Multi-Agent System Observer: Intelligent Support for Engaged E-Learning
Само за регистроване кориснике
2021
Аутори
Vuković, IgorKuk, Kristijan
Čisar, Petar
Banđur, Miloš
Banđur, Đoko
Milić, Nenad
Popović, Brankica
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
Moodle is a widely deployed distance learning platform that provides numerous opportunities to enhance the learning process. Moodle’s importance in maintaining the continuity of education in states of emergency and other circumstances has been particularly demonstrated in the context of the COVID-19 virus’ rapid spread. However, there is a problem with personalizing the learning and monitoring of students’ work. There is room for upgrading the system by applying data mining and different machine-learning methods. The multi-agent Observer system proposed in our paper supports students engaged in learning by monitoring their work and making suggestions based on the prediction of their final course success, using indicators of engagement and machine-learning algorithms. A novelty is that Observer collects data independently of the Moodle database, autonomously creates a training set, and learns from gathered data. Since the data are anonymized, researchers and lecturers can freely use the...m for purposes broader than that specified for Observer. The paper shows how the methodology, technologies, and techniques used in Observer provide an autonomous system of personalized assistance for students within Moodle platforms.
Кључне речи:
educational data mining / engaged learning / intelligent tutoring systems / Moodle / multi-agent systemИзвор:
Electronics, 2021, 10, 12, 1370-Издавач:
- Basel : MDPI
Институција/група
JakovTY - JOUR AU - Vuković, Igor AU - Kuk, Kristijan AU - Čisar, Petar AU - Banđur, Miloš AU - Banđur, Đoko AU - Milić, Nenad AU - Popović, Brankica PY - 2021 UR - http://jakov.kpu.edu.rs/handle/123456789/1531 AB - Moodle is a widely deployed distance learning platform that provides numerous opportunities to enhance the learning process. Moodle’s importance in maintaining the continuity of education in states of emergency and other circumstances has been particularly demonstrated in the context of the COVID-19 virus’ rapid spread. However, there is a problem with personalizing the learning and monitoring of students’ work. There is room for upgrading the system by applying data mining and different machine-learning methods. The multi-agent Observer system proposed in our paper supports students engaged in learning by monitoring their work and making suggestions based on the prediction of their final course success, using indicators of engagement and machine-learning algorithms. A novelty is that Observer collects data independently of the Moodle database, autonomously creates a training set, and learns from gathered data. Since the data are anonymized, researchers and lecturers can freely use them for purposes broader than that specified for Observer. The paper shows how the methodology, technologies, and techniques used in Observer provide an autonomous system of personalized assistance for students within Moodle platforms. PB - Basel : MDPI T2 - Electronics T1 - Multi-Agent System Observer: Intelligent Support for Engaged E-Learning VL - 10 IS - 12 SP - 1370 DO - 10.3390/electronics10121370 ER -
@article{ author = "Vuković, Igor and Kuk, Kristijan and Čisar, Petar and Banđur, Miloš and Banđur, Đoko and Milić, Nenad and Popović, Brankica", year = "2021", abstract = "Moodle is a widely deployed distance learning platform that provides numerous opportunities to enhance the learning process. Moodle’s importance in maintaining the continuity of education in states of emergency and other circumstances has been particularly demonstrated in the context of the COVID-19 virus’ rapid spread. However, there is a problem with personalizing the learning and monitoring of students’ work. There is room for upgrading the system by applying data mining and different machine-learning methods. The multi-agent Observer system proposed in our paper supports students engaged in learning by monitoring their work and making suggestions based on the prediction of their final course success, using indicators of engagement and machine-learning algorithms. A novelty is that Observer collects data independently of the Moodle database, autonomously creates a training set, and learns from gathered data. Since the data are anonymized, researchers and lecturers can freely use them for purposes broader than that specified for Observer. The paper shows how the methodology, technologies, and techniques used in Observer provide an autonomous system of personalized assistance for students within Moodle platforms.", publisher = "Basel : MDPI", journal = "Electronics", title = "Multi-Agent System Observer: Intelligent Support for Engaged E-Learning", volume = "10", number = "12", pages = "1370", doi = "10.3390/electronics10121370" }
Vuković, I., Kuk, K., Čisar, P., Banđur, M., Banđur, Đ., Milić, N.,& Popović, B.. (2021). Multi-Agent System Observer: Intelligent Support for Engaged E-Learning. in Electronics Basel : MDPI., 10(12), 1370. https://doi.org/10.3390/electronics10121370
Vuković I, Kuk K, Čisar P, Banđur M, Banđur Đ, Milić N, Popović B. Multi-Agent System Observer: Intelligent Support for Engaged E-Learning. in Electronics. 2021;10(12):1370. doi:10.3390/electronics10121370 .
Vuković, Igor, Kuk, Kristijan, Čisar, Petar, Banđur, Miloš, Banđur, Đoko, Milić, Nenad, Popović, Brankica, "Multi-Agent System Observer: Intelligent Support for Engaged E-Learning" in Electronics, 10, no. 12 (2021):1370, https://doi.org/10.3390/electronics10121370 . .