No abstract

No abstract

The quality of teaching and learning in higher education in many developing countries can be improved as institutions in this region adopt evidence-based practices that emphasize empirical measurements, observations, analysis and reports of learning outcomes. This article presents and analyses data on the academic performances of undergraduate students for duration of three semesters across the three major colleges of Landmark University, a private University in Nigeria. The colleges include the college of Agricultural Sciences (CAS), college of Business and Social Sciences (CBSS), and the college of Science and Engineering (CSE). Furthermore, population samples of 82, 577 and 812 undergraduates were selected randomly from CAS, CBSS and CSE respectively; totaling a population of sample of 1471 undergraduates from all academic levels (200L–500L) with the exception of first year students. The random selection was drawn from three consecutive semesters- the first and second semesters of academic 2016/2017 session and first semester of 2017/2018 academic session. The cumulative GPA of the sample population of students for the semester highlighted was obtained from the Centre for Systems and Information Services Units of the University. Motivated by the need to promote evidence-based research in academic excellence, a spread-sheet containing the detailed dataset is attached to this article. The descriptive statistics and frequency distributions of academic performance data are presented in with the use of tables and graphs for easy data interpretations. The data provided in this article supports the goal of a regional policy towards the realization of qualitative sustainable education.

The advent of technology has crept into virtually all sectors and this has culminated in automated processes making use of the Internet in executing various tasks and actions. Web services have now become the trend when it comes to providing solutions to mundane tasks. However, this development comes with the bottleneck of authenticity and intent of users. Providers of these Web services, whether as a platform, as a software or as an Infrastructure use various human interaction proof’s (HIPs) to validate authenticity and intent of its users. Completely automated public turing test to tell computer and human apart (CAPTCHA), a form of IDS in web services is advantageous. Research into CAPTCHA can be grouped into two -CAPTCHA development and CAPTCH recognition. Selective learning and convolutionary neural networks (CNN) as well as deep convolutionary neural network (DCNN) have become emerging trends in both the development and recognition of CAPTCHAs. This paper reviews critically over fifty article publications that shows the current trends in the area of the CAPTCHA scheme, its development and recognition mechanisms and the way forward in helping to ensure a robust and yet secure CAPTCHA development in guiding future research endeavor in the subject domain.

No abstract