Pregled bibliografske jedinice broj: 1088518
Emerging opportunities for education in the time of COVID-19: Adaptive e-learning intelligent agent based on assessment of emotion and attention
Emerging opportunities for education in the time of COVID-19: Adaptive e-learning intelligent agent based on assessment of emotion and attention // Proceedings of 31st Central European Conference on Information and Intelligent Systems (CECIIS 2020) / Strahonja, Vjeran ; Kirinić, Valentina (ur.).
Varaždin: Fakultet organizacije i informatike Sveučilišta u Zagrebu, 2020. str. 203-210 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1088518 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Emerging opportunities for education in the
time of COVID-19: Adaptive e-learning
intelligent agent based on assessment of
emotion and attention
Autori
Horvat, Marko ; Jagušt, Tomislav
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of 31st Central European Conference on Information and Intelligent Systems (CECIIS 2020)
/ Strahonja, Vjeran ; Kirinić, Valentina - Varaždin : Fakultet organizacije i informatike Sveučilišta u Zagrebu, 2020, 203-210
Skup
31st Central European Conference on Information and Intelligent Systems (CECIIS 2020)
Mjesto i datum
Varaždin, Hrvatska, 07.10.2020. - 09.10.2020
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
digital learning ; adaptive learning ; technology enhanced learning ; intelligent algorithms ; emotion recognition
Sažetak
The COVID-19 pandemic is a disastrous and rapidly evolving situation. It brings changes to many aspects of life, including teaching. A very important area for improvement of e- learning pedagogies is the ability to assess student engagement and the learning curve objectively and continuously. To this regard, we propose a novel procedure for personalized and adaptive assessment of learning performance based on methods for automated estimation of affective states. In this preliminary report we envision an intelligent agent which constantly monitors students’ behaviour parameters during online learning classes. Using unobtrusive video surveillance and machine learning the agent appraises key psychophysiological features related to emotion and attention.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb