TY - GEN
T1 - Empirical usage metadata in learning objects
AU - Nugent, Gwen
AU - Kupzyk, Kevin
AU - Riley, S. A.
AU - Miller, L. D.
AU - Hostetler, Jesse
AU - Soh, Leen Kiat
AU - Samal, Ashok
N1 - Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2009
Y1 - 2009
N2 - The iLOG Project (Intelligent Learning Object Guide) is designed to augment multimedia learning objects with information about (1) how a learning object has been used, (2) how it has impacted instruction and learning, and (3) how it should be used. The goal of the project is to generate metadata tags from data collected while students interact with learning objects; these metadata tags can then be used to help teachers identify learning objects that match the educational and experiential backgrounds of their students. The project involves the development of an agent-based intelligent system for tracking student interaction with learning objects, in tandem with an extensive learning research agenda. This paper provides an overview of this NSF-funded project, focusing on the instructional approach and research on varying levels of active learning and feedback. Using a randomized design and a hierarchical linear modeling framework, research showed that the active learning conditions resulted in significantly higher student learning. The elaborative feedback results approached (p = .056), but did not reach, the established significance criteria of alpha = .05. Both active learning conditions and one of the elaborative feedback conditions resulted in significantly higher content assessment scores compared to a control group.
AB - The iLOG Project (Intelligent Learning Object Guide) is designed to augment multimedia learning objects with information about (1) how a learning object has been used, (2) how it has impacted instruction and learning, and (3) how it should be used. The goal of the project is to generate metadata tags from data collected while students interact with learning objects; these metadata tags can then be used to help teachers identify learning objects that match the educational and experiential backgrounds of their students. The project involves the development of an agent-based intelligent system for tracking student interaction with learning objects, in tandem with an extensive learning research agenda. This paper provides an overview of this NSF-funded project, focusing on the instructional approach and research on varying levels of active learning and feedback. Using a randomized design and a hierarchical linear modeling framework, research showed that the active learning conditions resulted in significantly higher student learning. The elaborative feedback results approached (p = .056), but did not reach, the established significance criteria of alpha = .05. Both active learning conditions and one of the elaborative feedback conditions resulted in significantly higher content assessment scores compared to a control group.
KW - Active learning
KW - Computer science education
KW - Feedback
KW - Learning objects
UR - http://www.scopus.com/inward/record.url?scp=77951469890&partnerID=8YFLogxK
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U2 - 10.1109/FIE.2009.5350779
DO - 10.1109/FIE.2009.5350779
M3 - Conference contribution
AN - SCOPUS:77951469890
SN - 9781424447152
T3 - Proceedings - Frontiers in Education Conference, FIE
BT - 39th Annual Frontiers in Education Conference
T2 - 39th Annual Frontiers in Education Conference: Imagining and Engineering Future CSET Education, FIE 2009
Y2 - 18 October 2009 through 21 October 2009
ER -