Research suggests that parents with high levels of self-efficacy tend to make positive decisions about active engagement in the child's education, while parents with weak self-efficacy are often associated with less parental involvement. Therefore, endowing intelligent tutoring systems with the ability to adapt the level of support provided for the parent based on their self-efficacy may be of great benefit. Such a system might provide high levels of support for parents with low self-efficacy, while providing lower levels of support for parents with high self-efficacy. This paper explores the effect of using such an adaptive system in the home-tutoring context and, in particular, reports on two complementary empirical studies. In the first study, a dynamic self-efficacy model, learned from runtime self-report data is used to provide adaptive support for the parent. In the second empirical study, the dynamic self-efficacy model was expanded to allow parents to request for further support outside what is deemed necessary based on their self-efficacy model. Both studies comprised a control group which received full support regardless of their self-efficacy throughout the entire experiment. Results indicate clear increases in parental self-efficacy as a result of the provision of adaptive support throughout the home-tutoring process.

Research informs us of the benefit of parental involvement in learning activities in the home (Hoover-Dempsey & Sandler 1997, Kay et al. 1994). However parental involvement may not occur due to lack of knowledge or a lack of belief in the benefit of their involvement (Greenwood et al. 1991). The Parent and Child Tutor (P.A.C.T.) is one approach which may be of benefit in developing home tutor best practice. P.A.C.T. is a novel Adaptive Education System (AES) in so far as it is a dual-user AES, which supports both the parent and child during the tutoring process. This paper describes how P.A.C.T.'s dual user architecture simultaneously supports both users, parent and child, through the provision of dual user models, dual domain models and an adaptive engine which comprises a set of pedagogical rules. This paper describes the development and implementation of the dual user architecture.

Adaptive educational systems capture and represent, for each student, various characteristics such as knowledge and traits in an individual learner model. However, there are some unresolved issues in building adaptive educational systems that adapt to individual traits. For example it is not obvious what is the appropriate educational theory with which to develop instructional resources and model individual traits. This paper describes an experiment using the Multiple Intelligence (MI) based adaptive intelligent educational system, EDUCE, that explores how different categories of resources are used when the learner has complete control and when adaptive presentation strategies are employed. In particular it explores how Musical/Rhythmic traits and resources impact on performance. Results suggest that students prefer using Musical/Rhythmic resources to other types of resources, however it is not clear how this preference can be best employed to enhance learning performance.

Research indicates a high correlation between parental involvement and a child's learning. The most effective parental involvement is when parents engage in learning activities with their child at home. However, parental involvement around learning activities may not occur spontaneously due to lack of domain knowledge, teaching strategies or structured support. This paper discusses how these issues can be addressed through the Parent and Child Tutor (P.A.C.T.). In particular, P.A.C.T will provide support for Suzuki parents during violin practice at home. This paper presents two studies; the first study identifies a set of best practice exemplars through lesson observations and interviews with the domain expert which informs the design of P.A.C.T. The second study validates the design of the system through analysing parent-child practice with and without the support of P.A.C.T. Results suggests that P.A.C.T. is effective in significantly increasing the use of best practice exemplars, in particular positive reinforcement and motivational games.

Motivation plays a key role in learning and teaching, in particular in technology enhanced learning environments. According to motivational theories, proper contingency design is an important prerequisite to motivate learners. In this paper, we demonstrate how confidence levels in an adaptive educational system can be raised using a contingency design technique. Learners that saw parts of a complete picture depending on their performance were more confident to solve the next task than learners who did not. Results suggest that it is possible to raise confidence levels of learners through appropriate contingency design and thus to automatically adapt to their motivational states.

Learning characteristics, as informed by research, vary for each individual learner. Research suggests that knowledge is processed and represented in different ways and that students prefer to use different types of resources in distinct ways. However, building Adaptive Educational systems that adapt to different learning characteristics is not easy. Major research questions exist such as: how are the relevant learning characteristics identified, how does modelling of the learner take place and in what way should the learning environment change for users with different learning characteristics? EDUCE is one system that addresses these challenges by using Gardner's theory of multiple intelligences (MI) as the basis for dynamically modelling learning characteristics and for designing instructional material. This paper describes a research study, using EDUCE, that explores the effect of using different adaptive presentation strategies and the impact on learning performance when material is matched and mismatched with learning preferences. The results suggest that students with low levels of learning activity, and who use only a limited number of the resources available, have the most to benefit from adaptive presentation strategies and that surprisingly learning gain increases when they are provided with resources not normally preferred.

Research on learning has shown that students learn differently and that they prefer to use different type of resources. Adaptive educational systems can support different learning characteristics by building a model of the student's learning behaviour and subsequently adapting the learning environment to match different needs. However major challenges exist, as it is not clear how a student model of learning style can be accurately built. One solution may be in the use of machine learning techniques. This paper presents 'First Aid For You', a novel adaptive educational system that dynamically determines learning style using machine learning techniques. The paper describes how it uses the Feider & Solomon Index of Learning Style to design an environment for different learning styles. It also describes how, as the student interacts with the learning environment, it uses the Naive Bayes algorithm to predict the student's preferred learning style and adaptively customize the learning environment.

Research informs us that learning characteristic differ, that knowledge is processed and represented in different ways and that students prefer to use different types of resources in distinct ways. However, building Adaptive Educational systems that adapt to different learning characteristics is not easy. Major research questions exist such as: how are the relevant learning characteristics identified, how does modelling of the learner take place and in what way should the learning environment change for users with different learning characteristics? EDUCE is one such system that addresses these challenges by using Gardner's theory of Multiple Intelligences (MI) as the basis for dynamically modelling learning characteristics and for designing instructional material. This paper describes a research study, using EDUCE, that explores the effect of using different adaptive presentation strategies and the impact on learning performance when material is matched and mismatched with learning preferences. The results suggest that students with low levels of learning activity, and who use only a limited number of the resources available, have the most to benefit from adaptive presentation strategies and that surprisingly learning gain increases when they are provided with resources not normally preferred.

Most current Adaptive Educational Systems model cognitive characteristics of students such as learning goals, knowledge and preferences. However, motivation obviously plays a key role in education. This paper reviews the state-of-the-art regarding adaptation to motivation. Open research issues that need to be addressed are identified.

EDUCE is an Intelligent Tutoring System for which a set of learning resources has been developed using the principles of Multiple Intelligences. It can dynamically identify learning characteristics and adaptively provide a customised learning material tailored to the learner. This paper describes a research study using EDUCE that examines the relationship between the adaptive presentation strategy, the level of choice available and the learning performance of science school students aged 12 to 14. The paper presents some preliminary results from a group of 18 students that have participated in the study so far. Results suggest that learning strategies that encourage the student to use as many resources as possible are the most effective. They suggest that learning gain can improve by presenting students initially with learning resources that are not usually used and subsequently providing a range of resources from which students may choose.

Research on learning has shown that students learn differently and that they process knowledge in various ways. EDUCE is an Intelligent Tutoring System for which a set of learning resources has been developed using the principles of Multiple Intelligences. It can dynamically identify user learning characteristics and adaptively provide a customised learning material tailored to the learner. This paper introduces the predictive engine used within EDUCE. It describes the input representation model and the learning mechanism employed. The input representation model consists of input features that describe how different resources were used and inferred from fine-grained information collected during student computer interactions. The predictive engine employs the Naive Bayes classifier and operates online using no prior information. Using data from a previous experimental study, a comparison was made between the performance of the predictive engine and the actual behaviour of a group of students using the learning material without any guidance from EDUCE. Results indicate correlation between student's behaviour and the predictions made by EDUCE. These results suggest that the concept of learning characteristics can be modelled using a learning scheme with appropriately chosen attributes.

To deliver quality education universities have to devise a proper and effective learning and teaching strategy. The aim of this strategy should be to develop the potential of learners, which can only be achieved through the provision of a learner-centred environment. This study was conducted at the National College of Ireland and focussed exclusively on developing the potential of the learner. First year students were assessed and evaluated to ascertain their learning styles using Kolb Learning Style Inventory. Faculty received induction on various learning techniques. Online environments were created to adapt content to student learning styles. Detailed strategy, its effectiveness, on line learning environment and results obtained are discussed in this paper.