Expanding the Database Curriculum

As database concepts and technologies continue to evolve there exists a need to expand the topics included in database curricula. This is challenging given the restraints on the number of courses that can be included in a typical CS or IS program. While a set of commonly identified core concepts and principles exists, there is little consensus on what supplemental materials should be included in database courses. Through an NSF proof-of-concept grant, we designed and developed courseware incorporating the use of animations to deepen and enrich standard presentations of core database concepts and to complement database teachings as found in the most popular texts. This paper proposes extending that model to modules of relevant and advanced database topics. Modules covered include data warehousing, data exchange via XML, database security and database system architecture, performance and tuning. Students in the computing disciplines must develop a strong knowledge base and build a firm foundation in core database concepts and principles. They must also extend this to supplemental and advanced topics. The dilemma for CS educators is to find avenues through which these topics can be explored in an efficient and effective manner

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

A Review of the First Phase of a Project to Develop and Utilize Animated Database Courseware

Database technologies have revolutionized information technologies and information systems and, as such, are recommended components of any undergraduate program in a computing discipline. However, there are many obstacles to teaching database concepts including prioritizing the many important database concepts, incorporating new technologies into an already full curriculum and supporting students who find the topic challenging. This paper presents a review of the development of a set of database courseware animation prototypes designed to support and strengthen the presentation of database concepts. The work was part of a proof-of-concept NSF grant. Preliminary evaluation of the prototypes suggests they enhance student learning and, consequently, future enhancements to the courseware have been proposed

Web-based interactive computer-aided learning package on open channel flow : innovations, challenges and experiences

Author name used in this publication: K. W. Chau2006-2007 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe