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

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

Learning styles and courseware design

In this paper we examine how (courseware) can accommodate differences in preferred learning style. A review of the literature on learning styles is followed by a discussion of the implications of being able to accurately classify learners, and key issues that must be addressed are raised. We then present two courseware design solutions that take into account individual learning‐style preference: the first follows on from traditional research in this area and assumes that learners can be classified in advance. The second solution takes account of the issues raised previously. We conclude by discussing the feasibility of adapting learning to suit the needs of individual learners, and suggest further research investigating the relationship between preferred learning style and the design of effective interactive learning environments

Climbing the Great Wall: Linking Teacher Beliefs and Learning Styles in Cross-Cultural Teaching - Observations from cross-cultural teaching in Mainland China

Scholars have suggested separate relationships between culture and learning styles, and between culture and teaching beliefs. In this essay I suggest that interrelated relationships between culture, learning styles and teacher beliefs may exist. Drawing on personal observations from cross-cultural teaching experiences in Mainland China, the essay illustrates how culture, learning styles and teacher beliefs inform each other and how they might be combined into an inclusive framework. Such a framework could aid in identifying and overcoming challenges from cross-cultural teaching and cross-cultural learning. The observations shed further light onto the on-going debate of how Chinese learn. The essay concludes with directions for future research for further development of the framework and our understanding of cross-cultural differences in the classroom

Learning styles: Individualizing computer‐based learning environments

In spite of its importance, learning style is a factor that has been largely ignored in the design of educational software. Two issues concerning a specific set of learning styles, described by Honey and Mumford (1986), are considered here. The first relates to measurement and validity. This is discussed in the context of a longitudinal study to test the predictive validity of the questionnaire items against various measures of academic performance, such as course choice and level of attainment in different subjects. The second issue looks at how the learning styles can be used in computer‐based learning environments. A re‐examination of the four learning styles (Activist, Pragmatist, Reflector and Theorist) suggests that they can usefully be characterized using two orthogonal dimensions. Using a limited number of pedagogical building blocks, this characterization has allowed the development of a teaching strategy suitable for each of the learning styles. Further work is discussed, which will use a multi‐strategy basic algebra tutor to assess the effect of matching teaching strategy to learning style

On site challenges for the construction of 16-storey condominium: as observed by a young civil engineering technologist

The difference between an engineer and an engineering technologist is that, an engineer would mainly focus and produce structural designs based on engineering calculations, while the job of an engineering technologist is to execute the design in the real working environment by adopting flexible and critical technical ideas on-site. The challenges can be divided into two categories, namely design challenges faced by an engineer and the construction challenges faced by an engineering technologist. Thus, the job scope of an engineering technologist is relatively wider when compared to that of an engineer, as the engineering technologist would be dealing with the consultant, contractors and suppliers on site, while handling the in situ construction challenges. This requires basic understanding of engineering principles and technology, critical thinking and problem-solving skills, modern tools competency in software applications, designs and construction calculations, as well as communication and leadership skills all rolled into one. I have recorded my experience as a junior civil engineering technologist engaged in the construction works of a 16-storey condominium at Langkawi, Kedah. Included in the descriptions are in situ technical problems encountered, potentially unsafe working conditions, foundations, scheduling and housekeeping on site, among others. I hope that the information shared in this entry would make a good introduction and induction for juniors entering the work site, where my personal undertakings could serve as a guide and reminder for them

The effect of adaptive performance support system on learning achievements of students

The study compares the effectiveness of two performance support systems, adaptive and non-adaptive, on learning achievements of engineering students. In addition, the research design controls for a possible effect of learning style. The analysis reveals that students working with an adaptive performance support system score significantly higher than students using a non-adaptive performance system on a performance test across different learning styles. The only variation in the two experimental conditions, manipulated in the study, is embedded adaptive arrangement based on learning style. Embedded adaptation mode accommodates learning preferences of students through the structure of learning content as an association between types of learning content and different learning styles is assumed. Learning style does not produce a significant difference in the performance achievements of students and there is no indication for an interaction effect between performance support system as a method of instruction and learning style. These results are explained by two theoretical positions introduced in the background of the study, namely coping behaviour and the distinction between level and style type of cognitive constructs

The Structured Process Modeling Method (SPMM) : what is the best way for me to construct a process model?

More and more organizations turn to the construction of process models to support strategical and operational tasks. At the same time, reports indicate quality issues for a considerable part of these models, caused by modeling errors. Therefore, the research described in this paper investigates the development of a practical method to determine and train an optimal process modeling strategy that aims to decrease the number of cognitive errors made during modeling. Such cognitive errors originate in inadequate cognitive processing caused by the inherent complexity of constructing process models. The method helps modelers to derive their personal cognitive profile and the related optimal cognitive strategy that minimizes these cognitive failures. The contribution of the research consists of the conceptual method and an automated modeling strategy selection and training instrument. These two artefacts are positively evaluated by a laboratory experiment covering multiple modeling sessions and involving a total of 149 master students at Ghent University