Search results

Across the nation, African-American and Latino males have experienced limited access to placement in gifted education programs. This paper aims to pinpoint and describe the factors that frequently influence access to gifted education programming among African-American and Latino males.

African-American and Latino males are persistently underrepresented in gifted education for reasons such as teachers’ narrow conceptions of giftedness, teachers’ bias in the nomination process and teachers’ inappropriate usage and interpretation of intelligence measures. When these students qualify for such services, they often experience feelings of isolation and loneliness due to scarce representation of other African-American and Latino male students. A review of extant literature was conducted to identify factors that influence access to gifted education programming among African-American and Latino males.

African-American and Latino males encounter roadblocks in being identified for gifted placement and many also experience implicit biases and stereotypical beliefs about their ability. The need for culturally competent professionals is critical to meet the academic and social-emotional needs of gifted African-American and Latino males.

Recommendations for school psychologists and school counselors are offered to support the needs of gifted African-American and Latino males, assist in increasing their identification and participation in gifted education, and promote academic success.

There is an urgent need for research on access and placement in gifted programming among African-American and Latino males. Moreover, the role of school psychologists and school counselors should be considered in facilitation of gifted identification and placement.

The purpose of this paper is to use the data analysis method of sentiment analysis to improve the understanding of a large data set of employee comments from an annual employee job satisfaction survey of a US hospitality organization.

Sentiment analysis is used to examine the employee comments by identifying meaningful patterns, frequently used words and emotions. The statistical computing language, R, uses the sentiment analysis process to scan each employee survey comment, compare the words with the predefined word dictionary and classify the employee comments into the appropriate emotion category.

Employee responses written in English and in Spanish are compared with significant differences identified between the two groups, triggering further investigation of the Spanish comments. Sentiment analysis was then conducted on the Spanish comments comparing two groups, front-of-house vs back-of-house employees and employees with male supervisors vs female supervisors. Results from the analysis of employee comments written in Spanish point to higher scores for job sadness and anger. The negative comments referred to desires for improved healthcare, requests for increased wages and frustration with difficult supervisor relationships. The findings from this study add to the growing body of literature that has begun to focus on the unique work experiences of Latino employees in the USA.

This is the first study to examine a large unstructured English and Spanish text database from a hospitality organization’s employee job satisfaction surveys using sentiment analysis. Applying this big data analytics process to advance new insights into the human capital aspects of hospitality management is intriguing to many researchers. The results of this study demonstrate an issue that needs to be further investigated particularly considering the hospitality industry’s employee demographics.