How might people interact with agents
Communications of the ACM
Entertaining agents: a sociological case study
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Theory and models for creating engaging and immersive ecommerce Websites
SIGCPR '00 Proceedings of the 2000 ACM SIGCPR conference on Computer personnel research
Evolution of user interaction: the case of agent adele
Proceedings of the 8th international conference on Intelligent user interfaces
Interaction tactics for socially intelligent pedagogical agents
Proceedings of the 8th international conference on Intelligent user interfaces
InfoCruise: Information Navigation Presenting a Focus Facet Based on Context
ACHI '08 Proceedings of the First International Conference on Advances in Computer-Human Interaction
Content Matters: An Investigation of Feedback Categories within an ITS
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
AutoTutor: A simulation of a human tutor
Cognitive Systems Research
EC-TEL'10 Proceedings of the 5th European conference on Technology enhanced learning conference on Sustaining TEL: from innovation to learning and practice
Exploring user satisfaction in a tutorial dialogue system
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
Hi-index | 0.01 |
Researchers of educational technologies are often asked to do the impossible: make students learn and have them enjoy it. These two objectives, though not mutually exclusive, are frequently at odds with each other. Effective learning strategies require active knowledge use on the part of the student. Meanwhile, students typically seek to learn through the path of least effort. This can cause conflict during system interaction, and it is often the case that attitudes toward the learning environment suffer. The current study indicates that students' prior expectations of what technology can (or cannot) do may actually have a greater impact than their initial level of motivation, previous domain knowledge, and familiarity with technology, combined. Knowing these prior expectations may be a crucial step to help researchers perform the impossible.