An E-Learning Investigation into Learning Style Adaptivity

Abstrac

A novel algorithm for dynamic student profile adaptation based on learning styles

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.E-learning recommendation systems are used to enhance student performance and knowledge by providing tailor- made services based on the students’ preferences and learning styles, which are typically stored in student profiles. For such systems to remain effective, the profiles need to be able to adapt and reflect the students’ changing behaviour. In this paper, we introduce new algorithms that are designed to track student learning behaviour patterns, capture their learning styles, and maintain dynamic student profiles within a recommendation system (RS). This paper also proposes a new method to extract features that characterise student behaviour to identify students’ learning styles with respect to the Felder-Silverman learning style model (FSLSM). In order to test the efficiency of the proposed algorithm, we present a series of experiments that use a dataset of real students to demonstrate how our proposed algorithm can effectively model a dynamic student profile and adapt to different student learning behaviour. The results revealed that the students could effectively increase their learning efficiency and quality for the courses when the learning styles are identified, and proper recommendations are made by using our method

Designing for change: mash-up personal learning environments

Institutions for formal education and most work places are equipped today with at least some kind of tools that bring together people and content artefacts in learning activities to support them in constructing and processing information and knowledge. For almost half a century, science and practice have been discussing models on how to bring personalisation through digital means to these environments. Learning environments and their construction as well as maintenance makes up the most crucial part of the learning process and the desired learning outcomes and theories should take this into account. Instruction itself as the predominant paradigm has to step down. The learning environment is an (if not 'the�) important outcome of a learning process, not just a stage to perform a 'learning play'. For these good reasons, we therefore consider instructional design theories to be flawed. In this article we first clarify key concepts and assumptions for personalised learning environments. Afterwards, we summarise our critique on the contemporary models for personalised adaptive learning. Subsequently, we propose our alternative, i.e. the concept of a mash-up personal learning environment that provides adaptation mechanisms for learning environment construction and maintenance. The web application mash-up solution allows learners to reuse existing (web-based) tools plus services. Our alternative, LISL is a design language model for creating, managing, maintaining, and learning about learning environment design; it is complemented by a proof of concept, the MUPPLE platform. We demonstrate this approach with a prototypical implementation and a – we think – comprehensible example. Finally, we round up the article with a discussion on possible extensions of this new model and open problems

Layered evaluation of interactive adaptive systems : framework and formative methods

Peer reviewedPostprin

Learning design approaches for personalised and non-personalised e-learling systems

Recognizing the powerful role that technology plays in the lives of people, researchers are increasingly focusing on the most effective uses of technology to support learning and teaching. Technology enhanced learning (TEL) has the potential to support and transform students’ learning and allows them to choose when, where and how to learn. This paper describes two different approaches for the design of personalised and non-personalised online learning environments, which have been developed to investigate whether personalised e-learning is more efficient than non-personalised e-learning, and discuss some of the student’s experiences and assessment test results based on experiments conducted so far

The impact of learning styles on student grouping for collaborative learning: a case study

The final publication is available at Springer via http://dx.doi.org/10.1007/s11257-006-9012-7Learning style models constitute a valuable tool for improving individual learning by the use of adaptation techniques based on them. In this paper, we present how the benefit of considering learning styles with adaptation purposes, as part of the user model, can be extended to the context of collaborative learning as a key feature for group formation. We explore the effects that the combination of students with different learning styles in specific groups may have in the final results of the tasks accomplished by them collaboratively. With this aim, a case study with 166 students of computer science has been carried out, from which conclusions are drawn. We also describe how an existing web-based system can take advantage of learning style information in order to form more productive groups. Our ongoing work concerning the automatic extraction of grouping rules starting from data about previous interactions within the system is also outlined. Finally, we present our challenges, related to the continuous improvement of collaboration by the use and dynamic modification of automatic grouping rules.This project has been funded by the Spanish Ministry of Science and Education, TIN2004-03140

The guiding process in discovery hypertext learning environments for the Internet

Hypertext is the dominant method to navigate the Internet, providing user freedom and control over navigational behaviour. There has been an increase in converting existing educational material into Internet web pages but weaknesses have been identified in current WWW learning systems. There is a lack of conceptual support for learning from hypertext, navigational disorientation and cognitive overload. This implies the need for an established pedagogical approach to developing the web as a teaching and learning medium. Guided Discovery Learning is proposed as an educational pedagogy suitable for supporting WWW learning. The hypothesis is that a guided discovery environment will produce greater gains in learning and satisfaction, than a non-adaptive hypertext environment. A second hypothesis is that combining concept maps with this specific educational paradigm will provide cognitive support. The third hypothesis is that student learning styles will not influence learning outcome or user satisfaction. Thus, providing evidence that the guided discovery learning paradigm can be used for many types of learning styles. This was investigated by the building of a guided discovery system and a framework devised for assessing teaching styles. The system provided varying discovery steps, guided advice, individualistic system instruction and navigational control. An 84 subject experiment compared a Guided discovery condition, a Map-only condition and an Unguided condition. Subjects were subdivided according to learning styles, with measures for learning outcome and user satisfaction. The results indicate that providing guidance will result in a significant increase in level of learning. Guided discovery condition subjects, regardless of learning styles, experienced levels of satisfaction comparable to those in the other conditions. The concept mapping tool did not appear to affect learning outcome or user satisfaction. The conclusion was that using a particular approach to guidance would result in a more supportive environment for learning. This research contributes to the need for a better understanding of the pedagogic design that should be incorporated into WWW learning environments, with a recommendation for a guided discovery approach to alleviate major hypertext and WWW issues for distance learning

Adaptive intelligent personalised learning (AIPL) environment

As individuals the ideal learning scenario would be a learning environment tailored just for how we like to learn, personalised to our requirements. This has previously been almost inconceivable given the complexities of learning, the constraints within the environments in which we teach, and the need for global repositories of knowledge to facilitate this process. Whilst it is still not necessarily achievable in its full sense this research project represents a path towards this ideal.In this thesis, findings from research into the development of a model (the Adaptive Intelligent Personalised Learning (AIPL)), the creation of a prototype implementation of a system designed around this model (the AIPL environment) and the construction of a suite of intelligent algorithms (Personalised Adaptive Filtering System (PAFS)) for personalised learning are presented and evaluated. A mixed methods approach is used in the evaluation of the AIPL environment. The AIPL model is built on the premise of an ideal system being one which does not just consider the individual but also considers groupings of likeminded individuals and their power to influence learner choice. The results show that: (1) There is a positive correlation for using group-learning-paradigms. (2) Using personalisation as a learning aid can help to facilitate individual learning and encourage learning on-line. (3) Using learning styles as a way of identifying and categorising the individuals can improve their on-line learning experience. (4) Using Adaptive Information Retrieval techniques linked to group-learning-paradigms can reduce and improve the problem of mis-matching. A number of approaches for further work to extend and expand upon the work presented are highlighted at the end of the Thesis