Relationship between self-regulated learning and academic procrastination
TL;DR: This paper investigated the relationship between the components of motivation in self-regulated learning and academic procrastination and found that intrinsic goal orientation, task values, rehearsal, elaboration, meta cognitive self-regulation, resource management strategies, organisation, and critical thinking were correlated with academic performance.
Abstract: This study aims to investigate the relationship between the components of motivation in self-regulated learning as well as the components of learning strategies in self-regulated learning and academic procrastination. Academic procrastination creates problems for undergraduates such as stress and poor academic performance which should be investigated as a serious issue in the educational context. The participants in this study included 100 undergraduates in Universiti Putra Malaysia. The result of a Pearson correlation analysis revealed intrinsic goal orientation, task values, rehearsal, elaboration, meta cognitive self-regulation, resource management strategies, organisation and critical thinking as self-regulated learning components that have significant negative correlations with academic procrastination. In addition, anxiety was found to have a significant positive correlation with academic procrastination. Extrinsic goal orientation and control of learning beliefs were not significantly correlated to academic procrastination. The findings suggested that in order to cope with academic procrastination, an academic procrastinator might consider being a self-regulated learner as most of the components of self-regulated learning indicated a strong relationship with academic procrastination that can be encouraged in order to improve those lacking components of self-regulated learning. Also, to help undergraduates to improve on the components of self-regulated learning that they lack, strategies can be planned by educators to deal with academic procrastination and to increase academic performance.
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TL;DR: The study has highlighted the non-significant moderating role of gender in relation to EI and CT disposition that was missing in the existing literature.
22 citations
TL;DR: In this paper, the authors reviewed and synthesized past empirical findings on the relationship between metacognition and procrastination, and found that metacognitive belief and metACognitive regulation were significantly associated with passive procrastention; however, metACognition (regardless the types) was not significantly associated this paper.
Abstract: Procrastination is a universal phenomenon that occurs to most individuals in various settings. Such prevalence of academic procrastination suggests a need for systematic research that documents potential factors that lead to academic procrastination and subsequently explores potential ways to reduce procrastination, such as metacognition. Grounded upon the Self-Regulatory Executive Function (Wells and Matthews in Cognit Emot 8(3):279–295. https://doi.org/10.1080/026999394084089421994
), metacognition plays an essential role in explaining and predicting procrastination. As the first attempt, this study aims to review and synthesize past empirical findings on the relationship between metacognition and procrastination. Fifty-nine relevant articles involving a total of 23,627 participants were synthesized in this meta-analysis. Using the robust variance estimation, results showed significant small effect sizes of metacognition for passive procrastination (− .28), but not for active procrastination (.03). Further, different dimensions of metacognition showed different relation patterns with procrastination. In particular, metacognitive belief and metacognitive regulation were significantly associated with passive procrastination; however, metacognition (regardless the types) was not significantly associated with active procrastination. After controlling for all proposed moderators (grade level, individualistic index, and gender), no significant moderation effects were found in the overall metacognition–active procrastination relationship or metacognition–passive procrastination relationship. The implications of the findings were discussed.
4 citations
Journal Article•
TL;DR: In this paper, a quasi-experimental study with a pretest-posttest and a control group design was conducted to assess an educational package developed based on self-regulation strategies, academic engagement, and self-handicapping and explore its effect on high school students' procrastination.
Abstract: Background & Objectives: Procrastination has many different dimensions. Academic procrastination is among the most common problems at various levels of education, as a set of behavioral problems that numerous factors could reduce it. One of the most significant variables in explaining the cause of procrastination is self–regulation. The current study aimed to assess an educational package developed based on self– regulation strategies, academic engagement, and self–handicapping and explore its effect on high school students' procrastination. Methods: This was a quasi–experimental study with a pretest–posttest and a control group design. The statistical population of the study consisted of all 10– and 11–grade students in Sardasht, Iran in the academic year of 2018–2019. Accordingly, 60 study subjects were randomly assigned in the experimental and control groups, each consisting of 30 individuals. The study participants were selected using a purposive sampling method. A designed training package based on self–regulation strategies, academic engagement, and self–handicapping was provided to the experimental group in ten 90–minute sessions. The required data were collected at pretest and posttest phases using the Tuckman Procrastination Scale (Tuckman, 1991). The scale’s reliability was established by a Cronbach's alpha coefficient of 0.86. Descriptive statistics indices, including mean and standard deviation, were used for the statistical analysis of the descriptive data. One–way Analysis of Variance (ANOVA), Analysis of Covariance (ANCOVA), Pearson’s correlation coefficient, and Independent Samples t–test were used for running inferential statistics evaluations. Results: The obtained quantitative data revealed a significant difference between the control and experimental groups; thus, the provided training package presented a significant effect on the procrastination of investigated high school students (p<0.001). The results of Independent Samples t–test and one–way ANOVA suggested no significant relationship between age, household size, and economic status, and academic procrastination (p=0.624, p=0.784, p=0.802, respectively). The mean (SD) pretest scores of procrastination in the experimental and control groups were equal to 48.89(4.31) and 45.65(5.77), respectively. However, the posttest mean (SD) scores of the experimental and control groups were calculated as 27.89(3.50) and 44.68(6.10), respectively. These data indicate that compared to the controls, the procrastination scores of the students in the experimental group have changed after receiving the training. Conclusion: Based on the current study findings, the presented training package based on self–regulation strategies, academic engagement, and self–handicapping could improve procrastination among the investigated high school students.
1 citations
TL;DR: In this paper , a study was conducted to predict variations in academic procrastination by considering two constructs related to self-regulation: motivational factors (i.e., achievement goals), and learning strategies: deep learning cognitive strategies -Elaboration and Critical thinking-, effort regulation management.
Abstract: Procrastination could be conceptualized as a self-regulation failure. However, it is still not clear what type of self-regulation processes are precisely underlying the students’ tendency to procrastinate. The main objective of our study was therefore to predict variations in academic procrastination by considering two constructs related to self-regulation: motivational factors (i.e., achievement goals), and learning strategies: deep learning cognitive strategies -Elaboration and Critical thinking-, effort regulation management. The results of an online study on 249 first-year humanities and social sciences French students showed that 30% of the variance in procrastination was predicted positively by avoidance goals and negatively by effort regulation management. The effort regulation management strategy alone contributed to 24% of the variance in procrastination. Furthermore, the results confirmed the negative relationship between academic performance and procrastination tendency. Added together, these results support the conceptualization of procrastination as a self-regulation failure and specially of learning such as effort regulation management. Results are discussed in relation to possible interventions that aim to reduce procrastination in order to promote academic success and students’ well-being.
TL;DR: Ayrıca et al. as discussed by the authors proposed a duygusal zekası geliştikçe, eğitimin en önemli ilkelerinden biri olup, her ülkenin büyüme ve refaha ulaşmak için eleştirel düşünmeye ihtiyacı bulunmaktadır.
Abstract: Eleştirel düşünme, eğitimin en önemli ilkelerinden biri olup, her ülkenin büyüme ve refaha ulaşmak için eleştirel düşünmeye ihtiyacı bulunmaktadır.Duygusal zeka, eleştirel düşünmenin önemli bir yordayıcısıdır. Bir kişinin duygusal zekası geliştikçe, eleştirel düşünme eğilimi de artmaktadır. Bu doğrultuda araştırmada, lisans düzeyinde turizm eğitimi alan öğrencilerin duygusal zeka düzeylerinin eleştirel düşünme eğilimi üzerindeki etkisinin belirlenmesi amaçlanmıştır. Araştırmada veri toplama tekniği olarak anket kullanılmış ve 3 fakülte, 2 yüksekokul olmak üzere 5 farklı üniversitenin lisans düzeyindeki turizm okullarından toplam 471 öğrenci üzerinde uygulama gerçekleştirilmiştir. Verilerin analizinde frekans, yüzde dağılımı, aritmetik ortalama, standart sapma, t testi, varyans analizi ve Pearson korelasyon analizi kullanılmıştır. Araştırma sonucunda, öğrencilerin duygusal zeka düzeyleri ile eleştirel düşünme eğilimlerinin orta düzeyin oldukça üzerinde olduğu tespit edilmiştir. Bununla birlikte öğrencilerin duygusal zeka düzeyleri ile eleştirel düşünme eğilimleri arasında r=0,671’lik pozitif ve kuvvetli bir korelasyon bulunmuştur. Ayrıca, öğrencilerin duygusal zeka düzeyleri ile eleştirel düşünme eğilimlerinin cinsiyet, akademik başarı ve aylık harcama değişkenlerine göre anlamlı farklılıklar gösterdiği saptanmıştır.
References
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Book•
01 Dec 1969TL;DR: The concepts of power analysis are discussed in this paper, where Chi-square Tests for Goodness of Fit and Contingency Tables, t-Test for Means, and Sign Test are used.
Abstract: Contents: Prefaces. The Concepts of Power Analysis. The t-Test for Means. The Significance of a Product Moment rs (subscript s). Differences Between Correlation Coefficients. The Test That a Proportion is .50 and the Sign Test. Differences Between Proportions. Chi-Square Tests for Goodness of Fit and Contingency Tables. The Analysis of Variance and Covariance. Multiple Regression and Correlation Analysis. Set Correlation and Multivariate Methods. Some Issues in Power Analysis. Computational Procedures.
115,069 citations
"Relationship between self-regulated..." refers methods in this paper
...Using Cohen’s power analysis for sample size to determine the sample size for a one tailed test correlation analysis with a significance level of 0.025, power of 0.80 and medium effect size (r = 0.30) a minimum of 84 participants are required (Cohen, 1977)....
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TL;DR: In this article, a correlational study examined relationships between motivational orientation, self-regulated learning, and classroom academic performance for 173 seventh graders from eight science and seven English classes.
Abstract: A correlational study examined relationships between motivational orientation, self-regulated learning, and classroom academic performance for 173 seventh graders from eight science and seven English classes. A self-report measure of student self-efficacy, intrinsic value, test anxiety, self-regulation, and use of learning strategies was administered, and performance data were obtained from work on classroom assignments. Self-efficacy and intrinsic value were positively related to cognitive engagement and performance. Regression analyses revealed that, depending on the outcome measure, self-regulation, self-efficacy, and test anxiety emerged as the best predictors of performance. Intrinsic value did not have a direct influence on performance but was strongly related to self-regulation and cognitive strategy use, regardless of prior achievement level. The implications of individual differences in motivational orientation for cognitive engagement and self-regulation in the classroom are discussed. Self-regulation of cognition and behavior is an important aspect of student learning and academic performance in the classroom context (Corno & Mandinach, 1983; Corno & Rohrkemper, 1985). There are a variety of definitions of selfregulated learning, but three components seem especially important for classroom performance. First, self-regulated learning includes students' metacognitive strategies for planning, monitoring, and modifying their cognition (e.g., Brown, Bransford, Campione, & Ferrara, 1983; Corno, 1986; Zim
7,442 citations
Book•
01 Jan 1996
TL;DR: This book provides the reader with a review of correlation and covariance among variables, followed by multiple regression and path analysis techniques to better understand the building blocks of structural equation modelling.
Abstract: This book provides the reader with a review of correlation and covariance among variables, followed by multiple regression and path analysis techniques to better understand the building blocks of structural equation modelling. The concepts behind measurement models are introduced to illustrate how measurement error impacts statistical analyses, and structural models are presented that indicate how latent variable relationships can be established. Examples are included throughout to make the concepts clear to the reader. The structural equation modelling examples are presented using either EQS5.0 or LISREL8-SIMPLIS programming language, both of which have an easy-to-use set of commands to specify measurement and strucural models. No complicated programming is required, nor does the reader need an advanced understanding of statistics of matrix algebra. A goal in writing this volume was to focus conceptually on the steps one takes in analyzing theoretical models. These steps encompass: specifying a model based upon theory or prior research; determining whether the model can be identified to have unique estimates for variables in the model; selecting an appropriate estimation method based on the distributional assumptions of variables; testing the model and interpreting fit indices; and finally respecifying a model based on suggested modification indices, which involves adding or dropping paths in the model to obtain a better model fit. The resources and references provided in this book should equip faculty, students and researchers to enhance their working knowledge of structural equation modelling. Not intended as an in-depth presentation of statistics or factor analysis, this text focuses on the basic ideas and principles behind structural equation modelling. Assuming that the reader has a basic understanding of correlation, the authors have built upon this understanding to present these basic ideas and principles.
6,348 citations
02 Mar 2022
TL;DR: This book provides the reader with a review of correlation and covariance among variables, followed by multiple regression and path analysis techniques to better understand the building blocks of structural equation modelling.
Abstract: This book provides the reader with a review of correlation and covariance among variables, followed by multiple regression and path analysis techniques to better understand the building blocks of structural equation modelling. The concepts behind measurement models are introduced to illustrate how measurement error impacts statistical analyses, and structural models are presented that indicate how latent variable relationships can be established. Examples are included throughout to make the concepts clear to the reader. The structural equation modelling examples are presented using either EQS5.0 or LISREL8-SIMPLIS programming language, both of which have an easy-to-use set of commands to specify measurement and strucural models. No complicated programming is required, nor does the reader need an advanced understanding of statistics of matrix algebra. A goal in writing this volume was to focus conceptually on the steps one takes in analyzing theoretical models. These steps encompass: specifying a model based upon theory or prior research; determining whether the model can be identified to have unique estimates for variables in the model; selecting an appropriate estimation method based on the distributional assumptions of variables; testing the model and interpreting fit indices; and finally respecifying a model based on suggested modification indices, which involves adding or dropping paths in the model to obtain a better model fit. The resources and references provided in this book should equip faculty, students and researchers to enhance their working knowledge of structural equation modelling. Not intended as an in-depth presentation of statistics or factor analysis, this text focuses on the basic ideas and principles behind structural equation modelling. Assuming that the reader has a basic understanding of correlation, the authors have built upon this understanding to present these basic ideas and principles.
4,660 citations
TL;DR: The social cognitive conception of self-regulated learning presented in this article involves a triadic analysis of component processes and an assumption of reciprocal causality among personal, behavioral, and environmental triadic influences.
Abstract: Researchers interested in academic self-regulated learning have begun to study processes that students use to initiate and direct their efforts to acquire knowledge and skill. The social cognitive conception of self-regulated learning presented here involves a triadic analysis of component processes and an assumption of reciprocal causality among personal, behavioral, and environmental triadic influences. This theoretical account also posits a central role for the construct of academic self-efficacy beliefs and three self-regulatory processes: self-observation, self-judgment, and self-reactions. Research support for this social cognitive formulation is discussed, as is its usefulness for improving student learning and academic achievement.
3,062 citations
"Relationship between self-regulated..." refers background in this paper
...As stated by Zimmerman (1989), we can define undergraduates as self-regulated to the extent that their goals are achieved cognitively, motivationally and behaviourally by actively participating in their own learning stages and processes....
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