A meta-analysis of self-regulated learning in work-related training and educational attainment: what we know and where we need to go.

Researchers have been applying their knowledge of goal-oriented behavior to the self-regulated learning domain for more than 30 years. This review examines the current state of research on self-regulated learning and gaps in the field's understanding of how adults regulate their learning of work-related knowledge and skills. Self-regulation theory was used as a conceptual lens for deriving a heuristic framework of 16 fundamental constructs that constitute self-regulated learning. Meta-analytic findings (k=430, N=90,380) support theoretical propositions that self-regulation constructs are interrelated-30% of the corrected correlations among constructs were .50 or greater. Goal level, persistence, effort, and self-efficacy were the self-regulation constructs with the strongest effects on learning. Together these constructs accounted for 17% of the variance in learning, after controlling for cognitive ability and pretraining knowledge. However, 4 self-regulatory processes-planning, monitoring, help seeking, and emotion control-did not exhibit significant relationships with learning. Thus, a parsimonious framework of the self-regulated learning domain is presented that focuses on a subset of self-regulatory processes that have both limited overlap with other core processes and meaningful effects on learning. Research is needed to advance the field's understanding of how adults regulate their learning in an increasingly complex and knowledge-centric work environment. Such investigations should capture the dynamic nature of self-regulated learning, address the role of self-regulation in informal learning, and investigate how trainees regulate their transfer of training.

The effects of technical difficulties on learning and attrition during online training.

Although online instruction has many potential benefits, technical difficulties are one drawback to the increased use of this medium. A repeated measures design was used to examine the effect that technical difficulties have on learning and attrition from voluntary online training. Adult learners (N = 530) were recruited online and volunteered to participate in a 4-hr training program on using computer spreadsheets. Technical difficulties were inserted in some of the training modules in the form of error messages. Using multilevel modeling, the results indicated that the presence of these technical difficulties impaired learning, such that test scores were lower in modules where trainees encountered technical difficulties than in modules where they did not encounter technical difficulties. Furthermore, the effect on learning was greater among trainees who eventually withdrew from the course than among trainees who completed the course. With regards to attrition, pretraining motivation provided a buffer against dropping out, especially when trainees encountered technical difficulties. Learning also predicted attrition from the subsequent module, such that attrition was higher among trainees with low test scores in the previous module. The current study disentangles some of the implications of technical difficulties and suggests that organizations should provide trainees with the technical support required to overcome technical difficulties in training. Furthermore, the findings contribute to our theoretical understanding of the implications of interruptions on performance in online training.

Sometimes you need a reminder: the effects of prompting self-regulation on regulatory processes, learning, and attrition.

Prompting self-regulation involves asking trainees reflective questions to stimulate self-regulatory engagement. Research has found positive effects for prompting self-regulation on learning, but a scarcity of evidence exists regarding whether self-regulatory processes mediate the effect of prompting self-regulation, whether the intervention reduces attrition, and the optimal timing of implementing the intervention. Using a longitudinal design, we found that prompting self-regulation throughout training increased learning and reduced attrition, relative to the control condition. Moreover, the effect on learning was fully mediated by time on task. The intervention also moderated the effect of learning on subsequent self-regulatory activity and attrition. Learning performance had less of a positive effect on subsequent self-regulatory activity and less of a negative effect on subsequent attrition when trainees were prompted to self-regulate. These results highlight the importance of adopting a longitudinal design to examine how self-regulatory interventions affect the cyclical relationships among self-regulatory processes, learning, and attrition.

A review and meta-analysis of the nomological network of trainee reactions.

A review and meta-analysis of studies assessing trainee reactions are presented. Results suggest reactions primarily capture characteristics of the training course, but trainee characteristics (e.g., anxiety and pretraining motivation) and organizational support also have a moderate effect on reactions. Instructional style (rho = .66) followed by human interaction (rho = .56) were the best predictors of reactions. Reactions predicted pre-to-post changes in motivation (beta = .51) and self-efficacy (beta = .24) and were more sensitive than affective and cognitive learning outcomes to trainees' perceptions of characteristics of the training course. Moderator analyses revealed reactions- outcomes correlations tended to be stronger in courses that utilized a high level rather than a low level of technology, and affective and utility reactions did not differ in their relationships with learning outcomes. The current study clarifies the nomological network of reactions and specifies outcomes that are theoretically related to reactions.