Author:
Ebinç Senar,Kalkan Ziya,Oruç Zeynep,Sezgin Yasin,Urakçı Zuhat,Küçüköner Mehmet,Kaplan Muhammet Ali,Işıkdoğan Abdurrahman
Reference71 articles.
1. Ba, S., Hu, X., Stein, D. & Liu, Q. (2023). Assessing cognitive presence in online inquiry-based discussion through text classification and epistemic network analysis. British Journal of Educational Technology, 54, 247-266. https://doi.org/10.1111/bjet.13285
2. Baron, N. (2023). Even kids are worried ChatGPT will make them lazy plagiarists, says a linguist who studies tech's effect on reading, writing and thinking. Fortune. https://fortune.com/2023/01/19/what-is-chatgpt-ai-effect-cheating-plagiarism-laziness-education-kids-students/
3. Bengio, Y. & Senecal, J.S. (2008). Adaptive importance sampling to accelerate training of a neural probabilistic language model. IEEE Transactions on Neural Networks, 19(4), 713-722. https://doi.org/10.1109/TNN.2007.912312
4. Beseiso, M., Alzubi, O.A. & Rashaideh, H. (2021). A novel automated essay scoring approach for reliable higher educational assessments. Journal of Computing in Higher Education, 33(3), 727-746. https://doi.org/10.1007/s12528-021-09283-1
5. Botarleanu, R.M., Dascalu, M., Allen, L.K., Crossley, S.A. & McNamara, D.S. (2021). Automated Summary Scoring with ReaderBench. In A. Cristea & C. Troussas (Eds.), Intelligent Tutoring Systems (ITS 2021), 321-332. Springer. https://doi.org/10.1007/978-3-030-80421-3_35