Aderemi A. Atayero

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.

Smart campus: Data on energy consumption in an ICT-driven university

In this data article, we present a comprehensive dataset on electrical energy consumption in a university that is practically driven by Information and Communication Technologies (ICTs). The total amount of electricity consumed at Covenant University, Ota, Nigeria was measured, monitored, and recorded on daily basis for a period of 12 consecutive months (January–December, 2016). Energy readings were observed from the digital energy meter (EDMI Mk10E) located at the distribution substation that supplies electricity to the university community. The complete energy data are clearly presented in tables and graphs for relevant utility and potential reuse. Also, descriptive first-order statistical analyses of the energy data are provided in this data article. For each month, the histogram distribution and time series plot of the monthly energy consumption data are analyzed to show insightful trends of energy consumption in the university. Furthermore, data on the significant differences in the means of daily energy consumption are made available as obtained from one-way Analysis of Variance (ANOVA) and multiple comparison post-hoc tests. The information provided in this data article will foster research development in the areas of energy efficiency, planning, policy formulation, and management towards the realization of smart campuses.

A Case-Based Reasoning approach for speech-enabled e-Learning system

E-Learning plays an important role in our society today; hence, higher institutions now offer courses through distance learning. Several studies and methodologies towards improving e-Learning have been proposed and provided. However, not too many works are dedicated to the design and implementation of e-Learning for the visually impaired learners. Sight challenge is a serious form of disability, yet, the existing e-Learning platform (web, mobile, etc) have not devoted enough attention to the plight of the visually impaired particularly in the area of usability. The objective of this paper is to present an intelligent speech-based e-Learning system with dual interface -Voice User Interface (VUI) and Web User Interface (WUI). Case-Based Reasoning (CBR) was engaged to provide intelligent services. Voice Extensible Markup Language (VoiceXML) was used to develop the VUI, Hypertext Preprocessor (PHP) for the WUI and Apache as the middle ware. The VUI and WUI are accessed through mobile phone by dialing a telephone number and the WUI using the Internet respectively. The e-Learning system will especially be useful for students who are visually impaired and those with dyslexia ailment that make reading, writing and spelling difficult. The application will complement the existing e-Learning systems such as web-based learning, m-Learning and others.

Data on the key performance indicators for quality of service of GSM networks in Nigeria

In this data article, the Key Performance Indicators (KPIs) for Quality of Service (QoS) of Global System for Mobile Communications (GSM) networks in Nigeria are provided and analyzed. The data provided in this paper contain the Call Setup Success Rate (CSSR), Drop Call Rate (DCR), Stand-alone Dedicated Channel (SDCCH) congestion, and Traffic Channel (TCH) congestion for the four GSM network operators in Nigeria (Airtel, Etisalat, Glo, and MTN). These comprehensive data were obtained from the Nigerian Communications Commission (NCC). Significant differences in each of the KPIs for the four quarters of each year were presented based on Analysis of Variance (ANOVA). The values of the KPIs were plotted against the months of the year for better visualization and understanding of data trends across the four quarters. Multiple comparisons of the mean-quarterly differences of the KPIs were also presented using Tukeys Post Hoc test. Public availability and further interpretation and discussion of these useful information will assist the network providers, Nigerian government, local and international regulatory bodies, policy makers, and other stakeholders in ensuring access of people, machines, and things to high quality telecommunications services.

Dataset on statistical analysis of editorial board composition of Hindawi journals indexed in Emerging sources citation index

This data article contains the statistical analysis of the total, percentage and distribution of editorial board composition of 111 Hindawi journals indexed in Emerging Sources Citation Index (ESCI) across the continents. The reliability of the data was shown using correlation, goodness-of-fit test, analysis of variance and statistical variability tests.

Received signal strength and local terrain profile data for radio network planning and optimization at GSM frequency bands

The behaviour of radio wave signals in a wireless channel depends on the local terrain profile of the propagation environments. In view of this, Received Signal Strength (RSS) of transmitted signals are measured at different points in space for radio network planning and optimization. However, these important data are often not publicly available for wireless channel characterization and propagation model development. In this data article, RSS data of a commercial base station operating at 900 and 1800 MHz were measured along three different routes of Lagos-Badagry Highway, Nigeria. In addition, local terrain profile data of the study area (terrain elevation, clutter height, altitude, and the distance of the mobile station from the base station) are extracted from Digital Terrain Map (DTM) to account for the unique environmental features. Statistical analyses and probability distributions of the RSS data are presented in tables and graphs. Furthermore, the degree of correlations (and the corresponding significance) between the RSS and the local terrain parameters were computed and analyzed for proper interpretations. The data provided in this article will help radio network engineers to: predict signal path loss; estimate radio coverage; efficiently reuse limited frequencies; avoid interferences; optimize handover; and adjust transmitted power level.

Programming Development of Kolmogorov-Smirnov Goodness-of-Fit Testing of Data Normality as a Microsoft Excel® Library Function

This paper deliberates on the programming development of the Kolmogorov-Smirnov goodness-of-fit testing of data Normality as a library function in the Microsoft Excel® spreadsheet software, in which researchers normally store data for analysis and processing. The algorithmic program procedure utilised developed implementation of the Normality Kolmogorov-Smirnov D statistics for the one-sided and the two-sided test criteria as a library function in the Microsoft Excel® environment. For these programming developments, the Visual Basic for Applications® was employed for deploying macro embedment in the spreadsheet software. Successful programming development of the Normality K-S D statistics fosters implementation of the Normality K-S p-value estimation procedure also as a library function in the Microsoft Excel® environment. Test-applications of these programming developments in the study portray potency of accurate, speedy and economical procedure for testing compatibility of univariate data of real numbers to the Normal distribution, for datasets of n ≤ 2000 sample size.

Dataset and analysis of editorial board composition of 165 Hindawi journals indexed and abstracted in PubMed based on affiliations

This article explores the editorial board composition (across the six continents) of Hindawi journals indexed in PubMed. The dataset used is the official affiliation of the board members available at the various webpages of Hindawi journal website and not the countries of origin of the editorial board members. Summary statistics were presented and the raw dataset was provided for further analysis by interested scholars. The percentage of the editorial board composition across the continents was presented, the dataset of Hindawi journals indexed in both Hindawi and Scopus were also presented and measured in terms of Citescore and percentiles. The dataset can be used in journal evaluation, auditing, bibliometric analysis, management of smart campus; ranking and the analysis can be extended to other journal indexations.

Adaptive Neuro-Fuzzy Inference System for Dynamic Load Balancing in 3GPP LTE

ANFIS is applicable in modeling of key parameters when investigating the performance and functionality of wireless networks. The need to save both capital and operational expenditure in the management of wireless networks cannot be over-emphasized. Automation of network operations is a veritable means of achieving the necessary reduction in CAPEX and OPEX. To this end, next generations networks such WiMAX and 3GPP LTE and LTE-Advanced provide support for self-optimization, self-configuration and self-healing to minimize human-to-system interaction and hence reap the attendant benefits of automation. One of the most important optimization tasks is load balancing as it affects network operation right from planning through the lifespan of the network. Several methods for load balancing have been proposed. While some of them have a very buoyant theoretical basis, they are not practically implementable at the current state of technology. Furthermore, most of the techniques proposed employ iterative algorithm, which in itself is not computationally efficient. This paper proposes the use of soft computing, precisely adaptive neuro-fuzzy inference system for dynamic QoS-aware load balancing in 3GPP LTE. Three key performance indicators (i.e. number of satisfied user, virtual load and fairness distribution index) are used to adjust hysteresis task of load balancing.

Exploration of editorial board composition, Citescore and percentiles of Hindawi journals indexed in Scopus

The statistical analysis of editorial board composition, Citescore and percentile of 180 Hindawi journals currently indexed in Scopus are presented in this data article. The three indicators (editorial board composition, Citescore and percentile) can be helpful for researchers to make informed decision about the impact of Hindawi journals. The last two indicators are components of Scopus Citescore metrics.