Career Technical Education Adjunct Faculty Teacher Readiness: An Investigation of Teacher Excellence and Variables of Preparedness

The purpose of this research was to examine the relationship between teaching readiness and teaching excellence with three variables of preparedness of adjunct professors teaching career technical education courses through student surveys using a correlational design of two statistical techniques; least-squares regression and one-way analysis of variance. That is, the research tested the relationship between teacher readiness and teacher excellence with the number of years teaching, the number of years of experience in the professional field and exposure to teaching related professional development, referred to as variables of preparedness. The results of the research provided insight to the relationship between the variables of preparedness and student assessment of their adjunct professors. Concerning the years of teaching experience, this research found a negative inverse relationship with how students rated their professors’ teaching readiness and excellence. The research also found no relationship between years of professional experience and the students’ assessment. Lastly, the research found a significant positive relationship between the amount of teaching related professional development taken by an adjunct professor and the students’ assessment in teaching readiness and excellence. This research suggests that policies and practices at colleges should address the professional development needs of adjunct professors. Also, to design a model that meets the practices of inclusion for adjunct faculty and to make professional development a priority within the organization. Lastly, implement that model over time to prepare adjuncts in readiness and excellence.

Comparison and Validation of Tropical Rainfall Measuring Mission (TRMM) Rainfall Algorithms in Tropical Cyclones

Tropical Rainfall Measuring Mission (TRMM) rainfall retrieval algorithms are evaluated in tropical cyclones (TCs). Differences between the Precipitation Radar (PR) and TRMM Microwave Imager (TMI) retrievals are found to be related to the storm region (inner core vs. rainbands) and the convective nature of the precipitation as measured by radar reflectivity and ice scattering signature. In landfalling TCs, the algorithms perform differently depending on whether the rainfall is located over ocean, land, or coastal surfaces. Various statistical techniques are applied to quantify these differences and identify the discrepancies in rainfall detection and intensity. Ground validation is accomplished by comparing the landfalling storms over the Southeast US to the NEXRAD Multisensor Precipitation Estimates (MPE) Stage-IV product. Numerous recommendations are given to algorithm users and developers for applying and interpreting these algorithms in areas of heavy and widespread tropical rainfall such as tropical cyclones.