Browsing by Author "Odukoya JA"

Now showing 1 - 5 of 5
  • Thumbnail Image
    Item
    Datasets linking ethnic perceptions to undergraduate students learning outcomes in a Nigerian Tertiary Institution
    (2018) Badejo JA; John TM; Omole DO; Ucheaga EG; Popoola SI; Odukoya JA; Ajayi PO; Aboyade M; Atayero AA
    This data article represents academic performances of undergraduate students in a select Nigerian Private Tertiary institution from 2008 to 2013. The 2413 dataset categorizes students with respect to their origins (ethnicity), pre-university admission scores and Cumulative Grade Point Averages earned at the end of their study at the university. We present a descriptive statistics showing mean, median, mode, maximum, minimum, range, standard deviation and variance in the performances of these students and a boxplot representation of the performances of these students with respect to their origins.
  • Thumbnail Image
    Item
    Datasets on demographic trends in enrollment into undergraduate engineering programs at Covenant University, Nigeria
    (2018) Popoola SI; Atayero AA; Badejo JA; Odukoya JA; Omole DO; Ajayi P
    In this data article, we present and analyze the demographic data of undergraduates admitted into engineering programs at Covenant University, Nigeria. The population distribution of 2649 candidates admitted into Chemical Engineering, Civil Engineering, Computer Engineering, Electrical and Electronics Engineering, Information and Communication Engineering, Mechanical Engineering, and Petroleum Engineering programs between 2002 and 2009 are analyzed by gender, age, and state of origin. The data provided in this data article were retrieved from the student bio-data submitted to the Department of Admissions and Student Records (DASR) and Center for Systems and Information Services (CSIS) by the candidates during the application process into the various engineering undergraduate programs. These vital information is made publicly available, after proper data anonymization, to facilitate empirical research in the emerging field of demographics analytics in higher education. A Microsoft Excel spreadsheet file is attached to this data article and the data is thoroughly described for easy reuse. Descriptive statistics and frequency distributions of the demographic data are presented in tables, plots, graphs, and charts. Unrestricted access to these demographic data will facilitate reliable and evidence-based research findings for sustainable education in developing countries.
  • Thumbnail Image
    Item
    Learning analytics for smart campus: Data on academic performances of engineering undergraduates in Nigerian private university
    (2018) Popoola SI; Atayero AA; Badejo JA; John TM; Odukoya JA; Omole DO
    Empirical measurement, monitoring, analysis, and reporting of learning outcomes in higher institutions of developing countries may lead to sustainable education in the region. In this data article, data about the academic performances of undergraduates that studied engineering programs at Covenant University, Nigeria are presented and analyzed. A total population sample of 1841 undergraduates that studied Chemical Engineering (CHE), Civil Engineering (CVE), Computer Engineering (CEN), Electrical and Electronics Engineering (EEE), Information and Communication Engineering (ICE), Mechanical Engineering (MEE), and Petroleum Engineering (PET) within the year range of 2002–2014 are randomly selected. For the five-year study period of engineering program, Grade Point Average (GPA) and its cumulative value of each of the sample were obtained from the Department of Student Records and Academic Affairs. In order to encourage evidence-based research in learning analytics, detailed datasets are made publicly available in a Microsoft Excel spreadsheet file attached to this article. Descriptive statistics and frequency distributions of the academic performance data are presented in tables and graphs for easy data interpretations. In addition, one-way Analysis of Variance (ANOVA) and multiple comparison post-hoc tests are performed to determine whether the variations in the academic performances are significant across the seven engineering programs. The data provided in this article will assist the global educational research community and regional policy makers to understand and optimize the learning environment towards the realization of smart campuses and sustainable education.
  • Thumbnail Image
    Item
    Learning analytics: Dataset for empirical evaluation of entry requirements into engineering undergraduate programs in a Nigerian university
    (2018) Odukoya JA; Popoola SI; Atayero AA; Omole DO; Badejo JA; John TM; Olowo OO
    In Nigerian universities, enrolment into any engineering undergraduate program requires that the minimum entry criteria established by the National Universities Commission (NUC) must be satisfied. Candidates seeking admission to study engineering discipline must have reached a predetermined entry age and met the cut-off marks set for Senior School Certificate Examination (SSCE), Unified Tertiary Matriculation Examination (UTME), and the post-UTME screening. However, limited effort has been made to show that these entry requirements eventually guarantee successful academic performance in engineering programs because the data required for such validation are not readily available. In this data article, a comprehensive dataset for empirical evaluation of entry requirements into engineering undergraduate programs in a Nigerian university is presented and carefully analyzed. A total sample of 1445 undergraduates that were admitted between 2005 and 2009 to study Chemical Engineering (CHE), Civil Engineering (CVE), Computer Engineering (CEN), Electrical and Electronics Engineering (EEE), Information and Communication Engineering (ICE), Mechanical Engineering (MEE), and Petroleum Engineering (PET) at Covenant University, Nigeria were randomly selected. Entry age, SSCE aggregate, UTME score, Covenant University Scholastic Aptitude Screening (CUSAS) score, and the Cumulative Grade Point Average (CGPA) of the undergraduates were obtained from the Student Records and Academic Affairs unit. In order to facilitate evidence-based evaluation, the robust dataset is made publicly available in a Microsoft Excel spreadsheet file. On yearly basis, first-order descriptive statistics of the dataset are presented in tables. Box plot representations, frequency distribution plots, and scatter plots of the dataset are provided to enrich its value. Furthermore, correlation and linear regression analyses are performed to understand the relationship between the entry requirements and the corresponding academic performance in engineering programs. The data provided in this article will help Nigerian universities, the NUC, engineering regulatory bodies, and relevant stakeholders to objectively evaluate and subsequently improve the quality of engineering education in the country.
  • Thumbnail Image
    Item
    The role of gender on academic performance in STEM-related disciplines: Data from a tertiary institution
    (2018) John TM; Badejo JA; Popoola SI; Omole DO; Odukoya JA; Ajayi PO; Aboyade M; Atayero AA
    This data article presents data of academic performances of undergraduate students in Science, Technology, Engineering and Mathematics (STEM) disciplines in Covenant University, Nigeria. The data shows academic performances of Male and Female students who graduated from 2010 to 2014. The total population of samples in the observation is 3046 undergraduates mined from Biochemistry (BCH), Building technology (BLD), Computer Engineering (CEN), Chemical Engineering (CHE), Industrial Chemistry (CHM), Computer Science (CIS), Civil Engineering (CVE), Electrical and Electronics Engineering (EEE), Information and Communication Engineering (ICE), Mathematics (MAT), Microbiology (MCB), Mechanical Engineering (MCE), Management and Information System (MIS), Petroleum Engineering (PET), Industrial Physics-Electronics and IT Applications (PHYE), Industrial Physics-Applied Geophysics (PHYG) and Industrial Physics-Renewable Energy (PHYR). The detailed dataset is made available in form of a Microsoft Excel spreadsheet in the supplementary material of this article.