Addressing complexities in self-regulated learning: a focus on contextual factors, contingencies, and dynamic relations

Foreword ( Wilbert J. McKeachie). 1. Introduction: Self-Regulation of Learning in Postsecondary Education (Hefer Bembenutty) The chapter introduces this volume on self-regulation of learning and highlights the current trends on self-regulation presented by each of the authors. 2. Purpose of Engagement in Academic Self-Regulation (Einat Lichtinger, Avi Kaplan) This chapter argues that self-regulated learning is not a unitary construct, with students different purposes of engagement in the task meaningfully distinguishing between different types of self-regulation. 3. Self-Regulation and Achievement Goals in the College Classroom (Akane Zusho, Kelcey Edwards) This chapter links self-regulation of learning with achievement goal theory and offers practical tips to educators who struggle with disinterested learners. 4. Understanding and Facilitating Self-Regulated Help Seeking (Stuart A. Karabenick, Myron H. Dembo) The authors describe interventions to develop the competencies and resources that facilitate student help seeking as an effective selfregulated learning strategy. 5. Self-Regulation and Learning Strategies (Claire Ellen Weinstein, Taylor W. Acee, JaeHak Jung) This chapter reviews research on learning strategies, describes a model of strategic and self-regulated learning, and reports on instructional methods, interventions, and assessments that instructors can use to help students develop lifelong learning strategies they need to succeed in college. 6. Academic Delay of Gratifi cation and Academic Achievement (Hefer Bembenutty) This chapter provides a review of research on delay of gratifi cation and suggests ways in which educators can instill in students to delay gratifi cation. 7. Resistance and Disidentifi cation in Refl ective Practice with Preservice Teaching Interns (Michael Middleton, Eleanor Abrams, Jayson Seaman) This chapter examines case studies to identify contextual factors in teacher education programs that promote or inhibit teaching interns' self-refl ective practices. 8. Professional Development Needs and Practices Among Educators and School Psychologists (Timothy J. Cleary) This chapter summarizes research illustrating the importance and need for motivation and self-regulation professional development training for teachers and school psychologists across assessment, intervention, and instructional activities. 9. Transitioning from College Classroom to Teaching Career: Self-Regulation in Prospective Teachers (Judi Randi, Lyn Corno, Elisabeth Johnson) This chapter explores how preservice teachers prepare for the transition from college classroom to career through assignments and features of the learning environment designed to approximate the demands of work settings and job-related tasks. 10. The Role of Web 2.0 Technologies in Self-Regulated Learning (Anastasia Kitsantas, Nada Dabbagh) This chapter demonstrates how Web 2.0 technologies can be used to facilitate self-regulated learning in postsecondary education. The authors show how instructors can integrate social software into course design to promote students self-regulation of learning. 11. Self-Regulation of Learning with Computer-Based Learning Environments (Jeffrey A. Greene, Daniel C. Moos, Roger Azevedo) This chapter outlines how self-regulated learning skills can facilitate learning with computer-based learning environments and how educators can diagnose and build on students' self-regulated learning ability. 12. New Directions for Self-Regulation of Learning in Postsecondary Education (Hefer Bembenutty) This chapter puts into context the major contributions of this volume on self-regulation of learning and provides new directions for its promotion in postsecondary education. INDEX.

This study investigates secondary education students' self-regulated learning (SRL) processes with digital trace data, particularly whether SRL processes found in secondary education are comparable to those observed in higher education. We therefore adapted a digital learning environment and rule-based AI algorithm originally designed to measure SRL in higher education and collected multi-trace data from 13-year-old students ( N = 179) across three European countries during an essay-writing task. Hidden Markov modeling was employed to capture latent SRL processes. Four latent SRL processes emerged: orientation, first-reading, writing, and re-reading combined with monitoring. By clustering sequences of these latent SRL processes, we identified four sequential patterns of SRL processes at the task level: writing with metacognitive monitoring, writing intensively, reading first, writing next, and reading and writing simultaneously. Our findings highlight how AI and multi-trace data can be used to measure SRL during learning, providing a basis for enhancing personalized support. Self-regulated learning (SRL) is vital in the digital world. In this study, we investigated secondary education students' SRL processes with digital trace data. We also demonstrated that the instrumentation of a digital learning environment and rule-based AI algorithm originally designed to measure SRL processes in higher education can be leveraged to measure SRL processes in secondary education students. The real-time measurement of covert SRL processes is important as this information can 1) raise students' awareness on their learning, 2) help teachers to support students' learning, and 3) form the basis for providing personalized support for these processes with the help of AI-enhanced learning technologies. • Collected multi-trace data from 13-year-old students during an essay-writing task. • Found four SRL processes: orientation, reading, writing, re-reading with monitoring. • Identified four sequential patterns of SRL processes at the task level. • Students' task knowledge was associated with SRL at the task level.

Researchers, especially in the field of educational psychology, have argued that self-efficacy plays an important role in self-regulated learning. As such, teaching of self-regulated learning often focuses on enhancing self-efficacy. However, few studies have examined how the process of self-regulated learning might lead to the enhancement of self-efficacy. Therefore, the aim of this study was to investigate the effects of the self-regulated vocabulary learning process on self-efficacy. A total of 303 learners of English as a Foreign Language from two universities participated in the study. They completed a questionnaire measuring the process of self-regulated learning (i.e. forethought, performance or volitional control, and self-reflection). They also completed a vocabulary test as a measure of learning outcome. Correlation analysis was employed to evaluate the relationships between various variables investigated in the study. The results showed that self-regulated learning process can boost self-efficacy and increase vocabulary knowledge. The pedagogical implications of the current study are discussed mainly in terms of incorporating instructions aimed at enhancing self-efficacy and self-regulation in vocabulary teaching.

Strategic Self‐Regulation for Speaking English as a Foreign Language: Scale Development and Validation

Self‐regulated learning (SRL) is an active process involving multiple interacting components that evolve over time, exhibiting characteristics of complex systems such as non‐linearity, emergent behaviour, self‐organization, and hierarchy. These interactions unfold at different temporal levels, each warranting a dedicated lens to capture their distinct dynamics. In this study, we apply a complex dynamic systems lens to analyse the longitudinal dynamics of SRL. We map how different SRL processes interact with each other across time and scales: (1) the stable between‐person level, which represents the dominant approach to learning or roughly the trait of SRL, (2) the contemporaneous level, which maps how SRL processes influence each other within the same time and (3) the temporal level, which captures how processes predict or influence each other in the future. Data were collected through a weekly survey administered over 4 weeks in five courses at two institutions, complemented by LMS behavioural engagement data. A panel vector autoregression model was employed to examine the structure and dynamics of SRL and LMS behavioural engagement at the three levels. The findings suggest that central SRL processes, such as planning and adapting, take place in separate stages, in accordance with the classic SRL models, whereas other processes, like effort regulation, are more pervasive, co‐occurring with most other regulatory processes. At the aggregate level, adjusting was the most central process that drove students' SRL. As such, our results align with the main characteristics of complex systems, including non‐linearity and hierarchy. These findings have implications for the design of SRL interventions, where effort can benefit from real‐time prompts, whereas metacognitive processes might require long‐term scaffolding. Furthermore, the weak association between LMS engagement and SRL processes across all levels highlights the limitations of relying solely on behavioural trace data to infer regulation. Practitioner notes What is already known about this topic? Self‐regulated learning (SRL) is an important driver of academic success and can be influenced through targeted interventions. Most SRL research is based on group‐level data, often using static, cross‐sectional designs that overlook temporal dynamics. Recent work has highlighted that SRL can and should be modelled as a complex dynamic system. What this paper adds? There is evidence of complex systems characteristics in SRL such as hierarchy, non‐linearity and feedback loops. SRL processes follow distinct temporal phases, with some processes persisting throughout all phases. Metacognition is the most central process at the between‐person level, whereas effort is central at the within‐person level. LMS behavioural data is weakly linked to self‐reported SRL. Implications for practice and/or policy SRL interventions should consider how regulatory processes unfold over time, rather than treating SRL as a static trait. Interventions targeting effort regulation and metacognition have the potential to be the most consequential. Caution must be exerted when using average or between‐person data to inform individualized support. LMS metrics should be interpreted with care and ideally complemented by self‐report or observational data.

Deep versus surface knowledge is widely discussed by educational practitioners. A corresponding construct, levels of processing, has received extensive theoretical and empirical attention in learning science and psychology. In both arenas, lower levels of information and shallower levels of processing are predicted and generally empirically demonstrated to limit knowledge learners gain, curtail what they can do with newly acquired knowledge, and shorten the life span of recently acquired knowledge. I recapitulate major accounts of levels or depth of information and information processing to set a stage for conceptualizing, first, self-regulated learning (SRL) from this perspective and, second, how a "levels-sensitive" approach might be implemented in research about SRL. I merge the levels construct into a model of SRL (Winne, 2011, Handbook of self-regulation of learning and performance (pp. 15-32), New York: Routledge; Winne, 2017b, Handbook of self-regulation of learning and performance (2nd ed.), New York: Routledge; Winne & Hadwin, 1998, Metacognition in educational theory and practice (pp.277-304). Mahwah, NJ: Lawrence Erlbaum) conceptually and with respect to operationally defining the levels construct in the context of SRL in relation to each of the model's four phases - surveying task conditions, setting goals and planning, engaging the task, and composing major adaptations for future tasks. Select illustrations are provided for each phase of SRL. Regarding phase 3, a software system called nStudy is introduced as state-of-the-art instrumentation for gathering fine-grained, time-stamped trace data about information learners select for processing and operations they use to process that information. Self-regulated learning can be viewed through a lens of the levels construct, and operational definitions can be designed to research SRL with respect to levels. While information can be organized arbitrarily deeply, the levels construct may not be particularly useful for distinguishing among processes except in a sense that, because processes in SRL operate on information with depth, they epiphenomenally acquire characteristics of levels. Thus, SRL per se is not a deeper kind of processing. Instead, it is processing more complex - deeper - information about a different topic, namely processes for learning.

Self-regulated learning (SRL) process is temporally bounded and unfolds over time. Achieving insights into temporal dynamics in SRL processing, however, requires not only fine-grained data collection and methods but also modeling approaches that accurately account for the sequential structure of the events during the SRL process. Addressing this, we investigate an understudied aspect of SRL theory: how temporal SRL processing, as captured by multimodal traces, differs between mastery and progressing groups in the context of technology-enhanced biology learning in a lab setting. We examine count-based, duration-based, and sequence-based models of SRL processing to unveil diverse dimensions of the process across different achievement levels. Specifically, for the sequence-based model, we employed a multi-method approach, incorporating epistemic network analysis (ENA) to analyze the temporal and sequential dynamics of multimodal events during SRL processing and then qualitative analysis to delve deeper into those dynamics. Our study offered valuable methodological contributions by empirically comparing different methods for modeling temporality and by utilizing a novel analytic approach (i.e., ENA) to model the temporal dynamics of SRL processes. Additionally, it makes theoretical contributions to advancing the understanding of the dynamic interplay between SRL processing and academic performance, thereby informing targeted interventions to support students’ learning processes, ultimately enhancing their learning outcomes in technology-enhanced STEM education. Future researchers can leverage these contributions to advance models of the dynamic and temporal nature of SRL.

Measuring self-regulated learning (SRL) skills of engineering college students while using enhanced guided notes (EGN) promotes a better understanding of how students deal with notetaking activities. This study focused on students’ task interpretation, cognitive strategies, and SRL processes including planning, monitoring, and regulating strategies while using EGN in an electric circuits course. The main objectives of this study were to (1) understand how students’ SRL skills changed after using EGN; and (2) evaluate how students’ conceptual understanding on electric circuits improved after using EGN. Ninety-seven students enrolled in the Fundamental Electronics for Engineers course at a land grant university participated in the study. A self-regulated learning survey instrument developed using Butler and Cartier’s SRL model was used to capture students’ task interpretation, cognitive strategies, and SRL processes. In addition, Holton’s DC/AC Circuits Concept Inventory was used to measure students’ conceptual understanding of DC/AC concepts. Participants were asked to complete both surveys twice online, at the beginning and end of the semester. Descriptive statistics, independent and paired t-tests, and a cluster analysis technique were used to analyze data. Data analysis of the SRL survey revealed that students had different SRL profiles. Students in the improved group reported a significant improvement in planning, monitoring, and regulating strategies. On the other hand, those in the declined group showed a lower awareness of all SRL skills at the end of semester. Furthermore, the findings revealed an improvement in students’ conceptual understanding. This article will also discuss the potential implications for electric circuit concepts instruction.

Parenting Strategy for Enhancing Children’s Self-Regulated Learning

Self-regulated learning (SRL) is a cyclical process through which individuals plan their objectives, execute them and self-evaluate their own behavior so as to obtain their proposed goals. SRL has been investigated by different authors such as Zimmerman, Boekaerts, Winne and Hadwin, Pintrich, Efklides and Hadwin, Jarvela and Miller and it's being applied in learning environments. This systematic review describes the current state of the art in terms of the support for SRL in Massive Online Open courses (MOOC) using technologies based on psychological models. 66 studies conducted between 2010 and 2020 were analyzed by searching three multidisciplinary databases: Scopus, Web of Science and Google Scholar. The review methodology steps were the review planning, the search, literature analysis and the results report. Results show SRL in MOOCs is an emerging study area incentivized by the high dropout rate of the participants in MOOC. Regarding models of SRL, the most representative author reported was identified as Zimmerman. The most prominent self-regulation strategies used by MOOCs participants are: Goal setting, Help Seeking, Time management, Self-evaluation and Strategic planning. The platforms with research on SRL in MOOCs that stand out are Coursera, Edx, Open Edx and Moodle. We identified tools which have been developed to support SRL in MOOC and a set of good practices useful to support SLR that can be used by MOOC designers and tutors. Finally, a series of open problems and challenges that could lead to new research on the topic of SRL in MOOCs have been identified.

The focus of education is increasingly on learners’ ability to regulate their own learning within technology-enhanced learning environments. Prior research has shown that self-regulated learning (SRL) leads to better learning performance. However, many learners struggle to productively self-regulate their learning, as they typically need to navigate the myriad of cognitive, metacognitive, and motivational processes that SRL demands. To address these challenges, the FLoRA engine is developed to help students, workers, and professionals improve their SRL skills and become productive lifelong learners. FLoRA incorporates several learning tools that are grounded in SRL theory and enhanced with learning analytics (LA), aimed at improving learners’ mastery of different SRL skills. The engine tracks learners’ SRL behaviours during a learning task and provides automated scaffolding to help learners effectively regulate their learning. The main contributions of FLoRA include (1) creating instrumentation tools that unobtrusively collect intensively sampled, fine-grained, and temporally ordered trace data about learners’ learning actions; (2) building a trace parser that uses LA and related analytical techniques (e.g., process mining) to model and understand learners’ SRL processes; and (3) providing a scaffolding module that presents analytics-based adaptive, personalized scaffolds based on students’ learning progress. The architecture and implementation of the FLoRA engine are also discussed in this paper.

The increasing development in smart and mobile technologies supports transformation of learning environment into a smart learning environment to meet diverse learners’ needs. Learning in an online environment requires motivation and skills to succeed. Self-regulated learning has been identified as one of the strategies for supporting students in an online learning environment. However, despite these innovations, there is a scarcity of well exploratory works to understand if students have skills, experiences, and technology resources for implementing a self-regulated smart learning environment. This study attempts to fill the gaps by investigating these experiences and technology resources. The study used mixed-method to investigate the readiness of 157 undergraduate students. The quantitative data used an online self-regulated learning questionnaire (OSLQ), and the qualitative data used focus group discussions. The quantitative data were analyzed using descriptive statistics and correlations, and the qualitative data used a thematic method to understand students’ experiences. The investigations show that students understood the self-regulated learning process and its’ role in their academic success. Moreover, they have access to mobile internet services and smart devices, mostly an android operating system. More so, goal setting, time management, and self-evaluation are among the top-level skills readiness, and there is a relationship between previous online learning experiences and these skills. Future research could investigate facilitators’ readiness to develop a self-regulated smart learning environment for engaging online learning experiences and skills development.

Self-regulated learning (SRL) is the degree to which students are metacognitively, motivationally, and behaviourally active participants in their own learning process. It involves the self-regulation of cognitive, behavioural, and affective processes. SRL holds significant potential for enhancing practise and achievement. Although affect is acknowledged as one of the three fundamental processes in SRL, there is limited research investigating it. However, emotions have been found to influence SRL efficiency while emotion regulation (ER) can impact learning outcomes. Thus, this study sought to investigate how ER processes relate to SRL among professional musicians who perform Western classical music. Four forms of regulation (reappraisal, suppression, rumination, repression) were examined in relation to the SRL three-phase model. Professional musicians (N = 334) of 39 nationalities (age: 18–66; [M = 28]; female = 215; male = 119) completed a survey comprising the Self-Regulated Learning in Music Questionnaire, the Emotion Regulation Questionnaire, and demographic items. A significant positive correlation emerged between SRL and reappraisal, and significant negative correlations emerged between SRL and the other three processes. Further multiple linear regression analysis revealed that reappraisal, practise hours, and expertise accounted for 26% of the variance in SRL. Finally, a factorial (2 × 2 × 2) ANOVA yielded significant group differences on ER as a function of gender, expertise, and occupation. Results suggest that reappraisal can enhance the use of SRL in musicians, thus highlighting the potential utility in considering ER as part of SRL. These results suggest that by including training on emotion regulation strategies within musicians’ educational institutions and workplaces, efficiency and engagement in SRL can be enhanced. This could produce more effective learning strategies and outcomes, together with higher musical achievements.

The purpose of this study was to investigate whether the effectiveness of prompts depends on their point of presentation time in self-regulated learning. First, based on the cognitive load theory, we investigated whether presenting prompts during the learning process instead of before has a positive effect on the process and outcome of self-regulated learning. Second, based on an integration of the cognitive load theory and a model of learning regulation goals, we investigated whether presenting prompts during learning and according to a theoretically optimal course of learning regulation has a positive effect on the process and outcome of self-regulated learning. In an experimental study with a one-factorial between-subjects design, with “point of presentation time of prompts” as independent variable and strategy use as well as learning outcome as dependent variables, 95 students were randomly assigned to one of three conditions. During self-regulated learning in a computer-based learning environment on a physics domain, all conditions got the same prompts, whereas their point of presentation time differed (according to optimal regulation course/contrary to optimal regulation course/before learning). Results revealed that presenting prompts during learning instead of before had a positive effect on learning outcome. Results further revealed that adapting the presentation of prompts according to an optimal course of learning regulation did not have an additional positive effect on learning outcome. A mediator analysis showed that the effect of point of presentation time of prompts on learning outcome was mediated by strategy use during learning. Results are discussed with respect to new directions for assessment and support of self-regulated learning.

The competence approach in pre-school education, which recommends the promotion of self-regulated learning, raises questions about its impact on the development of children’s self-regulation. As a cross-cutting skill, self-regulated learning is essential for today’s society. It provides for a person’s ability to self-educate and develop effectively and successfully. The environment of the pre-school institution and the teacher, who equips and improves this environment, play an important role in promoting the child’s self-regulated learning. In the third stage of pre-school education children have reached the age of 5 to 6 years old and are being prepared to start school. The aim of this study is to assess and analyse children’s self-regulation skills in a pre-primary education environment in the third stage of self-regulated learning. Self-regulated learning is when a student is able to function and use cognitive, emotional processes and behavioural regulation tools to achieve learning goals. The following research tasks were included: 1) analyse the essence and development of self-regulation, and guidelines for organising a self-regulated learning process; and 2) carry out pedagogical observations of children’s self-regulatory abilities within the framework of the self-regulated learning process. The research methods included analysis of pedagogical and psychological literature and sources, pedagogical observation, and statistical analysis of data. The study involved 41 children who were 5 to 6 years old. The results of the study show that self-directed learning can significantly promote the development of self-regulation skills in 5 to 6-year-old children. To better develop the process of self-regulation for 5 to 6-year-old children, the self-regulated learning process must be easier to understand, with an emphasis on updating, understanding, and reflecting on the learned content, while also clearly articulating the expected outcomes and providing feedback.