The predictive power of the presences on cognitive load

<p>The current study had a multi-purpose and complementary research agenda focusing on the predictive power of teaching, cognitive and social presence on intrinsic, extraneous and germane cognitive load. More specifically, this study investigated the predictive relationships between the presences and cognitive load types through multiple regression analyses. This provided insights into the predictive validity of the presences with regard to cognitive load. Five graduate-level fully online courses delivering instruction in the field of learning, design and technology comprised the research context. Data from 103 graduate students were used for multiple regression purposes. Results revealed that (a) the presences can significantly predict extraneous and germane load with and without perceived learning and satisfaction; (b) the presences can significantly predict intrinsic load as a group without controlling for perceived learning and satisfaction, and together with perceived learning and satisfaction after controlling for it; (c) cognitive presence is the best predictor of both intrinsic and germane load with increased cognitive presence associated with increased intrinsic and germane load; (d) teaching presence is the best predictor of extraneous load with increases in teaching presence associated with decreases in extraneous load; and (e) perceived learning and satisfaction are significant predictors of extraneous and germane load especially while showing a strong trend to be significant predictors of intrinsic load. Overall, the current results suggested a strong and joint predictive power of the presences on cognitive load with or without perceived learning and satisfaction. All of the presences may not strongly relate to or predict cognitive load types individually though. This may imply a strong interrelation among the presences, and that the presences can work quite effectively all together in relation to cognitive load. Perceived learning and satisfaction appear to be strong collaborators with the presences as well. All these insights warrant future research in different learning contexts, possibly integrating other potential variables as well, which would foster cross-validation.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Curriculum & Instruction

Year

2015

Author dc:creator

• Kozan, Kadir

Contributors dc:contributor

• Jennifer C. Richardson
• Timothy J. Newby
• William Watson
• Aman Yadav
• Karen Swan

• Instructional Design
• Education Technology
• Education

Repository record dc:identifier

https://docs.lib.purdue.edu/open_access_dissertations/491

OAI identifier oai:identifier

oai:docs.lib.purdue.edu:open_access_dissertations-1391