6 documents found, page 1 of 1
ChatGPT in exam rooms
Pesovski, Ivica; Santos, Ricardo; Henriques, Roberto; Trajkovik, VladimirPesovski, I., Santos, R., Henriques, R., & Trajkovik, V. (2024). ChatGPT in exam rooms: preliminary insights into student performance with and without AI assistance. In L. G. Chova, C. G. Martínez, & J. Lees (Eds.), 16th International Conference on Education and New Learning Technologies 1-3 July, 2024 Palma, Spain (pp. 5908-5914). (EDULEARN24 Proceedings; No. 2024). IATED Academy. https://doi.org/10.21125/edul...
Predicting bootcamp success
Santos, Ricardo; Pesovski, Ivica; Henriques, Roberto; Trajkovik, VladimirSantos, R., Pesovski, I., Henriques, R., & Trajkovik, V. (2024). Predicting bootcamp success: using regression to leverage preparatory course data for tech career transitions. In L. G. Chova, C. G. Martínez, & J. Lees (Eds.), 16th International Conference on Education and New Learning Technologies 1-3 July, 2024 Palma, Spain (pp. 6181-6190). (EDULEARN24 Proceedings; No. 2024). IATED Academy. https://doi.org/10....
Generative AI for Customizable Learning Experiences
Pesovski, Ivica; Santos, Ricardo Miguel; Henriques, Roberto; Trajkovik, VladimirPesovski, I., Santos, R. M., Henriques, R., & Trajkovik, V. (2024). Generative AI for Customizable Learning Experiences. Sustainability, 16(7), 1-23. Article 3034. https://doi.org/10.3390/su16073034; The introduction of accessible generative artificial intelligence opens promising opportunities for the implementation of personalized learning methods in any educational environment. Personalized learning has been...
Comparing Perception and Reality
Pesovski, Ivica; Santos, Ricardo; Trajkovik, Vladimir; Henriques, RobertoPesovski, I., Santos, R., Trajkovik, V., & Henriques, R. (2023). Comparing Perception and Reality: Exploring Test Complexity and Student Performance in Higher Education. In L. G. Chova, C. G. Martínez, & J. Lees (Eds.), 16th International Conference of Education, Research and Innovation (pp. 6654-6660). (ICERI Proceedings). IATED Academy. https://doi.org/10.21125/iceri.2023.1660; Building on previous work where...
A toolbox of machine learning software to support microbiome analysis
Marcos-Zambrano, Laura Judith; López-Molina, Víctor Manuel; Bakir-Gungor, Burcu; Frohme, Marcus; Karaduzovic-Hadziabdic, Kanita; Klammsteiner, ThomasFunding Information: This study was supported by COST Action CA18131 “Statistical and machine learning techniques in human microbiome studies.” LM-Z is supported by Spanish State Research Agency Juan de la Cierva Grant IJC2019-042188-I (LM-Z). MB is supported by Metagenopolis grant ANR-11-DPBS-0001. MLC was partially supported by the Spanish Ministry of Economy, Industry and Competitiveness, Reference PID2019-1...
Advancing microbiome research with machine learning
D’Elia, Domenica; Truu, Jaak; Lahti, Leo; Berland, Magali; Papoutsoglou, Georgios; Ceci, Michelangelo; Zomer, Aldert; Lopes, Marta B.; Ibrahimi, ElianaFunding Information: The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study is based upon work from COST Action ML4Microbiome “Statistical and machine learning techniques in human microbiome studies” (CA18131), supported by COST (European Cooperation in Science and Technology), www.cost.eu . MB acknowledges support through the Metagenopo...