Conference Paper

Sensors Model Student Self Concept in the Classroom

DOI: 10.1007/978-3-642-02247-0_6 Conference: User Modeling, Adaptation, and Personalization, 17th International Conference, UMAP 2009, formerly UM and AH, Trento, Italy, June 22-26, 2009. Proceedings
Source: DBLP


In this paper we explore ndings from three experiments that use minimally invasive sensors with a web based geometry tutor to create a user model. Minimally invasive sensor technology is mature enough to equip classrooms of up to 25 students with four sensors at the same time while using a computer based intelligent tutoring system. The sensors, which are on each student's chair, mouse, monitor, and wrist, provide data about posture, movement, grip tension, arousal, and facially expressed mental states. This data may provide adaptive feedback to an intelligent tutoring system based on an individual student's aective states. The experiments show that when sensor data supplements a user model based on tutor logs, the model reects a larger percentage of the students' self-concept than a user model based on the tutor logs alone. The models are further expanded to classify four ranges of emotional self-concept including frustration, interest, condence, and excitement with over 78% accuracy. The emotional predictions are a rst step for intelligent tutor systems to create sensor based personalized feedback for each student in a classroom environment. Bringing sensors to our children's schools addresses real problems of students' relationship to mathematics as they are learning the subject.

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Available from: Ivon Arroyo, Aug 12, 2014
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    • "Predicting the students' affective states, that is, attempting to determine these states while students interact with the system, is a challenging problem in education research, and is the focus of several current research efforts [6], [7]. Methods that have been implemented in ITS to predict the affective state include human observation [5], [8], [9], learners' self-reported data of their affective state [10], [11], mining the system's log file [12], [13], modeling affective states [11], [14], facebased emotion recognition systems [4], [3], analyzing the data from physical sensors [15], [16], [10], and more recently, sensing devices such as physiological sensors [17], [18]. Advances in these methods look promising in a lab setting. "
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    ABSTRACT: The importance of affect in learning has led many intelligent tutoring systems (ITS) to include learners' affective states in their student models. The approaches used to identify affective states include human observation, self-reporting, data from physical sensors, modeling affective states, and mining students' data in log files. Among these, data mining and modeling affective states offer the most feasible approach in real-world settings, which may involve a huge number of students. Systems using data mining approaches to predict frustration have reported high accuracy, while systems that predict frustration by modeling affective states, not only predict a student's affective state but also the reason for that state. In our approach, we combine these approaches. We begin with the theoretical definition of frustration, and operationalize it as a linear regression model by selecting and appropriately combining features from log file data. We illustrate our approach by modeling the learners' frustration in Mindspark, a mathematics ITS with large-scale deployment. We validate our model by independent human observation. Our approach shows comparable results to existing data mining approaches and also the clear interpretation of the reasons for the learners' frustration.
    IEEE Transactions on Learning Technologies 10/2013; 6(4):378-388. DOI:10.1109/TLT.2013.31 · 1.28 Impact Factor
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    • "Several approaches have been used for affect recognition and researchers have explored how these approaches complement or supplement each other in learning scenarios [1]. Affect recognition using facial expression as input has been regarded as the most accurate measurement [2]. However, brain-computer interfaces (BCI) have not yet been incorporated. "
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    ABSTRACT: The ability of a learning system to infer a student’s affects has become highly relevant to be able to adjust its pedagogical strategies. Several methods have been used to infer affects. One of the most recognized for its reliability is face- based affect recognition. Another emerging one involves the use of brain-computer interfaces. In this paper we compare those strategies and explore if, to a great extent, it is possible to infer the values of one source from the other source.
    Proceedings of the 13th IEEE International Conference on Advanced Learning Technologies (ICALT); 07/2013
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    • "In addition , emoting-aloud is likely to interfere with normal collaborative and help-seeking behaviors which students use in classroom settings (e.g., [28], [51]). Self-report given through a user interface is more feasible, and has been frequently used in classrooms (e.g., [13], [49]), but carries challenges of its own; in particular, self-report of affect can disrupt student concentration and even change affect (e.g., it can annoy students), if requested in the middle of a problem. Self-report requested between problems may be less useful for studying fine-grained affect during learning, as current self-report methods are not able to capture the sequence of affect while solving a problem. "
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    ABSTRACT: We study the affective states exhibited by students using an intelligent tutoring system for Scatterplots with and without an interactive software agent, Scooter the Tutor. Scooter the Tutor had been previously shown to lead to improved learning outcomes as compared to the same tutoring system without Scooter. We found that affective states and transitions between affective states were very similar among students in both conditions. With the exception of the "neutral state,” no affective state occurred significantly more in one condition over the other. Boredom, confusion, and engaged concentration persisted in both conditions, representing both "virtuous cycles” and "vicious cycles” that did not appear to differ by condition. These findings imply that—although Scooter is well liked by students and improves student learning outcomes relative to the original tutor—Scooter does not have a large effect on students' affective states or their dynamics.
    IEEE Transactions on Affective Computing 04/2012; 3(2):224-236. DOI:10.1109/T-AFFC.2011.41 · 2.68 Impact Factor
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