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

Roger Azevedo, Amy Johnson

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

4 Citations (Scopus)

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
Pages16-26
Number of pages11
VolumeFS-08-04
StatePublished - Dec 1 2008
Externally publishedYes
Event2008 AAAI Fall Symposium - Arlington, VA, United States
Duration: Nov 7 2008Nov 9 2008

Other

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

Fingerprint

Intelligent buildings

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Azevedo, R., & Johnson, A. (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 (Vol. FS-08-04, pp. 16-26)

Detecting, tracking, and modeling self-regulatory processes during complex learning with hypermedia. / Azevedo, Roger; Johnson, Amy.

Biologically Inspired Cognitive Architectures - Papers from the AAAI Fall Symposium, Technical Report. Vol. FS-08-04 2008. p. 16-26.

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

Azevedo, R & Johnson, A 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. vol. FS-08-04, pp. 16-26, 2008 AAAI Fall Symposium, Arlington, VA, United States, 11/7/08.
Azevedo R, Johnson A. 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. Vol. FS-08-04. 2008. p. 16-26
Azevedo, Roger ; Johnson, Amy. / Detecting, tracking, and modeling self-regulatory processes during complex learning with hypermedia. Biologically Inspired Cognitive Architectures - Papers from the AAAI Fall Symposium, Technical Report. Vol. FS-08-04 2008. pp. 16-26
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