Learning analytics for smart campus: Data on academic performances of engineering undergraduates in Nigerian private university

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dc.contributor.authorPopoola SI
dc.contributor.authorAtayero AA
dc.contributor.authorBadejo JA
dc.contributor.authorJohn TM
dc.contributor.authorOdukoya JA
dc.contributor.authorOmole DO
dc.date.accessioned2022-07-26T12:18:22Z
dc.date.available2022-07-26T12:18:22Z
dc.date.issued2018
dc.descriptionData in Brief
dc.description.abstractEmpirical 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.
dc.identifier.citation10.1016/j.dib.2017.12.059
dc.identifier.issn2352-3409
dc.identifier.urihttps://nerd.ethesis.ng/handle/123456789/320
dc.language.isoen
dc.subjectSmart campus
dc.subjectLearning analytics
dc.subjectSustainable education
dc.subjectNigerian university
dc.subjectEducation data mining
dc.subjectEngineering
dc.titleLearning analytics for smart campus: Data on academic performances of engineering undergraduates in Nigerian private university
dc.typeArticle
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