Archived project

Affective Meta Tutor

Goal: The Affective Meta-Tutoring system is comprised of (1) a tutor that teaches system dynamics modeling, (2) a meta-tutor that teaches good strategies for learning how to model from the tutor, and (3) an affective learning companion that encourages students to use the learning strategy that the meta-tutor teaches. The affective learning companion’s messages are selected by using physiological sensors and log data to determine the student’s affective state. Its efficacy (and our hypotheses) will be tested by seeing if it creates lasting changes in the students' modeling practices when compared to the unaugmented modeling tool. Evaluations compared the learning gains of three conditions: the tutor alone, the tutor plus meta-tutor and the tutor, meta-tutor and affective learning companion.

Date: 1 May 2009 - 30 April 2013

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Project log

Lishan Zhang
added a research item
This project aimed to improve students’ learning and task performance using a non-cognitive learning companion in the context of both a tutor and a meta-tutor. The tutor taught students how to construct models of dynamic systems and the meta-tutor taught students a learning strategy. The non-cognitive learning companion was designed to increase students’ effort and persistence in using the learning strategy. It decided when to intervene and what to say using both log data and affective state monitoring via a fa-cial expression camera and a posture sensor. Experiments with high school students showed that the non-cognitive learning compan-ion increased students’ learning and performance. However, it had no effect on performance during a transfer phase in which the learning companion, meta-tutor and tutor were all absent. The transfer phase null effect must be interpreted with caution due to low power, a possible floor effect and other issues.
Javier Gonzalez-Sanchez
added an update
Project goal
The Affective Meta-Tutoring system is comprised of (1) a tutor that teaches system dynamics modeling, (2) a meta-tutor that teaches good strategies for learning how to model from the tutor, and (3) an affective learning companion that encourages students to use the learning strategy that the meta-tutor teaches. The affective learning companion’s messages are selected by using physiological sensors and log data to determine the student’s affective state. Its efficacy (and our hypotheses) will be tested by seeing if it creates lasting changes in the students' modeling practices when compared to the unaugmented modeling tool. Evaluations compared the learning gains of three conditions: the tutor alone, the tutor plus meta-tutor and the tutor, meta-tutor and affective learning companion.
Background and motivation
Research problem. In science and mathematics education, a well-known problem is that students often understand and manipulate scientific representations (models) in a shallow manner (Stratford,1997). For instance, when drawing causal networks, students sometimes pay little attention to what the nodes and links denote (Biswas et al., 2005). For instance, they might copy and paste a node's name from the text or change a link's label from + to - without considering what these edits say about causation. Although this is a well-known problem, it has no standard name, so let us call it the shallow modeling problem.
Hypothesized solution. When the modeling is done with a software tool, then it is becoming possible to detect episodes of shallow modeling from patterns of usage. Currently, this capability has been used for meta-tutoring. That is, when the meta-tutor detects shallow modeling, it gently reminds students to do deeper modeling and explains how, if asked. This is called meta-tutoring because it does not tutor the scientific domain (e.g., stream ecology) but it does tutor meta-cognitive practices for learning the scientific domain. Unfortunately, current work also indicates that when the meta-tutor is removed, students resume shallow modeling (Schwartz, Chase, Chin et al., in press; Roll, Aleven, McLaren et al., 2006). In line with current theories (e.g., Dweck's; Picard's), we hypothesize that lasting benefits require first changing students' cost-benefit beliefs about shallow vs. deep modeling practices, and then breaking their old modeling habits and instilling new ones. Moreover, these changes are more easily accomplished in a supportive social context. Thus, our solution is to combine meta-tutoring technology with the technology of affective learning companions (e.g., Bickmore & Picard, 2005), which have been used successfully to get people to make persistent changes such as adopting safer sexual practices (Read et al., 2006) or persevering in the face of frustration (Burleson & Picard, 2007).
Research plan. In order to generalize from earlier work, we will use system dynamics diagrams, which are a more sophisticated modeling language than causal networks. We will construct modules that use a systems dynamics modeling tool to teach interesting science to high school students during summer camps. We will collect log data, verbal protocols and non-invasive affect sensor data. These will be used to develop and calibrate a combined meta-tutor and affective learning companion. Its efficacy (and our hypotheses) will be tested by seeing if it creates lasting changes in the students' modeling practices when compared to the unaugmented modeling tool.
 
Javier Gonzalez-Sanchez
added 7 research items
Intelligent Tutoring Systems are software applications capable of complementing and enhancing the learning process by providing direct customized instruction and feedback to students in various disciplines. Although Intelligent Tutoring Systems could differ widely in their attached knowledge bases and user interfaces (including interaction mechanisms), their behaviors are quite similar. Therefore, it must be possible to establish a common software model for them. A common software model is a step forward to move these systems from proof-of-concepts and academic research tools to widely available tools in schools and homes. The work reported here addresses: (1) the use of Design Patterns to create an object-oriented software model for Intelligent Tutoring Systems; (2) our experience using this model in a three-year development project and its impact on facets such as creating a common language among stakeholders, supporting an incremental development, and adjustment to a highly shifting development team; and (3) the qualities achieved and trade-offs made.
The Affective Meta-Tutoring system is comprised of (1) a tutor that teaches system dynamics modeling, (2) a meta-tutor that teaches good strategies for learning how to model from the tutor, and (3) an affective learning companion that encourages students to use the learning strategy that the meta-tutor teaches. The affective learning companion’s messages are selected by using physiological sensors and log data to determine the student’s affective state. Evaluations compared the learning gains of three conditions: the tutor alone, the tutor plus meta-tutor and the tutor, meta-tutor and affective learning companion.
The level up procedure is a method for evaluating the learning gains of educational software, and tutoring systems in particular, that includes some form of embedded assessment. The instruction is arranged in levels that take only a few minutes to master, and students level up when the software indicates they have achieved mastery. This paper reports some methodological lessons learned from applying this procedure in studies of a tutoring system that taught high school students how to model dynamic systems.
Javier Gonzalez-Sanchez
added a project goal
The Affective Meta-Tutoring system is comprised of (1) a tutor that teaches system dynamics modeling, (2) a meta-tutor that teaches good strategies for learning how to model from the tutor, and (3) an affective learning companion that encourages students to use the learning strategy that the meta-tutor teaches. The affective learning companion’s messages are selected by using physiological sensors and log data to determine the student’s affective state. Its efficacy (and our hypotheses) will be tested by seeing if it creates lasting changes in the students' modeling practices when compared to the unaugmented modeling tool. Evaluations compared the learning gains of three conditions: the tutor alone, the tutor plus meta-tutor and the tutor, meta-tutor and affective learning companion.
 
Javier Gonzalez-Sanchez
added a research item
Intelligent Tutoring Systems (ITSs) constitute an alternative to expert human tutors, providing direct customized instruction and feedback to students. ITSs could positively impact education if adopted on a large scale, but doing that requires tools to enable their mass production. This circumstance is the key motivation for this work. We present a component-based approach for a system architecture for ITSs equipped with meta-tutoring and affective capabilities. We elicited the requirements that those systems might address and created a system architecture that models their structure and behavior to drive development efforts. Our experience applying the architecture in the incremental implementation of a four-year project is discussed.