A GRID-BASED E-LEARNING MODEL FOR OPEN UNIVERSITIES

E-learning has grown to become a widely accepted method of learning all over the world. As a result, many e-learning platforms which have been developed based on varying technologies were faced with some limitations ranging from storage capability, computing power, to availability or access to the learning support infrastructures. This has brought about the need to develop ways to effectively manage and share the limited resources available in the e-learning platform. Grid computing technology has the capability to enhance the quality of pedagogy on the e-learning platform. In this paper we propose a Grid-based e-learning model for Open Universities. An attribute of such universities is the setting up of multiple remotely located campuses within a country. The grid-based e-learning model presented in this work possesses the attributes of an elegant architectural framework that will facilitate efficient use of available e-learning resources and cost reduction, leading to general improvement of the overall quality of the operations of open universities

CAES: A Model of an RBR-CBR Course Advisory Expert System

Academic student advising is a gargantuan task that places heavy demand on the time, emotions and mental resources of the academic advisor. It is also a mission critical and very delicate task that must be handled with impeccable expertise and precision else the future of the intended student beneficiary may be jeopardized due to poor advising. One integral aspect of student academic advising is course registration, where students make decisions on the choice of courses to take in specific semesters based on their current academic standing. In this paper, we give the description of the design, implementation and trial evaluation of the Course Advisory Expert System (CAES) which is a hybrid of a rule based reasoning (RBR) and case based reasoning (CBR). The RBR component was implemented using JESS. The result of the trial experiment revealed that the system has high performance/user satisfaction rating from the sample expert population conducted

The impact of internet access on cloud computing research in Africa: Analysis of bibliometric and online search data

The objective of this paper is to uncover the relationship between inter- net penetration and cloud computing research output and to underst and the connection between the interest in cloud computing on research out-puts in cloud computing from African countries. For the period of 2009 to 2017, bibliometric data on cloud computing research was retrieved from the Scopus database. Online search traffic data on the search term “cloud computing” was obtained from Google trends for the period under review. Our results show that there was a strong significant correla- tion between internet penetration and the total number of scholarly outputs within the given period for all countries studied, suggesting that an increase in internet penetration is directly proportional to increase in cloud computing research activities and outputs from a given region. The penetration of internet technologies contributes significantly to the advancement of research efforts and scholarly outputs in ICT-related endeavours

Early Identification of Implicit Requirements with the COTIR Approach using Common Sense, Ontology and Text Mining

The ability of a system to meet its requirements is a strong determinant of success. Thus effective Software Requirements Specification (SRS) is crucial. Explicit Requirements are well-defined needs for a system to execute. IMplicit Requirements (IMRs) are assumed needs that a system is expected to fulfill though not elicited during requirements gathering. Studies have shown that a major factor in the failure of software systems is the presence of unhandled IMRs. Since relevance of IMRs is important for efficient system functionality, there are methods developed to aid the identification and management of IMRs. In this research, we emphasize that commonsense knowledge, in the field of Knowledge Representation in AI, would be useful to automatically identify and manage IMRs. This research is aimed at identifying the sources of IMRs and also proposing an automated support tool for managing IMRs within an organizational context. Since this is found to be a present gap in practice, our work makes a contribution here. We propose a novel approach called COTIR (Commonsense, Ontology and Text mining for Implicit Requirements) to identify and manage IMRs. As the name implies, COTIR is based on an integrated framework of three core technologies: commonsense knowledge (CSK), text mining and ontology. We claim that discovery and handling of unknown and non-elicited requirements would reduce risks and costs in software development

Implementation of an Intelligent Course Advisory Expert System

Academic advising of students is an expert task that requires a lot of time, and intellectual investments from the human agent saddled with such a responsibility. In addition, good quality academic advising is subject to availability of experienced and committed personnel to undertake the task. However, there are instances when there is paucity of capable human adviser, or where qualified persons are not readily available because of other pressing commitments, which will make system-based decision support desirable, and useful. In this work, we present the design, implementation, of an intelligent Course Advisory Expert System (CAES) that uses a combination of rule based reasoning (RBR), and case based reasoning (CBR) to recommend courses that a student should register in a specific semester by making recommendation based on the student’s academic history. The evaluation CAES yielded satisfactory performance in terms of credibility of its recommendations, and usability

PROMIRAR: Tool for Identifying and Managing Implicit Requirements in SRS Documents

Implicit requirements (IMRs) in software requirements specifications (SRS) are subtle and need to be identified as users may not provide all information upfront. It is found that successful functioning of a software crucially depends on addressing its IMRs. This work presents a novel system called PROMIRAR with an integrated framework of Natural Language Processing, Ontology and Analogy based Reasoning for managing Implicit Requirements. It automates early identification and management of IMRs and is found helpful in targeted application domain. We present the PROMIRAR system with its architecture, demo and evaluation

Identifying Implicit Requirements in SRS Big Data

Over the past few years, we have worked on pioneering an approach that employs Commonsense Knowledge (CSK) to automate the identification of Implicit Requirements (IMRs) from text in large Software Requirements Specifications (SRS) documents. This paper builds on our IMR-identification approach by adding CNN-based deep learning to detect IMRs from complex SRS big data such as images and tables

Tool Support for Cascading Style Sheets’ Complexity Metrics

Tools are the fundamental requirement for acceptability of any metrics programme in the software industry. It is observed that majority of the metrics proposed and are available in the literature lack tool support. This is one of the reasons why they are not widely accepted by the practitioners. In order to improve the acceptability of proposed metrics among software engineers that develop Web applications, there is need to automate the process. In this paper, we have developed a tool for computing metrics for Cascading Style Sheets (CSS) and named it as CSS Analyzer (CSSA). The tool is capable of measuring different metrics, which are the representation of different quality attributes: which include understandability, reliability and maintainability based on some previously proposed metrics. The tool was evaluated by comparing its result on 40 cascading style sheets with results gotten by the manual process of computing the complexities. The results show that the tool computes in far less time when compared to the manual process and is 51.25% accurate