A semi-supervised spam mail detector

This document describes a novel semi-supervised approach to spam classification, which was successful at the ECML/PKDD 2006 spam classification challenge. A local learning method based on lazy projections was successfully combined with a variant of a standard semi-supervised learning algorithm

Effective Assessment in Art and Design : writing learning outcomes and assessment criteria in art and design

This document has been written to help teachers in art and design who are writing project briefs or unit outlines in learning outcomes form for the first time. It is not meant to be prescriptive but rather a general guide that attempts to clarify the purposes of outcome-led learning and identify some of the pitfalls you might encounter. You will find that the most successful examples of outcome-led learning come from competency-based learning where it is relatively straightforward for students to provide evidence of their learning because the outcomes are almost always skills oriented. Increasingly, universities are adopting the learning outcomes approach (student-centred) in preference to the aims and objectives approach (teacher-centred). Many examples now exist of text-based subjects working with learning outcomes. One of the major challenges for them is to take the term 'understanding' and redefine it in terms of more specific measurable cognitive (thinking) outcomes. In art and design our challenge is greater because we work with rather more ambiguous terms such as 'creativity', 'imagination', 'originality' etc as well as 'understanding'. A significant challenge for you then will be to articulate learning outcomes in a way which promotes these important cognitive attributes but at the same time provides some useful methods of measuring their achievement

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