An Assessment of ICT Literacy Among Secondary School Students in a Rural Area of Kwara State, Nigeria: A Community Advocacy Approach

In recent times, public schools in Nigeria have enjoyed some benefits in terms of deployment of Information and Communication Technologies (ICTs), but no constant attention and continuous interest is paid to fill the digital gap between schools in the rural and urban areas. The contribution of private sectors in the education system has elevated the use of ICT in both private and public schools especially in the urban areas of Nigeria. However, schools in rural areas have not benefitted much in this area. This research used community advocacy program referred to as COBES (Community Based Experience Scheme) to assess ICT literacy of secondary school students in a rural area of Kwara State, Nigeria. The study employed mixed research approach that combined both quantitative and qualitative data collection strategies. The initial findings of the study revealed low level of ICT skills among secondary school students in the rural area. Although, majority of the students who served as the respondents claimed they have computer teacher and can operate computer systems, yet, the study showed that there is dearth of ICT facilities for hands-on training. Nevertheless, through the one week long COBES program, the findings from three focus group discussion conducted at the end of the COBES program showed that students‟ interest to use ICT increased and majority of them expressed their willingness to continue interacting with computer and internet facilities. Findings further revealed that the main reason for low ICT skills is the lack of ICT facilities for teaching and learning. The study recommended that ICT project implementation should be uniform in all public schools in Nigeria, irrespective of whether it is located in the urban or rural area, adequate and skilled computer studies teachers should be made available and government should put in place mechanisms that will ensure proper maintenance of the ICT facilitie

Affective e-learning approaches, technology and implementation model: a systematic review

A systematic literature study including articles from 2016 to 2022 was done to evaluate the various approaches, technologies, and implementation models involved in measuring student engagement during learning. The review’s objective was to compile and analyze all studies that investigated how instructors can gauge students’ mental states while teaching and assess the most effective teaching methods. Additionally, it aims to extract and assess expanded methodologies from chosen research publications to offer suggestions and answers to researchers and practitioners. Planning, carrying out the analysis, and publishing the results have all received significant attention in the research approach. The study’s findings indicate that more needs to be done to evaluate student participation objectively and follow their development for improved academic performance. Physiological approaches should be given more support among the alternatives. While deep learning implementation models and contactless technology should interest more researchers. And, the recommender system should be integrated into e-learning system. Other approaches, technologies, and methodology articles, on the other hand, lacked authenticity in conveying student feeling

Performance Analysis of Particle Swarm Optimization for Feature Selection

One of the key task in data mining is the selection of relevant features from datasets with high dimensionality. This is expected to reduce the time and space complexity, and consequently improve the performance of data mining algorithms for tasks such as classification. This study presents an empirical study of the effect of particle swarm optimization as a feature selection technique on the performance of classification algorithms. Two dataset from different domains were used: SMS spam detection and sentiment analysis datasets. Particle swarm optimization is applied on the datasets for feature selection. Both the reduced and raw dataset are separately classified using C4.5 decision tree, k-nearest neighbour and support vector machine. The result of the analysis showed that the improvement of classifier performance is case-dependent; some significant improvements are noticed in the sentiment analysis datasets and not in the SMS spam dataset. Although some marginal effect are observed on performance, it implies that with particle swarm optimization features selection the space complexity is reduced while maintaining the accuracy of the classifiers. Keywords—classification, feature selection, machine learning, particle swarm optimization, text mining  

Software Defect Prediction Using Ensemble Learning: An ANP Based Evaluation Method

Software defect prediction (SDP) is the process of predicting defects in software modules, it identifies the modules that are defective and require extensive testing. Classification algorithms that help to predict software defects play a major role in software engineering process. Some studies have depicted that the use of ensembles is often more accurate than using single classifiers. However, variations exist from studies, which posited that the efficiency of learning algorithms might vary using different performance measures. This is because most studies on SDP consider the accuracy of the model or classifier above other performance metrics. This paper evaluated the performance of single classifiers (SMO, MLP, kNN and Decision Tree) and ensembles (Bagging, Boosting, Stacking and Voting) in SDP considering major performance metrics using Analytic Network Process (ANP) multi-criteria decision method. The experiment was based on 11 performance metrics over 11 software defect datasets. Boosted SMO, Voting and Stacking Ensemble methods ranked highest with a priority level of 0.0493, 0.0493 and 0.0445 respectively. Decision tree ranked highest in single classifiers with 0.0410. These clearly show that ensemble methods can give better classification results in SDP and Boosting method gave the best result. In essence, it is valid to say that before deciding which model or classifier is better for software defect prediction, all performance metrics should be considered.Keywords— Data mining, Machine Learning,  Multi Criteria Decision Making, Software Defect Predictio

Energy-Efficient Virtual Machine Placement using Enhanced Firefly Algorithm

The consolidation of the virtual machines (VMs) helps to optimise the usage of resources and hence reduces the energy consumption in a cloud data centre. VM placement plays an important part in the consolidation of the VMs. The researchers have developed various algorithms for VM placement considering the optimised energy consumption. However, these algorithms lack the use of exploitation mechanism efficiently. This paper addresses VM placement issues by proposing two meta-heuristic algorithms namely, the enhanced modified firefly algorithm (MFF) and the hierarchical cluster based modified firefly algorithm (HCMFF), presenting the comparative analysis relating to energy optimisation. The comparisons are made against the existing honeybee (HB) algorithm, honeybee cluster based technique (HCT) and the energy consumption results of all the participating algorithms confirm that the proposed HCMFF is more efficient than the other algorithms. The simulation study shows that HCMFF consumes 12% less energy than honeybee algorithm, 6% less than HCT algorithm and 2% less than original firefly. The usage of the appropriate algorithm can help in efficient usage of energy in cloud computing

OBJECTIVE MAINTAINABILITY MEASUREMENT MODEL FOR OBJECTORIENTED SOFTWARE DESIGN

Software maintainability is an important quality attribute due to the fact that the maintenance phase of the Software Life Cycle has been attributed to be the most costly phase in terms of software budget and programmers' effort. Software metrics, which is the measurement of software structural and quality attributes, can be used to facilitate the engineering of quality into software, especially during the design phase of software development at which design refinement is easier and less costly than after implementation or when the software is already in use

OBJECTIVE MAINTAINABILITY MEASUREMENT MODEL FOR OBJECTORIENTED SOFTWARE DESIGN

Software maintainability is an important quality attribute due to the fact that the maintenance phase of the Software Life Cycle has been attributed to be the most costly phase in terms of software budget and programmers' effort. Software metrics, which is the measurement of software structural and quality attributes, can be used to facilitate the engineering of quality into software, especially during the design phase of software development at which design refinement is easier and less costly than after implementation or when the software is already in use

A PROMETHEE based evaluation of software defect predictors

A software defect is an error, flaw, mistake, or fault in a computer program or system that produces incorrect or unexpected results and the process of locating defective modules in software is software defect prediction. Defect prediction in software improves quality and testing efficiency by constructing predictive stand-alone classifier models or by the use of ensembles methods to identify fault-prone modules. Selection of the appropriate set of single classifier models or ensemble methods for the software defect prediction over the years has shown inconsistent results. In previous analysis, inconsistencies exist and the performance of learning algorithms varies using different performance measures. Therefore, there is need for more research in this field to evaluate the performance of single classifiers and ensemble algorithms in software defect prediction. This study assesses the quality of the ensemble methods alongside single classifier models in the software defect prediction using Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), a multi criteria decision making (MCDM) approach. Using PROMETHEE, the performance of some popular ensemble methods based on 11 performance metrics over 10 public-domain software defect datasets from the NASA Metric Data Program (MDP) repository was evaluated. Noise is removed from the dataset by performing attribute selection. The classifiers and ensemble methods are applied on each dataset; Adaboostgave the best results. Boosted PART comes first followed by Naïve Bayes and then Bagged PART as the best models for mining of datasets.Keywords: Ensemble; Classification; Software Defect Prediction; PROMETHEE; MCD