Detecting, tracking, and modeling self-regulatory processes during complex learning with hypermedia

Roger Azevedo, Amy M. Witherspoon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Self-regulated learning (SRL) involves a complex set of interactions between cognitive, metacognitive, motivational and affective processes. The key to understanding the influence of these self-regulatory processes on learning with open-ended, non-linear learning computer-based environments involves detecting, capturing, identifying, and classifying these processes as they temporally unfold during learning. Understanding the complex nature of the processes is key to building intelligent learning environments that adapt to learners' fluctuations in their SRL processes and emerging understanding of the topic of domain. The foci of this paper are to: (1) introduce the complexity of SRL with hypermedia, (2) briefly present an information processing theory (IPT) of SRL and using it to analyze the temporally, unfolding sequences of processes during learning, (3) present and describe sample data to illustrate the nature and complexity of these processes, and (4) present challenges for future research that combine several techniques and methods to design intelligent learning environments that trace, model, and foster SRL.

Original languageEnglish (US)
Title of host publicationBiologically Inspired Cognitive Architectures - Papers from the AAAI Fall Symposium, Technical Report
PublisherAmerican Association for Artificial Intelligence
Pages16-26
Number of pages11
ISBN (Print)9781577353966
StatePublished - Jan 1 2008
Event2008 AAAI Fall Symposium - Arlington, VA, United States
Duration: Nov 7 2008Nov 9 2008

Publication series

NameAAAI Fall Symposium - Technical Report
VolumeFS-08-04

Other

Other2008 AAAI Fall Symposium
CountryUnited States
CityArlington, VA
Period11/7/0811/9/08

ASJC Scopus subject areas

  • Engineering(all)

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    Azevedo, R., & Witherspoon, A. M. (2008). Detecting, tracking, and modeling self-regulatory processes during complex learning with hypermedia. In Biologically Inspired Cognitive Architectures - Papers from the AAAI Fall Symposium, Technical Report (pp. 16-26). (AAAI Fall Symposium - Technical Report; Vol. FS-08-04). American Association for Artificial Intelligence.