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Quincy Brown

An Experiment Using CTAT to Explore the Role of Self-Regulation in the Robust Learning of Middle School Math. Research Questions & Hypotheses Theoretical Assumptions: Good, Bad & Ugly Using CTAT to test hypotheses The Interface Beneath the Interface: Models & Behavior Graphs

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Quincy Brown

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  1. An ExperimentUsing CTAT to Explore the Role of Self-Regulation in the Robust Learning of Middle School Math • Research Questions & Hypotheses • Theoretical Assumptions: Good, Bad & Ugly • Using CTAT to test hypotheses • The Interface • Beneath the Interface: Models & Behavior Graphs • Lessons Learned • Extensions to the CTAT Interface Tools • Future work Quincy Brown Kallen Tsikalas

  2. Research Questions & Hypotheses • Effect of providing a self-regulatory goal. What is the effect of giving students an explicit self-regulatory goal [to be “error detectives”] on their robust learning and the accuracy of their self-efficacy ratings? • Effect of providing self-regulatory feedback and practice opportunities. What is the effect of providing students with feedback on and practice with a self-regulatory skill [error detection and correction] on their robust learning and the accuracy of their self-efficacy ratings? • Predictive power of accurate self-efficacy ratings. To what extent does the accuracy of students’ self-efficacy ratings effect their learning curve and help-seeking behavior? • Outcome Variables • Accuracy of self-efficacy ratings • Learning curves from CTAT data • Pre-, post-, and delayed post-test scores How sure are you that you can solve this problem? Likert scale (1-10)

  3. Theoretical Assumptions • Interventions that target students’ self-regulatory processes can lead to improved cycles of learning and improved academic and non-academic outcomes. • Examples of self-regulatory interventions are training and/or feedback on motivational beliefs, goal-setting, monitoring, self-judgments, etc. • Providing feedback on self-regulatory skills effects students’ • Ability to create internal feedback and self-assess • Attributions about success or failure • Proficiency at help-seeking • Willingness to invest effort in dealing with feedback information • Cognitive load theory may suggest that attending to errors introduces extraneous load which may diminish robust learning.

  4. Using CTAT to Test Hypotheses • 2x2 factorial design • Control condition = Cognitive Tutor with no self-regulation enhancements’ • Opportunities for assisted practice of cognitive skills • Multiple versions of Cognitive Tutor Error ID Feedback

  5. The Interface Two Versions • Example-Tracing Tutor • Executed in Flash • Steps on separate screens • Dynamic feedback: Students have opportunity to interact with feedback screens • Full Cognitive Tutor • Executive in Flash • Interface represents deep mathematical structure

  6. The CTAT Example-Tracing Interface • Executed in Flash • Steps on separate screens (Flash frames) • Dynamic feedback: Students have opportunity to interact with error feedback on screens (through Flash movies)

  7. The CTAT Cognitive Tutor Interface • Executed in Flash • Streamlined format representing deep structure of mathematics

  8. The CTAT Full Cognitive Tutor Behavior Graph Working Memory Conflict Tree Cognitive Model

  9. The CTAT Full Cognitive TutorProduction Rules • All production rules functioning

  10. The CTAT Example-Tracing Behavior Graph for the CogTutor Interface

  11. Lessons Learned • How to use the CTAT tools • Importance of think-alouds for building example-tracing and production rules • To create correct branching structure • To optimize the number of rules – not more than needed • Potential threats to the efficacy of our intervention: Ken’s talk on design principles • Ideas about new types of learning outcomes (learning curves, help requests that lead to greater learning)

  12. Extensions to CTAT Interface Tools • Multiple screens for one tutor • Navigation between screens that communicates with CTAT • Via ActionScript • Intratutor communication • Separate functions (e.g., visible and invisible Flash movies) for displaying feedback • Adjustments to Flash Widgets • Widgets just to log student actions/ideas rather than to tutor • Debugging of Flash tutorials

  13. Future Work • Extension to mobile devices • Use of student characteristics (e.g., self-efficacy ratings) to guide specific tutoring actions • Use of student characteristics (e.g., accuracy of self-efficacy ratings) to predict learning curves

  14. Special Thanks to… Everyone who helped us figure out what’s going on! • John and Brett for assistance with Flash widgets and communication between Example-Tracing functions and Flash interface • Jonathan and Vincent for assistance with full cognitive tutor development and production • Noboru for assistance with SimStudent • The PLSC Summer School students and staff for their good humor and great ideas!

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