The influence of cognitive styles on the design of adaptive web-based learning materials

This research addresses the issues of adaptation and personalisation of the computer interface for Web-based learning materials taking into consideration key characteristics of learners and particularly their cognitive style. The thesis examines main concerns driving learning towards individualisation. Different approaches to adaptation and personalisation are analysed, as are a range of adaptive systems. The need for further research regarding individual differences is identified; it is argued that cognitive styles should be allowed for in designing adaptive learning materials. A comprehensive review of cognitive style classifications is presented, from which key defining attributes and advantageous instructional conditions are identified and a number of adaptive variables derived. LEARNINT, a prototype based on these variables was developed and used in two experimental studies. Results show a relationship between Interface Affect and learning outcomes and also between the variables underpinning the interface style used and variation in user reactions and performance; however, little interaction is observed between these variables and cognitive style. It is suggested that for most learners using Web-based learning materials performance may improve if they experience positive affect towards the interface; also, that the proposed variables stand as good candidates for providing adaptivity. A methodological approach is presented that extends the functionality of LEARNINT. The generic aspects of the research are further elaborated offering guidance on future directions for the design of adaptive Web-based learning materials

Personalised trails and learner profiling within e-learning environments

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

Data mining technology for the evaluation of learning content interaction

Interactivity is central for the success of learning. In e-learning and other educational multimedia environments, the evaluation of interaction and behaviour is particularly crucial. Data mining – a non-intrusive, objective analysis technology – shall be proposed as the central evaluation technology for the analysis of the usage of computer-based educational environments and in particular of the interaction with educational content. Basic mining techniques are reviewed and their application in a Web-based third-level course environment is illustrated. Analytic models capturing interaction aspects from the application domain (learning) and the software infrastructure (interactive multimedia) are required for the meaningful interpretation of mining results

A Longitudinal Study on the Effect of Hypermedia on Learning Dimensions, Culture and Teaching Evaluation

Earlier studies have found the effectiveness of hypermedia systems as learning tools heavily depend on their compatibility with the cognitive processes by which students perceive, understand and learn from complex information\ud sources. Hence, a learner’s cognitive style plays a significant role in determining how much is learned from a hypermedia learning system. A longitudinal study of Australian and Malaysian students was conducted over two semesters in 2008. Five types of predictor variables were investigated with cognitive style: (i) learning dimensions (nonlinear learning, learner control, multiple tools); (ii)\ud culture dimensions (power distance, uncertainty avoidance, individualism/collectivism, masculinity/femininity, long/short term orientation); (iii) evaluation of units; (iv) student demographics; and (v) country in which students studied. This study uses both multiple linear regression and linear mixed effects to model the relationships among the variables. The results from this study support the findings of a cross-sectional study conducted by Lee et al. (2010); in particular, the predictor variables are significant to determine students’ cognitive style

A conceptual architecture for interactive educational multimedia

Learning is more than knowledge acquisition; it often involves the active participation of the learner in a variety of knowledge- and skills-based learning and training activities. Interactive multimedia technology can support the variety of interaction channels and languages required to facilitate interactive learning and teaching. A conceptual architecture for interactive educational multimedia can support the development of such multimedia systems. Such an architecture needs to embed multimedia technology into a coherent educational context. A framework based on an integrated interaction model is needed to capture learning and training activities in an online setting from an educational perspective, to describe them in the human-computer context, and to integrate them with mechanisms and principles of multimedia interaction

An E-Learning Investigation into Learning Style Adaptivity

Abstrac

Information systems for interactive learning: Design perspective

This paper aims to present and discuss educational issues and relevant research to universities and colleges in the Arabian Gulf Region. This include cultural, students’ learning preferences and the use of information and communication technology. It particularly focuses on interactive learning through the consideration of learning styles. It explores the sequential-global learning styles profile of undergraduate students as part of a continuous research in Information Systems design with a particular focus on the design of Interactive Learning Systems (ILSs). A study to examine the learning style profile of undergraduate students in a cohort of Management Information Systems at a UAE university has been conducted, and a discussion and recommendations on how these findings can be reflected on the design of ILSs are provided

The role of unit evaluation, learning and culture dimensions related to student cognitive style in hypermedia learning

Recent developments in learning technologies such as hypermedia are\ud becoming widespread and offer significant contributions to improving the delivery\ud of learning and teaching materials. A key factor in the development of hypermedia\ud learning systems is cognitive style (CS) as it relates to users‟ information\ud processing habits, representing individual users‟ typical modes of perceiving,\ud thinking, remembering and problem solving.\ud \ud \ud \ud \ud A total of 97 students from Australian (45) and Malaysian (52) universities\ud participated in a survey. Five types of predictor variables were investigated with\ud the CS: (i) three learning dimensions; (ii) five culture dimensions; (iii) evaluation\ud of units; (iv) demographics of students; and (v) country in which students studied.\ud Both multiple regression models and tree-based regression were used to analyse\ud the direct effect of the five types of predictor variables, and the interactions within\ud each type of predictor variable. When comparing both models, tree-based\ud regression outperformed the generalized linear model in this study. The research\ud findings indicate that unit evaluation is the primary variable to determine students‟\ud CS. A secondary variable is learning dimension and, among the three dimensions,\ud only nonlinear learning and learner control dimensions have an effect on students‟\ud CS. The last variable is culture and, among the five culture dimensions, only\ud power distance, long term orientation, and individualism have effects on students‟\ud CS. Neither demographics nor country have an effect on students‟ CS.\ud These overall findings suggest that traditional unit evaluation, students‟\ud preference for learning dimensions (such as linear vs non-linear), level of learner\ud control and culture orientation must be taken into consideration in order to enrich\ud students‟ quality of education. This enrichment includes motivating students to\ud acquire subject matter through individualized instruction when designing,\ud developing and delivering educational resources

Mining learning preferences in web-based instruction: Holists vs. Serialists

Web-based instruction programs are used by learners with diverse knowledge, skills and needs. These differences determine their preferences for the design of Web-based instruction programs and ultimately influence learners' success in using them. Cognitive style has been found to significantly affect learners' preferences of web-based instruction programs. However, the majority of previous studies focus on Field Dependence/Independence. Pask's Holist/Serialist dimension has conceptual links with Field Dependence/Independence but it is left mostly unstudied. Therefore, this study focuses on identifying how this dimension of cognitive style affects learner preferences of Web-based instruction programs. A data mining approach is used to illustrate the difference in preferences between Holists and Serialists. The findings show that there are clear differences in regard to content presentation and navigation support. A set of design features were then produced to help designers incorporate cognitive styles into the development of Web-based instruction programs to ensure that they can accommodate learners' different preferences.This work is partially funded by National Science Council, Taiwan, ROC (NSC 98-2511-S-008-012- MY3; NSC 99- 2511-S-008 -003 -MY2; NSC 99-2631-S-008-001)

Possible versus desirable in instructional systems: Who's driving?

This paper takes a pragmatic stance that the key to successful application of technology in education is good teaching: using technology only when it is a cost‐effective servant of pedagogy. The paper discusses some fundamental issues in the production of computer‐based materials, and considers them in the context of an on‐going evaluation of an Internet courseware project