Abstract—Pedagogical models development requires several
steps, one of which is the mapping of tasks and skills, also
known as the educational items clustering. This activity of
clustering educational items usually requires the participation
of domain experts. However, discovering the exact skills
involved in performing the tasks is a complex activity for them.
This paper aims at solving the task and skill-mapping problem
by proposing an approach based on the Weighted
Multi-Relational Matrix Factoring technique to help experts in
this task. This approach relies on two types of relationship, the
“ student does task” relationship and the “student has skills”
relationship through a latent factor model to reconstruct the
“ task requires skill” relationship, the latter being the mapping
between tasks and skills. An evaluation conducted on a group of
two hundred (200) students in lower 6th class in a general
secondary school (Côte d'Ivoire), showed that this approach
brought an improvement rate of about 82.8% of the skill-task
mapping proposed by the experts in the field. This result
confirms that our approach not only allows us to map tasks and
skills but also to significantly improve the updating of curricula.
Index Terms—Pedagogical models, skills discovery, matrix factorization, Q-Matrix, WMRMF.
The authors are with Research Laboratory in Computer Science and Telecommunications (LARIT) at the Institut National Polytechnique Felix HOUPHOUET Boigny (INP-HB) Yamoussoukro, Côte d'Ivoire.
Cite: Denon Arthur Richmond Gono*, Bi Tra Goore, Yves Tiecoura, and Kouamé Abel Assielou, "Multi-relational Matrix Factorization Approach for Educational Items Clustering," International Journal of Information and Education Technology vol. 13, no. 1, pp. 42-47, 2023.Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).