Ranking Feature Sets for Emotion Models Used in Classroom Based Intelligent Tutoring Systems

Abstract. Recent progress has been made in using sensors with Intel-ligent Tutoring Systems in classrooms in order to predict the affective state of students users. If tutors were able to interpret sensor data with new students based on past experience, rather than having to be indi-vidually trained, then tutor developers could evaluate various methods of adapting to each student’s affective state using consistent predictions. Our classifiers for emotion have predicted student emotions with an accu-racy between 78 % and 87%. However, it is still unclear which sensors are best, and the educational technology community needs to know this to develop better than baseline classifiers, e.g. ones that use only frequency of emotional occurrence to predict affective state. This paper suggests a method for comparing classifiers using different sensors as well as a method for validating the classifiers on a novel population. This involves training our classifiers on data collected in the Fall of 2008 and testing them on data collected in the Spring of 2009. Results of the comparison show that the classifiers for some affective states are significantly better than the baseline, and a validation study found that not all classifier rankings generalize to new settings. The analysis suggests that though there is some benefit gained from simple linear classifiers, more advanced methods are needed for better results.

Metacognition and learning in spoken dialogue computer tutoring

We investigate whether four metacognitive metrics derived from student correctness and uncertainty values are predictive of student learning in a fully automated spoken dialogue computer tutoring corpus. We previously showed that these metrics predicted learning in a comparable wizarded corpus, where a human wizard performed the speech recognition and correctness and uncertainty annotation. Our results show that three of the four metacognitive metrics remain predictive of learning even in the presence of noise due to automatic speech recognition and automatic correctness and uncertainty annotation. We conclude that our results can be used to inform a future enhancement of our fully automated system to track and remediate student metacognition and thereby further improve learning. © Springer-Verlag Berlin Heidelberg 2010

Modeling student benefit from illustrations and graphs

We examine a corpus of physics tutorial dialogues between a computer tutor and students. Either graphs or illustrations were displayed during the dialogues. In this work, stepwise linear regression, augmented to remove unwanted terms, is used to build models that identify situations when each graphic may aid learning. Our experimental results show that grouping students by pretest score, then by gender produces a model that significantly outperforms the baseline. © 2014 Springer International Publishing Switzerland