes the research and results of employing a cognitive theory of Human Plausible Reasoning Theory in error diagnosis for users interacting with an operating system. This theory has formalized the reasoning based on similarities, generalizations and specializations that people use to make plausible guesses about questions. Here we exploit the fact that plausible guesses can be incorrect and thus turned into human errors. The error diagnosis is performed by the user modelling component of an IHS, called RESCUER.

Towards Improving Visual-Facial Emotion Recognition through Use of Complementary Keyboard-Stroke Pattern Information

In this paper, we investigate the possibility of improving the accuracy of visual-facial emotion recognition through use of additional (complementary) keyboard-stroke information. The investigation is based on two empirical studies that we have conducted involving human subjects and human observers. The studies were concerned with the recognition of emotions from a visual-facial modality and keyboard-stroke information, respectively. They were inspired by the relative shortage of such previous research in empirical work concerning the strengths and weaknesses of each modality so that the extent can be determined to which the keyboard-stroke information complements and improves the emotion recognition accuracy of the visual-facial modality. Specifically, our research focused on the recognition of six basic emotion states, namely happiness, sadness, surprise, anger and disgust as well as the emotionless state which we refer to as neutral. We have found that the visual-facial modality may allow the recognition of certain states, such as neutral and surprise, with sufficient accuracy. However, its accuracy in recognizing anger and happiness can be improved significantly if assisted by keyboard-stroke information.

Personalised e-learning through an educational virtual reality game using Web services

This paper describes how a personalised educational game architecture has been used in conjunction with Web services to provide remote access to the system. The educational game is a virtual reality adventure game that performs affective user modelling by measuring emotional characteristics of users. Virtual reality (VR) games are so popular among children and adolescents that can be used for the purposes of educational software to render it more attractive and motivating. The benefits of such an application can be maximised if it is available over the Web. Software applications that operate over the Web are targeted to a wide range of users. Hence they need a high degree of adaptivity and dynamic individualisation to each user that interacts with the application. This should include the students’ emotional state that affects their learning. However, the environment of a Web-based VR-game that performs user modelling is so demanding that the technology of Web services is necessary for its effective operation and interoperability. Moreover, reusability may be achieved for the user modelling component.