Athena: um modelo computacional para serviços inteligentes na educação a distância usando históricos de contextos

Abstract

The educational environment has undergone numerous transformations and among them, the expansion and democratization of teaching. Distance Education aims to offer a complete, dynamic and efficient teaching and learning process, mediated by technological resources. It has been growing and assuming an important role in the educational environment. It has been of paramount importance in the expansion and access to higher education, driven by the use of New Information and Communication Technologies (NICT) and new behaviors in the teaching and learning process. However, the high dropout rate in distance courses has caused many concerns, as not all students who enroll in a course manage to reach the end for different reasons. Evasion occurs in all teaching modalities, whether face-to-face, blended and distance learning, both in public and private institutions. However, distance education needs a deeper look, since it is mediated by technologies. Given this context, this thesis proposal proposes the development of a computational model for intelligent services focused on distance education based on historical contexts. The model aims to assist managers and teachers in strategic planning, allowing them to monitor the student's academic progress, as well as assist students with difficulties in the learning process through website recommendations, complementary teaching materials, videos, among other resources. The aim of offering intelligent services is to offer solutions such as: monitoring, intervention, recommendation, formation of study groups, motivation and improvement in the students' learning process, avoiding failure and consequently reducing the dropout rate. The model uses an ontology to represent knowledge in the field of distance education. Furthermore, it explores elements of the students' context that are used in composing context histories. Context history analysis is used to personalize services that deliver useful information for distance learning. An Athena prototype was created and tested with information from 25 students enrolled in the Technology in Systems Analysis and Development course, supporting the implementation and evaluation of two intelligent services, Forecasting to predict students' academic performance and Grouping for training study group. The results obtained reinforce the hypothesis that it is possible to develop a computational model that generically supports the creation and use of intelligent services for distance education based on students' context histories.