Supporting Self-Regulated Learning Research Articles

Adaptive support for self‐regulated learning in digital learning environments

AbstractA core focus of self‐regulated learning (SRL) research lies in uncovering methods to empower learners within digital learning environments. As digital technologies continue to evolve during the current hype of artificial intelligence (AI) in education, the theoretical, empirical and methodological nuances to support SRL are emerging and offering new ways for adaptive support and guidance for learners. Such affordances offer a unique opportunity for personalised learning experiences, including adaptive interventions. Exploring the application of adaptivity to enhance SRL is an important and emerging area of research that requires further attention. This editorial introduces the contributions of seven papers for the special section on adaptive support for SRL within digital learning environments. These papers explore various themes related to enhancing SRL strategies through technological interventions, offering valuable insights and paving the way for future advancements in this dynamic area.

Investigating the effects of different levels of students' regulation support on learning process and outcome: In search of the optimal level of support for self-regulated learning

Students in higher education who actively manage and control their own learning processes have better learning outcomes, more efficient learning progress, and other non-academic benefits than those who do not. However, students often have poor self-regulation practices, lack reflective thinking, and fail to monitor their learning against defined goals. Therefore, a variety of advanced learning technologies and features have been developed to support students with regulation. Nevertheless, the optimal level of regulation support and the most effective combination of these features remain unestablished in the existing research literature. Therefore, this study employs quantitative research to examine the effectiveness of two learning environments with different levels of self-regulation support (less and more) on students' learning academic outcomes, learning process, and satisfaction. Both variants combine the elements of learning dashboards, goal-setting activity, self-assessment task, reflection support, and personal recommendation, with differences in the level of supporting students' regulation. Our findings reveal that students who received higher levels of self-regulation support tended to be more active readers and visited online learning materials and course overviews more frequently. While they outperformed students in the control group, it's noteworthy that these students did not necessarily outperform their peers who received less support, particularly in the context of self-assessment tasks. This study has also highlighted the need for further research in this area (e.g., self-assessment accuracy), including the development of an instrument that can objectively measure and evaluate the level of self-regulated learning support.

Taking adaptive learning in educational settings to the next level: leveraging natural language processing for improved personalization

AbstractEducational technology plays an increasingly significant role in supporting Self-Regulated Learning (SRL), while the importance of Adaptive Learning Technology (ALT) grows due to its ability to provide personalized support for learners. Despite recognizing the potential of ALT to be influential in SRL, effectively addressing pedagogical concerns about using ALT to enhance students’ SRL remains an ongoing challenge. Consequently, learners can develop perceptions that ALT is not customized to their specific needs, resulting in critical or dismissive attitudes towards such systems. This study therefore explores the potential of combining Natural Language Processing (NLP) to enhance real-time contextual adaptive learning within an ALT to support learners’ SRL. In addressing this question, our approach consisted of two steps. Initially, we focused on developing an ALT that incorporates learners’ needs. Subsequently, we explored the potential of NLP to capture pertinent learner information essential for providing adaptive support in SRL. In order to ensure direct applicability to pedagogical practice, we engaged in a one-year co-design phase with a high school. Qualitative data was collected to evaluate the implementation of the ALT and to check complementary possibilities to enhance SRL by potentially adding NLP. Our findings indicate that the learning technology we developed has been well-received and implemented in practice. However, there is potential for further development, particularly in terms of providing adaptive support for students. It is evident that a meaningful integration of NLP and ALT holds substantial promise for future enhancements, enabling sustainable support for learners SRL.

How do students learn with real‐time personalized scaffolds?

AbstractScaffolds that support self‐regulated learning (SRL) have been found to improve learning outcomes. The effects of scaffolds can differ depending on how learners use them and how specific scaffolds might influence learning processes differently. Personalized scaffolds have been proposed to be more beneficial for learning due to their adaptivity to learning progress and individualized content to learning needs. The present study investigated finer‐grained effects of how personalized scaffolds driven by a rule‐based artificial intelligence system influenced SRL processes, especially how students learned with them. Using a pre‐post experimental design, we investigated personalized scaffolds based on university students' real‐time learning processes in a technologically enhanced learning environment. Students in the experimental group (n = 30) received personalized scaffolds, while the control group (n = 29) learned without scaffolds. All students completed a 45‐minute learning task with trace data recorded. Findings indicated scaffold effects on students' subsequent learning behaviour. Additionally, only scaffold interaction correlated to essay performance and suggests that the increase in frequencies of SRL activities alone does not contribute directly to learning outcomes. As guidelines for real‐time SRL support are lacking, this study provides valuable insights to enhance SRL support with adaptive learning technologies. Practitioner notesWhat is already known about this topic Self‐regulated learning scaffolds, especially adaptive scaffolds, improve learning. Personalized scaffolds have effects on self‐regulated learning activities. Past research focused on aggregated effects of scaffolds. What this paper adds Investigates how students learn with personalized scaffolds in terms of frequencies of learning activities and scaffold interaction. Takes a closer look at which learning activities and when the effects of personalized scaffolds occur. Examines how finer‐grained effects of personalized scaffolds correspond to learning outcomes. Implications for practice and/or policy Personalized scaffold effects vary across learning, and future research should consider finer‐grained investigations of SRL support in order to better understand their influence on learning. The number of personalized scaffolds provided should be reconsidered in the future as students only use some of the support provided, especially when task demands increase. Personalized scaffold interaction is linked to improvement in task performance, so future research should also focus on students' appropriate use of self‐regulated learning support.

Capturing self‐regulated learning processes in virtual reality: Causal sequencing of multimodal data

AbstractThis study examines the embodied ways in which learners monitor their cognition while learning about exponential functions in an immersive virtual reality (VR) based game, Pandemic by Prisms of Reality. Traditionally, metacognitive monitoring has been assessed through behavioural traces and verbalised instances. When learning in VR, learners are fully immersed in the learning environment, actively manipulating it based on affordances designed to support learning, offering insights into the relationship between physical interaction and metacognition. The study collected multimodal data from 15 participants, including think‐aloud audio, bird's‐eye view video recordings and physiological data. Metacognitive monitoring was analysed through qualitative coding of the think‐aloud protocol, while movement was measured via optical flow analysis and cognitive load was assessed through heart rate variability analysis. The results revealed embodied metacognition by aligning the data to identify learners' physical states alongside their verbalised metacognition. The findings demonstrated a temporal interplay among cognitive load, metacognitive monitoring, and motion during VR‐based learning. Specifically, cognitive load, indicated by the low‐ and high‐frequency heart rate variability index, predicted instances of metacognitive monitoring, and monitoring predicted learners' motion while interacting with the VR environment. This study further provides future directions in understanding self‐regulated learning processes during VR learning by utilizing multimodal data to inform real‐time adaptive personalised support within these environments. Practitioner notesWhat is already known about this topic Immersive virtual reality (VR) environments have the potential to offer personalised support based on users' individual needs and characteristics. Self‐regulated learning (SRL) involves learners monitoring their progress and strategically regulating their learning when needed. Multimodal data captured during VR learning, such as birds‐eye‐view video, screen recordings, physiological changes and verbalisations, can provide insights into learners' SRL processes and support needs. What this paper adds Provides insights into the embodied aspects of learners' metacognitive monitoring during learning in an immersive VR environment. Demonstrates how SRL processes can be captured via the collection and analysis of multimodal data, including think‐aloud audio, bird's‐eye view video recordings and physiological data, to capture metacognitive monitoring and movement during VR‐based learning. Contributes to the understanding of the interplay between cognitive load, metacognitive monitoring, and motion in immersive VR learning. Implications for practice and/or policy Researchers and practitioners can use the causal relationships identified in this study to identify instances of SRL in an immersive VR setting. Educational technology developers can consider the integration of online measures, such as cognitive load and physiological arousal, into adaptive VR environments to enable real‐time personalised support for learners based on their self‐regulatory needs.

Supporting self-regulated learning in a secondary applied mathematics course

<p>This study examines how one teacher supported low-achieving students’ self-regulated learning (SRL) in the context of a secondary mathematics class. The teacher’s scaffolding provided students with multiple opportunities to use feedback and adapt learning and study strategies. Data compared pre- and postmeasures of metacognitive skills, motivational beliefs, and learning and study behaviours, and examined the effects of directed practice on students’ developing SRL as well as their mathematics achievement. Results suggest the need for more research into the effects of individualized, targeted supports, particularly in assisting students in using metacognitive feedback to adapt learning strategies.</p>

Teachers’ assessment of self-regulated learning: Linking professional competences, assessment practices, and judgment accuracy

Self-regulated learning (SRL) is crucial for successful lifelong learning and an important educational goal. For students to develop SRL skills, they need appropriate SRL support from teachers in the classroom. Teachers, who are aware of their students’ strengths and weaknesses in SRL, can promote SRL more adaptively. This requires teachers to assess students’ SRL skills accurately. However, there is little research on teachers’ diagnostic competences in SRL. To address this research gap, the present exploratory study investigates teachers’ content knowledge about SRL, assessment activities, and accuracy in judging their students’ SRL. Furthermore, the study examines whether teachers’ characteristics and competences in SRL are associated with the accuracy of their judgments. The study included 41 lower secondary school teachers and their 173 students. The students completed metacognitive knowledge tests on several SRL skills while the teachers made predictions about the students’ metacognitive knowledge of those SRL skills. The results indicate that not all teachers were familiar with the assessment of SRL. Moreover, teachers exhibited greater familiarity with offline assessments of SRL than online assessments and a noteworthy proportion of teachers employed assessment activities that were not diagnostic of SRL. Low correlations between students’ actual test scores and teachers’ judgments generally revealed low accuracy for teachers in assessing their students’ metacognitive knowledge of various SRL skills. Teachers’ characteristics and competences in SRL were mainly uncorrelated with their judgment accuracy. Overall, these results highlight the need for further attention and support for teachers in developing their diagnostic competences in SRL.

The role of reference frames in learners’ internal feedback generation with a learning analytics dashboard

Being able to self-regulate can positively impact learners’ academic achievement. An inherent catalyst of Self-Regulated Learning (SRL) is internal feedback, the new knowledge which is generated when comparing current knowledge against reference information. Learners may not always generate internal feedback, hampering further SRL. Supporting SRL can be done with a Learning Analytics Dashboard (LAD), in which reference frames allow for comparisons and facilitate internal feedback generation. This study explores internal feedback generation using a LAD and the effect of reference frame availability. A multiple method design examined the interplay of reference frames, comparison processes, internal feedback generation and preparatory activities engagement. Differences between three conditions were explored using Bain ANOVA’s. Results showed that reference frames almost exclude other external comparators and are used in parallel with an internal comparator. A peer reference frame leads to most verbalizations of internal feedback, and potentially to most verbalizations of preparatory activities.

Support for self-regulated learning in the online environment

Support for self-regulated learning in the online environment

Supporting self-regulated learning in clinical problem-solving with a computer-based learning environment: the effectiveness of scaffolds

Supporting self-regulated learning in clinical problem-solving with a computer-based learning environment: the effectiveness of scaffolds

You’ve Got Mail: A Technology-Mediated Feedback Strategy to Support Self-Regulated Learning in First-Year University Students

Self-regulated learning (SRL) is associated with university student academic success outcomes, however students often need support to develop these skills. Technology-mediated feedback is one strategy that may aid educators in supporting students’ SRL development. This study aims to explore whether a technology-mediated feedback strategy targeting tutorial preparation for flipped classrooms enhances first-year students’ self-report SRL and observed implementation of the strategy. Self-report SRL was measured using the Motivated Strategies for Learning Questionnaire (MSLQ); strategy implementation profiles were based on lecture video access patterns. First-year psychology students (n = 99) were sent technology-mediated feedback emails aimed at developing their SRL. Paired-samples t-tests revealed significant increases in post-intervention self-reported motivational SRL subscales; self-reported and observed learning strategies implementation did not improve. Future research could build upon this exploratory work to form a multi-pronged strategy to increase understanding of the role of technology-mediated feedback in first-year students’ SRL development for flipped classroom learning.

Çocuğun Öz-Düzenleme Gelişimi: Okul Öncesi Eğitim Süresi ve Öğretmenlerin Öz-Düzenleme Düzeyi Bağlamında İncelenmesi

This study was conducted to examine the self-regulation of teachers working in preschool education institutions, their practices to support self-regulated learning, and the relationship between preschool education status and children's self-regulation skills. The sample of this study, in which the relational survey model, one of the quantitative research methods, was used, consisted of 316 teachers and 213 children working in primary school preschool classes and independent kindergartens affiliated with the Ministry of National Education in Mersin province. The demographic information of the participants was collected through the Demographic Information Form prepared by the researcher. Teachers' self-regulation was assessed with the "Self-Regulation Scale", while the practices they used to support self-regulated learning were assessed with the "Scale for Evaluating Preschool Teachers' Practices to Support Self-Regulated Learning" (SC-SRLS). Children's self-regulation skills were assessed with the "Preschool Self-Regulation Scale" (PSRS), which requires face-to-face practice with each child. As a result of the study, it was found that children's self-regulation skills differed significantly in favor of children older than 68 months. In addition, it was determined that children's self-regulation and emotion regulation differed in favor of girls according to gender. In the study, the self-regulation skills of children with more than one year of preschool education were significantly higher than those of children with less preschool education. As a result of the study, it was determined that there was a positive and significant relationship between teachers' self-regulation and classroom practices to support self-regulated learning. In addition, there was a positive and significant relationship between teachers' self-regulation and the practices they used to support self-regulated learning and children's self-regulation skills.

Supporting the Self-Regulated Learning of Primary School Students With a Performance-Based Assessment Platform for Programming Education

This study implemented and evaluated the innovative use of a performance-based assessment platform to support the development of self-regulated learning (SRL) in senior primary students as they completed programming tasks. We embedded SRL support features into a performance-based assessment platform as scaffolding to help the students implement problem-solving strategies. A mixed-methods approach was adopted to evaluate the intervention. The students’ perceptions of their SRL skills after working through the programming tasks were measured by a survey of 45 students. The quantitative results suggested that the students benefited from the performance-based assessment platform in developing their SRL skills. A thematic analysis of interview data from 20 students further indicated that the embedded SRL scaffolding and automatic marking function helped them to solve the programming tasks. The results demonstrate that a well-designed performance-based assessment platform with embedded SRL support can be an effective tool for developing students’ SRL. The qualitative results further revealed that algorithmic thinking is an aspect of programming for which students need more SRL support.

Clustering children's learning behaviour to identify self-regulated learning support needs

When children are learning using adaptive learning technologies (ALTs), the technology builds a learner model, which creates temporal trajectories providing insight into how children's knowledge develops. Based on this learner model, ALTs adjust the difficulty of problems for each child, yet children still need to regulate their practice behaviour and uphold effort and accuracy. The temporal trajectories are consequently likely to, besides showing children's knowledge development, be indicative of children's regulation. Therefore, we explore clusters of these trajectories to further identify failure in children's self-regulated learning (SRL) and potential support needs. We propose a data-driven approach to cluster 354 trajectories of 134 5th graders learning three skills with different complexity. The resulting 9 clusters were interpreted using practice accuracy and effort as indicators of regulation of practice behaviour and prior and post-knowledge and learning gain as indicators of knowledge development. The differences between clusters regarding these indicators signal there are different levels of SRL failure and, consequently, different SRL support needs: high accuracy and knowledge development indicate minimal support needs, whereas clusters with low accuracy, showing no knowledge development, indicate extensive SRL support needs. In conclusion: clusters of temporal patterns in children's learning data can identify SRL support is needed.

A Philosophical Discussion of the Support of Self-Regulated Learning in Medical Education: The Treasure Hunt Approach Versus the (Dutch) “Dropping” Approach

Issue: Many current educational approaches are intended to cultivate learners’ full (learning) potential by fostering self-regulated learning (SRL), as it is expected that those learners with a high degree of SRL learn more effectively than those with a low degree of SRL. However, these attempts to foster SRL are not always successful. Evidence: We considered complexities related to fostering self-regulated learning by use of an analogy. This analogy was based on two (Dutch) children’s games: the treasure hunt (children can find a “treasure” by following directions, completing assignments and/or answering questions) and the dropping (pre-teens are dropped in the woods at nighttime with the assignment to find their way back home). We formulated four interrelated philosophical questions. These questions were not formulated with the intention to provide clear-cut answers, but were instead meant to evoke contemplation about the SRL concept. During this contemplation, the implications of definitional issues regarding SRL were discussed by use of the first question: What are the consequences of the difficulties to explicate what is (not) SRL? The second question (How does SRL relate to autonomy?) touched upon the intricate relationship between SRL and autonomy, by discussing the role of social interaction and varying degrees of instruction when fostering SRL. Next, a related topic was addressed by the third question: How much risk are we willing and able to take when fostering SRL? And finally, the importance of and possibilities to assess SRL were discussed by the fourth question (Should SRL be assessed?). Implications: From our contemplations it has become clear that approaches to foster SRL are often insufficiently aligned with the experience and needs of learners. Instead these approaches are commonly defined by contextual factors, such as misconceptions about SRL and lack of leeway for learners. Consequently, we have used principles that apply to both treasure hunts and droppings, to provide guidelines on how to align one’s approach to foster SRL with the educational context and experience and needs of learners.

How does portfolio use support self-regulated learning during general practitioner specialty training? A qualitative focus group study

ObjectivesPortfolios are used to support self-regulated learning (SRL), but the research literature is still inconclusive on their effectiveness. This study explored experiences with portfolio use among different stakeholders, to answer...

Mobile Learning to Support Self-Regulated Learning

This paper discusses the possibilities of using and designing mobile technology for learning purposes coupled with learning analytics to support self-regulated learning (SRL). Being able to self-regulate one's own learning is important for academic success but is also challenging. Research has shown that without instructional support, students are often not able to effectively regulate their own learning. This is problematic for effective self-study and stands in the way of academic success. Providing instructional support for both metacognitive processes such as planning, monitoring, and reflection and cognitive processes such as learning strategies can help students to learn in a self-regulated way more optimally. Mobile learning provides opportunities to provide ‘just in time' support for both cognitive and metacognitive processes. To provide insights into how mobile learning can support SRL, this theoretical review discusses selected studies that have used mobile learning to support SRL in different domains.

Adaptive or adapted to: Sequence and reflexive thematic analysis to understand learners' self‐regulated learning in an adaptive learning analytics dashboard

AbstractThe real‐time and granularized learning information and recommendations available from adaptive learning technology can provide learners with feedback that is personalized. However, at an individual level, learners often experience technological and pedagogical conflicts. Learners have more freedom to accept, ignore or reject the feedback while also having the challenges of building learning strategies and utilizing learning information that requires self‐regulated learning skills. Given the conflicts, both understanding how learners learn and providing support for learners to be more self‐regulated in the learning environment are imperative. This investigation explores how learners processed their learning in an adaptive technology‐integrated learning analytics dashboard (ALAD). It employed mixed‐methods using a lens of self‐regulated learning (SRL). Three groups were identified based on clustering analysis of the learners' usage of warm‐up (WU) tests. Sequence analysis revealed the time trends of each group's interactions with course content. Reflexive thematic analysis brought insights on how learners built their learning strategies (eg, ways of using WU tests and submodule assessments) and how they monitored and controlled their learning. It showed their dynamic interactions with core adaptive learning analytics dashboard elements. Challenges such as difficulties in rehearsing and monitoring through segmented course content arose from the new structural changes. We suggest the need of future improvement to individual learning support through the learning analytics dashboard to be more diverse and dynamic (real‐time) over the course of learning while reducing potential undesirable consequences. Practitioner notesWhat is already known about this topic One purpose of learning analytics and adaptive learning is to help learners identify learning goals and take action to achieve their goals. Learning analytics intervention showed support in learners' reflection phase SRL. However, it is not clear how to better support actionable and strategic changes to learning. Learning improvement through learning analytics interventions varies depending on how learners utilize feedback and monitor their learning progress, interacting with their digital learning environments. What this paper adds Learners established certain learning strategies with core adaptive learning analytics dashboard elements (eg, use of assessments, monitoring strategies). Learners' perceptions about learning support from the ALAD were built by interacting with the learners' task and cognitive conditions. Based on the perceptions, individuals' various SRL strategies and the need for diverse support were found. Monitoring and rehearsals can be challenging when course content is broken down for individuals' support. Implications for practice and/or policy Courses with adaptive learning analytics dashboards need to be designed more carefully, considering possible undesirable consequences, and to improve SRL support. Learners need more diverse learning support at an individual level based on how they interact with the learning environment.

Effects of real-time analytics-based personalized scaffolds on students’ self-regulated learning

Self-Regulated Learning (SRL) is related to increased learning performance. Scaffolding learners in their SRL activities in a computer-based learning environment can help to improve learning outcomes, because students do not always regulate their learning spontaneously. Based on theoretical assumptions, scaffolds should be continuously adaptive and personalized to students' ongoing learning progress in order to promote SRL. The present study aimed to investigate the effects of analytics-based personalized scaffolds, facilitated by a rule-based artificial intelligence (AI) system, on students' learning process and outcomes by real-time measurement and support of SRL using trace data. Using a pre-post experimental design, students received personalized scaffolds (n = 36), generalized scaffolds (n = 32), or no scaffolds (n = 30) during learning. Findings indicated that personalized scaffolds induced more SRL activities, but no effects were found on learning outcomes. Process models indicated large similarities in the temporal structure of learning activities between groups which may explain why no group differences in learning performance were observed. In conclusion, analytics-based personalized scaffolds informed by students’ real-time SRL measured and supported with AI are a first step towards adaptive SRL supports incorporating artificial intelligence that has to be further developed in future research.

Transition to Medical School: Use of Learning Strategies to Support Self-Regulated Learning in Integrated, Learner-Centered Curricula.

Purpose: Medical education research has demonstrated the importance of supporting students during the transition to medical school, a transition rendered even more complex by many medical schools’ shift toward integrated, learner-centered curricula. 1 Despite prior academic success, many students begin medical school without the learning strategies needed to handle the depth, pace, and structure of the curricula. 2 When students are unable to transition effectively, their academic performance suffers. Self-regulated learning (SRL), a commonly used framework for teaching medical students effective study habits, is a process that students use to acquire academic skills, including setting goals, selecting and developing study strategies, and monitoring effectiveness. 3 Zimmerman proposed 3 phases of SRL: forethought, performance, and self-reflection. 4 While past research shows that successful medical students in a traditional 2 plus 2 curriculum engage in SRL, little is known about which specific strategies first-year medical students use to support SRL when transitioning to an integrated, learner-centered curriculum, and how they employ those strategies. The aim of this study was to document and codify the specific strategies students use in a learner-centered, integrated medical school curriculum to provide a roadmap that future students and educators can use to guide the transition to medical school. Method: Two United States medical schools with integrated, learner-centered curricula collaborated to examine which learning strategies students use to support SRL and how they use them. Thirty-two first-year medical students participated in an interview and were asked to reflect on their approach to SRL at the start and at the end of their first year of medical school. Students were asked to describe what strategies they used to plan (forethought), monitor (performance), and reflect (self-reflection) on their learning. A qualitative analysis of the data was performed using deductive coding that was based on a theoretical framework combining aspects of Zimmerman’s SRL model. Results: The data revealed that most students used a variety of learning strategies during all 3 stages of SRL described by Zimmerman 3 throughout their first year of medical school. Students were able to articulate specific examples of how they used strategies to plan, monitor, and reflect on their learning in an integrated, first-year medical school curriculum. Results indicated that as students progressed through their first year of medical school, they refined their strategy use to better fit the needs of an integrated, learner-centered medical curriculum. Discussion: This work contributes to existing medical education literature by providing concrete examples of strategies that first-year medical students in an integrated, learner-centered curriculum can use when adjusting to medical school. Medical educators recognize that SRL strategies need to be explicitly taught and that the specific SRL strategies used will vary based on the learning environment. The specific strategies, tasks, and behaviors identified in this study represent a bridge between theory and practical steps students can take to engage in SRL. Although students learn about the phases of SRL, they often have difficulty translating each phase into actionable steps as first-year medical students. The specific information provided by this study can aid in diagnosing gaps in learners’ existing processes or serve as concrete suggestions for new strategies, tasks, and behaviors to try when coaching first-year medical students. Significance: This work provides a roadmap of strategies for all stages of SRL that faculty, staff, administrators, and students can use to support learning in first-year, integrated medical school curricula.