6 research outputs found
E-Deposit in Academic Use
Get PDFThe e-Deposit is a deposit that can be managed in electronic form. The concept of a fund accessible for different financial transactions makes the e-Deposit appropriate for use at universities. The application is implemented in three-tier architecture. Because of the extensive exchange of financial data over the internet, the data integrity is secured. The business and data access tier are implemented in a modular manner helping the robustness of the application and reducing the risk of unwanted behavior. Special modules are used, that enable the integration of an e-commerce scenario in an already existing university information system
Curriculum analysis for data systems education.
Get PDFThe field of data systems has seen quick advances due to the popularization of data science, machine learning, and real-time analytics. In industry contexts, system features such as recommendation systems, chatbots and reverse image search require efficient infrastructure and data management solutions. Due to recent advances, it remains unclear (i) which topics are recommended to be included in data systems studies in higher education, (ii) which topics are a part of data systems courses and how they are taught, and (iii) which data-related skills are valued for roles such as software developers, data engineers, and data scientists. This working group aims to answer these points to explain the state of data systems education today and to uncover knowledge gaps and possible discrepancies between recommendations, course implementations, and industry needs. We expect the results to be applicable in tailoring various data systems courses to better cater to the needs of industry, and for teachers to share best practices
Data systems education: curriculum recommendations, course syllabi, and industry needs.
Get PDFData systems have been an important part of computing curricula for decades, and an integral part of data-focused industry roles such as software developers, data engineers, and data scientists. However, the field of data systems encompasses a large number of topics ranging from data manipulation and database distribution to creating data pipelines and data analytics solutions. Due to the slow nature of curriculum development, it remains unclear (i) which data systems topics are recommended across diverse higher education curriculum guidelines, (ii) which topics are taught in higher education data systems courses, and (iii) which data systems topics are actually valued in data-focused industry roles. In this study, we analyzed computing curriculum guidelines, course contents, and industry needs regarding data systems to uncover discrepancies between them. Our results show, for example, that topics such as data visualization, data warehousing, and semi-structured data models are valued in industry, yet seldom taught in courses. This work allows professionals to further align curriculum guidelines, higher education, and data systems industry to better prepare students for their working life by focusing on relevant skills in data systems education
Data systems education : curriculum recommendations, course syllabi, and industry needs
Get PDFData systems have been an important part of computing curricula for decades, and an integral part of data-focused industry roles such as software developers, data engineers, and data scientists. However, the field of data systems encompasses a large number of topics ranging from data manipulation and database distribution to creating data pipelines and data analytics solutions. Due to the slow nature of curriculum development, it remains unclear (i) which data systems topics are recommended across diverse higher education curriculum guidelines, (ii) which topics are taught in higher education data systems courses, and (iii) which data systems topics are actually valued in data-focused industry roles. In this study, we analyzed computing curriculum guidelines, course contents, and industry needs regarding data systems to uncover discrepancies between them. Our results show, for example, that topics such as data visualization, data warehousing, and semi-structured data models are valued in industry, yet seldom taught in courses. This work allows professionals to further align curriculum guidelines, higher education, and data systems industry to better prepare students for their working life by focusing on relevant skills in data systems education
Model and implementation of a self-adaptive social navigation system for public information systems
No full textThis paper presents a model of a generic navigation system of a public information
system, that can be used to improve the structure and content of the information repository via
self-organization capabilities based on social navigation and interaction. This model has the
primary goal of establishing a generic and adaptive social-based self-structuring navigation
system. The model integrates the concepts of social navigation, interaction and self-adaptivity
in a feedback control loop. The model focuses on self-adaptivity and includes elements of
social navigation in all parts of the system, which enables the implementations based on this
model to get social adaptability based on user actions individually, but also as a social
environment, in every possible aspect of the functioning of the system. The introduced
feedback control loop gives the possibility for further autonomous improvements of the
organization of the information. As a proof of concept, this model is then used to build
a prototype implementation solution that can be used to guide students towards better course
selection during the semester enrolment process
Module Advisor: A Hybrid Recommender System for Elective Module Exploration
Get PDFThe 12th ACM Conference on Recommender Systems (RecSys '18), Vancouver, Canada, 2 October 2018Recommender systems are omni-present in our every day lives, guiding us through the vast amount of information available. However, in the academic world, personalised recommendations are less prominent, leaving students to navigate through the typically large space of available courses and modules manually. Since it is crucial for students to make informed choices about their learning pathways, we aim to improve the way students discover elective modules by developing a hybrid recommender system prototype that is specifically designed to help students find elective modules from a diverse set of subjects. We can improve the discoverability of long-tail options and help students broaden their horizons by combining notions of similarity and diversity.Science Foundation IrelandInsight Research Centr
