Deeper Modeling via Affective Meta-tutoring Deeper Modeling via Affective Meta-tutoring 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., Dwecks; Picards), 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). REU Supplement: Deeper Modeling via Affective Meta-tutoring REU-Deeper Modeling via affective meta-tutoring REU: Deeper Modeling via Affective Meta-tutoring The PI is currently funded by the NSF grant: Deeper Modeling via affective meta-tutoring. This proposal seeks REU supplemental funding for two undergraduate students; the funding will serve the following two purposes: (1) provide undergraduate students with the opportunity to gain experience with research through a set of well defined, hands-on tasks; (2) enhance the abovestated project by increasing the project instruments and applications affective feedback capabilities. The objective of the above-stated project is to encourage students to reason effectively in instructional situations, first via scaffolding for self regulation, and then after this scaffolding is removed. We have identified problems that on-line tutors have with creating conditions for students to regulate their own behavior. For instance, instead of taking advantage of the available learning opportunities, some students ask for hints until the tutor gives them the correct answer. To foster lasting changes, we propose that student beliefs about shallow compared with deep modeling practices must be changed. To accomplish these changes, we propose to integrate into our learning environment (1) a meta-tutor to help students regulate their learning and (2) an affective agent to persuade them of the utility of doing so; one specific affective strategy that we are exploring is fostering interest in the instructional activities via various means outlined in the social cognitive psychology literature. To date, we have focused on developing an affective meta-tutor (AMT) for the domain of scientific modeling that includes a teachable agent wrapper. We have also begun exploring the design of the meta-cognitive and affective agents. This summer, we will conduct the first of a series of evaluations of our (AMT) with high school students enrolled in summer schools at Arizona State University. This proposal extends the scope of our project through additional research and development tasks that are appropriate for undergraduate students these tasks are described below.
|Effective start/end date||9/1/09 → 8/31/13|
- National Science Foundation (NSF): $1,011,363.00
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