Publication date: 29/07/2022

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Summary: Concept maps are forms of graphic representation that establish relationships between concepts. They can
be used in education in different situations and purposes: as a learning resource, cognitive representation, means of
evaluation, instructional organization and knowledge sharing. The knowledge evidenced in a conceptual map
involves implications between meanings. Meanings are everything that can be said about an object, such as a
description of its properties, as well as everything that we can observe in it. In addition, a meaning or implication
also refers to everything we can think of objects (classifying them, establishing some kind of relationship, among
others). In the classroom, concept maps can be applied as a way of mediating teaching-learning, stimulating
meaningful learning. There are many challenges for using concept maps to be more effective in measuring learning
and evaluating the learner’s cognitive processes and their interactions with other learning participants. The
assessment of learning is a very complex task, especially when the objective is to automate the assessment process,
which demands, among other obligations, a formalization of knowledge representation structures. Furthermore, the
evaluation of a concept map becomes more complex when the author does not represent his knowledge, but the
knowledge expressed in a text or in a map made by someone else. In this context, we carried out a literature review
between the years 2015 and 2020 on the development of technological approaches that help or automate the
process of evaluating concept maps in the educational environment, through the use of significant implications. We
also sought to identify limitations and gather the best features of related works to propose our approach. Among the
limitations found, we found that none of the approaches found applied semantic analysis in the evaluation of
concept maps. The application of semantic analysis makes it possible to achieve a deeper perception of the supposed
knowledge represented in the concept maps, seeking to transform evidence into evidence of the learning, in fact,
carried out by the learner, helping the teacher in the evaluation of the concept maps. Furthermore, an activity that
involves concept maps in a classroom would result in the construction of different maps, and with that, the
evaluation would become costly and of great cognitive effort for the teacher. Therefore, the automation of this
process is of great value. In order to develop a computational architecture capable of performing semantic analysis
of concept maps through the significant implications defined by Piaget, this research resulted in an API that aims to
provide a deeper semantic dimension in the analysis of significant implications and to extract more accurate
information about the representation of knowledge and the understanding of an individual from the representation
of his map. The architecture is capable of processing concept maps written in English and Portuguese. The
development took place using natural language processing techniques and pre-trained neural network models with
billions of texts for word prediction and semantic similarity calculation. In order to obtain a quantitative and
qualitative analysis, the conceptual architecture was applied in a classroom environment, presenting satisfactory
results. In addition, the architecture will be integrated as a tool to support education in an environment of intelligent
tutors, providing semantic information about the students’ conceptual maps, in order to facilitate the tutors’

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