Self-regulated Learning Behaviors Research Articles

The mediating role of self-regulated online learning behaviors: Exploring the impact of personality traits on student engagement

AbstractOnline learning environments have become increasingly prevalent in higher education, necessitating an understanding of factors influencing student engagement. This study examines the mediating role of self-regulated online learning in the relationship between five-factor personality traits and student engagement among university students. A sample of 437 university students from educational sciences, social sciences, and health sciences disciplines participated in the study. Data were collected using ‘The Big Five Inventory’ to assess personality traits, the ‘Self-Regulated Online Learning Questionnaire’ to measure self-regulated online learning, and the ‘Student Engagement Scale in the Online Learning Environment’ to evaluate student engagement. Structural equation modeling with bootstrap analysis was employed to analyze the data. The study findings indicate that the five factor personality traits significantly predict self-regulated online learning. Furthermore, self-regulated online learning is a significant predictor of students’ engagement in the online learning environment. Additionally, the five factor personality traits are found to be significant predictors of student engagement in the online learning environment. Lastly, self-regulated online learning plays a partially mediating role in the relationship between the five factor personality traits and student engagement in the online learning environment. This study underscores the importance of considering individual differences in personality traits and fostering self-regulated learning strategies to enhance student engagement in online learning environments. Understanding these dynamics can inform the design of effective interventions aimed at improving student outcomes in online education.

Reciprocal Relationship Between Learning Interest and Learning Persistence: Roles of Strategies for Self-Regulated Learning Behaviors and Academic Performance.

Learning interest (internal driving motivation) and learning persistence (explicit behaviors) are important factors affecting students' academic development, yet whether they operate reciprocally and how to bolster them are still issues requiring attention. This study aimed to examine the reciprocal relationship between learning interest and persistence as well as the potential mechanisms behind the relationship from the perspectives of internal self-regulation and external feedback (i.e., academic performance). 510 students (Mage = 13.71, SD = 1.77, 44.1% girls) were tracked for one year using questionnaires. Results showed that higher learning interest was linked to greater subsequent learning persistence and vice versa; and both predicted each other over time indirectly through academic performance and the multiple mediating paths from strategies for self-regulated learning behaviors to academic performance. Ancillary analysis verifies the robustness of these results. The findings not only provide evidence of a dynamic relationship between learning motivation and behaviors, highlighting the important role of positive performance feedback in leading to a benign cycle, but also contribute to understanding the potential avenue (i.e., teaching strategies for self-regulation) for optimizing student learning.

Differential Effects of Additional Formative Assessments on Student Learning Behaviors and Outcomes

It is well-established that formative assessments with accompanying feedback can enhance learning. However, the degree to which additional formative assessments on the same material further improve learning outcomes remains an open research question. Moreover, it is unclear whether providing additional formative assessments impacts self-regulated learning behavior, and if the benefits of such assessments depend on students' self-regulated learning behavior. The current study, conducted in a real-world blended learning setting and using a Learning Analytics approach, compares 154 students who completed additional formative assessments with 154 students who did not. The results indicate that the additional formative assessments led to an improvement in learning outcomes, but also had both positive and negative effects on students' self-regulated learning behavior. Students who completed additional formative assessments performed better on the assessments but reported lower levels of subjective comprehension and devoted more time to completing exercises. Simultaneously, they devoted less effort to additional learning activities (additional investment), such as class preparation and post-processing. Furthermore, the impact of additional formative assessments on learning success depended on students' self-regulated learning behavior. It was primarily the students who invested above-average time during formative assessments (time investment) who benefited from the additional exercises. Cluster analysis revealed that high-effort students (those with above-average time investment and above-average additional investment) gained the most from the extra exercises. In contrast, low-effort students and those who achieved high performance with relatively low effort (efficient students) did not benefit from additional formative assessments. In conclusion, providing students with additional formative assessments can enhance learning, but it should be done with caution as it can alter self-regulated learning behavior in both positive and negative ways, and not all students may benefit from it equally.

Decoding Student Success in Higher Education: A Comparative Study on Learning Strategies of Undergraduate and Graduate Students


 
 Learning management systems (LMS) provide a rich source of data about the engagement of students with courses and their materials that tends to be underutilized in practice. In this paper, we use data collected from the LMS to uncover learning strategies adopted by students and compare their effectiveness. Starting from a sample of over 11,000 enrollments at a Portuguese information management school, we extracted features indicative of self-regulated learning (SRL) behavior from the associated interactions. Then, we employed an unsupervised machine learning algorithm (k-means) to group students according to the similarity of their patterns of interaction. This process was conducted separately for undergraduate and graduate students. Our analysis uncovered five distinct learning strategy profiles at both the undergraduate and graduate levels: 1) active, prolonged and frequent engagement; 2) mildly frequent and task-focused engagement; 3) mildly frequent, mild activity in short sessions engagement; 4) likely procrastinators; and 5) inactive. Mapping strategies with the students' final grades, we found that students at both levels who accessed the LMS early and frequently had better outcomes. Conversely, students who exhibited procrastinating behavior had worse end-of-course grades. Interestingly, the relative effectiveness of the various learning strategies was consistent across instruction levels. Despite the LMS offering an incomplete and partial view of the learning processes students employ, these findings suggest potentially generalizable relationships between online student behaviors and learning outcomes. While further validation with new data is necessary, these connections between online behaviors and performance could guide the development of personalized, adaptive learning experiences.
 

Unveiling the dynamics of self-regulated learning in project-based learning environments

Project-based learning (PBL) has been found to exert a positive influence on learners' academic achievements, while also fostering their intrinsic motivation and playing a crucial role in nurturing sustainable learning capacities. Understanding individual differences in self-regulated learning (SRL) within project-based foreign language learning can guide language teachers in delivering personalized instruction. This study utilized a blended learning management system to collect and analyze data on SRL from 95 learners enrolled in a project-based College English course. Through microanalysis, latent profile analysis (LPA), and epistemic network analysis (ENA), the study identified distinct learner profiles and traced their developmental trajectories in project-based language learning. The findings revealed that PBL facilitates the occurrence of SRL behaviors and strengthens connections across different regulation phases. Notably, learners with diverse profiles displayed variations in their SRL epistemic network structures and developmental trajectories. These results highlight the dynamic nature of SRL within the PBL context, underscoring the significance of considering individual differences and supporting learners’ evolving self-regulatory behaviors and strategies throughout their engagement in PBL activities. To enhance SRL in PBL, educators are encouraged to provide scaffolding support, promote help-seeking behaviors, and implement interventions targeting metacognitive processes and reflective practices.

Attitudes in music practice: a survey exploring the self-regulated learning processes of advanced Brazilian and Portuguese musicians.

This study aimed to investigate the Self-Regulated Learning behaviors of advanced Brazilian and Portuguese musicians and how these processes vary in terms of gender, nationality, musical instrument, quantity of practice, expertise, and professional experience. 300 participants fully completed the 22-item questionnaire "Attitudes in music practice". The sample comprised of 54.3% males, 44.0% females, and 1% non-binary; 0.7% did not respond. 68.0% (n = 204) were Brazilian, and 32.0% (n = 96) were Portuguese. The mean age was 32.70 years old (SD = 11.261), the mode was 22 years old, with a range of 18 to 66 years. Data analysis procedure included exploratory factor analysis, internal consistency, independent sample t test, analysis of variance (ANOVA), and chi-square tests. Exploratory Factor Analysis generated three factors: Practice Organization, Personal Resources, and External Resources. The results report there are no differences in SRL scores in terms of gender, nationality, and musical instrument. However, One-way ANOVA test results convey differences in SRL scores and the quantity of practice and expertise with those musicians who reported practicing for longer periods scoring more highly than participants who declared spending less time on daily practice. The results for the expertise variables suggest that more experienced and older musicians scored higher in Personal Resources and lower in External Resources indicating that, as musicians gain in experience, their metacognitive processes become more evident than the social factors of their performance.

The Effects of the ADMIRE Program on the Learning Motivation and Self-regulated Learning of University Students

<p style="text-align: justify;">Learning motivation and self-regulated learning are prominent and essential characteristics to possess in this time of change, leading to lifelong learners. The aim of this study was to investigate the impact of the ADMIRE program on the learning motivation and self-regulated learning behavior of undergraduate students. The research consisted of a quasi-experimental design conducted for two months. The participants were divided into two homogenous groups including the intervention group (n = 85) and the control group (n = 79). Data were collected before and after the intervention using the learning motivation scale and self-regulated learning behavior scale. Finally, data were analyzed using MANOVA. The study results revealed that the ADMIRE program made a significant impact on the learning motivation and self-regulated learning behavior. Therefore, it can be concluded that the use of the ADMIRE program can lead to greater learning motivation and self-regulated learning behavior in the intervention group compared to the control group. The ADMIRE program gives students the opportunity to participate in initiatives to reach the desired learning outcomes. Instructors can also use the ADMIRE program during the process of proactive learning in various forms.</p>

Towards prescriptive analytics of self‐regulated learning strategies: A reinforcement learning approach

AbstractSelf‐regulated learning (SRL) is an essential skill to achieve one's learning goals. This is particularly true for online learning environments (OLEs) where the support system is often limited compared to a traditional classroom setting. Likewise, existing research has found that learners often struggle to adapt their behaviour to the self‐regulatory demands of OLEs. Even so, existing SRL analysis tools have limited utility for real‐time or individualised support of a learner's SRL strategy during a study session. Accordingly, we explore a reinforcement learning based approach to learning optimal SRL strategies for a specific learning task. Specifically, we utilise the sequences of SRL processes acted by 44 participants, and their assessment scores for a prescribed learning task, in a purpose‐built OLE to develop a long short‐term memory (LSTM) network based reward function. This is used to train a reinforcement learning agent to find the optimal sequence of SRL processes for the learning task. Our findings indicate that the developed agents were able to effectively select SRL processes so as to maximise a prescribed learning goal as measured by predicted assessment score and predicted knowledge gains. The contributions of this work will facilitate the development of a tool which can detect sub‐optimal SRL strategy in real‐time and enable individualised SRL focused scaffolding. Practitioner notesWhat is already known about this topic Learners often fail to adequately adapt their behavior to the self‐regulatory demands of e‐Learning environments. In order to promote effective Self‐regulated learning (SRL) capabilities, researchers and educators need tools that are able to analyze and diagnose a learner's SRL strategy use. Current methods for SRL analysis are more often descriptive as opposed to prescriptive and have limited utility for real‐time analysis or support of a learner's SRL behavior. What this paper adds This paper proposes the use of Reinforcement Learning for prescriptive analytics of SRL. We train a Reinforcement Learning agent on sequences of SRL processes acted by learners in order to learn the optimal SRL strategy for a given learning task. Implications for practice and/or policy Our work will facilitate the development of a tool which can detect sub‐optimal SRL strategy in real‐time and enable individualized SRL focused scaffolding. The implications of our work can aid in course design by predicting the self‐regulatory load imposed by a given task. The ability to model SRL strategies using Reinforcement Learning can be extended to simulate or test SRL theories.

An analysis of technological resources to encourage self-regulated learning behaviour in virtual learning environments in the last decade

An analysis of technological resources to encourage self-regulated learning behaviour in virtual learning environments in the last decade

An analysis of technological resources to encourage self-regulated learning behaviour in virtual learning environments in the last decade

An analysis of technological resources to encourage self-regulated learning behaviour in virtual learning environments in the last decade

Examining change in students’ self-regulated learning patterns after a formative assessment using process mining techniques

The growing popularity of e-learning platforms, such as learning management systems, has foregrounded the role of self-regulated learning (SRL) in student success. In many e-learning environments, students typically complete learning assignments outside of school hours with little or no instructor support, which requires students to be highly self-regulated. The current study used trace data from a Moodle platform to examine both the temporal pattern of students' SRL behaviors and changes in high- and low-performing students' SRL behaviors following a formative in-course exam. This study employed repeated-measures ANOVA, multivariate ANOVA, Fuzzy miner, and pMineR on 122,167 event logs from 527 undergraduate students. Findings revealed that students engaged in a loosely sequenced recursive SRL cycle. Following the formative assessment, each group made different adjustments to their SRL processes. High-performing students exhibited more SRL behaviors and developed more structured and interconnected SRL patterns. Low-performing students displayed a smaller increase in SRL behaviors while maintaining their established SRL patterns. Findings from this study could provide a more in-depth theoretical understanding of the nature of SRL cycles. Furthermore, students’ adjustment of SRL patterns in response to assessment may be informative for practitioners to assist students in enhancing their SRL through formative feedback.

The interplay between self-regulated learning behavioral factors and students’ performance in English language learning through Moodle

The increasing popularity of online learning environments, such as Moodle LMS, has led to a growing interest in identifying factors contributing to student success in language learning. Self-regulated learning behaviors, such as goal setting, planning, and self-monitoring, have been identified as key predictors of academic achievement. However, limited research on how these behaviors relate to success in online language learning environments is limited. This study aimed to identify self-regulation learning factors and assess behaviors in English language teaching through Moodle LMS by analyzing trace data. The study analyzed trace data from 1523 English language learners in a Moodle course and identified several behavioral factors. The final course point in English language learning is significantly predicted, including the number of completed quizzes, middle course points, engagement with course materials, time spent on tasks, completion score quizzes, access time in total, and pacing. The study found that completing quizzes was the strongest predictor of the final course point, followed by time spent on task, access time in total, and middle course point. The findings suggest that educators can use the identified behavioral factors to promote self-regulated learning online and develop interventions to support students struggling with self-regulated learning. The studies include using trace data to analyze behavioral patterns and focusing on self-regulated learning factors in online language learning. The study provides important insights into self-regulated learning factors and behaviors in English language learning through Moodle LMS, which can inform the development of effective interventions to support students in online language learning environments.

Role of Theory in Analysing the dynamic of Self-regulated Learning process based on students’ event logs data: A scoping review

Students' capability to metacognitively regulate themselves—cognitively or behaviourally—in their learning plays a pivotal role in determining their academic performance. The dynamic aspect of Self-Regulated Learning (SRL) will be challenging to capture if it relies on perceptual data obtained from students through questionnaires and interviews and only at specific points in time. One potential alternative is to use an approach that captures student activity data in real-time throughout the learning period. Given the context-sensitivity involved in measuring SRL via event data, a solid theoretical foundation is essential in analysing patterns of SRL behaviour using event log data. This scoping review paper aims to identify and map how the empirical studies in this area consider SRL theory or models, not only when interpreting analytical results but also when designing instruction and interpreting SRL indicators from raw data. A thorough literature search was performed on various online databases, including Scopus, IEEExplorer, ProQuest, and Web of Science, to identify relevant studies. Following the PRISMA scoping review (PRISMA-ScR) as a protocol for the review, 39 studies published between 2012 and 2023 were included. This study found limited studies incorporating SRL theory in every analysis stage, from designing instruction to preprocessing event data and interpreting analytical models. This study also highlighted the importance of including contextual and theoretical factors when assessing self-regulatory behavioural patterns.

The Influence of Democratic Parenting and Self-Regulated Learning on Students’ Academic Procrastination in Ngaglik

Procrastination can cause significant problems and disrupt the education systems of high school students, including those at State Senior High School 1 Ngaglik. This study examines the influence of democratic parenting and self-regulated learning on students’ academic procrastination. We performed this quantitative study using a cluster-random sampling technique. Three measurement tools were used: academic procrastination scale, democratic parenting scale, and self-regulated learning scale. In total, 86 participants completed the survey. Data analysis used analyze multiple regression on SPSS. The findings support our initial hypothesis that democratic parenting and self-regulated learning significantly influence academic procrastination. However, the coefficient value of democratic parenting styles’ influence on academic procrastination was greater than 0.01 (0.245), indicating that the first minor hypothesis is unsupported. The subsequent minor hypothesis testing shows a coefficient value of -0.366 0.01, indicating that self-regulated learning has a significant positive influence on academic procrastination. This finding becomes the basis for explaining how self-regulated learning could contribute to lowering students’ academic procrastination. Increasing self-regulated learning behavior could motivate students to improve their cognitive and problem-solving skills. We believe that the findings from this research still require more exploration from future studies.

Self-Regulated Learning Behaviors of Pre-School Teacher Candidates in Flipped Learning

Self-Regulated Learning Behaviors of Pre-School Teacher Candidates in Flipped Learning

Cognitive load patterns affect temporal dynamics of self-regulated learning behaviors, metacognitive judgments, and learning achievements

This study investigated how students' cognitive load patterns related to their self-regulated learning (SRL) and performance. One hundred and eleven medical students were required to accomplish a diagnostic task in a computer-based learning environment. The metacognitive aspects of SRL were reported by students at different points during the task, and cognitive load was rated after students finished the task. We extracted SRL behaviors from system logfiles, and students' performance was measured as diagnostic accuracy, which indicated whether they achieved an accurate diagnosis. A two-step cluster analysis was conducted to identify students’ cognitive load patterns based on their perceived intrinsic, extraneous, and germane load. We found three cognitive load patterns: knowledge builders with a low-level intrinsic and extraneous load but high-level germane load, task-loaded learners with a high-level intrinsic and extraneous load but low-level germane load, and overloaded learners with high-level intrinsic, extraneous, and germane load. Moreover, we performed epistemic network analyses to compare the SRL behavior networks of the three clusters. The results showed that knowledge builders exhibited stronger connections associated with monitoring behaviors, while task-loaded and overloaded learners constructed more connections associated with help-seeking behaviors. The transitions between evaluation and solution elaboration behaviors were more frequent for knowledge builders than for the other clusters. Furthermore, knowledge builders reported higher scores in two types of metacognitive judgments: ease-of-learning and judgment-of-learning. Knowledge builders also achieved significantly higher diagnostic accuracy than task-loaded and overloaded students. These findings contributed to a more comprehensive understanding of how cognitive load interacts with SRL.

The Effect of Self-Regulated Learning Behavior on Online Academic Procrastination and the Moderating Role of Parental Academic Involvement Latent Profiles Among Middle School Students

Objectives: This study aimed to examine the effect of self-regulated learning on online academic procrastination and the moderating effects of parental academic involvement latent profile.Methods: The participants were 527 middle school students who completed questionnaires to assess online academic procrastination, self-regulated learning behavior, and parental academic involvement. The data were analyzed using descriptive statistics, Pearson’s correlation coefficients, latent profile analysis, and hierarchical regression analysis with SPSS 26.0 and Mplus 8.4.Results: First, latent profile analysis identified four groups of parental academic involvement: support and pressure-emphasizing type, encouragement and active support type, autonomy-supportive type, and lack of support and high-pressure type. Second, results revealed that higher levels of selfregulated learning behavior and being in the encouragement and active support type group; were associated with reduce online academic procrastination behavior. Furthermore, the study found that the negative impact of self-regulated learning behavior on online academic procrastination weakened for students in the lack of support and high-pressure type or the encouragement and active support type groups.Conclusion: This study highlights the significance of improving self-regulated learning behaviors in the context of online learning. Moreover, the results indicate that parents’ positive support and encouragement, coupled with high expectations for academic growth, are more effective in fostering academic success than solely applying academic pressure or offering autonomous learning support. These findings offer valuable insights for developing interventions to prevent and address online academic procrastination.

Rapport, Course Technology, and Self-Regulated Learning as Predictors of Student Satisfaction in an Online Horticulture Program

Online courses have proliferated in higher education, which has provided greater opportunities for institutions and students. However, student attrition from online programs has been a perennial problem. One potential solution to help increase retention is to improve student satisfaction. Instructor characteristics, technology, and self-regulated learning are all variables, which could contribute to greater student satisfaction in online courses. However, little research exists regarding these variables within the context of online agriculture programs. Therefore, the purpose of this study was to determine predictors of student satisfaction in an online, multi-institutional, horticulture program. Students enrolled in the ACCEPtS horticulture program in spring and fall of 2020 were surveyed to determine their perceptions regarding professor-student rapport, technology, self-regulated learning, and course satisfaction. Results showed that students had favorable perceptions regarding professor-student rapport and technology, they mostly agreed they used self-regulated learning behaviors, and they were generally satisfied with the ACCEPtS program. Technology and professor-student rapport were significant predictors of student satisfaction; however, rapport was more robust and explained about a quarter of the variance in satisfaction. Rapport is an important contributor to student satisfaction in online courses, and instructors should utilize behaviors that contribute to the building of relationships.

Basic psychological needs satisfaction as a mediator between clinical learning climate, self-regulated learning and perceived learning in the nursing education context

Purpose Self-regulated learning (SRL) can enhance students’ learning process. Students need support to effectively regulate their learning. However, the effect of learning climate on SRL behavior, its ultimate effect on learning and the underlying mechanisms have not yet been established. We explored these relationships using self-determination theory. Materials and methods Nursing students (N = 244) filled in questionnaires about SRL behavior, perceived learning, perceived pedagogical atmosphere and Basic Psychological Needs (BPN) satisfaction after their clinical placement. Structural equation modelling was used to test a model in which perceived pedagogical atmosphere affects SRL behavior and subsequent perceived learning through BPN satisfaction. Results The tested model had an adequate fit (RMSEA = 0.080, SRMR = 0.051; CFI = 0.972; TLI = 0.950). A positively perceived pedagogical atmosphere contributed to SRL behavior, which was fully explained by BPN satisfaction. SRL partially mediated the contribution of pedagogical atmosphere/BPN to perceived learning. Conclusions A learning climate that satisfies students’ BPN contributes to their SRL behavior. SRL behavior plays a positive but modest role in the relationship between climate and perceived learning. Without a culture that is supportive of learning, implementation of tools to apply SRL behavior may not be effective. Study limitations include reliance on self-report scales and the inclusion of a single discipline.

The mediating role of self-efficacy in the relationship between the online learning environment and academic self-regulation

PurposeThe purpose of the study is to explore the interrelation between internal factors of learners and the external environment. The results of this study help to design a learning environment that improves students' self-efficacy and consequently self-regulated learning (SRL) behaviors of students.Design/methodology/approachThe study adopts a quantitative approach to explore the relationship between learner's self-efficacy, self-regulation behavior and three factors in the online learning environment: course design, learning activities and relationship with instructors and peers. Participants of the study are 350 students in two universities in Vietnam.FindingsThe study finds that factors in the learning environment namely course design, learning activities and relationship within class significantly affect students' self-regulation. Moreover, results show that students' self-efficacy plays the mediating role in the relationship between learning environment and self-regulation.Research limitations/implicationsSamples are taken by convenience sampling method, which may lead to sampling bias, and results may, to some extent, be misleading. The study was conducted in only two universities with limited student populations. A larger sample of students from other institutions may contribute to a better explanation of the relationships.Practical implicationsThe study has a practical implication of contributing to the limited understanding of learners in an underdeveloped-research country context. The study also implies necessary changes to the long-standing, prevalent yet ineffective teaching and learning style.Social implicationsThe study calls for a renovation in the nation's traditional educational practices, having a social implication of creating a learning environment beneficial for learners.Originality/valueThis study is the first to investigate the impact of online learning environment and students' internal factors on their learning behaviors in Vietnam. The study is among the very few empirical research studies on the country's education generally and on self-regulation specifically, contributing to better understanding of learning experiences and the improvement of teaching.