The Effect of Self-Regulatory Strategy Modeling on Students' Performance During Online Inquiry

• In Event: Poster Session 15
  In Poster Session: Technology, Instruction, Cognition and Learning SIG Poster Session: Issues in Technology, Instruction, Cognition, and Learning

Abstract

This paper investigates the impact of expert-modeling of a self-regulatory strategy in an open online inquiry learning environment (Google) on forty (n = 40) community college students’ performance on an online inquiry task and the role of key self-regulatory measures during the online inquiry phases searching/evaluating, and synthesizing. Theory-driven data for the study was gathered by employing a microanalytic data method. The results generally supported the hypotheses and showed that expert-modeling of a self-regulatory strategy helped students to improve performance during online inquiry, while maintaining a higher level of self-efficacy during the inquiry phases searching/evaluating.

Author

• Christoph Winkler, Baruch College - CUNY