Technology may be of benefit in providing the necessary support for both parent and child. Previously, technology has played a role in supporting the home tutoring process through the provision of on-line learning resources (National ParentNet Association, 2008, NumberWorks@Home, 2008, Moravian Academy Suzuki Violin Website, 2008). However, little research exists which investigates how personalised learning environments may be used effectively in the domain of home tutoring (Brusilovsky, 2003). This section provides an overview of adaptive educational systems. Firstly, an overview of the area of adaptive educational systems is presented. Secondly, it reviews a number of adaptive collaborative systems illustrating the design issues in building such systems, and in particular, how adaptive techniques can be used to support the learning process. Thirdly, it reviews a number of sample systems, which incorporate user’s self- efficacy. Finally, it describes a number of adaptive educational systems, which
incorporate the learner’s affect. Particular emphasis is placed on how affect can be elicited and adapted to.
Educational systems that treat all students in the same way by providing the same level of support and instructional approach may be ineffective where there are students with various goals, levels of knowledge, and preferences. Adaptive and Intelligent Educational Systems attempt to overcome this problem by building a model of the goals, preferences and knowledge of each individual student, and by subsequently using this generated model to dynamically adapt the learning environment for each student in a manner that best supports their needs (Brusilovsky, 2001). This may involve the provision of scaffolding, identifying misconceptions. (Mitrovic, 2003), or modifying the presentation in order to adapt to the knowledge level of the student (De Bra & Calvi, 1998).
Adaptive educational systems and intelligent educational systems share similarities, however, each have distinct emphasis. In adaptive systems, the emphasis is on providing a personalised environment for each student using information collected in the student model. Intelligent systems place the emphasis on the application of techniques from the field of Artificial Intelligence in the provision of greater support for the student (Brusilovsky & Peylo, 2003).
The term ‘technologies’ is used to describe the myriad of approaches to the inclusion of adaptive and intelligent functionality, which can be used (Brusilovsky, 1998). Brusilovsky & Peylo, (2003) propose five major groups of technologies: intelligent tutoring, adaptive information filtering, intelligent class monitoring, intelligent collaboration support and adaptive hypermedia.
The major intelligent tutoring technologies are curriculum sequencing, problem solving support and intelligent solution analysis. The purpose of curriculum sequencing is to help the student find the most suitable path through learning material by making decisions on what content to present next (Weber & Brusilovsky, 2001). The goal of interactive problem solving support is to provide help with problem solving by giving hints or executing the next step (Melis et al., 2001). With intelligent solution, analysis attempts are made to find out what exactly is wrong or incomplete, identify what piece of incorrect knowledge that may be responsible for the error and provide suitable feedback (Mitrovic, 2003).
Adaptive information filtering (AIF) selects a subset of items, which are relevant to a user’s interests from a large pool of information. It adapts the search by ordering and
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filtering the results, and subsequently recommends the most relevant documents. Two categories of AIF technologies exist, namely content based filtering and collaborative filtering. With the content-based approach, the behaviour of a user is predicted from their past behaviour (e.g. MLTutor, Smith & Blandford, 2003). While with the collaborative approach, the behaviour of the user is predicted from the behaviour of other like-minded people, for example (e.g. WebCOBALT, Mitsuhara et al., 2003).
Intelligent collaborative learning technologies can be used in an endeavour to support collaboration between students. Collaborations are supported using three types of technologies: adaptive collaboration support, adaptive group formation and peer help and virtual students. Adaptive collaboration support technologies provide interactive support to help collaboration using knowledge about good and bad collaborations (Soller & Lesgold, 2003). Adaptive group formation and peer help technologies use knowledge about collaborating peers to form matching groups for different tasks (Greer at al, 1998). Virtual student technology attempts to introduce virtual peers into the learning environment (Chan & Baskin, 1990).
Intelligent class monitoring technologies recognize students who need support or extra challenges. These technologies use Artificial Intelligence techniques to explore large amounts of information that is collected when tracking student actions (Maceron & Yacef, 2003).
Adaptive hypermedia includes two major technologies: adaptive navigation and adaptive presentation. Adaptive navigation supports the student by changing the appearance of links. For example, it can adaptively sort, annotate, or partly hide the links of the current page to make it easier to choose where to go next (de Bra, 1996). Adaptive presentation adapts the content to be presented by dynamically generating the content for individual students according to their needs (Weber & Brusilovsky, 2001).
As has been described, there is a diverse range of technologies, which can be used in the development of adaptive and intelligent educational systems. Clearly, this research falls under the umbrella of collaborative learning as there are two users (parent and child) working together to achieve a common goal (home practice). However, instead of focusing on the collaborative process this research will focus in particular on how adaptive hypermedia technologies can be incorporated in order to provide personalised domain support for both users throughout their collaborative activities.