Challenges for data collecting of teacher and student' behavior in different types of class using video and wearable device

Matsui, Kunihiko; Kasai, Tatsuhiko; Sakai, Keiya

doi:10.1109/iciev.2019.8858558

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…However, there is no defined record structure to store user interaction data in intelligent educational environments. Even though works such as [13,[24][25][26][27][28][29][30][31] use sensor data to address the situations of interest of each job, no defined structure is observed that they share among themselves.…”

Section: Behavior Detectionmentioning

confidence: 99%

From Interaction Data to Personalized Learning: Mining User-Object Interactions in Intelligent Environments

Hernández-Calderón,

Benítez-Guerrero,

Rojano-Cáceres

et al. 2024

Proceedings of ISP RAS

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The aim of this work is to contribute to the personalization of intelligent learning environments by analyzing user-object interaction data to identify On-Task and Off-Task behaviors. This is accomplished by monitoring and analyzing users' interactions while performing academic activities with a tangible-intangible hybrid system in a university intelligent environment configuration. With the proposal of a framework and the Orange Data Mining tool and the Neural Network, Random Forest, Naive Bayes, and Tree classification models, training and testing was carried out with the user-object interaction records of the 13 students (11 for training and two for testing) to identify representative sequences of behavior from user-object interaction records. The two models that had the best results, despite the small number of data, were the Neural Network and Naive Bayes. Although a more significant amount of data is necessary to perform a classification adequately, the process allowed exemplifying this process so that it can later be fully incorporated into an intelligent educational system to contribute to build personalized environments.

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