Ecosystem for production and consumption of connected open data and its application in educational settings

• Grant number: 15/24507-2
• Support Opportunities: Regular Research Grants
• Start date: October 01, 2017
• End date: March 31, 2020
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Agreement: MCTI/MC
• Principal Investigator: Seiji Isotani
• Grantee: Seiji Isotani
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

Linked open data are structured data, available on the Web with an open license and using formats and standards supported by the W3C. There are several benefits to provide data as linked open data (LOD). Among them, we emphasize the transparency and access to information, the ability to connect different data and generate new knowledge and ultimately enable the creation of new services and computational tools to support human decision-making. Unfortunately, the adoption of LOD in Brazil (and elsewhere) faces major challenges such as lack of adequate processes and governance methods for generation of LOD with quality, as well as lack of resources and computational tools that support the automation of production of LOD in different areas of knowledge. Thus, this research project aims to build an ecosystem for the production and consumption of LOD that enable transparency and access to information, generating new services to society by using government data, particularly from the Brazilian educational system. To do so, we first define a process for high-quality LOD creation. Then, through institutional partnerships nationally and internationally, we will study mechanisms of management and data governance for monitoring, enhancement, distribution and integration of data. For the production process and governance data to be effective, tools and computational models will be developed to manage and automate parts of the activities. Finally, an economical system for open data and and professional training will be proposed to foster the generation of new services and products that add value and knowledge to the data. To validate the proposal, use cases will be held in conjunction with partner government agencies and professionals associated with this project. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

PENTEADO, BRUNO ELIAS; ISOTANI, SEIJI; PEREIRA PAIVA, PAULA MARIA; MORETTIN-ZUPELARI, MARINA; FERRARI, DEBORAH VIVIANE; CHANG, M; SAMPSON, DG; HUANG, R; GOMES, AS; CHEN, NS; et al. Detecting behavioral trajectories in continued education online courses. 2019 IEEE 19TH INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES (ICALT 2019), v. N/A, p. 2-pg., 2019-01-01. (15/24507-2)

PENTEADO, BRUNO ELIAS; ISOTANI, SEIJI; PAIVA, PAULA M.; MORETTIN-ZUPELARI, MARINA; FERRARI, DEBORAH VIVIANE; ROSE, CP; MARTINEZ-MALDONADO, R; HOPPE, HU; LUCKIN, R; MAVRIKIS, M; et al. Prediction of Interpersonal Help-Seeking Behavior from Log Files in an In-Service Education Distance Course. ARTIFICIAL INTELLIGENCE IN EDUCATION, PT II, v. 10948, p. 5-pg., 2018-01-01. (15/24507-2)

PENTEADO, BRUNO ELIAS; ISOTANI, SEIJI; PEREIRA PAIVA, PAULA MARIA; MORETTIN-ZUPELARI, MARINA; FERRARI, DEBORAH VIVIANE; ISOTANI, S; MILLAN, E; OGAN, A; HASTINGS, P; MCLAREN, B; et al. Discovery of Study Patterns that Impacts Students' Discussion Performance in Forum Assignments. ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2019, PT II, v. 11626, p. 6-pg., 2019-01-01. (15/24507-2)