Document Type : Original Article
Authors
1
Assistant Professor, Department of Public Administration, Faculty of Management and Economics, Lorestan University, Khorramabad, Iran. Corresponding Author, Email: shariat.al@lu.ac.ir
2
Ph.D. Student, Human Resource Management, Faculty of Management and Economics, Lorestan University, Khorramabad, Iran. Email: hasanvand.fa@fc.lu.ac.ir
3
Ph.D. Student, Human Resource Management, Faculty of Management and Economics, Lorestan University, Khorramabad, Iran. Email: minahosseini7615@gmail.com
4
Ph.D. Student, Human Resource Management, Faculty of Management and Economics, Lorestan University, Khorramabad, Iran. Email: anismoayed1@yahoo.com
Abstract
Purpose: Today is an era in which managers face countless challenges. Meanwhile, self-regulated learning emerges as an inevitable necessity. Managers who ignore this principle may face problems and lose their job opportunities. Therefore, it is necessary to include self-regulated learning as a central principle in managers' strategies and plans. Therefore, the current research has been carried out to provide a fuzzy cognitive map of the mechanisms and consequences of self-regulated learning using the method (FCM).
Design/Methodology/Approach: The present study is a mixed research in terms of method and is applied in terms of purpose and is classified as a descriptive survey research in terms of data collection and as a deductive-inductive research in terms of research philosophy. The statistical population of this study is experts and scholars familiar with the subject under study, of whom 25 were selected as sample members using a purposive sampling method. The reason for selecting the population and statistical sample is that the concept of self-regulated learning has a theoretical concept with a scientific origin in management and human resources, and the selection of the sample should be done in such a way that all theoretical and practical dimensions of this concept are also examined by the statistical sample; Therefore, a set of experts who had relevant knowledge, expertise, work experience in the relevant field, and relevant activities were selected as sample members of this study. Given the familiarity of the sample members in the qualitative section with the research topic and approach through in-depth interviews, and to increase the accuracy and coherence of the research findings, it was decided to use the same experts in the quantitative section. In the qualitative section, the data collection tool was an interview, and in the quantitative section, a questionnaire; in such a way that first, qualitative data was collected using the opinions of 25 experts until theoretical saturation of information, and then, it was analyzed using MaxQDA software and content analysis and labeling methods. Content analysis is a systematic method for extracting and interpreting key concepts from qualitative data. In the present study, this method was used to identify the mechanisms and consequences of self-regulated learning to provide a basis for modeling with fuzzy cognitive mapping. Content analysis, focusing on extracting conceptual structures, is more suitable than quantitative or other qualitative methods. Its implementation steps include data collection, initial labeling, concept classification, and final extraction, which ultimately prepare the qualitative data for fuzzy analysis. The validity and reliability of the data collection tool in the interview section were confirmed using the content validity ratio and Cohen's Kappa test. Also, in the questionnaire section, it was confirmed using content validity and test-retest reliability. In the next stage, the quantitative data were analyzed using the fuzzy cognitive mapping method. Fuzzy cognitive mapping is an analytical method for identifying the main dimensions of a concept, which extracts its most important components using centrality indices. This method also examines and explains the interactions and connections between variables, relying on causal relationships between them.
Findings: In this study, the results can be presented in both quantitative and qualitative sections. In the qualitative part of the research, self-regulated learning mechanisms were identified, which include: cognitive self-regulation, behavioral self-regulation, motivational self-regulation, metacognition, causal attributions, avoiding procrastination, critical thinking, rethinking, questioning, and effective communication. Also, a set of consequences of using self-regulated learning was identified, which includes: strengthening leadership skills, improving self-efficacy, learning strategies, self-awareness, strengthening problem-solving skills, self-assessment, developing creative thinking, taking responsibility, increasing performance, and increasing motivation. The results of the quantitative part of the research also confirm that among the 20 factors identified, cognitive self-regulation was recognized as the most important self-regulated learning mechanism with the highest degree of centrality (27.05), followed by behavioral self-regulation, motivational self-regulation, and metacognition, respectively, as the most important consequences of self-regulated learning. Also, according to the research findings, strengthening leadership skills with a centrality degree of (24.86) has been identified as the most important outcome of self-regulated learning, followed by improving self-efficacy, learning strategies, and self-awareness, respectively, as the most important mechanisms of self-regulated learning.
Discussion and Conclusion: The results of this study show that the most important learning mechanism for self-regulation is cognitive self-regulation, and its most important consequence is strengthening leadership skills.
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