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A Multilevel Analysis of the Effect of Prompting Self-Regulation in Technology-Delivered Instruction

dc.contributor.authorSitzmann, Traci
dc.contributor.authorBell, Bradford S.
dc.contributor.authorKraiger, Kurt
dc.contributor.authorKanar, Adam M.
dc.date.accessioned2020-11-25T14:55:49Z
dc.date.available2020-11-25T14:55:49Z
dc.date.issued2008-11-01
dc.description.abstractWe used a within-subjects design and multilevel modeling in two studies to examine the effect of prompting self-regulation, an intervention designed to improve learning from technology-delivered instruction. The results of two studies indicate trainees who were prompted to self-regulate gradually improved their knowledge and performance over time, relative to the control condition. In addition, Study 2 demonstrated that trainees’ cognitive ability and self-efficacy moderated the effect of the prompts. Prompting self-regulation resulted in stronger learning gains over time for trainees with higher ability or higher self-efficacy. Overall, the two studies demonstrate that prompting self-regulation had a gradual, positive effect on learning, and the strength of the effect increased as trainees progressed through training. The results are consistent with theory suggesting self-regulation is a cyclical process that has a gradual effect on learning and highlight the importance of using a within-subjects design in self-regulation. research.
dc.description.legacydownloadsWP08_121.pdf: 1111 downloads, before Oct. 1, 2020.
dc.identifier.other692339
dc.identifier.urihttps://hdl.handle.net/1813/77378
dc.language.isoen_US
dc.relation.hasversionAn updated version of this paper can be found here: https://hdl.handle.net/1813/74889.
dc.relation.urihttps://hdl.handle.net/1813/74889
dc.subjectself-regulation
dc.subjectlearner control
dc.subjecttechnology-delivered instruction
dc.titleA Multilevel Analysis of the Effect of Prompting Self-Regulation in Technology-Delivered Instruction
dc.typepreprint
local.authorAffiliationSitzmann, Traci: Advanced Distributed Learning Co-Laboratory
local.authorAffiliationBell, Bradford S.: bb92@cornell.edu Cornell University
local.authorAffiliationKraiger, Kurt: Colorado State University
local.authorAffiliationKanar, Adam M.: amk58@cornell.edu Cornell University

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