On Recommendation of Learning Objects using Felder-Silverman Learning Style Model

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.The e-learning recommender system in learning institutions is increasingly becoming the preferred mode of delivery, as it enables learning anytime, anywhere. However, delivering personalised course learning objects based on learner preferences is still a challenge. Current mainstream recommendation algorithms, such as the Collaborative Filtering (CF) and Content-Based Filtering (CBF), deal with only two types of entities, namely users and items with their ratings. However, these methods do not pay attention to student preferences, such as learning styles, which are especially important for the accuracy of course learning objects prediction or recommendation. Moreover, several recommendation techniques experience cold-start and rating sparsity problems. To address the challenge of improving the quality of recommender systems, in this paper a novel recommender algorithm for machine learning is proposed, which combines students actual rating with their learning styles to recommend Top-N course learning objects (LOs). Various recommendation techniques are considered in an experimental study investigating the best technique to use in predicting student ratings for e-learning recommender systems. We use the Felder-Silverman Learning Styles Model (FSLSM) to represent both the student learning styles and the learning object profiles. The predicted rating has been compared with the actual student rating. This approach has been experimented on 80 students for an online course created in the MOODLE Learning Management System, while the evaluation of the experiments has been performed with the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of the experiment verify that the proposed approach provides a higher prediction rating and significantly increases the accuracy of the recommendation

Web-Mediated Education and Training Environments: A Review of Personalised Interactive Learning.

This chapter reviews the concept of personalised eLearning resources in relation to integrating interactivity into asynchronous learning. Personalised eLearning resources are learning resources which are selected to suit a specific student or trainee’s individual learning requirements. The affordance of personalised eLearning would provide educators with the opportunity to shift away from eLearning content that is retrieved and move towards the provision of personalised interactive content to provide a form of asynchronous learning to suit students at different degree levels. A basic introduction to the concept of ePedagogy in online learning environments is explored and the impacts these systems have on students learning experiences are considered. Issues, controversies, and problems associated with the creation of personalised interactive eLearning resources are examined, and suggested solutions and recommendations to the identified issues, controversies, and problems are reviewed. Personalised interactive asynchronous learning resources could potentially improve students’ learning experiences but more research on the human computer interface of these authoring tools is required before personalised eLearning resources are available for use by non-technical authors

Flexible learning in computer science

This paper outlines the concept of Flexible Pedagogy and how it can assist in addressing some of the issues facing STEM disciplines in general, and Computer Science in particular. The paper considers what flexible pedagogy is and how technologies developed by Computer Science can enable flexibility. It then describes some of the issues facing STEM education, with a particular focus on Computer Science education in Higher Education. Finally, it considers how flexible approaches to teaching and learning are particularly pertinent to the issues faced in Computer Science and future opportunities

Development of a Myers-Briggs Type Indicator Based Personalised E-Learning System

The major challenge of the traditional learning system is space-time restriction and it is teacher-centred. The emergence of Information Technology gave rise for e-learning systems which are characterized with the components of teacher-centred and one-size-fits-all strategy. Subsequently, the concept of personalisation with learning technology was introduced that provides adaptation of learning contents to learning requirements of the learners. Hence, this research paper develops a personalised e-learning system that matches teaching strategy with learners’ learning style using Myers-Briggs Type Indicator (MBTI).  The emphasis is laid on adaptive teaching strategy and revising the teaching strategy for the purpose of increasing learners’ learning performance. The mathematical model is developed for profiling learners to determine their learning style based on the MBTI questionnaire and Dynamic Bayesian Network is applied to revise the teaching strategy. The system is implemented using PHP and Wamp server and the database is designed using Structured Query Language (SQL). The developed system is tested using Undergraduate students studying Information Technology at Federal University of Technology, Minna. The percentage analysis of the students’ scores shows that 78% of students passed and the remaining 22% passed when the strategy was revised. The performance evaluation of the system is carried out and from the analysis it can be concluded that the Myers-Briggs Type Indicator Based Personalised E-learning System developed is appealing to students and the performance of students improved significantly

Cloud eLearning - Personalisation of learning using resources from the Cloud

With the advancement of technologies, the usage of alternative eLearning systems as complementary systems to the traditional education systems is becoming part of the everyday activities. At the same time, the creation of learning resources has increased exponentially over time. However, the usability and reusability of these learning resources in various eLearning systems is difficult when they are unstandardised and semi-standardised learning resources. Furthermore, eLearning activities’ lack of suitable personalisation of the overall learning process fails to optimize resources’ and systems’ potentialities. At the same time, the evolution of learning technologies and cloud computing creates new opportunities for traditional eLearning to evolve and place the learner in the center of educational experiences. This thesis contributes to a holistic approach to the field by using a combination of artificial intelligence techniques to automatically generate a personalized learning path for individual learners using Cloud resources. We proposed an advancement of eLearning, named the Cloud eLearning, which recognizes that resources stored in Cloud eLearning can potentially be used for learning purposes. Further, the personalised content shown to Cloud Learners will be offered through automated personalized learning paths. The main issue was to select the most appropriate learning resources from the Cloud and include them in a personalised learning path. This become even more challenging when these potential learning resources were derived from various sources that might be structured, semi- structure or even unstructured, tending to increase the complexity of overall Cloud eLearning retrieval and matching processes. Therefore, this thesis presents an original concept,the Cloud eLearning, its Cloud eLearning Learning Objects as the smallest standardized learning objects, which permits reusing them because of semantic tagging with metadata. Further, it presents the Cloud eLearning Recommender System, that uses hierarchical clustering to select the most appropriate resources and utilise a vector space model to rank these resources in order of relevance for any individual learner. And it concludes with Cloud eLearning automated planner, which generates a personalised learning path using the output of the CeL recommender system

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

Personalisation in MOOCs: a critical literature review

The advent and rise of Massive Open Online Courses (MOOCs) have brought many issues to the area of educational technology. Researchers in the field have been addressing these issues such as pedagogical quality of MOOCs, high attrition rates, and sustainability of MOOCs. However, MOOCs personalisation has not been subject of the wide discussions around MOOCs. This paper presents a critical literature survey and analysis of the available literature on personalisation in MOOCs to identify the needs, the current states and efforts to personalise learning in MOOCs. The findings illustrate that there is a growing attention to personalisation to improve learners’ individual learning experiences in MOOCs. In order to implement personalised services, personalised learning path, personalised assessment and feedback, personalised forum thread and recommendation service for related learning materials or learning tasks are commonly applied

Academics\u27 Views on Personalised e-Learning in Higher Education

The challenges for academics in meeting the learning requirements of students are many and varied. This research focuses on the concept of personalised learning, where activities are specifically selected to suit the learning requirements of individual students. The creation of personalised learning activities to suit every student’s learning needs, are not easily achieved. A survey was conducted in June 2012 to determine academics awareness of, and views on, the ‘novel teaching approach’ of personalised e-learning in higher education. Forty academics participated in this study. 60% of academic respondents agreed with the statement: “There is a need to personalise e-learning to suit individual student’s learning requirements”. 85% of respondents agreed that e-learning can enhance the learning experience of students, and 70% were of the opinion that the use of personalised e- learning activities would enhance the learning experience of students. 43% of respondents agreed that they would use an authoring tool for personalising e-learning if one was available, and 43% did not know if they would use one or not. ‘Prior knowledge’ was perceived as the most important student characteristic on which to base personalisation and the easiest to achieve, and ‘web navigational behaviour’ was seen as the least important and most difficult to achieve. This study contributes to existing research into the development of authoring tools to facilitate the creation of personalised e-learning activities by non-technical authors

Challenges Encountered in Creating Personalised Learning Activities to Suit Students Learning Preferences

This book chapter reviews some of the challenges encountered by educators in creating personalised e-learning activities to suit students learning preferences. Technology-enhanced learning (TEL) alternatively known as e-learning has not yet reached its full potential in higher education. There are still many potential uses as yet undiscovered and other discovered uses which are not yet realisable by many educators. TEL is still predominantly used for e-dissemination and e-administration. This chapter reviews the potential use of TEL to provide personalised learning activities to suit individual students learning preferences. In particular the challenges encountered by educators when trying to implement personalised learning activities based on individual students learning preferences

Develop a User Behavior Analysis Tool in ETHOL Learning Management System

Students have different learning styles when studying online. Meanwhile, lecturers use the same method for all students who take their online lectures. These different learning styles can affect the level of understanding and the results obtained by students. By knowing student learning styles, lecturers are expected to be able to use the right way in delivering material. In this research, we developed a student behavior analysis feature on self-developed Virtual Learning Environment (VLE) called Enterprise Hybrid Online Learning (ETHOL). Students’ data collected includes data on online activities, personal data, and survey data on student learning styles. User behavior analysis was carried out by dividing into three clusters: average scores, time to collect assignments, and student learning styles. The clustering method used is the Hierarchical K-Means. The results obtained are students who have the habit of collecting assignments on time have higher scores than others. In addition, the lecturer is able to see the results of the analysis of the behavior and learning styles of each student. These results can be used as information in delivering lecture material