Students’ characteristics of student model in intelligent programming tutor for learning programming: a systematic literature review

This study describes preliminary results of a research related to Intelligent Programming Tutor (IPT) which is derived from Intelligent Tutoring System (ITS). The system architecture consists of four models. However, in this study student model mainly student characteristic was focused. From literature, 44 research articles were identified from a number of digital databases published between 1997 to 2022 base on systematic literature review (SLR) method. The findings show that the majority 48% of IPT implementation focuses on knowledge and skills. While 52% articles focused on a combination of two to three student characteristics where one of the combinations is knowledge and skill. When narrow down, 25% focused on knowledge and skills with errors or misconceptions; 4% focused on knowledge and skill with cognitive features; 5% focused focus on knowledge and skill with affective features; 2% focused on knowledge and skill with motivation; and 9% based on knowledge and skill with learning style and learning preferences as students’ characteristics to build their student model. Whereas 5% focused on a combination of three student characters which are knowledge and skill with cognitive and affective features and 2% focused on knowledge and skill with learning styles and learning preferences and motivation as students’ characteristics to construct the tutoring system student model. To provide an appropriate tutoring system for the students, students’ characteristic needs to decide for the student model before developing the tutoring system. From the findings, it can say that knowledge and skills is an essential students’ characteristic used to construct the tutoring system student model. Unfortunately, other students’ characteristic is less considered especially students’ motivation

Proposed architecture for intrusion detection system for software as a service in cloud computing environment

The purpose of this paper is to propose an architecture for intrusion detection based on Software as a Service (SaaS) called Software as a Service Intrusion Detection Services (SaaSIDS) in a cloud environment. Therefore, this research focusing on developing Software As A Service IDS (SaaSIDS) where the traffic at different points of the network is sniffed and the interested packets would be transferred to the SaaSIDS for further inspection. The main engine of SaaSIDS is the hybrid analysis engine where the signature based engine and anomaly based engine which using artificial immune system will work in parallel. The SaaSIDS is able to identify malicious activity and would generate appropriate alerts and notification accordingly

Fuzzy Delphi method for evaluating HyTEE model (hybrid software change management tool with test effort estimation)

When changes are made to a software system during development and maintenance, they need to be tested again i.e. regression test to ensure that changes behave as intended and have not impacted the software quality. This research will produce an automated tool that can help the software manager or a maintainer to search for the coverage artifact before and after a change request. Software quality engineer can determine the test coverage from new changes which can support cost estimation, effort, and schedule estimation. Therefore, this study is intended to look at the views and consensus of the experts on the elements in the proposed model by benefitting the Fuzzy Delphi Method. Through purposive sampling, a total of 12 experts from academic and industrial have participated in the verification of items through 5-point linguistic scales of the questionnaire instrument. Outcome studies show 90% of elements in the proposed model consists of change management, traceability support, test effort estimation support, regression testing support, report and GUI meet, the value threshold (d construct) is less than 0.2 and the percentage of the expert group is above 75%. It is shown that elements of all the items contained in the venue are needed in the HyTEE Model (Hybrid Software Change Management Tool with Test Effort Estimation) based on the consensus of experts

Sustainable Software Engineering:A Perspective of Individual Sustainability

Sustainable software engineering is a mean of developing sustainable software with sustainable software engineering process activities while balancing its various dimensions for instance economic, environmental, social, technical and individual. It is conveyed that the economic, technical, environmental and social dimensions are explored to satisfactory degree however the individual dimension of sustainable software engineering which is concerned with wellbeing of software engineers is not explored to satisfactory degree with respect to its understanding and challenges. Therefore, the aim of the study is to highlight and prioritize the challenges regarding individual sustainability dimension. The study also provides the mitigation strategies for the top five individual sustainability challenges. The systematic literature review has been performed to report the challenges and mitigation strategies. The study finding shows that lack of domain knowledge, lack of methodologies and tool support, lack of education, varying and unidentified situations and lack of sustainable software engineering practices are top most challenges regarding individual sustainability. These challenges need an urgent attention to achieve the goal of sustainable software engineering. The study also reports various mitigation strategies to overcome the risk of identified top most individual sustainability challenges such as to introduce sustainable software engineering education and knowledge in software engineering curricula, development of knowledge sharing frameworks and awareness regarding unclear and varying situations for each software engineering activity etc.  The study will be beneficial for sustainable software engineering body of knowledge, sustainable software engineering practitioners and researchers by providing classified list of individual sustainability challenges and their mitigation strategies

Support Vector Machine Algorithm for SMS Spam Classification in The Telecommunication Industry

In recent years, we have withnessed a dramatic increment volume in the number of mobile users grows in telecommunication industry. However, this leads to drastic increase to the number of spam SMS messages. Short Message Service (SMS) is considered one of the widely used communication in telecommunication service. In reality, most of the users ignore the spam because of the lower rate of SMS and limited amount of spam classification tools. In this paper, we propose a Support Vector Machine (SVM) algorithm for SMS Spam Classification. Support Vector Machine is considered as the one of the most effective for data mining techniques. The propose algorithm have been evaluated using public dataset from UCI machine learning repository. The performance achieved is compared with other three data mining techniques such as Naïve Bayes, Multinominal Naïve Bayes and K-Nearest Neighbor with the different number of K= 1,3 and 5. Based on the measuring factors like higher accuracy, less processing time, highest kappa statistics, low error and the lowest false positive instance, it’s been identified that Support Vector Machines (SVM) outperforms better than other classifiers and it is the most accurate classifier to detect and label the spam messages with an average an accuracy is 98.9%. Comparing both the error parameter overall, the highest error has been found on the algorithm KNN with K=3 and K=5. Whereas the model with less error is SVM followed by Multinominal Naïve Bayes. Therefore, this propose method can be used as a best baseline for further comparison based on SMS spam classification

Trusted cloud computing framework for healthcare sector

Cloud computing is rapidly evolving due to its efficient characteristics such as cost-effectiveness, availability and elasticity. Healthcare organizations and consumers lose control when they outsource their sensitive data and computing resources to a third party Cloud Service Provider (CSP), which may raise security and privacy concerns related to data loss and misuse appealing threats. Lack of consumers' knowledge about their data storage location may lead to violating rules and regulations of Health Insurance Portability and Accountability Act (HIPAA) that can cost them huge penalty. Fear of data breach by internal or external hackers may decrease consumers' trust in adopting cloud computing and benefiting from its promising features. We designed a HealthcareTrusted Cloud Computing (HTCC) framework that maintains security, privacy and considers HIPAA regulations. HTCC framework deploys Trusted Computing Group (TCG) technologies such as Trusted Platform Module (TPM), Trusted Software Stack (TSS), virtual Trusted Platform Module (vTPM), Trusted Network Connect (TNC) and Self Encrypting Drives (SEDs). We emphasize on using strong multi-factor authentication access control mechanisms and strict security controls, as well as encryption for data at storage, in-transit and while process. We contributed in customizing a cloud Service Level Agreement (SLA) by considering healthcare requirements. HTCC was evaluated by comparing with previous researchers' work and conducting survey from experts. Results were satisfactory and showed acceptance of the framework. We aim that our proposed framework will assist in optimizing trust on cloud computing to be adopted in healthcare sector

Mobile Business Intelligence Acceptance Model for Organisational Decision Making

Mobile Business Intelligence (BI) is the ability to access BI-related data such as key performance indicators (KPIs), business metric and dashboard through mobile device. Mobile BI addresses the use-case of remote or mobile workers that need on-demand access to business-critical data. User acceptance on mobile BI is an essential in order to identify which factors influence the user acceptance of mobile BI application. Research on mobile BI acceptance model on organizational decision-making is limited due to the novelty of mobile BI as newly emerged innovation. In order to answer gap of the adoption of mobile BI in organizational decision-making, this paper reviews the existing works on mobile BI Acceptance Model for organizational decision-making. Two user acceptance models which are Technology Acceptance Model and Technology Acceptance Model for Mobile Services will be review. Realizing the essential of strategic organizational decision-making in determining success of organizations, the potential of mobile BI in decision-making need to be explore. Since mobile BI still in its infancy, there is a need to study user acceptance and usage behavior on mobile BI in organizational decision-making. There is still opportunity for further investigate the impact of mobile BI on organizational decision-making

Acceptance of cloud computing in the Malaysian public sector: A proposed model

The Malaysian government has initiated a cloud government project as an integration of cloud computing and unified communication-based applications toward the digital and cloud work environment. However, the impact studies have found that the implementation of this project has several weaknesses such as lack of infrastructure support, weak IT knowledge, and lack of awareness among public sector employees causing applications not to be fully utilized. Therefore, it is crucial to conduct a study to measure the acceptance of government cloud project because there has been much investment in the project. This study applied Unified Theory of Acceptance and Use of Technology (UTAUT), Technology Readiness Index (TRI) and several factors to develop the research model which is divided into two main factors: technological and human. The technological factor might determine the likelihood of its acceptance by the public sector and might stimulate them to accept it. The human factor as the characteristics of the people in the public sector that may contribute to creating the need for and ability to accept cloud computing. This proposed model will be used to evaluate the individual acceptance of cloud computing in the Malaysian public sector. For future work, this model needs to be enriched with interview sessions and quantitative surveys to validate the findings

The validity and reliability evaluation of instruments for cloud computing acceptance study

Online data storage technology over the cloud network has become an option for many organizations, even for personal use. The benefits of cloud computing enable many organizations, including the public sector, to use this technology to provide the best service experience. However, there is an issue with the implementation of cloud-based applications when their usage is less than the number of applications offered. Therefore, a study on the acceptance of cloud computing in the public sector should be conducted. This paper aims to evaluate the validity and reliability of the instrument for cloud computing acceptance in Malaysian public sectors. The developed instruments are analyzed through validity and reliability phases. The validity analysis phase involves two stages of face validity and expert validity. The Content Validity Index (CVI) is used, and the feedback of the panel is considered in improving the items used. The reliability phase was conducted by performing an analysis to evaluate Cronbach 'alpha for each item and also testing using Exploratory Factor Analysis (EFA). The final instrument contained 71 items of 5-point Likert scale multiple-choice options, classified under 15 variables. As a result, this instrument is successfully validated and are reliable to be used in the actual data collection