A Fuzzy Petri Net Model for Assessing Analogical Reasoning in Children Ranging from 5 to 8 Years Old.

In this research, we implement an intelligent quantitative model to assess a specific qualitative intelligence scale in children between 5 and 8 years old, based on augmented reality and the well known WISC-IV test. The output of the model is a cognitive factor associated with the analogical reasoning level of the child, and the ulterior analysis of the evaluation measure is intended to serve as an aid for the teacher to discover problems related to the child’s ability to solve visual analogies. A quantitative approach to assess analogical reasoning is suitable to avoid ambiguous evaluations of qualitative results. Also, given that the assessment employs a visual WISC subtest, it constitutes a non-verbal evaluation. Finally, the fact that the model is based on an intelligent approach guarantees that the assessment process is impartial, based on the quantitative scores obtained, instead of an interpretation of the results. The purpose of this work is to give evidence that a computer-aided adaption, employing augmented reality and a Fuzzy Petri Net, for the WISC test, will improve the teaching-learning process in children ranges from 5 to 8 years old. A case study is analyzed, where both the paper-based and the augmented reality versions are applied to five children with Spanish as their native tongue. We show the feasibility and potentiality of implementing the test in a multimedia version to provide teachers with a more reliable resource for the diagnosis and treatment of possible learning deficiencies in the child regarding disambiguation, non-verbality, and impartiality.