A Comparative Study of Learning Styles of Business Students in The United States and the Dominican Republic

The Index of Learning Styles (ILS) instrument based on the Felder-Silverman Learning Style Model was used to compare distribution of learning styles of business students in the United States and the Dominican Republic. Results show that majority of business students have a balanced learning style in each of the four learning styles dimensions examined. Difference in learning style preference between United States and Dominican Republic was statistically significant only for the sensing-intuitive and active-reflective dimensions of the FelderSilverman model. The knowledge of statistically significant difference in learning styles of the U.S. and non-U.S. students may help American faculty pay attention to special needs of international students attending universities in the U.S

LEARNING STYLES OF EGYPTIAN BUSINESS STUDENTS

The Index of Learning Styles (ILS) instrument based on the Felder-Silverman Learning Style Model was used to determine distribution of learning styles of eighty Egyptian business students enrolled in an Egyptian institution of higher education. Results show that Egyptian business students surveyed in this study prefer sensing, visual, active, and sequential learning styles over intuitive, verbal, reflective, and global learning styles respectively. The majority of business students have a balanced learning style in all four dimensions of the Felder-Silverman model. Gender difference in learning style preference was statistically significant for only two of the four dimensions. The small gender difference was deemed inconsequential for designing teaching and learning methods. More than 85 percent of Egyptian business students are likely to benefit from teaching methods geared toward sensing, visual, active, and sequential learners

IS Course Success in Liberal Arts Institutions - What\u27s the Formula?

Much of IS pedagogy research has focused on IS programs in business schools or in computer science departments. Insufficient attention has been given to assessing IS pedagogy in business schools without an IS major and in a strong liberal arts environment where skepticism about IS education is high. We describe a newly-designed IS core course that succeeded in such an environment. Our formula for success comprises of inculcating the notion that IS knowledge has both a business and a technology dimension to it, treating these two dimensions as co-equals, and closely integrating the two dimensions while continuing to deliver technical education using the time-tested active learning approach. The active learning component of this course included working on a set of three software development projects focused on an e-business theme. Furthermore, given our perspective that IS knowledge consists of a business and a technology dimension, we also developed an entirely new approach to measuring learning outcomes. Student outcomes were measured in terms of movements in the two-dimensional IS knowledge space and detected via multivariate analysis of variance. Data on student outcomes were collected from three classes held in consecutive semesters. This study breaks new ground both in terms of how IS learning is conceptualized and measured and in demonstrating the success of a technology-driven pedagogical approach in an essentially non-technical culture

Does Culture Influence Learning Styles of Business Students? A Comparative Study of Two Cultures

This paper presents the usage of the Index of Learning Styles (ILS) instrument based on the Felder-Silverman Learning Style Model to investigate the influence of culture on learning style distribution of business students. Western culture was represented by the United States and was compared with middle-eastern culture represented by Egypt. Results of this study show that majority of business students have a balanced learning style in each of the four learning style dimensions of the Felder-Silverman model both in the U. S. and in Egypt. Difference in learning style distribution of business students between the U. S. and Egypt was statistically significant only for the sensing-intuitive and visual-verbal dimensions of the Felder-Silverman model. The difference was not statistically significant for the active-reflective and the sequential-global dimensions

Using Neural Networks to Predict MBA Student Success

Predicting MBA student performance for admission decisions is crucial for educational institutions. This paper evaluates the ability of three different models--neural networks, logit, and probit to predict MBA student performance in graduate programs. The neural network technique was used to classify applicants into successful and marginal student pools based on undergraduate GPA, GMAT scores, undergraduate major, age and other relevant data. The results of this study show that the neural network model performs as well as the statistical models and is a useful tool in predicting MBA student performance. Several limitations of this study are discussed

Predicting Corporate Acquisitions: An Application of Uncertain Reasoning Using Rule Induction

Artificial Intelligence (AI)-based rule induction techniques such as IXL and ID3 are powerful tools that can be used to classify firms as acquisition candidates or not, based on financial and other data. The purpose of this paper is to develop an expert system that employs uncertainty representation and predicts acquisition targets. We outline in this paper, the features of IXL, a machine learning technique that we use to induce rules. We also discuss how uncertainty is handled by IXL and describe the use of confidence factors. Rules generated by IXL are incorporated into a prototype expert system, ACQTARGET, which evaluates corporate acquisitions. The use of confidence factors in ACQTARGET allows investors to specifically incorporate uncertainties into the decision making process. A set of training examples comprising 65 acquired and 65 non-acquired real world firms is used to generate the rules and a separate holdout sample containing 32 acquired and 32 non-acquired real world firms is used to validate the expert system results. The performance of the expert system is also compared with a conventional discriminant analysis model and a logit model using the same data. The results show that the expert system, ACQTARGET, performs as well as the statistical models and is a useful evaluation tool to classify firms into acquisition and non-acquisition target categories. This rule induction technique can be a valuable decision aid to help financial analysts and investors in their buy/sell decisions

Induction

The authors wish to thank two anonymous referees for their helpful comments. Please address your correspondence to Srin