Ghanaian universities have employed various marketing mix to stimulate demand for their services; particularly in recruitment and engagement of home and international students. This paper examines the relative influence of factors affecting the college choice decisions of students. It is based on a survey of 2,534 admitted students at a major university in Ghana, to which 39 percent of the sample responded. We used Factor analysis to rate 26 University borne attributes upon which student decisions are based. The outcome was used to build a five-scale preference measure, which were evaluated with a qualitative approach in analyzing gathered data using descriptive statistics, cross tabulation and chi square. The results revealed some marketing strategies that can be used in institutions of higher learning to boost students’ enrolment in their order of perceived effectiveness. The following were found to influence decisions: word of mouth (family and friends), proximity/nearness, accreditation/affiliation, affordability and lecturing Style.

The authors explore how the milieu of workplace diversity affects the relationship between employee and performance. In particular, we theorize and empirically examine the moderating effects of four (4) categories of diversity context variables: age, gender, ethnicity, and educational background. The authors perform analyses on 175 out of 320 respondents consisting of academic and administrative staff of four selected private universities in Ghana. The results showed, for example, that workplace diversity has an overall influence on employee performance, however, educational diversity has more effect on employee performance in the universities compared to the other competing variables used in this study. Age and educational diversity had a significant impact on employee performance in the universities whilst gender and ethnicity diversity had no influence on their performance. We discuss future research directions regarding diversity, workgroup context, and performance outcomes and outline some recommendations for administrators and university leaders. Request access from your librarian to read this article's full text.

The increasing complexity of administrative tasks in educational institutions has necessitated the adoption of automated systems powered by artificial intelligence (AI) and other advanced technologies. This chapter explores the role of AI in streamlining administrative processes such as grading, scheduling, and attendance tracking. It highlights the benefits of automation, including improved efficiency, reduced workload for educators, and enhanced accuracy. However, the chapter also addresses the challenges associated with automation, such as data privacy concerns, implementation costs, and resistance to change. By examining current research and case studies, this chapter provides a comprehensive overview of the potential and pitfalls of automating administrative tasks in education.

Video semantic concept analysis has received a lot of research attention in the area of human computer interactions in recent times. Reconstruction error classification methods based on sparse coefficients do not consider discrimination, essential for classification performance between video samples. To further improve the accuracy of video semantic classification, a video semantic concept classification approach based on sparse coefficient vector (SCV) and a kernel-based weighted KNN (KWKNN) is proposed in this paper. In the proposed approach, a loss function that integrates reconstruction error and discrimination is put forward. The authors calculate the loss function value between the test sample and training samples from each class according to the loss function criterion, and then vote on statistical results. Finally, this paper modifies the vote results combined with the kernel weight coefficient of each class and determine the video semantic concept. The experimental results show that this method effectively improves the classification accuracy for video semantic analysis and shorten the time used in the semantic classification compared with some baseline approaches.