Using a simple expert system to assist a powered wheelchair user

A simple expert system is described that helps wheelchair users to drive their wheelchairs. The expert system takes data in from sensors and a joystick, identifies obstacles and then recommends a safe route. Wheelchair users were timed while driving around a variety of routes and using a joystick controlling their wheelchair via the simple expert system. Ultrasonic sensors are used to detect the obstacles. The simple expert system performed better than other recently published systems. In more difficult situations, wheelchair drivers did better when there was help from a sensor system. Wheelchair users completed routes with the sensors and expert system and results are compared with the same users driving without any assistance. The new systems show a significant improvement

An adaptation algorithm for an intelligent natural language tutoring system

The focus of computerised learning has shifted from content delivery towards personalised online learning with Intelligent Tutoring Systems (ITS). Oscar Conversational ITS (CITS) is a sophisticated ITS that uses a natural language interface to enable learners to construct their own knowledge through discussion. Oscar CITS aims to mimic a human tutor by dynamically detecting and adapting to an individual's learning styles whilst directing the conversational tutorial. Oscar CITS is currently live and being successfully used to support learning by university students. The major contribution of this paper is the development of the novel Oscar CITS adaptation algorithm and its application to the Felder–Silverman learning styles model. The generic Oscar CITS adaptation algorithm uniquely combines the strength of an individual's learning style preference with the available adaptive tutoring material for each tutorial question to decide the best fitting adaptation. A case study is described, where Oscar CITS is implemented to deliver an adaptive SQL tutorial. Two experiments are reported which empirically test the Oscar CITS adaptation algorithm with students in a real teaching/learning environment. The results show that learners experiencing a conversational tutorial personalised to their learning styles performed significantly better during the tutorial than those with an unmatched tutorial

Using open ended, ill formed problems to develop and assess Engineering Mathematics competencies.

The purpose of this paper is to report upon how an engineering mathematics class was used to provide a vehicle for students to develop mathematical competencies and hence higher order thinking skills within the broader field of engineering education. Specifically it provided students with the opportunities to think mathematically, reason mathematically, pose and resolve mathematical problems, to use technology to model resolutions, interpret and handle mathematical symbolism and to communicate their resolutions to peers and staff. Using the report produced by the Mathematics Working Group of SEFI (European Society for Engineering Education), which details a framework for mathematics curricula in engineering education (SEFI, 2013), a methodology was identified. This methodology was also based on work previously undertaken by the author (Peters, 2017; Peters, 2015). In section 2.1 (p 13) the report lists and describes a set of eight mathematical competencies: (1) Thinking mathematically, (2) reasoning mathematically, (3) posing and solving mathematical problems, (4) modelling mathematically, (5) representing mathematical entities, (6) handling mathematical symbols and formalism, (7) communicating in, with, and about mathematics and, (8) making use of aids and tools. The report also points out the importance of developing assessment procedures pertinent to competency acquisition (p7). The evidence from this investigation concludes that the majority of students found the experience challenging but worthwhile. They considered they had learnt important skills including the ability to form assumptions, persistence, time management, project management and an enhancement of their mathematical skills in relation to engineering

Adaptação de apresentação de conteúdos de objeto de aprendizagem considerando estilos de aprendizagem

Orientador : Andrey Ricardo PimentelTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 01/09/2017Inclui referências : p. 167-175Resumo: Os estilos de aprendizagem (EA) referem-se a preferências individualizadas de uma pessoa, em relação aos modos e formas que ela prefere aprender no processo de ensino e aprendizagem. O conhecimento dos estilos de aprendizagem permite fazer proposições para o ensino rearranjando os métodos instrucionais e as estratégias de aprendizagem. Uma das possibilidades de realizar isso é através da apresentação do conteúdo do objeto de aprendizagem (OA) usando o conhecimento sobre o estilo de aprendizagem do aluno. Isso permite oferecer aos alunos recursos educacionais digitais adaptados as suas preferências individuais de aprendizagem. Pois acreditamos que a criação de novas formas/formatos de apresentação dos conteúdos dos objetos de aprendizagem levando em consideração o EA do aluno, pode gerar uma motivação maior por parte do aluno no uso desse tipo de recurso educacional, no caso o OA, pois os alunos receberiam esse recurso adaptado de acordo com as suas preferências individuais de aprendizagem. Neste contexto, foram investigados e estudados a teoria dos EA e os seus modelos, além dos princípios da Teoria Cognitiva da Aprendizagem Multimídia (TCAM), pois eles ajudam a evitar o uso inadequado de recursos nos mais variados formatos, que podem acarretar na distração e desmotivação do aluno no uso desse tipo de recurso, podendo causar insucesso no processo de aprendizagem, e foram usados para melhorar a adaptação da apresentação dos conteúdos do OA. Foram mapeadas e associadas as características mais relevantes dos EA, com as formas mais adequadas de apresentação do conteúdo do OA para cada EA, para definir a composição do modelo de adaptação da apresentação de conteúdos do OA considerando os estilos de aprendizagem (AdaptCOAEA). Foi criado um protótipo do OA com a interface adaptada com base no EA, a partir do modelo criado para avaliação das abordagens usadas através de experimentos com alunos. Os resultados obtidos das medidas subjetivas de satisfação e de respostas emocionais do aluno, e de aspectos da usabilidade em relação a interface do OA, demonstraram que o AdaptCOAEA atingiu os resultados almejados, em relação a adequação da interface do OA de acordo com os estilos do modelo de Felder-Silverman. Portanto os resultados obtidos com essa pesquisa também espera trazer contribuições futuras no sentido de possibilitar o aumento da motivação e satisfação no uso de OA adaptados, como recurso educacional no processo de aprendizagem, tanto pelo professor como para o aluno, através do fornecimento e recebimento desses recursos educacionais, adequados as preferências individuais de aprendizagem do aluno. Palavras-chave: Estilo de Aprendizagem, Adaptação, Objeto de Aprendizagem.Abstract: Learning styles (LS) refer to a person's individual preferences in respect to the ways and forms they prefer to learn in the teaching and learning process. Knowledge of learning styles allows to make propositions for teaching by rearranging instructional methods and learning strategies. One of the possibilities to accomplish this is through the presentation of the object learning (LO) content using knowledge about the learner's learning style. This allows students to offer digital educational resources tailored to their individual learning preferences. Because we believe that the creation of new forms / formats of presentation of the contents of the learning objects taking into account the student's learning, can generate a greater motivation on the part of the student in the use of this type of educational resource, in this case the LO, since the Students would receive this resource tailored to their individual learning preferences. In this context, the LS theory and its models, as well as the principles of the Cognitive Theory of Multimedia Learning (CTML), were investigated and studied, since they help to avoid the inappropriate use of resources in the most varied formats, which can lead to distraction and demotivation of the student in the use of this type of resource, what could cause failure in the learning process, and were used to improve the adaptation of the presentation of LO contents. The most relevant characteristics of the LS were mapped and associated with the most appropriate forms of presentations of the content of the LO for each LS, to define the composition of the adaptation model of LO content presentation considering the learning styles (AdaptCOAEA). A prototype of the LO with the interface adapted based on the LS was created, from the proposed model for evaluation of the approaches used through experiments with students. The results obtained from the subjective measures of satisfaction and emotional responses of the student, and aspects of usability in relation to the LO interface, demonstrated that AdaptCOAEA achieved the desired results, in relation to the adequacy of the LO interface according to the styles of the Felder-Silverman Model. Therefore, the results obtained with this research also hope to bring future contributions in order to increase motivation and satisfaction in the use of adapted LO as an educational resource in the learning process, both by the teacher and the student, through the provision and reception of these educational resources appropriate to individual student learning preferences. Keywords: Learning Style, Adaptation, Learning Object

Personalising Learning with Dynamic Prediction and Adaptation to Learning Styles in a Conversational Intelligent Tutoring System

This thesis presents research that combines the benefits of intelligent tutoring systems (ITS), conversational agents (CA) and learning styles theory by constructing a novel conversational intelligent tutoring system (CITS) called Oscar. Oscar CITS aims to imitate a human tutor by implicitly predicting individuals’ learning style preferences and adapting its tutoring style to suit them during a tutoring conversation. ITS are computerised learning systems that intelligently personalise tutoring based on learner characteristics such as existing knowledge and learning style. ITS are traditionally student-led, hyperlink-based learning systems that adapt the presentation of learning resources by reordering or hiding links. Research suggests that students learn more effectively when instruction matches their learning style, which is typically modelled explicitly using questionnaires or implicitly based on behaviour. Learning is a social process and natural language interfaces to ITS, such as CAs, allow students to construct knowledge through discussion. Existing CITS adapt tutoring according to student knowledge, emotions and mood, however no CITS adapts to learning styles. Oscar CITS models a human tutor by directing a tutoring conversation and automatically detecting and adapting to an individual’s learning styles. Original methodologies and architectures were developed for constructing an Oscar Predictive CITS and an Oscar Adaptive CITS. Oscar Predictive CITS uses knowledge captured from a learning styles model to dynamically predict learning styles from an individual’s tutoring dialogue. Oscar Adaptive CITS applies a novel adaptation algorithm to select the best tutoring style for each tutorial question. The Oscar CITS methodologies and architectures are independent of the learning styles model and subject domain. Empirical studies involving real students have validated the prediction and adaptation of learning styles in a real-world teaching/learning environment. The results show that learning styles can be successfully predicted from a natural language tutoring dialogue, and that adapting the tutoring style significantly improves learning performance