Bridging the divide between theory and practice. Experiential learning approaches for tourism and hospitality education

Experiential learning approaches such as role-play have been found to be valuable methods of bridging the divide between academic knowledge and practical skills, a problem often cited in tourism and hospitality management education. Such approaches have been found to contribute towards deeper learning by enhancing students' interest, motivation, participation, knowledge and skill development. This paper reports on the implementation of an experiential learning approach designed to encourage and facilitate deeper learning approaches, with the contributing aims of providing students with a more interesting learning experience and a broader set of skills for future employment.

Incremental learning for interactive e-mail filtering

In this article, we propose a framework, namely, Prediction-Learning-Distillation (PLD) for interactive document classification and distilling misclassified documents. Whenever a user points out misclassified documents, the PLD learns from the mistakes and identifies the same mistakes from all other classified documents. The PLD then enforces this learning for future classifications. If the classifier fails to accept relevant documents or reject irrelevant documents on certain categories, then PLD will assign those documents as new positive/negative training instances. The classifier can then strengthen its weakness by learning from these new training instances. Our experiments’ results have demonstrated that the proposed algorithm can learn from user-identified misclassified documents, and then distil the rest successfully.

Interactive email filtering: Learning from misclassified examples

Learning from mistakes has proven to be an effective way of learning in the interactive document classifications. In this paper we propose an approach to effectively learning from mistakes in the email filtering process. Our system has employed both SVM and Winnow machine learning algorithms to learn from misclassified email documents and refine the email filtering process accordingly. Our experiments have shown that the training of an email filter becomes much effective and faster

Machine learning for matching astronomy catalogues

An emerging issue in the field of astronomy is the integration, management and utilization of databases from around the world to facilitate scientific discovery. In this paper, we investigate application of the machine learning techniques of support vector machines and neural networks to the problem of amalgamating catalogues of galaxies as objects from two disparate data sources: radio and optical. Formulating this as a classification problem presents several challenges, including dealing with a highly unbalanced data set. Unlike the conventional approach to the problem (which is based on a likelihood ratio) machine learning does not require density estimation and is shown here to provide a significant improvement in performance. We also report some experiments that explore the importance of the radio and optical data features for the matching problem.

SQLator: An online SQL learning workbench

SQL (Structured Query Language) is one of the essential topics in foundation databases courses in higher education. Due to its apparent simple syntax, learning to use the full power of SQL can be a very difficult activity. In this paper, we introduce SQLator, which is a web-based interactive tool for learning SQL. SQLator's key function is the evaluate function, which allows a user to evaluate the correctness of his/her query formulation. The evaluate engine is based on complex heuristic algorithms. The tool also provides instructors the facility to create and populate database schemas with an associated pool of SQL queries. Currently it hosts two databases with a query pool of 300+ across the two databases. The pool is divided into 3 categories according to query complexity. The SQLator user can perform unlimited executions and evaluations on query formulations and/or view the solutions. The SQLator evaluate function has a high rate of success in evaluating the user's statement as correct (or incorrect) corresponding to the question. We will present in this paper, the basic architecture and functions of SQLator. We will further discuss the value of SQLator as an educational technology and report on educational outcomes based on studies conducted at the School of Information Technology and Electrical Engineering, The University of Queensland.

Learning from the story of a great leader

This paper reports on research findings from a larger study which seeks to understand leadership from the experiences of well-known and well-recognised Australian leaders across a spectrum of endeavours such as the arts, business, science, the law and politics. To date there appears to be limited empirical research that has investigated the insights of Australian leaders regarding their leadership experiences, beliefs and practices. In this paper, the leadership story of a well-respected medical scientist is discussed revealing the contextual factors that influenced her thinking about leadership as well as the key values she embodies as a leader. The paper commences by briefly considering some of the salient leadership literature in the field. In particular, two prominent theoretical frameworks provided by Leavy (2003)and Kouzes and Posner (2002) are explored. While Leavy’s framework construes leadership as consisting of three “C’s” – context , conviction and credibility, Kouzes and Posner (2002)refer to five practices of exemplary leadership. The paper provides a snapshot of the life forces and context that played an important role in shaping the leader’s views and practices. An analytical discussion of these practices is considered in the light of the earlier frameworks identified. Some implications of the findings from this non-education context for those in schools are briefly noted.