An Effective and Secure Quality Assurance System for a Computer Science Program

Improving the quality assurance (QA) processes and acquiring accreditation are top priorities for academic programs. The learning outcomes (LOs) assessment and continuous quality improvement represent core components of the quality assurance system (QAS). Current assessment methods suffer deficien-cies related to accuracy and reliability, and they lack well-organized processes for continuous improvement planning. Moreover, the absence of automation, and integration in QA processes forms a major obstacle towards developing efficient quality system. There is a pressing need to adopt security protocols that provide required security services to safeguard the valuable information processed by QAS as well. This research proposes an effective methodology for LOs assessment and continuous improvement processes. The proposed approach ensures more accurate and reliable LOs assessment results and provides systematic way for utilizing those results in the continuous quality improvement. This systematic and well-specified QA processes were then utilized to model and implement automated and secure QAS that efficiently performs quality-related processes. The proposed system adopts two security protocols that provide confidentiality, integrity, and authentication for quality data and reports. The security protocols avoid the source repudiation, which is important in the quality reporting system. This is achieved through implementing powerful cryptographic algorithms. The QAS enables efficient data collection and processing required for analysis and interpretation. It also prepares for the development of datasets that can be used in future artificial intelligence (AI) researches to support decision making and improve the quality of academic programs. The proposed approach is implemented in a successful real case study for a computer science program. The current study serves scientific programs struggling to achieve academic accreditation, and gives rise to fully automating and integrating the QA processes and adopting modern AI and security technologies to develop effective QAS.