Affiliations: [a] Facultad de Ciencias Químicas e Ingeniería, Universidad Autónoma de Baja California, Tijuana, Baja California, México | [b] Facultad de Ingeniería Mochis, Universidad Autónoma de Sinaloa, Los Mochis, Sinaloa, México
Correspondence:
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Corresponding author. Alan Ramírez-Noriega, Facultad de Ciencias Químicas e Ingeniería, Universidad Autónoma de Baja California, Tijuana, Baja California, México. E-mail: [email protected].
Abstract: Course sequencing plays a major role in Intelligent Tutoring Systems because it determines the learning path of the student. However, it is difficult to define this order during early stages when there is no interaction with the student. The objective of this study is to determine the sequence of learning concepts considering an ontology and Wikipedia information. We used a text mining algorithm using Wikipedia to determine course sequencing. The knowledge base is formed by concepts and relationships in an ontology, in addition to Wikipedia articles of the same concepts. To evaluate the accuracy of the algorithm, we made a comparison against domain experts. According to the Pearson test, a correlation of 0.664 between the algorithm and experts was obtained, with a confidence level higher than 99%. The learning sequence can be defined with this method when we do not have evidence of student knowledge, to be later modified according to the interaction of the student.
