Self-Directed Learning and the Sensemaking Paradox

Abstract

Educative sensemaking focuses on the needs of self-directed learners, a nonexpert population of thinkers who must locate relevant information sources, evaluate the applicability and accuracy of digital resources for learning, and determine how and when to use these resources to complete educational tasks. Self-directed learners face a sensemaking paradox: They must employ deep-level thinking skills to process information sources meaningfully, but they often lack the requisite domain knowledge needed to deeply analyze information sources and to successfully integrate incoming information with their own existing knowledge. In this article, we focus on the needs of college-aged students engaged in learning about natural sciences using web-based learning resources. We explored the impact of cognitive personalization technologies on students' sensemaking processes using a controlled study in which students' cognitive and metacognitive processes were analyzed as they completed a common educational task: writing an essay. We coded students' observable on-screen behaviors, self-reported processes, final essays, and responses to domain assessments to assess benefits of personalization technologies on students' educative sensemaking. Results show that personalization supported students' analysis of knowledge representations, helped students work with their representations in meaningful ways, and supported effective encoding of new knowledge. We discuss implications for new technologies to help students overcome the educative sensemaking paradox.

Notes

Acknowledgments. We are grateful to Sebastian de la Chica and Faisal Ahmad for their contributions to this research and for running experimental sessions. We thank Shaw Ketels for his contributions to the student processes/revisions analyses. We also thank our colleagues at Digital Learning Sciences/UCAR for their help with gathering digital library resources.

Support. This material is based in part on work supported by the National Science Foundation under Grant Numbers IIS-0537194, DRL-0835393, and DRL-0835454. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

HCI Editorial Record. First manuscript received February 16, 2009. Revision received November 30, 2009. Accepted by Peter Pirolli. Final manuscript received November 30, 2009. — Editor