Smart Blended Learning Framework based on Artificial Intelligence using MobileNet Single Shot Detector and Centroid Tracking Algorithm

The Covid-19 pandemic has affected all aspects of human life and has even forced humans to shift their life habits, including in the world of education. The learning model must shift from the traditional face-to-face pattern to a modern face-to-face pattern or an asynchronous pattern with information technology-based applications. Blended learning is one of the appropriate solutions to adjust the limited face-to-face learning conditions. Blended learning can be done, for example, by scheduling learning by dividing the number of participants by 50% and entering on a scheduled basis. However, the problem is that the time and effort used are less efficient. Blended learning can also be done by conducting learning simultaneously with 50% of students in class and the remaining 50% through conferences. This concept will streamline the time and effort used. However, the problem is that there is a gap in the learning experience between students in class and students who do learning via conference. This innovative blended learning system framework is proposed to overcome these problems. The system built seeks to present an online learning experience atmosphere so that it is expected to be able to resemble an offline learning atmosphere. We created a system using camera technology and object detection that will track the movement of the teacher so that the teacher can move freely in the room without having to be stuck in front of the computer holding the conference. The algorithms used are MobileNet Single Shot Detector and Centroid Tracking. This research produces an accurate model for detecting teacher movement at a distance of 2, 4, and 6 meters with a camera installation height of 1.5 and 3 meters.

@article{Wahid2022,
  title     = {Smart Blended Learning Framework based on Artificial Intelligence using MobileNet Single Shot Detector and Centroid Tracking Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {5},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Abdul Wahid and Muhammad Fajar B and Jumadi M. Parenreng and Seny Luhriyani and Puput Dani Prasetyo Adi},
  doi       = {10.14569/IJACSA.2022.0130543},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130543}
}