A Recommender System Method for Children's Education Using Mobile Technology

• Published in: IEEE access 2022, Vol.10, p.123679-123696
• Main Authors: Moradi, Mohammad, Fard, Kheirollah Rahsepar, Akhlaqi, Mohammad Yahya
• Format: Article
• Language: English
• Subjects: Bayes methods; Cell phones; Child; Children; Children & youth; Decision making; dissemination of information; Documents; Education; Educational materials; mobile; Numerical models; Portable computers; Real-time systems; recommender system; Recommender systems; Technology utilization; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3223663

In the last decade, we have seen attention and scientific research in using technology, especially the mobile technology, in education. The critical effects of technology in child education are increasing motivation, the self-confidence of children and their group cooperation. The purpose of this research is to provide a child educational recommender system using mobile technology, which includes decision making and real-time monitoring of the educational status of each child, personalization of educational materials based on the educational status of each child, recommendation of educational materials based on the extent of their dissemination in the children's educational network, etc. In this research, the type of research based on the objective is applied research. The statistical population includes children between the ages of 8 and 10 years. The child's educational recommender system includes input, processing and output. The mobile technology was used to track each child's educational activity and educational status and turn them into input keywords of the recommender system. In the processing section, various matching functions are provided to match each child's detected educational status with the educational documents. Finally, in the output section, educational documents adapted to each child's needs are e-mailed to the mobile phone of the child's parents. Because children cannot recognize and express their educational needs, in the proposed recommender system, all phases, from the recognition of children's educational activity and diagnosis of the educational status of each child to the presentation of educational documents related to the diagnosed educational status, have been done automatically. The educational recommender system is simulated using NetLogo and R. Also, the implementation of the educational recommender system has been done on 36 children. According to the results, the educational recommender system was able to increase children's learning more than twice.