Rule extraction from technology IPOs in the US stock market

Machine learning techniques for prediction and rule extraction from artificial neural network methods are used. The hypothesis that market sentiment and IPO specific attributes are equally responsible for first-day IPO returns in the US stock market is tested. Machine learning methods used are Bayesian classifications, support vector machines, decision tree techniques, rule learners and artificial neural networks. The outcomes of the research are predictions and rules associated With first-day returns of technology IPOs. The hypothesis that first-day returns of technology IPOs are equally determined by IPO specific and market sentiment is rejected. Instead lower yielding IPOs are determined by IPO specific and market sentiment attributes, while higher yielding IPOs are largely dependent on IPO specific attributes.

Fostering professional development through integrating work and formal learning

The Undergraduate Site Learning Program (USLP) is an innovative work-based learning program that addresses the call to develop a broader set ofattributes in engineering graduates. Unlike cooperative education programs, site learning can give students full academic credit for their placement without extending the duration of the degree through the use of an innovative learning alignment model. A cenrralpart ofthis program is a unique course entitled Professional Development in which students articulate and reflect upon the lessons they leom while on placement in industry. Students spend the bulk ofa semester on-site often in remote locations, which requires a flexible approach to course operation and fosters independent learning. Thus the USLP challenges both staff and students and produces outcomes that bofh the alumni and industry value.

Investigating Concept Mapping and Stimulated Recall to Reveal Academic Teacher Beliefs and how they are enacted through learning designs for the Web

Increasingly, academic teachers are designing their own web sites to add value to or replace other forms of university teaching. These web sites are tangible and dynamic constructions that represent the teachers thinking and decisions derived from an implicit belief system about teaching and learning. The emphasis of this study is to explore the potential of the research techniques of concept-mapping and stimulated recall to locate the implicit pedagogies of academic teachers and investigate how they are enacted through the learning designs of their web sites. The rationale behind such an investigation is that once these implicit belief systems are made visible, then conversations can commence about how these beliefs are transformed into practice, providing a potent departure point for academic development.