Collecting 3A Data to Enhance HCI in AIS
Abstract
3A refers to content aware, context aware, and learner aware intelligent tutoring system (ITS) [2]. The idea behind this is that any ITS should be delivering content intelligently by knowing about the state of the user. The state of the user could be emotional or even physical. Almost all the ITS are more or less intelligent to deliver content. But less intelligent to know whether the learner is accepting the content. In addition, the context consists of two components: context of the content and context of the environment. It is easy for an ITS to be aware of the context of the content (e.g., calculus in case of integrals) but very few ITS take into account the context of the environment. For example, a learner is accessing content in a crowded environment from her cell phone and a learner is accessing content inside a library where it is calm. Contexts of these two learners are different. Moreover, the learner awareness includes emotional states as well as physical states of a learner. In this research our focus is to collect data by enabling a 3A learning system in AutoTutor [11]. AutoTutor is a conversation-based ITS that uses an expectation-misconception tailored dialogue to promote learning. Several questions involved in designing 3A enabled AutoTutor. How to collect 3A data without violating learners’ privacy is the most important one. All other design questions revolve around this.
Publication Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Recommended Citation
Ahmed, F., Shi, G., Shubeck, K., Wang, L., Black, J., Pursley, E., Hossain, I., & Hu, X. (2021). Collecting 3A Data to Enhance HCI in AIS. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12792 LNCS, 499-508. https://doi.org/10.1007/978-3-030-77857-6_35
