Profiling The Campus Recruiter At A Four-Year Hospitality Program

Profiling the Campus Recruiter At a Four-Year Hospitality Program, is a written profile, supported by anecdotal rather than stridently empirical evidence, by Al lzzolo, Assistant Professor, College of Hotel Administration, University of Nevada, Las Vegas. “Each year major chain corporations as well as single unit companies interview hospitality students throughout the country. A study conducted at the University of Nevada, Las Vegas, was designed to profile the hospitality industry campus recruiter and to provide meaningful data to college students who would be interviewing with these recruiters,” the author initially proffers. “Recruiting at the four-year hospitality program, by its nature, is not a science, nor is it highly quantifiable. The interviewing and selection processes are highly subjective and vary from company to company,” says Izzolo to preface his essay. “Data were collected via a questionnaire specifically designed to answer questions about the recruiters and/or the companies that sent interviewers to the placement office of the university's hospitality program,” our author says to explain the process used to gather information for the piece. Findings of the study indicate that the typical recruiter is male, college educated – but not necessarily in a Hospitality’ curriculum – and almost 80 percent of respondents said they had the authority to hire management trainees. Few campuses are visited by hospitality industry recruitment staff as evidenced by Izzolo’s observations/data. Table 3 analyzes the desirable traits a recruiter deems appropriate for the potential employee candidate. Personal appearance, work experience, grade point average, and verbal communication rank high on the list of distinguishable attributes. The most striking finding in this portion of the study is that a student’s GPA is virtually ignored. “Recruiting for the hospitality industry appears to be very subjective,” Izzolo says. “Recruiters are basing decisions to hire not on knowledge levels as determined by an academic grade point average but rather on criteria much less definitive, such as verbal skills and personal appearance,” our author opines. In closing, Izzolo concedes this is not a definitive study, but is merely a launching pad to a more comprehensive investigation on the recruitment subject.

Utilizing Traditional Cognitive Measures of Academic Preparation to Predict First-Year Science, Technology, Engineering, and Mathematics (STEM) Majors' Success in Math and Science Courses

For the past several years, U.S. colleges and universities have faced increased pressure to improve retention and graduation rates. At the same time, educational institutions have placed a greater emphasis on the importance of enrolling more students in STEM (science, technology, engineering and mathematics) programs and producing more STEM graduates. The resulting problem faced by educators involves finding new ways to support the success of STEM majors, regardless of their pre-college academic preparation. The purpose of my research study involved utilizing first-year STEM majors’ math SAT scores, unweighted high school GPA, math placement test scores, and the highest level of math taken in high school to develop models for predicting those who were likely to pass their first math and science courses. In doing so, the study aimed to provide a strategy to address the challenge of improving the passing rates of those first-year students attempting STEM-related courses. The study sample included 1018 first-year STEM majors who had entered the same large, public, urban, Hispanic-serving, research university in the Southeastern U.S. between 2010 and 2012. The research design involved the use of hierarchical logistic regression to determine the significance of utilizing the four independent variables to develop models for predicting success in math and science. The resulting data indicated that the overall model of predictors (which included all four predictor variables) was statistically significant for predicting those students who passed their first math course and for predicting those students who passed their first science course. Individually, all four predictor variables were found to be statistically significant for predicting those who had passed math, with the unweighted high school GPA and the highest math taken in high school accounting for the largest amount of unique variance. Those two variables also improved the regression model’s percentage of correctly predicting that dependent variable. The only variable that was found to be statistically significant for predicting those who had passed science was the students’ unweighted high school GPA. Overall, the results of my study have been offered as my contribution to the literature on predicting first-year student success, especially within the STEM disciplines.