Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userA Decentralized and Autonomous Model to Administer University Examinations
174
0
0.0
(
0
)
Added by
Arvind Kiwelekar
Publication date
2021
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Administering standardized examinations is a challenging task, especially for those universities for which colleges affiliated to it are geographically distributed over a wide area. Some of the challenges include maintaining integrity and confidentiality of examination records, preventing mal-practices, issuing unique identification numbers to a large student population and managing assets required for the smooth conduct of examinations. These challenges aggravate when colleges affiliated to universities demand academic and administrative autonomy by demonstrating best practices consistently over a long period. In this chapter, we describe a model for decentralized and autonomous examination system to provide the necessary administrative support. The model is based on two emerging technologies of Blockchain Technology and Internet of Things (IoT). We adopt a software architecture approach to describe the model. The prescriptive architecture consists of {em architectural mappings} which map functional and non-functional requirements to architectural elements of blockchain technology and IoT. In architectural mappings, first, we identify common use-cases in administering standardized examinations. Then we map these use-cases to the core elements of blockchain, i.e. distributed ledgers, cryptography, consensus protocols and smart-contracts and IoT. Such kind of prescriptive architecture guide downstream software engineering processes of implementation and testing
rate research
Read More
Decentralized autonomous organizations as a new form of online governance arecollections of smart contracts deployed on a blockchain platform that intercede groupsof people. A growing number of Decentralized Autonomous Organization Platforms,such as Aragon and Colony, have been introduced in the market to facilitate thedevelopment process of such organizations. Selecting the best fitting platform ischallenging for the organizations, as a significant number of decision criteria, such aspopularity, developer availability, governance issues, and consistent documentation ofsuch platforms, should be considered. Additionally, decision-makers at theorganizations are not experts in every domain, so they must continuously acquirevolatile knowledge regarding such platforms and keep themselves updated.Accordingly, a decision model is required to analyze the decision criteria usingsystematic identification and evaluation of potential alternative solutions for adevelopment project. We have developed a theoretical framework to assist softwareengineers with a set of Multi-Criteria Decision-Making problems in software production.This study presents a decision model as a Multi-Criteria Decision-Making problem forthe decentralized autonomous organization platform selection problem. Weconducted three industry case studies in the context of three decentralizedautonomous organizations to evaluate the effectiveness and efficiency of the decisionmodel in assisting decision-makers.
With the recent implementation of the K to 12 Program, academic institutions, specifically, Colleges and Universities in the Philippines have been faced with difficulties in determining projected freshmen enrollees vis-a-vis decision-making factors for efficient resource management. Enrollment targets directly impacts success factors of Higher Education Institutions. This study covered an analysis of various characteristics of freshmen applicants affecting their admission status in a Philippine university. A predictive model was developed using Logistic Regression to evaluate the probability that an admitted student will pursue to enroll in the Institution or not. The dataset used was acquired from the University Admissions Office. The office designed an online application form to capture applicants details. The online form was distributed to all student applicants, and most often, students, tend to provide incomplete information. Despite this fact, student characteristics, as well as geographic and demographic data based on the students location are significant predictors of enrollment decision. The results of the study show that given limited information about prospective students, Higher Education Institutions can implement machine learning techniques to supplement management decisions and provide estimates of class sizes, in this way, it will allow the institution to optimize the allocation of resources and will have better control over net tuition revenue.
In this early draft, we describe a decentralized, app-based approach to COVID-19 vaccine distribution that facilitates zero knowledge verification, dynamic vaccine scheduling, continuous symptoms reporting, access to aggregate analytics based on population trends and more. To ensure equity, our solution is developed to work with limited internet access as well. In addition, we describe the six critical functions that we believe last mile vaccination management platforms must perform, examine existing vaccine management systems, and present a model for privacy-focused, individual-centric solutions.
The sudden change in the landscape of Philippine education, including the implementation of K to 12 program, Higher Education institutions, have been struggling in attracting freshmen applicants coupled with difficulties in projecting incoming enrollees. Private HEIs Enrolment target directly impacts success factors of Higher Education Institutions. A review of the various characteristics of freshman applicants influencing their admission status at a Philippine university were included in this study. The dataset used was obtained from the Admissions Office of the University via an online form which was circulated to all prospective applicants. Using Logistic Regression, a predictive model was developed to determine the likelihood that an enrolled student would seek enrolment in the institution or not based on both students and institutions characteristics. The LR Model was used as the algorithm in the development of the Decision Support System. Weka was utilized on selection of features and building the LR model. The DSS was coded and designed using R Studio and R Shiny which includes data visualization and individual prediction.
With larger memory capacities and the ability to link into wireless networks, more and more students uses palmtop and handheld computers for learning activities. However, existing software for Web-based learning is not well-suited for such mobile devices, both due to constrained user interfaces as well as communication effort required. A new generation of applications for the learning domain that is explicitly designed to work on these kinds of small mobile devices has to be developed. For this purpose, we introduce CARLA, a cooperative learning system that is designed to act in hybrid wireless networks. As a cooperative environment, CARLA aims at disseminating teaching material, notes, and even components of itself through both fixed and mobile networks to interested nodes. Due to the mobility of nodes, CARLA deals with upcoming problems such as network partitions and synchronization of teaching material, resource dependencies, and time constraints.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
