Self-regulated Learning Profiles Research Articles

Linking self-regulated learning to community of inquiry in online undergraduate courses: A person-centered approach

AbstractThe Community of Inquiry (CoI) framework has gained widespread recognition as a theoretical model for understanding student learning in online environments. Despite its prevalence, CoI has been critiqued for its limited emphasis on learners’ proactive roles in self-regulating their own learning. To address this, researchers have suggested integrating self-regulated learning (SRL) into the CoI framework. This integration calls for empirical research to explore the relationship between SRL and the three established CoI presences: teaching, social, and cognitive. Using a person-centered approach, this study examines how varying SRL skills among 750 undergraduate students in an online introductory mathematics course are related to the three CoI components. Latent profile analyses identified five distinct SRL profiles: minimal regulators, low regulators with limited social skills, low regulators, moderate regulators, and competent regulators. We found that students in higher SRL profiles demonstrated higher perception of CoI, whereas those in relatively lower SRL profiles showed lower levels of perceived CoI. Our findings underscore the importance of incorporating self-regulation in the CoI framework for a more comprehensive understanding of online learning.

Exploring Primary School Students’ Self-Regulated Learning Profiles in a Web-Based Inquiry Science Environment

Exploring Primary School Students’ Self-Regulated Learning Profiles in a Web-Based Inquiry Science Environment

잠재전이분석과 머신러닝 기법을 적용한 초-중 학교급 전환에 따른 자기조절학습 변화양상과 예측변인 탐색

The purpose of this study is to examine the transition patterns of self-regulated learning (SRL) profiles among students transitioning from elementary to middle school and to identify factors influencing these changes. To do this, the「Korea Education Longitudinal Study 2013」data was utilized. Latent transition analysis and six machine learning algorithms (Random Forest, Decision Tree, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, and Artificial Neural Network) were approached for analysis. As results, first, the SRL profiles of elementary to middle school transition students were categorized into three levels: high, medium, and low. Second, Random Forest(RF) demonstrated the highest performance in classifying the transition groups of SRL patterns. Third, examining RF analysis to investigate factors influencing the transition of SRL profiles, academic achievement emerged as the most significant predictor, followed by academic self-concept, self-management, creativity, and comprehension of class materials, among others. Finally, based on the study’s results, educational implications for fostering positive development of students’ SRL were suggested.

Are profiles of self-regulated learning and intelligence mindsets related to students’ self-regulated learning and achievement?

BackgroundResearch emphasizes the relevance of mindsets for SRL and academic achievement. AimsThis paper reports three studies that aim to detect profiles of mindsets about SRL and intelligence, examine their relationship with students’ SRL, study-related beliefs, and academic achievement, and replicate the findings across samples. SampleParticipants were Ntotal = 633 university students, with NStudy1 = 254, NStudy2 = 187, and NStudy3 = 192. MethodsLPAs were run to identify mindset profiles, and Mplus’ auxiliary (BCH) function was applied to examine mean differences across profiles. ResultsAll three studies identified the mindset profiles “Entity-Theory”, “Balanced-Theory”, and “SRL-Theory”. The analyses revealed an SRL-Theory advantage regarding SRL, study-related beliefs, and academic achievement. ConclusionsThese are the first studies to explore mindset profiles based on mindsets about SRL and intelligence. The identified mindset-based meaning systems provide diagnostic information for teaching and tailored interventions and stimulate the advancement of SRL and mindset theories.

Measuring the Complexity of Self-Regulated Learning and Academic Challenges for Adolescents in Canada

Research that uses self-report measures to examine the complexity of self-regulated learning (SRL) and academic challenges for adolescents is limited. This study examined the psychometric property of the Self-Regulated Learning Profile and Self-Diagnostic (SRL-PSD) instrument and addressed the multi-components of SRL and academic challenges for adolescents. Participants were 358 adolescents from a Canadian middle school. The subscales of SRL-PSD were administered to students through LimeSurvey during a 25-min instructional session over two days. Results demonstrated the SRL-PSD was a reliable and valid self-report instrument to measure adolescents’ SRL practices and academic challenges. Also, all types of SRL practices and academic challenges were significantly intercorrelated. Additionally, all types of SRL practices were positively associated with school engagement, whereas all types of academic challenges were negatively associated with school engagement. Overall, this study provides a validated self-report measure for educators and researchers to examine adolescents’ SRL practices and academic challenges.

Self-regulated learning profiles including test anxiety linked to stress and performance: A latent profile analysis based across multiple cohorts.

Medical educators aim to understand why students differ in performance and stress. While performance and stress are associated with student demographics, school factors and aspects of self-regulated learning (SRL), it remains unclear how these elements interact within individuals. This multi-cohort study identified SRL profiles among medical students and explored their associations with performance and stress. Additionally, we examined the identified profiles' associations with gender, migration status and assessment policy. We used latent profile analysis (LPA) to identify profiles on Motivated Strategies for Learning Questionnaire (MSLQ) scores of six cohorts (2014-2019) of Year 1, first semester medical students (n = 1894) in a Dutch medical school. We used nine MSLQ subscales that measure test anxiety (TA), self-efficacy, deep learning, resource management and value. The university's assessment policy varied, demanding students to obtain 100% or 75% of Year 1 credits to remain enrolled. We defined optimal performance as obtaining all credits at the end of Year 1. Two cohorts completed the Perceived Stress Scale (PSS-14, n = 409) in the 2nd semester. We identified three distinct student profiles: 693/1894(36.6%) were classified as TAhigh SRLhigh , 661/1894(34.9%) as TAlow SRLhigh and 540/1894(28.5%) as TAmoderate SRLlow . Females were more likely to belong to TAhigh SRLhigh profiles compared to males (effect size [ES] Cramer's V = .13, small). Migration background was not associated with these profiles. The TAhigh SRLhigh profile was more prevalent under the 100% assessment policy (ES Cramer's V = .10, negligible). TAlow SRLhigh students demonstrated lower stress levels (PSS = 23.9 out of 56) compared to TAhigh SRLhigh students (PSS = 28.7, ES Cohen's d = .62, medium) and TAmoderate SRLlow students (PSS = 28.2, ES Cohen's d = .51, medium). Performance differed among the three profiles (ES Cramer's V = .16, small): 82.5% optimal performance in the TAlow SRLhigh , 71.9% in the TAhigh SRLhigh and 65.2% in the TAmoderate SRLlow profile. Three distinct SRL student profiles associated with gender, academic performance and perceived stress were identified. Test anxiety had additional value in distinguishing subgroups with differential academic performance and stress. These profiles may aid educators to inform personalised support strategies for novice learners.

Exploring the impact of chat-based collaborative activities and SRL-focused interventions on students’ self-regulation profiles, participation in collaborative activities, retention, and learning in MOOCs

AbstractDespite their potential to deliver a high-quality learning experience, massive open online courses (MOOCs) pose several issues, such as high dropout rates, difficulties in collaboration between students, low teaching involvement, and limited teacher–student interaction. Most of these issues can be attributed to the large number, diversity, and variation in self-regulated learning (SRL) skills of participants in MOOCs. Many instructional designers try to overcome these issues by incorporating collaborative activities. Others try to scaffold students’ SRL levels by making SRL-focused interventions. However, limited research combines the study of SRL-focused interventions with students’ engagement in collaborative activities, course retention, and learning outcomes of MOOC environments. We deployed a programming-oriented MOOC in which we incorporated chat-based collaborative activities, supported by a learning analytics dashboard. Students were asked to complete SRL-focused questionnaires at the beginning and the end of the course. Based on their score, we calculated an average score that forms their SRL level, creating three groups: (a) control, (b) general intervention, and (c) personalized intervention in which we provided personalized interventions. We compared the students’ learning outcomes, participation in collaborative activities, and retention in the MOOC. These comparisons provided evidence regarding the positive impact of different intervention modes on students’ engagement in collaborative activities and their learning outcomes, with respect to their various SRL profiles. Students allocated to the general and personalized intervention groups displayed increased participation in the collaborative activities and learning outcomes, as compared to students assigned to the control group. We also documented that the SRL interventions positively affected students’ course retention.

Individual differences in self-regulated learning profiles of Chinese EFL readers: A sequential explanatory mixed-methods study

AbstractThis study explored the latent profiles of self-regulated learning (SRL) strategies (cognitive, metacognitive, and motivational regulation) endorsed by Chinese English-as-a-foreign-language (EFL) readers in a high-stakes testing environment, and also their associations with individual factors (gender, grade, reading proficiency, and motivational beliefs). With a sequential explanatory mixed-methods design, students in grades 11 and 12 (n = 1,113) completed a reading comprehension test and a questionnaire regarding their strategy use and individual factors, and some (n = 16) were randomly selected for follow-up semi-structured interviews. Findings revealed three SRL profiles, characterized by high, medium, and low levels of SRL-strategy use. Self-efficacy and extrinsic motivation most powerfully predicted an individual’s profile membership; all the intrinsic and extrinsic motivation variables were significantly higher for learners from the higher strategy-use profile. Moreover, reading proficiency did not significantly predict profile membership, but more self-regulated students still achieved higher reading scores as a group tendency.

Influence of primary students' self-regulated learning profiles on their rating of a technology-enhanced learning environment for mathematics.

The importance of learning technologies for mathematics education is increasing as new opportunities arise for mathematics education for all students, in school and at home. These so-called technology-enhanced learning environments (TELEs) incorporating technology with mathematical content are useful for developing mathematical knowledge and can simultaneously foster self-regulated learning (SRL) and motivational learning in mathematics. However, how do primary students' differences in their SRL and motivation affect their rating of the quality of mathematical TELEs? To answer this research question, we asked third and fourth-grade primary students (n = 115) to evaluate both their SRL, including metacognition and motivation, and the quality characteristics of the ANTON application, a frequently and intensively used TELE in Germany. Using a person-centered research approach by conducting a cluster analysis, we identified three SRL profiles of primary students-motivated self-learners, non-motivated self-learners, and average motivated non-self-learners-who differ in their ratings of the quality characteristics of the TELE (output variables). Our results highlight that motivated self-learners and non-motivated self-learners vary significantly in their rating of the adequacy of the TELE to their mathematical learning and highly but not significantly concerning the TELE's reward system. Moreover, differences existed between the motivated self-learners and the average motivated non-self-learners regarding their rating of the characteristic differentiation. Based on these findings, we assume that technical elements associated with adequacy, differentiation, and rewards of mathematical TELEs should be tailorable to the needs of individuals and groups of primary schoolchildren.

Self-Regulated Online Learning Profiles of Medical Students amidst Curriculum Adaptation during COVID-19 Pandemic

Student-centered learning (SCL) is an approach in which the learning process depends significantly on the student. Hence, self-regulated learning (SRL) plays a crucial role in optimising SCL. SRL is defined as active participation in terms of metacognition, motivation, and behaviour during learning. The COVID-19 pandemic greatly impacted education, including medical education, which must adapt by implementing online and blended learning curricula. Thus, there is increasing urgency for both SCL and SRL. This cross-sectional study was performed with medical students at the Faculty of Medicine, Universitas Indonesia, using the Online Self-regulated Learning Questionnaire (OSLQ) to determine the validity and reliability of the OSLQ in the setting of Indonesian medical students and measure their online SRL profiles. We conducted cross-cultural validation, followed by exploratory factor analysis (EFA), quantitative analysis, and qualitative analysis to assess students’ SRL and the challenges of online learning environments. A total of 519 participants completed the questionnaire. The EFA confirmed the validity of the questionnaire, which comprised 23 items and five subscales, in the current population (Cronbach alpha = 0.86). Online SRL scores ranged between 65%–77%, with statistically significant differences in all subscales across six different academic years. Preclinical students had a higher level of online SRL compared to clinical students. This study supports the validity and reliability of using OSLQ in the context of Indonesian medical students. Student SRL is a dynamic process that evolves with changing learning contexts. Student support in online learning environments is essential for enhancing student SRL.

L2 learners’ self-regulated learning strategies and self-efficacy for writing achievement: A latent profile analysis

There is an increasing recognition that effective deployment of self-regulated learning (SRL) strategies is essential for the development of second/foreign language learners’ abilities to accomplish learning goals. Within models of SRL, a prerequisite for the active employment of SRL strategies is learners’ self-efficacy, which interacts with strategy use in all phases. In the domain of second language (L2) writing, previous research mainly adopted a variable-centred approach to investigate the relationship between SRL strategy use and self-efficacy whereas person-centred questions have largely been neglected. Performing latent profile analysis (LPA) with undergraduate students of English as a foreign language (EFL), we identified three SRL profiles that differed quantitatively concerning levels of self-efficacy. The efficacious self-regulators with the highest levels of cognitive, metacognitive, and motivational components of SRL reported the highest levels of self-efficacy, followed by the moderate strategists; EFL learners with the lowest levels on the main SRL components reported the lowest writing self-efficacy score. Learners of different profiles differed significantly in a writing performance test with the efficacious self-regulators outperforming the other two profiles. Implications for self-regulated language learning and L2 writing instruction are discussed.

Self-regulated learning, online mathematics learning engagement, and perceived academic control among Chinese junior high school students during the COVID-19 pandemic: A latent profile analysis and mediation analysis.

Under the COVID-19 prevention and control policy, online learning has been widely used. The current study aimed to identify latent profiles of self-regulated learning in the context of online mathematics learning during the recurrent outbreak of COVID-19, and examine the mechanisms underlying the relationship between self-regulated learning and online mathematics learning engagement among Chinese junior high school students using variable-and person-centered approaches. A sample of 428 Chinese junior high school students (47.66% female) completed questionnaires on self-regulated learning, perceived academic control, and learning engagement. Mplus7.0 was used to analyze the latent classes of self-regulated learning. A mediation model was conducted using the software SPSS PROCESS macro. Three profiles of self-regulated learning were identified and named as low self-regulated learning (16.12%), medium self-regulated learning (43.23%), and high self-regulated learning (40.65%). In the mediating analysis, results of the variable-centered approach showed that perceived academic control mediated the effects of self-regulated learning on learning engagement. For the person-centered approach, we selected the low self-regulated learning type as the reference profile, and the analysis revealed that compared with the reference profile, perceived academic control partially mediated the link between the medium self-regulated learning profile and learning engagement; perceived academic control partially mediated the relationship between the high self-regulated learning profile and learning engagement. This study showed the heterogeneity in the online mathematics self-regulated learning patterns of Chinese junior high school students during the COVID-19 pandemic, revealing the internal mechanisms of Chinese junior high school students' online mathematics learning engagement using variable-and person-centered approaches. Furthermore, the findings of the study have important implications for promoting online mathematics learning engagement among junior high students during the pandemic.

Changes in self-regulated learning profiles during an undergraduate peer-based intervention: A latent profile transition analysis

University students often have insufficient strategies to regulate their own learning independently, suggesting the need for adequate support. This study examined developmental changes in students’ self-regulated learning (SRL) profiles during their participation in Supplemental Instruction, a peer-based intervention, over the course of a semester and predictive variables of these changes. Latent profile transition analysis of a sample of 352 undergraduates revealed three distinct SRL profiles which emerged identically across time points: competent regulators, self-confident regulators, and goal-oriented regulators. Students in the competent regulators profile were most stable over time, while those in the goal-oriented regulators profile were most malleable, but in a positive sense. Results also indicate that students who attributed SI attendance to mastery goals were more likely to transition beneficially between SRL profiles. The observed predictors of transitions between profiles also highlighted the relative importance of motivational variables as opposed to cognitive-metacognitive variables in accounting for SRL development.

An Empirical Study on the Relationship between Cognition and Metacognition in Technology-Enhanced Self-Regulated Learning

Self-regulated learning (SRL) has become an indispensable ability for learners to succeed in self-study. A fast-growing number of learners worldwide opt for studying via technology-enhanced learning environments (TELEs) to enrich their education. Since the outbreak of the global COVID-19 pandemic, learners have relied more and more on online and distance learning for their own education purposes; this circumstance urges learners to self-regulate their learning processes. Striving for understanding learners’ SRL ability in TELEs to provide them with sufficient support, this research analyzed the relationship between the two key factors of SRL, cognition and metacognition, from the context of self-study in TELEs. Applying our proposed hypothetical model on the relationship between cognition and metacognition, we conducted a pilot study in which 20 postgraduate students solved a complex academic task delivered via a TELE—the learning management system Moodle. In this experiment, the correlation between the students’ cognitive and metacognitive scores was analyzed. The experimental results showed that there is a positive linear correlation between cognition and metacognition of a learner when he or she performs a complex task in TELEs, and such a correlation can be classified into different profiles. Implications include opportunities to help learners understand their SRL profiles and provide recommendations for further research on the granularity of SRL characteristics.

Using Educational Data Mining Techniques to Identify Profiles in Self-Regulated Learning: An Empirical Evaluation

With the increased emphasis on the benefits of self-regulated learning (SRL), it is important to make use of the huge amounts of educational data generated from online learning environments to identify the appropriate educational data mining (EDM) techniques that can help explore and understand online learners’ behavioral patterns. Understanding learner behaviors helps us gain more insights into the right types of interventions that can be offered to online learners who currently receive limited support from instructors as compared to their counterparts in traditional face-to-face classrooms. In view of this, our study first identified an optimal EDM algorithm by empirically evaluating the potential of three clustering algorithms (expectation-maximization, agglomerative hierarchical, and k-means) to identify SRL profiles using trace data collected from the Open University of the UK. Results revealed that agglomerative hierarchical was the optimal algorithm, with four clusters. From the four clusters, four SRL profiles were identified: poor self-regulators, intermediate self-regulators, good self-regulators, and exemplary self-regulators. Second, through correlation analysis, our study established that there is a significant relationship between the SRL profiles and students’ final results. Based on our findings, we recommend agglomerative hierarchical as the optimal algorithm to identify SRL profiles in online learning environments. Furthermore, these profiles could provide insights on how to design a learning management system which could promote SRL, based on learner behaviors.
  

Investigating self-regulation in the context of a blended learning computing course

PurposeMassive open online courses (MOOCs) provide an innovative educational technology, which has become widely used for distance learning by independent learners. However, there has been little work so far to study the effects of using MOOCs as part of a blended classroom approach in which learning activities take place both online and in a traditional classroom setting. The purpose of this study is to investigate the aspects of blended MOOC usage in the context of a computing course for first-year undergraduates at a UK university.Design/methodology/approachThe MOOC was implemented on a purpose-built platform that supports learners to make informed choices about their learning path. This research investigates students’ capacity for self-regulated learning (SRL) and understands their preparedness for independent study, profile the general areas of SRL strength and weakness, which may affect their ability to learn effectively in a self-directed environment. An existing survey instrument, based on a six-dimensional conceptualization of SRL was adapted to investigate the self-regulation in the MOOC study.FindingsThe results demonstrate that the dimensions of self-evaluation and time management represent particular areas of weakness for these students. Furthermore, profiles of SRL for individual students show considerable differences in capability within the study. However, the deficiencies in SRL dimensions contrast with the students’ of generally high levels of attainment. This leads us to question the validity of the existing SRL. Furthermore, a high level of social interaction and help-seeking was reported in relation to the MOOC study indicating the increasing importance of social learning and the importance of co-regulation for SRL.Research limitations/implicationsAlthough this study presents findings from a small data sample, it points to a number of areas for future implementation and exploration. Firstly, in line with the action research approach, students’ SRL could, in the future, be tested early in the course with the MOOC component being ideally placed to provide personalised support for each student in aspects which they may benefit from developing further. Secondly, for students in the cohort studied in this paper, a longitudinal study will track how their SRL develops as they progress through the degree. We feel that it is important to gain further qualitative data to understand how students work in practice and the strategies they adopt when confronted with different modes of learning. Finally, it is necessary to consider the conceptualisation of SRL to understand if existing instruments could be adapted to provide a more accurate assessment of the effectiveness of learners’ self-regulation.Originality/valueThere has been little research on the effects of using a MOOC as the online component of a blended classroom learning approach. This study has used a theoretical perspective of SRL to investigate the approaches to self-regulation adopted by undergraduate computer science students studying in a blended MOOC environment. The MOOC used for this purpose was developed on the innovative eLDa platform, allowing students to determine, track and visualise their individual path through topics and materials offered in the MOOC.

Online Self-Regulated Learning Profiles: A Study of Chinese as a Foreign Language Learners.

The role of self-regulated learning (SRL) in achieving academic success has been widely investigated for campus-based college students. However, research on online learners’ SRL is limited, while the number of online learners has been increasing tremendously in recent years, especially after the COVID-19 pandemic worldwide. As SRL is context-bound, differences caused by classroom and home environments may be expected. This study investigated the factor structures of online learners’ SRL in Chinese as a foreign language education and the existence of SRL profiles in online learners. Data were collected from 378 international students enrolled in online Chinese language courses in 2020. Ten latent factors were revealed by exploratory factor analysis with motivation and learning strategies scales of the Motivated Strategies for Learning Questionnaire (MSLQ). A follow-up latent profile analysis showed three profiles of low, moderate, and high self-regulated learners. The study supports the context-bound nature of SRL and calls for developing adaptive training programs according to SRL profiles of Chinese language learners.

Time-driven modeling of student self-regulated learning in network-based tutors

ABSTRACT This study applies a time-driven approach to model self-regulated learning (SRL) on the basis of elapsed time metrics in the context of open-ended learning environments (OELEs), specifically, network-based tutors. In doing so, we examine how students allocated attentional resources to distinct phases of SRL as a measure of depth of information processing. Student teachers (N=68) were assigned to two different versions of nBrowser: a static version where the network did not converge on the basis of student interactions and a dynamic version where the network was continually updated by the system. Students designed a lesson plan and completed pre- and post-test self-report measures of knowledge gains. In both the experimental conditions, the results show four distinct SRL profiles that are relatively consistent and can be detected on the basis of behavioral patterns logged by the system across behaviors, namely, planning, requesting hints, studying examples, and monitoring. Although students who allocated more attentional resources to studying examples performed more poorly, their efforts to engage in planning, requesting hints, and monitoring were found to predict knowledge gains and design skills. Furthermore, students assigned to the dynamic version of the system outperformed those assigned to the static version in pedagogical knowledge gains.

The counterintuitive self-regulated learning behaviours of healthcare providers from low-income settings

Self-regulated learning (SRL) is useful for understanding self-directed learning practices. However, SRL behaviours - despite being deemed highly context-dependent - remain mostly unexplored for healthcare workers in low-income countries. This study details how SRL strategies vary and impact on healthcare providers' learning gains when using digital learning platforms. We apply Latent Profile Analysis (LPA) to questionnaire responses from a sample of 264 healthcare providers, arguably the first time LPA has been applied for the context in this subject-domain. We identified four SRL profiles: High, Above-Average with Low Help-Seeking, Average, and Low SRL profiles with significant differences in SRL strategies between the four profiles confirmed by Kruskal-Wallis test and logistic regression. Healthcare providers with more specialised clinical training were most likely to be in the Low SRL profile, but compared to the other profiles, maximised possible learning gains in the fewest learning iterations. From our findings, SRL may not adequately represent the nature of the interaction between these learners and contextual characteristics. Exploring the important role of various external learning regulation behaviours that influence healthcare providers SRL might help address this shortcoming. These findings provide insights into the learner factors to consider when implementing technology-mediated learning in these resource-contexts. They also offer plausible future research directions into how to maximise healthcare providers’ learning gains on digital platforms that is informed by how learners in low-income contexts regulate their self-directed learning.

Secondary students’ online self-regulated learning during flipped learning: A latent profile analysis

Flipped learning (FL) makes greater use of students' self-regulated learning (SRL) skills when they regulate their online learning behavior. Previous research has shown the value of SRL support during FL to enhance students' SRL and learning outcomes. However, as previous studies have indicated that SRL behavior varies, should SRL support be tailored to these differences in SRL? We applied latent profile analysis to identify subgroups in 150 eighth-graders during FL. We used practically relevant online behavioral data to represent students’ online SRL activities, which we gathered unobtrusively in an ecologically valid secondary educational classroom setting. We found five distinct SRL profiles from low completion and no activity to full completion and very high activity. In addition, students in the profile who showed low SRL activity achieved significantly worse learning outcomes than students in the three profiles with higher SRL activity. Finally, we explored whether SRL activity profile membership can be explained by student characteristics (i.e., self-reported SRL, motivation, and prior knowledge). None of the student-level variables predicted profile membership, but our approach offers leads for future research to further investigate the potential of tailored SRL support.