Mining Students’ Messages to Discover Problems Associated with Academic Learning

WhatsApp has become the preferred choice of students for sending messages in developing countries. Due to its privacy and the ability to create groups, students are able to express their “feelings” to peers without fear. To obtain immediate feedback on problems hindering effective learning, supervised learning algorithms were applied to mine the sentiments in WhatsApp group messages of University students. An ensemble classifier made up of Naïve Bayes, Support Vector Machines, and Decision Trees outperformed the individual classifiers in predicting the mood of students with an accuracy of 0.76, 0.92 recall, 0.72 precision and 0.80 F-score. These results show that we can predict the mood and emotions of students towards academic learning from their private messages. The method is therefore proposed as one of the effective ways by which educational authorities can cost effectively monitor issues hindering students’ academic learning and by extension their academic progress. Keywords: WhatsApp; Sentiments; Ensemble; Classification; Naïve Bayes; Support Vector Machines.

Behavioural Dimension of the Growth of Informal Settlements in Kumasi city, Ghana

Informal settlements are among the major problems that confronts many cities in the world. Despite numerousefforts both at the international and local levels to address this problem, its existence keeps on rising. This paperused a theoretical approach to assess the growth of informal settlements in Kumasi, Ghana. It adapted the theoryof Planned Behaviour with informal settlement dwellers been the target population. A total of 238 informalsettlement dwellers were covered with questionnaires serving as the research instruments. It was found out thatinformal settlement dwellers have bad behaviour towards such settlements and this was underpinned by their badintentions about informal settlements. Low awareness of building regulations and poor perception of land useplanning were the issues that influenced their behaviour through their intensions. The major contribution of thispaper is that the growth of informal settlements is to a greater extent orchestrated by bad behaviour towards suchsettlements and that policy makers and international bodies should have a second thought on these settlementsand pay keen attention to human behaviours towards informal settlements.Keywords: urban; housing; informal settlements; behaviour; Kumasi, Ghan

Predicting Customer Preference of Mobile Service using Neural Network

In countries where several Mobile Communication Service providers operate, it is imperative on the service providers to recognize the aspects of their services that will attract new customers in order to help them stay on top of the competition. This is known to be expensive in terms of money and time. To address this problem, we present a Feed-forward Back-propagation Neural Network (FBNN) that is aimed at learning potential customers’ “would-be” pattern of choosing a Mobile Service based on selected criteria. The Neural Network is tested on sample data and predictions made to the same effect as already mentioned. The results show that the Neural Network is adequate for predicting customer preference of a mobile service. This research concentrates on predicting new customer preferences as opposed to the popular notion of models predicting (existing) customer churn. Keywords: Feed-forward Back-propagation, Mobile Service Provider, Neural Network, Prediction

Inflation Targeting Framework and Interest Rates Transmission in Ghana: An Empirical Investigation

This paper investigates the long-and short-run rate of transmission of the prime rate to interest rates since the implementation of inflation targeting policy in Ghana. Monthly data covering the period January 2002 to March 2016 is used. The Johansen and Hansen parameter instability cointegration, the FMOLS and DOLS estimation procedures were used. The long-run results show incomplete pass-through of the prime rate to commercial banks’ lending and deposit rates but over pass-through to the 91-day Treasury bill rate. The short-run adjustment shows relatively slow transmission of the prime rate to the respective interest rates. Given the findings, relevant policy suggestions are provided

An Assessment of Different Educational Background of Students Performance in Engineering Mathematics and on the Class of Award obtained at the Higher National Diploma (HND) Level at Cape Coast Polytechnic, Ghana.

The purpose of this research article is to find out an assessment of different educational background of students performance in engineering mathematics and on the class of award obtained at the Higher National Diploma (HND) level at Cape Coast Polytechnic. A descriptive survey was conducted on students of the Electricals/Electronics Department who graduated in each year from 2007 to 2011. A sample of ten (10) students was randomly selected from those who graduated with SSS, and ten (10) from Non-SSS background, constituting a total of twenty (20) students from each graduation year. In all, one hundred (100) graduated students were selected from 2007 to 2011 for the study. A secondary data consisted of information on student’s Department entry requirement, class assessment result, end-of-semester examination result for engineering mathematics and the class of award obtained were collected from the department. Correlation coefficient (Pearson’s product moment) was used for the analysis. The study revealed that the students’ educational background of study has effect on their performance in Engineering Mathematics, and also on the class of award obtained.   Key words: Correlation coefficient, Higher National Diploma (HND), Performance in Engineering Mathematics.

MAKING INCLUSIVE EDUCATION A REALITY: THE ROLE OF TEACHERS, PARENTS AND ADMINISTRATORS

The study used basically a survey design which employed the quantitative approach to investigate how children with special learning needs (SENs) are supported to learn together in the inclusive classroom with the focus on finding the level of support for children in five (5) inclusive schools in the Cape Coast Metropolis, Ghana. The sample size involved 86 teachers, 6 administrators (Head teachers) and 40 parents. In all, a total of 132 participants were involved in the study. Questionnaire was designed in close-ended format for the teachers, administrators and parents. The basic structure of the instrument was based on four Point Likert-type scales. The Likert-scale items questionnaire was analysed using frequencies, percentages, means and standard deviation. A tally sheet was used to generate frequency counts out of which percentages were calculated based on the scores assigned to each rating. These data were then analysed using the SPSS computer system. It was found from the study that, there are low levels of material or human support for pupils with special educational needs in inclusive classrooms. On material support, the parents indicated that their children do not receive any material support from Ghana Education Service to support their children and they do not get any financial support from Non-Governmental Organizations (NGO’s). It is recommended that teachers, parents and administrators should come together to provide the needed support for pupils with special education needs to ensure the success of the inclusive agenda.  Article visualizations

Revisiting the conclusion instability issue in software effort estimation

Conclusion instability is the absence of observing the same effect under varying experimental conditions. Deep Neural Network (DNN) and ElasticNet software effort estimation (SEE) models were applied to two SEE datasets with the view of resolving the conclusion instability issue and assessing the suitability of ElasticNet as a viable SEE benchmark model. Results were mixed as both model types attain conclusion stability for the Kitchenham dataset whilst conclusion instability existed in the Desharnais dataset. ElasticNet was outperformed by DNN and as such it is not recommended to be used as a SEE benchmark model

Failure to protect the myocardium against ischemia/reperfusion injury after chronic atorvastatin treatment is recaptured by acute atorvastatin treatment A potential role for phosphatase and tensin homolog deleted on chromosome ten?

ObjectivesWe sought to ascertain whether chronic oral therapy with atorvastatin protects against ischemia/reperfusion (I/R) injury.BackgroundWe have recently shown that acute atorvastatin treatment protects against reperfusion-induced injury by activating the PI3K/Akt/eNOS pathway. However, many patients are on chronic statin therapy, and it is necessary to investigate whether this, in itself, provides a therapeutic advantage.MethodsSprague-Dawley rats were orally treated for one day, three days, one week, or two weeks with 20 mg/kg of atorvastatin or vehicle, after which the hearts underwent 35 min of ischemia and 120 min reperfusion (IR). Two additional groups were treated for one or two weeks with atorvastatin and then received a supplementary dose of 40 mg/kg before IR. The risk zone was determined using Evans blue and infarct size (IR%) using triphenyltetrazolium chloride staining.ResultsTreatment with atorvastatin for one and three days significantly reduced infarct size versus controls (38.9 ± 3.1% vs. 56.4 ± 2.3%; 39.3 ± 2.4% vs. 61.3 ± 3.8%, respectively). However, after one or two weeks of treatment, no protection was observed (52.6 ± 3.8% vs. 58.6 ± 4.3%; 58.3 ± 2.7% vs. 52.4 ± 5.7%, respectively). Surprisingly, a supplementary dose of atorvastatin recaptured the protection in the groups treated chronically (36.2 ± 2.8% vs. 58.6 ± 4.3%; 26.8 ± 1.5% vs. 51.2 ± 6.7%, at one and two weeks, respectively). Interestingly, we observed an increased level of phosphatase and tensin homolog deleted on chromosome ten (PTEN), the phosphatidylinositol-3 kinase inhibitor, in the chronic treated hearts.ConclusionsIn conclusion, atorvastatin appears to have an acute protective effect that wanes with time associated with an increase in PTEN levels. This waning protection can be recaptured by an acute high dose given immediately before IR. These results may have protential clinical relevance

Rework Effort Estimation of Self-admitted Technical Debt

Programmers sometimes leave incomplete, temporary workarounds and buggy codes that require rework. This phenomenon in software development is referred to as Self- admitted Technical Debt (SATD). The challenge therefore is for software engineering researchers and practitioners to resolve the SATD problem to improve the software quality. We performed an exploratory study using a text mining approach to extract SATD from developers’ source code comments and implement an effort metric to compute the rework effort that might be needed to resolve the SATD problem. The result of this study confirms the result of a prior study that found design debt to be the most predominant class of SATD. Results from this study also indicate that a significant amount of rework effort of between 13 and 32 commented LOC on average per SATD prone source file is required to resolve the SATD challenge across all the four projects considered. The text mining approach incorporated into the rework effort metric will speed up the extraction and analysis of SATD that are generated during software projects. It will also aid in managerial decisions of whether to handle SATD as part of on-going project development or defer it to the maintenance phase

Multi-Objective Optimization for Software Testing Effort Estimation

Software Testing Effort (STE), which contributes about 25-40% of the total development effort, plays a significant role in software development. In addressing the issues faced by companies in finding relevant datasets for STE estimation modeling prior to development, cross-company modeling could be leveraged. The study aims at assessing the effectiveness of cross-company (CC) and within-company (WC) projects in STE estimation. A robust multi-objective Mixed-Integer Linear Programming (MILP) optimization framework for the selection of CC and WC projects was constructed and estimation of STE was done using Deep Neural Networks. Results from our study indicate that the application of the MILP framework yielded similar results for both WC and CC modeling. The modeling framework will serve as a foundation to assist in STE estimation prior to the development of new a software project