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. 2018 Jan 3;17:76–94. doi: 10.1016/j.dib.2017.12.059

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

Segun I Popoola a,⁎, Aderemi A Atayero a, Joke A Badejo a, Temitope M John a, Jonathan A Odukoya b, David O Omole c
PMCID: PMC5988220  PMID: 29876377

Abstract

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.

Keywords: Smart campus, Learning analytics, Sustainable education, Nigerian university, Education data mining, Engineering

Specifications Table

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 6.6718oN, Longitude 3.1581oE)
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.
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Value of the data

  • •

    Comprehensive academic performance datasets provided in this article will promote evidence-based 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.

1. 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 (Latitude 6.6718o N, Longitude 3.1581o E). It is a private Christian university affiliated with Living Faith Church Worldwide and a member of the Association of Commonwealth Universities (ACU), Association of African Universities (AAU), and National Universities Commission (NUC).

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. The earliest year of entry and the latest year of graduation are 2002 and 2014 respectively. Having excluded undergraduates with incomplete academic records, 198, 152, 374, 407, 349, 166, 195 undergraduates were pooled from CHE, CVE, CEN, EEE, ICE, MEE, and PET respectively. The descriptive statistics of the academic performances of undergraduates in each of the seven engineering programs at Covenant University are presented in Table 1, Table 2, Table 3, Table 4, Table 5, Table 6, Table 7.

Table 1.

Descriptive statistics of academic performances of undergraduates in CHE.

First Year GPA Second Year GPA Third Year GPA Fourth Year GPA Fifth Year GPA Cumulative GPA
Mean 4.02 3.49 3.52 3.77 3.79 3.70
Median 4.11 3.53 3.55 3.88 3.90 3.78
Mode 4.15 2.74 3.13 4.06 4.43 3.73
Standard Deviation 0.57 0.69 0.77 0.79 0.67 0.61
Variance 0.32 0.48 0.59 0.63 0.45 0.37
Kurtosis 4.07 2.69 2.40 2.70 3.45 2.39
Skewness −0.97 −0.34 −0.33 −0.64 −0.85 −0.36
Range 2.82 3.24 3.47 3.42 3.41 2.70
Minimum 2.09 1.54 1.47 1.55 1.59 2.16
Maximum 4.91 4.78 4.94 4.97 5.00 4.86
Total Samples 198 198 198 198 198 198
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Table 2.

Descriptive statistics of academic performances of undergraduates in CVE.

First Year GPA Second Year GPA Third Year GPA Fourth Year GPA Fifth Year GPA Cumulative GPA
Mean 3.67 3.13 3.33 3.78 3.91 3.54
Median 3.70 3.09 3.38 3.92 4.01 3.60
Mode 4.02 3.14 2.76 4.17 4.89 3.76
Standard Deviation 0.60 0.69 0.85 0.74 0.71 0.65
Variance 0.36 0.47 0.72 0.54 0.50 0.42
Kurtosis 3.48 2.55 2.28 2.24 2.60 2.27
Skewness −0.47 0.25 −0.15 −0.42 −0.57 −0.06
Range 3.36 3.22 3.94 3.03 3.15 2.96
Minimum 1.60 1.70 0.99 1.94 1.83 1.97
Maximum 4.96 4.92 4.93 4.97 4.98 4.93
Total Samples 152 152 152 152 152 152
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Table 3.

Descriptive statistics of academic performances of undergraduates in CEN.

First Year GPA Second Year GPA Third Year GPA Fourth Year GPA Fifth Year GPA Cumulative GPA
Mean 3.61 3.23 3.38 3.64 3.62 3.50
Median 3.71 3.22 3.51 3.72 3.68 3.56
Mode 4.00 3.20 4.47 4.07 4.25 3.21
Standard Deviation 0.71 0.76 0.90 0.77 0.72 0.69
Variance 0.50 0.58 0.81 0.59 0.52 0.48
Kurtosis 2.58 2.50 2.36 3.33 2.73 2.44
Skewness −0.43 0.03 −0.43 −0.61 −0.45 −0.24
Range 3.20 3.74 4.01 4.40 3.55 3.10
Minimum 1.73 1.19 0.97 0.60 1.39 1.80
Maximum 4.93 4.93 4.98 5.00 4.94 4.90
Total Samples 374 374 374 374 374 374
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Table 4.

Descriptive statistics of academic performances of undergraduates in EEE.

First Year GPA Second Year GPA Third Year GPA Fourth Year GPA Fifth Year GPA Cumulative GPA
Mean 4.03 3.49 3.60 3.54 3.58 3.66
Median 4.11 3.48 3.73 3.57 3.64 3.71
Mode 4.13 3.22 3.96 3.48 4.00 3.28
Standard Deviation 0.56 0.73 0.83 0.76 0.74 0.66
Variance 0.31 0.54 0.69 0.58 0.55 0.43
Kurtosis 3.07 2.50 2.56 2.59 2.49 2.43
Skewness −0.61 −0.17 −0.55 −0.38 −0.32 −0.29
Range 3.23 3.56 3.95 3.69 3.58 3.05
Minimum 1.71 1.34 1.05 1.31 1.42 1.83
Maximum 4.94 4.90 5.00 5.00 5.00 4.88
Total Samples 407 407 407 407 407 407
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Table 5.

Descriptive statistics of academic performances of undergraduates in ICE.

First Year GPA Second Year GPA Third Year GPA Fourth Year GPA Fifth Year GPA Cumulative GPA
Mean 3.56 3.18 3.30 3.58 3.74 3.47
Median 3.55 3.18 3.36 3.62 3.82 3.51
Mode 3.49 3.06 3.02 3.52 4.00 3.51
Standard Deviation 0.69 0.76 0.88 0.73 0.71 0.68
Variance 0.48 0.57 0.77 0.54 0.50 0.46
Kurtosis 2.57 2.42 2.32 2.66 2.72 2.44
Skewness −0.33 0.06 −0.24 −0.40 −0.48 −0.16
Range 3.32 3.49 3.89 3.49 3.23 3.09
Minimum 1.64 1.39 1.09 1.51 1.75 1.80
Maximum 4.96 4.88 4.98 5.00 4.98 4.89
Total Samples 349 349 349 349 349 349
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Table 6.

Descriptive statistics of academic performances of undergraduates in MEE.

First Year GPA Second Year GPA Third Year GPA Fourth Year GPA Fifth Year GPA Cumulative GPA
Mean 3.92 3.33 3.13 3.60 3.78 3.54
Median 4.00 3.32 3.04 3.73 3.96 3.57
Mode 4.00 3.69 3.13 4.55 4.30 3.95
Standard Deviation 0.60 0.72 0.87 0.76 0.73 0.66
Variance 0.36 0.52 0.76 0.58 0.54 0.43
Kurtosis 3.12 2.19 2.06 2.74 2.70 2.25
Skewness −0.69 0.03 0.05 −0.57 −0.67 −0.14
Range 2.67 3.32 3.58 3.72 3.25 2.89
Minimum 2.20 1.55 1.40 1.25 1.73 1.99
Maximum 4.87 4.87 4.98 4.97 4.98 4.88
Total Samples 166 166 166 166 166 166
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Table 7.

Descriptive statistics of academic performances of undergraduates in PET.

First Year GPA Second Year GPA Third Year GPA Fourth Year GPA Fifth Year GPA Cumulative GPA
Mean 3.86 3.24 3.32 3.54 3.71 3.54
Median 3.91 3.18 3.33 3.54 3.75 3.56
Mode 3.78 2.48 3.74 3.61 3.20 3.83
Standard Deviation 0.62 0.71 0.73 0.69 0.65 0.59
Variance 0.38 0.50 0.54 0.48 0.42 0.35
Kurtosis 3.83 2.54 2.46 2.67 2.39 2.43
Skewness −0.88 −0.04 −0.15 −0.03 −0.18 −0.01
Range 3.29 3.74 3.64 3.55 2.83 2.73
Minimum 1.64 1.22 1.18 1.45 2.13 2.07
Maximum 4.93 4.96 4.82 5.00 4.95 4.80
Total Samples 195 195 195 195 195 195
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The academic performances of engineering undergraduates vary as the students proceed from one level to another yearly. Fig. 1 shows the variations in the GPA data of all the engineering undergraduates under investigation. Fig. 2, Fig. 3, Fig. 4, Fig. 5, Fig. 6, Fig. 7, Fig. 8 illustrate the differences and trends in the GPA data of undergraduates in CHE, CVE, CEN, EEE, ICE, MEE, and PET respectively. The frequency distributions of the GPA data of undergraduates in CHE, CVE, CEN, EEE, ICE, MEE, and PET are shown in Fig. 9, Fig. 10, Fig. 11, Fig. 12, Fig. 13, Fig. 14, Fig. 15 respectively. Fig. 16, Fig. 17, Fig. 18 depict the proportions of engineering students that graduated with First Class, Second Class Upper, Second Class Lower, and Third Class in CHE, CVE, CEN, and EEE; ICE and MEE; and PET respectively.

Fig. 1.

Fig. 1

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Boxplot of GPA data of undergraduates in the seven engineering programs (2002–2014).

Fig. 2.

Fig. 2

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Boxplot of GPA data of undergraduates in CHE (2002–2014).

Fig. 3.

Fig. 3

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Boxplot of GPA data of undergraduates in CVE (2002–2014).

Fig. 4.

Fig. 4

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Boxplot of GPA data of undergraduates in CEN (2002–2014).

Fig. 5.

Fig. 5

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Boxplot of GPA data of undergraduates in EEE (2002–2014).

Fig. 6.

Fig. 6

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Boxplot of GPA data of undergraduates in ICE (2002–2014).

Fig. 7.

Fig. 7

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Boxplot of GPA data of undergraduates in MEE (2002–2014).

Fig. 8.

Fig. 8

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Boxplot of GPA data of undergraduates in PET (2002–2014).

Fig. 9.

Fig. 9

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Histogram distributions of GPA data of undergraduates in CHE.

Fig. 10.

Fig. 10

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Histogram distributions of GPA data of undergraduates in CVE.

Fig. 11.

Fig. 11

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Histogram distributions of GPA data of undergraduates in CEN.

Fig. 12.

Fig. 12

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Histogram distributions of GPA data of undergraduates in EEE.

Fig. 13.

Fig. 13

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Histogram distributions of GPA data of undergraduates in ICE.

Fig. 14.

Fig. 14

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Histogram distributions of GPA data of undergraduates in MEE.

Fig. 15.

Fig. 15

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Histogram distributions of GPA data of undergraduates in PET.

Fig. 16.

Fig. 16

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Proportions of class of degree in CHE, CVE, CEN, and EEE.

Fig. 17.

Fig. 17

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Proportions of class of degree in ICE and MEE.

Fig. 18.

Fig. 18

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Proportions of class of degree in PET.

2. 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 academic performances are significant across the seven engineering programs. Data showing whether there are significant differences in the GPA data of the engineering undergraduates throughout their five-year study period are presented in Table 8, Table 9, Table 10, Table 11, Table 12, Table 13. The boxplots of the GPA distribution by program are shown in Fig. 19, Fig. 20, Fig. 21, Fig. 22, Fig. 23, Fig. 24. The results of the post-hoc test conducted to understand the extent of significant variations in cumulative GPA across engineering Programs at Covenant University are presented in Table 14. Multiple comparison plots of Cumulative GPA data in Fig. 25, Fig. 26, Fig. 27, Fig. 28, Fig. 29, Fig. 30, Fig. 31 reveal groups (i.e. other engineering programs at Covenant University) whose statistical means are significantly different.

Table 8.

ANOVA test on first year GPA data of engineering programs at Covenant university.

Source of variation Sum of squares Degree of freedom Mean squares F Statistic Prob>F
Columns 69.15 6 11.52 28.95 2.99×10–33
Error 730.21 1834 0.40
Total 799.36 1840
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Table 9.

ANOVA test on second year GPA data of engineering programs at Covenant university.

Source of variation Sum of squares Degree of freedom Mean squares F statistic Prob>F
Columns 34.02 6 5.67 10.58 1.43×10–11
Error 983.13 1834 0.54
Total 1017.15 1840
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Table 10.

ANOVA test on third year GPA data of engineering programs at Covenant university.

Source of variation Sum of squares Degree of freedom Mean squares F statistic Prob>F
Columns 36.48 6 6.08 8.55 3.47×10-9
Error 1304.02 1834 0.71
Total 1340.51 1840
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Table 11.

ANOVA test on fourth year GPA data of engineering programs at Covenant university.

Source of variation Sum of squares Degree of freedom Mean squares F statistic Prob>F
Columns 12.99 6 2.16 3.83 8.53×10-4
Error 1037.83 1834 0.57
Total 1050.82 1840
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Table 12.

ANOVA test on fifth year GPA data of engineering programs at Covenant university.

Source of variation Sum of squares Degree of freedom Mean squares F statistic Prob>F
Columns 17.80 6 2.97 5.87 4.44 × 10-6
Error 926.63 1834 0.51
Total 944.43 1840
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Table 13.

ANOVA test on cumulative GPA data of engineering programs at Covenant university.

Source of variation Sum of squares Degree of freedom Mean squares F statistic Prob>F
Columns 12.13 6 2.02 4.70 9.39×10-5
Error 789.25 1834 0.43
Total 801.38 1840
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Fig. 19.

Fig. 19

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First year GPA data of all engineering programs.

Fig. 20.

Fig. 20

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Second year GPA data of engineering programs at Covenant university.

Fig. 21.

Fig. 21

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Third year GPA data of engineering programs at Covenant university.

Fig. 22.

Fig. 22

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Fourth year GPA data of engineering programs at Covenant university.

Fig. 23.

Fig. 23

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Fifth year GPA data of engineering programs at Covenant university.

Fig. 24.

Fig. 24

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Cumulative GPA data of engineering programs at Covenant university.

Table 14.

Post-hoc test on cumulative GPA for engineering programs at Covenant university.

Groups compared Lower limits for 95% confidence intervals Mean difference Upper limits for 95% confidence intervals p-value
CHE CVE −0.0469 0.1617 0.3703 0.2507
CHE CEN 0.0331 0.2031 0.3731 0.0078
CHE EEE −0.1222 0.0453 0.2129 0.9853
CHE ICE 0.0590 0.2310 0.4031 0.0015
CHE MEE −0.0450 0.1585 0.3621 0.2455
CHE PET −0.0333 0.1618 0.3570 0.1798
CVE CEN −0.1447 0.0414 0.2274 0.9948
CVE EEE −0.3002 −0.1164 0.0675 0.5029
CVE ICE −0.1186 0.0693 0.2573 0.9321
CVE MEE −0.2203 −0.0032 0.2139 1.0000
CVE PET −0.2091 0.0001 0.2094 1.0000
CEN EEE −0.2963 −0.1577 −0.0192 0.0139
CEN ICE −0.1160 0.0280 0.1719 0.9976
CEN MEE −0.2249 −0.0445 0.1358 0.9909
CEN PET −0.2121 −0.0412 0.1296 0.9919
EEE ICE 0.0446 0.1857 0.3268 0.0020
EEE MEE −0.0649 0.1132 0.2913 0.4979
EEE PET −0.0520 0.1165 0.2849 0.3898
ICE MEE −0.2549 −0.0725 0.1099 0.9047
ICE PET −0.2421 −0.0692 0.1037 0.9020
MEE PET −0.2009 0.0033 0.2076 1.0000
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Fig. 25.

Fig. 25

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Multiple comparison test on cumulative GPA for CHE.

Fig. 26.

Fig. 26

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Multiple comparison test on cumulative GPA for CVE.

Fig. 27.

Fig. 27

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Multiple comparison test on cumulative GPA for CEN.

Fig. 28.

Fig. 28

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Multiple comparison test on cumulative GPA for EEE.

Fig. 29.

Fig. 29

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Multiple comparison test on cumulative GPA for ICE.

Fig. 30.

Fig. 30

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Multiple comparison test on cumulative GPA for MEE.

Fig. 31.

Fig. 31

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Multiple comparison test on cumulative GPA for PET.

Acknowledgements

This work is carried out under the SmartCU and Covenant University Data Analytics Center (CUDAC) Research Clusters. This research is fully sponsored by Covenant University Centre for Research, Innovation and Development (CUCRID), Covenant University, Ota, Nigeria.

Footnotes

Transparency document

Transparency data associated with this article can be found in the online version at https://doi.org/10.1016/j.dib.2017.12.059.

Appendix A

Supplementary data associated with this article can be found in the online version at https://doi.org/10.1016/j.dib.2017.12.059.

Transparency document. Supplementary material

Supplementary material

mmc1.docx (12.8KB, docx)

.

Appendix A. Supplementary material

Supplementary material

mmc2.xlsx (160.7KB, xlsx)

.

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