@inproceedings{shehata-etal-2023-enhancing,
    title = "Enhancing Video-based Learning Using Knowledge Tracing: Personalizing Students{'} Learning Experience with {ORBITS}",
    author = "Shehata, Shady  and
      Santandreu Calonge, David  and
      Purnell, Philip  and
      Thompson, Mark",
    editor = {Kochmar, Ekaterina  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Madnani, Nitin  and
      Tack, Ana{\"\i}s  and
      Yaneva, Victoria  and
      Yuan, Zheng  and
      Zesch, Torsten},
    booktitle = "Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.bea-1.8",
    doi = "10.18653/v1/2023.bea-1.8",
    pages = "100--107",
    abstract = "As the world regains its footing following the COVID-19 pandemic, academia is striving to consolidate the gains made in students{'} education experience. New technologies such as video-based learning have shown some early improvement in student learning and engagement. In this paper, we present ORBITS predictive engine at YOURIKA company, a video-based student support platform powered by knowledge tracing. In an exploratory case study of one master{'}s level Speech Processing course at the Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) in Abu Dhabi, half the students used the system while the other half did not. Student qualitative feedback was universally positive and compared the system favorably against current available methods. These findings support the use of artificial intelligence techniques to improve the student learning experience.",
}

As the world regains its footing following the COVID-19 pandemic, academia is striving to consolidate the gains made in students{'} education experience. New technologies such as video-based learning have shown some early improvement in student learning and engagement. In this paper, we present ORBITS predictive engine at YOURIKA company, a video-based student support platform powered by knowledge tracing. In an exploratory case study of one master{'}s level Speech Processing course at the Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) in Abu Dhabi, half the students used the system while the other half did not. Student qualitative feedback was universally positive and compared the system favorably against current available methods. These findings support the use of artificial intelligence techniques to improve the student learning experience.
Shehata, Shady Sant reu Calonge, David Purnell, Philip Thompson, Mark
Enhancing Video-based Learning Using Knowledge Tracing: Personalizing Students' Learning Experience with ORBITS