Factors affecting the academic performance of real estate students in a specialized Federal University of Technology in Nigeria

Abstract

Purpose The purpose of this paper is to examine the factors affecting the academic outcome of real estate students in a specialized Federal University in Nigeria. Furthermore, this paper investigates the phenomenon of publication bias in the extant literature as such evidence poses severe threats to the validity of empirical findings on factors affecting the degree outcome of undergraduate students. Design/methodology/approach The standard statistical approach adopted was to examine whether the reported coefficient estimates from ten empirical studies (105 observations) are independent of their standard errors by employing both ordinary least squares (OLS) and weighted least squares (WLS). In this paper, this approach enabled evidence of publication bias in the cited literature to be refuted. In addition, data were also collected on the academic measure and demographic information of 449 students who graduated between 2005 and 2011. For the purpose of analysis, the study utilized a stepwise logistic regression technique to examine the factors impacting on the degree outcome of real estate students. Findings The results of the OLS and WLS regression indicate that there is no significant evidence of any empirical effect of publication bias in the extant literature. The results of the logistic regression also revealed that grade point average, gender differences, prior knowledge of real estate discipline and potential difference in year of enrollment impact on students’ academic performance in terms of their ability to graduate at first attempt. In addition, factors such as age, marital status, high school grade and geopolitical/ethnic background of undergraduate real estate students do not influence their opportunities to graduate at first attempt from the university. Research limitations/implications This paper focuses only on one specialized university of technology offering a bachelor’s program in real estate in Nigeria, so as to remove any extraneous factor(s) that could be present in the other institutional settings where students have completed such program. Extending similar study to tertiary institutions in Nigeria that share similar geographical characteristics and institutional settings can produce far-reaching generalization. Originality/value This paper contributes to the scanty literature on factors affecting the academic performance of students in an undergraduate real estate program in Nigeria. A scientific element of novelty in this paper is the evidence of the absence of the underlying effect of publication bias in the extant literature on students’ academic outcome in tertiary institutions. Findings from this study serve as the basis for university officers to monitor significant transitions in real estate students’ academic progress, so as to identify those who are unlikely to graduate at first attempt early at the entrant level. Generally, the outcome of this research could provide faculty and admission officers in tertiary institutions with complementary information in arriving at an informed decision in a non-discriminatory admission process.

[Retracted] Analysis of the Natural Conditions and Geographical Factors of the Formation of Hui‐Style Prints

“…On this basis, the formation of Hui-style prints is affected by economic and social benefits, resulting in increased interference factors in the formation of Hui-style prints, which is also harmful to the creation of artists. Therefore, all departments gradually realized the seriousness of this problem and gradually increased their attention to the formation of Hui-style prints [ 3 ].…”

Applying machine learning approach to predict students’ performance in higher educational institutions

“…Their study uses the following predictive algorithms: classification tree, random forest, Naïve Bayes and neural networks. Similarly, Ojetunde et al (2019) did not find evidence to support that the geopolitical/ethnic background of students determines the degree outcome of undergraduate students using a logistic regression model.…”