Abstract
Eye-movement tracking and student-system interaction logs provide different types of information which can be used as a potential source of real-time adaptation in learning environments. By analysing student interactions with an intelligent tutoring system (ITS), we can identify sub-optimal behaviour such as not paying attention to important interface components. On the basis of such findings, ITSs can be enhanced to be proactive, rather than reactive, to users’ actions. Tutorial dialogues are one of the teaching strategies used in ITSs which has been shown empirically to significantly improve learning. Enhanced entity-relationship (EER)-Tutor is a constraint-based ITS that teaches conceptual database design. This paper presents the preliminary results of a project that investigates how students interact with the tutorial dialogues in EER-Tutor using both eye-gaze data and student-system interaction logs. Our findings indicate that advanced students are selective of the interface areas they visually focus on, whereas novices waste time by paying attention to interface areas that are inappropriate for the task at hand. Novices are also unaware that they require help with the tutorial dialogues. Furthermore, we have demonstrated that the student’s prior knowledge, the problem complexity and the percentage of the dialogue’s prompts that are answered correctly are factors that can be used to predict future errors. The findings from our study can be used to further enhance EER-Tutor in order to support learning better, including real-time classification of students into novices and advanced students in order to adapt system feedback and interventions.
Highlights
Despite the proven effectiveness of intelligent tutoring systems (ITSs), studies indicate that some students only gain shallow knowledge which they have difficulty applying to new and different problems (Aleven et al 1999)
It is evident that there are some differences between novices and advanced students in terms of their behaviour as indicated by the collected Enhanced entity-relationship (EER)-Tutor and eye-tracking data
From the EER-Tutor logs, we see that there is no significant difference in the distributions of the percentage of help choices made by the two groups
Summary
Despite the proven effectiveness of intelligent tutoring systems (ITSs), studies indicate that some students only gain shallow knowledge which they have difficulty applying to new and different problems (Aleven et al 1999). Self-explanation is a constructive activity during which a person tries to make sense of new information by explaining it to him/herself (Chi 2000) This results in the revision of his/her knowledge for future application. Tutorial dialogues have been used in a number of ITSs in order to Elmadani et al Research and Practice in Techology Enhanced Learning (2015) 10:16 encourage deep learning. In some systems, such as Why2-Atlas (Vanlehn et al 2002) and Auto Tutor (Graesser et al, 2003), tutorial dialogues are used as the main learning activity. Systems like Geometry Explanation Tutor (Aleven et al 2004) and KERMIT-SE (Weerasinge and Mitrovic 2005) use problem-solving as the primary learning activity, while tutorial dialogues provide additional support. Tutorial dialogues have been evaluated empirically and shown to significantly improve learning (Olney et al 2010; Weerasinghe et al 2011)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Research and practice in technology enhanced learning
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
