Reexamining the Nature of Learner Control: Dimensionality and Effects on Learning and Training Reactions
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The purpose of this study was to advance learner control as a psychological construct by reexamining its dimensionality and effects on learning and reactions in the context of technology-delivered training.
Learners (n=384) completed a 2-h web-based Microsoft Excel training. The amount of instructional and scheduling control was manipulated in order to introduce variance in control perceptions. Outcome measures included off-task attention, declarative knowledge, and training reactions.
Learner control is a multidimensional construct with differential effects on training outcomes. By focusing on learners’ perceptions of control, we found that instructional control perceptions decreased learning by increasing off-task attention, while scheduling control perceptions increased learning.
Though both dimensions of perceived learner control are positively related to training reactions, they differentially predict learning. Combined with factor analytic evidence, our study suggests that learner control research should differentiate between objective and perceived learner control, and between instructional and scheduling control perceptions. Organizations should consider how learner control affects learning prior to designing training.
Scheduling control is an often used but rarely researched form of learner control. We address this gap by expanding the construct domain of learner control to include scheduling control. Further, this study provides the first empirical examination of learner control perceptions. Despite calls for learner control literature to differentiate between objective and perceived control, no study had previously examined control perceptions directly. Our results may be used to inform organizational decisions regarding the amount and type of control included in training.
KeywordsE-learning Distributed learning environments Off-task attention Perceived control Training reactions Web-based training
- Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.Google Scholar
- Aly, M. M., Elen, J. J., & Willems, G. G. (2005). Learner-control vs. program-control instructional multimedia: a comparison of two interactions when teaching principles of orthodontic appliances. European Journal of Dental Education, 9(4), 157–163. doi:10.1111/j.1600-0579.2005.00385.x.PubMedCrossRefGoogle Scholar
- Amazon Web Services (2005–2012). Amazon Mechanical Turk [Computer software]. Seattle, WA.Google Scholar
- Black, A. E., & Deci, E. L. (2000). The effects of instructors’ autonomy support and students’ autonomous motivation on learning organic chemistry. Science Education, 84(6), 740–756. doi:10.1002/1098-237X(200011)84:6<740:AID-SCE4>3.0.CO;2-3.CrossRefGoogle Scholar
- Gray, S. H. (1987). The effect of sequence control on computer assisted learning. Journal of Computer-Based Instruction, 2, 54–56.Google Scholar
- Kirkpatrick, D. L. (1996). Great ideas revisited. Training & Development, 50(1), 54–59.Google Scholar
- Kraiger, K., & Jerden, E. (2007). A meta-analytic investigation of learner control: Old findings and new directions. In S. M. Fiore & E. Salas (Eds.), Toward a science of distributed learning (pp. 65–90). Washington, DC: American Psychological Association. doi:10.1037/11582-004.CrossRefGoogle Scholar
- Noe, R. A. (2010). Employee training and development (5th ed.). Burr Ridge, IL: McGraw-Hill Irwin.Google Scholar
- Paolacci, G., Chandler, J., & Ipeirotis, P. G. (2010). Running experiments on amazon mechanical turk. Judgment and Decision Making, 5(5), 411–419.Google Scholar
- Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. doi:10.3758/BRM.40.3.879