Next Article in Journal
Himawari-8 Satellite Based Dynamic Monitoring of Grassland Fire in China-Mongolia Border Regions
Next Article in Special Issue
A Survey of Data Semantization in Internet of Things
Previous Article in Journal / Special Issue
A More Efficient Transportable and Scalable System for Real-Time Activities and Exercises Recognition
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(1), 267;

Application of Computational Intelligence to Improve Education in Smart Cities

Electrical Engineering and Computer College, State University of Campinas, Av. Albert Einsten, 400, Cidade Universitária Zeferino Vaz, Distrito Barão Geraldo, Campinas 13083-852 SP, Brazil
Computer Science Department, State University of Londrina, Rod. Celso Garcia Cid, Km 380, s/n, Campus Universitário, Londrina 86057-970 PR, Brazil
Author to whom correspondence should be addressed.
Received: 30 November 2017 / Revised: 8 January 2018 / Accepted: 14 January 2018 / Published: 18 January 2018
(This article belongs to the Special Issue Sensing, Data Analysis and Platforms for Ubiquitous Intelligence)
Full-Text   |   PDF [1607 KB, uploaded 18 January 2018]   |  


According to UNESCO, education is a fundamental human right and every nation’s citizens should be granted universal access with equal quality to it. Because this goal is yet to be achieved in most countries, in particular in the developing and underdeveloped countries, it is extremely important to find more effective ways to improve education. This paper presents a model based on the application of computational intelligence (data mining and data science) that leads to the development of the student’s knowledge profile and that can help educators in their decision making for best orienting their students. This model also tries to establish key performance indicators to monitor objectives’ achievement within individual strategic planning assembled for each student. The model uses random forest for classification and prediction, graph description for data structure visualization and recommendation systems to present relevant information to stakeholders. The results presented were built based on the real dataset obtained from a Brazilian private k-9 (elementary school). The obtained results include correlations among key data, a model to predict student performance and recommendations that were generated for the stakeholders. View Full-Text
Keywords: computational intelligence; smart education; smart city, adaptive learning; creative school; self-learning computational intelligence; smart education; smart city, adaptive learning; creative school; self-learning

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Gomede, E.; Gaffo, F.H.; Briganó, G.U.; de Barros, R.M.; Mendes, L.S. Application of Computational Intelligence to Improve Education in Smart Cities. Sensors 2018, 18, 267.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top