Adaptive and Intelligent Mentoring to Increase User Attentiveness in Learning Activities

• Published in: Advances in Artificial Intelligence - IBERAMIA 2018 Vol. 11238; pp. 145 - 155
• Main Authors: Toala, Ramón, Gonçalves, Filipe Manuel, Durães, Dalila, Novais, Paulo
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Switzerland Springer International Publishing AG 2018; Springer Verlag; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Adaptive systems; Attention; Ciências da Computação e da Informação; Ciências Naturais; Eletrónica e Informática; Engenharia e Tecnologia; Engenharia Eletrotécnica; Intelligent mentoring systems; Learning; Science & Technology
• ISBN: 3030039277; 9783030039271
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-03928-8_12

In the past decades intelligent mentoring systems have rapidly increased. In e-learning environment there has been an exponential growth in technological development environments and number of users that are addressed, hence an intelligent mentoring system should capture the user’s attention in order to improve results when focused in (e)learning tasks (i.e. serve both as a support of presence lessons and for distance form of studies – e-learning). It is important to note that the process of teaching-learning requires an interaction between the different actors involved: the tutor, the student, the expert domain and the learning environment or interface. In this paper we propose an innovative approach of an intelligent mentoring system that monitors the user’s biometric behaviour and measures his/her attention level during e-learning activities. Additionally, a machine learning categorisation model is presented that monitors students’ activity during school lessons. Nowadays computers are used as important working tools in many places, where we intend to use non-invasive methods of intelligent orientation through the observation of the user’s interaction with the computer. This work has been supported by: SENESCYT - Universidad do Minho and Secretaría de Educación Superior, Ciencia, Tecnología e Innovación within the Project: SENESCYT-SDFC-DSEFC-2017-2855-O; Part-funded by ERDF European Regional Development Fund and by National Funds through the FCT Portuguese Foundation for Science and Technology within project NORTE-01-0247-FEDER-017832. The work of Filipe Gonçalves is supported by a FCT grant with the reference ICVS-BI-2016-005.