Affective Meta Tutor

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.

Intellectual merits:

  1. Using non-invasive sensing and other data to better understand the multiple reasons that students engage in shallow modeling.
  2. Developing probabilistic algorithms for detecting shallow modeling.
  3. Developing meta-tutoring strategies for reducing shallow modeling.
  4. Developing decision-theoretic methods for choosing personalized meta-tutoring strategies for different students based on the taxonomy developed in (1).
  5. Using non-invasive sensing and other data to better understand why students resume shallow modeling after meta-tutoring has been removed.
  6. Developing a new meta-tutor that is more like an affective learning companion, and testing whether the affective strategies that it employs cause students to persist in continuing to engage in deep modeling even after the tutor/companion is removed.

Broad impacts:

  1. The studies will be conducted in high school science summer camps which usually have a large number of underrepresented groups and girls. Learning about modeling and participating in our studies may excite them about studying more science.
  2. Because the modeling tools can be used with many kinds of science, other programs (e.g., ASU's Mars Project's outreach and programs in the School of Education) have expressed interest in adapting the projects' materials and activities for their students.
  3. The PIs are helping start a new undergraduate major at ASU in Informatics that will include core courses in both modeling and educational informatics. Undergraduates will be encouraged to do their course projects using our research projects' personnel, software and materials.
  4. Although intelligent tutoring systems have been successful in teaching procedural cognitive skills, and learning companions have been successful in therapeutic applications, neither activity is as common in science classes as modeling. This project may pave the way for these effective technologies to be used more widely in high school and college.

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