Personalizing Quantitative Homework Assignments to Facilate Student Learning

Electronic business tools, technology, and approaches have enabled organizations to reach new customers and markets more effectively and efficiently than ever before. A parallel process has occurred in the education industry. In many of these educational ventures, the desire to reach underserved populations is a primary motivation. In others, efficiencies and cost reductions are paramount. Still other e-learning and innovative teaching initiatives have been initiated by the sincere and direct desire to improve existing educational effectiveness. The project described in this paper is focused on educational effectiveness of teaching operations management to college students. The idea is to have a student-friendly approach to developing customized homework problems where each student has the same problem type but unique values, answers, and decisions. In this way, we seek to encourage student interaction and discussion of the problems while minimizing the risk of cheating through rote copying

A Detailed Procedure for Using Copulas to Classify E-Business Data

Decision support systems are widely implemented to effectively utilize the tremendous amount of data generated by information systems throughout an organization. In one common implementation, the goal is to correctly classify a customer so that appropriate action can take place. This may take the form of a customized purchase incentive given to increase the probability that a transaction is completed, while enhancing profitability. Intelligent agents employing neural network technology that function as Bayesian classifiers are one approach used here. Another approach that has been around for decades, called copulas, to our knowledge has yet to be utilized for classification in e-business applications. Copulas are functions that can describe the dependence among random variables. The very fact that copulas directly address co-dependence among variables may make them especially attractive in e-business applications where large numbers of correlated attributes may be present that could negatively affect the performance of other methods. In this paper, the basics of Bayesian decision making and posterior probabilities are reviewed. A detailed procedure for using copulas as Bayesian classifiers for e-business data is presented. The emphasis in describing the method is placed upon practitioner understanding to facilitate replication in real situations while maintaining technical rigor to ease computerized implementation

A Principled Approach to Building and Evaluating Neural Network Models for E-Business Applications

Every commercial transaction generates large amounts of data on consumers for use by organizations. Data from ebusiness is typified by its complexity, quantity, and noisiness. Neural networks are ideally suited for these problem characteristics. Furthermore, the fact that neural networks can estimate the posterior probabilities associated with the group membership of objects of interest, makes them a powerful tool of great potential for e-business applications. As with all classification approaches, though, the neural network’s utility is based upon its generalization performance on new data. In this paper, we propose a principled approach to building and evaluation neural network models for e-business applications. First, the usefulness of neural networks for e-commerce applications and Bayesian classification is discussed. Next, the theory concerning model accuracy and generalization is presented. Then the principled approach is described including illustrative examples

THE HUMAN FACTOR IN ADVANCED MANUFACTURING TECHNOLOGY ADOPTION: AN EMPIRICAL ANALYSIS

Research Paper Series (National University of Singapore. Faculty of Business Administration); 1995-0021-3