Motivation Diagnosis in Intelligent Tutoring Systems
ITS '98 Proceedings of the 4th International Conference on Intelligent Tutoring Systems
Modeling and understanding students' off-task behavior in intelligent tutoring systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Word Sense Disambiguation for Vocabulary Learning
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Choosing Reading Passages for Vocabulary Learning by Topic to Increase Intrinsic Motivation
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
IEEE Transactions on Learning Technologies
Predicting change in student motivation by measuring cohesion between tutor and student
IUNLPBEA '11 Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications
Self-Assessment in the REAP Tutor: Knowledge, Interest, Motivation, & Learning
International Journal of Artificial Intelligence in Education
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Self-assessment motivation questionnaires have been used in classrooms yet many researchers find only a weak correlation between answers to these questions and learning. In this paper we postulate that more direct questions may measure motivation better, and they may also be better correlated with learning. In an eight week study with ESL students learning vocabulary in the REAP reading tutor, we administered two types of self-assessment questions and recorded indirect measures of motivation to see which factors correlated well with learning. Our results showed that some user actions, such as dictionary look up frequency and number of times a word is listened to, correlate well with self-assesment motivation questions as well as with how well a student performs on the task. We also found that using more direct self-assesment questions, as opposed to general ones, was more effective in predicting how well a student is learning.