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.

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Serious games and gamified interventions have become increasingly popular among researchers and therapists dealing with the autistic audience. The number of studies on technology for autism has multiplied, with the aim to foster independence and improve learning outcomes. Nevertheless, designing interventions for Autism Spectrum Disorder is challenging, due to the complex clinical conditions and the broad range of symptoms covered by the disturbance. Thus, this systematic review investigates the current status of gamification resources for autism, with a special interest in the gamification elements and the User Interface design. We describe the planning and the searching procedures and present the data extracted from 30 primary sources. The studies analyzed show a multitude of gamification elements and a plethora of methods and strategies to support decision-making and improve accessibility in the development of autism-specific software. It is concluded that the existence of methodological gaps related to the definition of the target audience and the conduction of testing may impose additional challenges to the development process, whilst the combination of gamification elements is generally positive.

Featured Application: Our results can be applied to identifying of students' learning style providing adaptation to e-learning systems.Abstract: It is possible to classify students according to the manner they recognize, process, and store information. This classification should be considered when developing adaptive e-learning systems. It also creates a comprehension of the different styles students demonstrate while in the process of learning, which can help adaptive e-learning systems offer advice and instructions to students, teachers, administrators, and parents in order to optimize students' learning processes. Moreover, e-learning systems using computational and statistical algorithms to analyze students' learning may offer the opportunity to complement traditional learning evaluation methods with new ones based on analytical intelligence. In this work, we propose a method based on deep multi-target prediction algorithm using Felder-Silverman learning styles model to improve students' learning evaluation using feature selection, learning styles models, and multiple target classification. As a result, we present a set of features and a model based on an artificial neural network to investigate the possibility of improving the accuracy of automatic learning styles identification. The obtained results show that learning styles allow adaptive e-learning systems to improve the learning processes of students.

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