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

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.

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.
& 2018 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Subject area
Engineering Education More specific subject area

Learning Analytics
Type of data Tables, graphs, figures, and spreadsheet file How data was acquired 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.

Data format
Raw, analyzed Experimental factors Undergraduates with incomplete academic records were excluded

Experimental features
Descriptive statistics, frequency distributions, one-way ANOVA and multiple comparison post-hoc tests were performed to determine whether the variations in the academic performances are significant across the seven engineering programs.

Data source location
The population sample and the academic performance data provided in this article were obtained at Covenant University, Canaanland, Ota, Nigeria (Latitude 6.6718 o N, Longitude 3.1581 o E) Data accessibility 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.

Value of the data
Comprehensive academic performance datasets provided in this article will promote evidencebased research in the emerging field of learning analytics in developing countries [1][2][3][4].
Easy access to this data 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 [5][6][7][8][9][10].
With the growing adoption of machine learning and artificial intelligence techniques in different fields, empirical data provided in this article will help in the development of predictive models for learning outcomes in engineering undergraduates [11][12][13][14][15][16][17][18].
Descriptive statistics, frequency distributions, one-way ANOVA and multiple comparison post-hoc tests that are presented in tables, plots, and graphs will make data interpretation much easier for useful insights and logical conclusions.
Detailed datasets that are made publicly available in a Microsoft Excel spreadsheet file attached to this article will encourage further explorative studies in this field of research.

Data
The emerging field of learning analytics may be exploited to improve learning outcomes of engineering undergraduates in higher institutions of developing countries towards attaining sustainable education in the region [19][20][21]. Useful information about the academic performances of undergraduates that studied engineering programs at Covenant University, Nigeria are presented and analyzed in this data article. Covenant University is located in Ota, Ogun State in Nigeria  Tables 1-7.
The academic performances of engineering undergraduates vary as the students proceed from one level to another yearly.

Experimental design, materials and methods
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          Table 14. Multiple comparison plots of Cumulative GPA data in Figs. 25-31 reveal groups (i.e. other engineering programs at Covenant University) whose statistical means are significantly different.