Scaffold Self-regulated Learning Research Articles

"I Should, but I Don't Feel Like It": Overcoming Obstacles in Upper Secondary Students' Self-regulation Using Learning Analytics


 
 While research has been conducted on self-regulated learning in relation to learning analytics, there remains a knowledge gap regarding the obstacles secondary education students face in regulating their learning and how learning analytics can support their self-regulation. This paper investigates two questions: 1) What challenges do secondary education students experience in the process of regulating their own learning?, and 2) What information and data do secondary education students need to better regulate their own learning? We conducted a study at a mid-sized upper secondary school in middle Sweden, to better understand how these issues manifest among students. We analyzed data collected by the school twice annually between 2015 and 2022, and administered a questionnaire to 224 students to answer the research questions. Through descriptive statistics and a thematic analysis, we identify prevalent problems that students encounter, as well as the necessary information that is essential for scaffolding self-regulated learning. We discuss the implications of our findings for the design of systems that provide students with relevant data to enhance their learning experiences.
 

Scaffolding Self-Regulated Learning in Operative Dentistry Through Self-Assessment Training.

This study aimed to investigate how effective self-assessment training is in improving the self-regulated learning (SRL) ability of operative dentistry students. The secondary aim was to explore students' attitudes towards self-assessment in clinical settings. Self-assessment has been the focus of dental education research for decades and has been claimed to be cornerstone in the SRL process. This is quasi-experimental study conducted at Damascus University Faculty of Dental Medicine, Syria. A modified direct observation of procedural skills (DOPS) assessment protocol with a self-assessment component was applied to a convenience sample of students across five clinical encounters in 2022. Data were collected during clinical training on the DOPS form and at the end of the experiment using a structured questionnaire with a single open-ended question. The qualitative part of the study followed a deductive theory-informed content analysis approach, relying on Zimmerman SRL theory. A total number of 32 students participated in the study, eight of whom were male. During the experiment assessment, participants showed overwhelming positive attitudes towards the utility of self-assessment (m ≥ 3.5; max:4) and the overall assessment method (3.3 ≥ m ≥ 3.5; max:4). Cronbach's alpha for the post-experiment questionnaire was 0.888. A total of 84.4% of the participants strongly agreed that self-assessment is useful in clinical training. Data collected (qualitative and quantitative) from participants revealed the positive effects of self-assessment on all three phases of SRL, especially the reflection phase. Self-assessment practice and training showed positive results in terms of improving SRL and performance in a clinical context; SRL appeared to be affected by self-assessment in all phases (forethought, performance, and reflection). Participants' perceptions of self-assessment were generally very positive. These findings imply the potential benefits of applying self-assessment training interventions to foster the SRL ability of learners within the clinical context.

Identifying the effects of scaffolding on learners' temporal deployment of self-regulated learning operations during game-based learning using multimodal data.

Self-regulated learning (SRL), or learners' ability to monitor and change their own cognitive, affective, metacognitive, and motivational processes, encompasses several operations that should be deployed during learning including Searching, Monitoring, Assembling, Rehearsing, and Translating (SMART). Scaffolds are needed within GBLEs to both increase learning outcomes and promote the accurate and efficient use of SRL SMART operations. This study aims to examine how restricted agency (i.e., control over one's actions) can be used to scaffold learners' SMART operations as they learn about microbiology with Crystal Island, a game-based learning environment. Undergraduate students (N = 94) were randomly assigned to one of two conditions: (1) Full Agency, where participants were able to make their own decisions about which actions they could take; and (2) Partial Agency, where participants were required to follow a pre-defined path that dictated the order in which buildings were visited, restricting one's control. As participants played Crystal Island, participants' multimodal data (i.e., log files, eye tracking) were collected to identify instances where participants deployed SMART operations. Results from this study support restricted agency as a successful scaffold of both learning outcomes and SRL SMART operations, where learners who were scaffolded demonstrated more efficient and accurate use of SMART operations. This study provides implications for future scaffolds to better support SRL SMART operations during learning and discussions for future directions for future studies scaffolding SRL during game-based learning.

How to design and evaluate personalized scaffolds for self-regulated learning

Self-regulation is an essential skill for lifelong learning. Research has shown that self-regulated learning (SRL) leads to greater academic achievement and sustainable education, but students often struggle with SRL. Scaffolds are widely reported as an effective and efficient support method for SRL. To further improve digital scaffolds’ effectiveness, real-time detection of learning behavior can be used to personalize scaffolds. Therefore, the present study aimed to inform the field of scaffolding SRL by reporting on the design and evaluation of digital scaffolds. We present decisions made during the design process of personalized scaffolds to inform future scaffold designs. We evaluated how scaffolds were personalized based on real-time detection SRL, how university students respond to the scaffolds (i.e., compliance), and how this response is related to learning outcomes (i.e., quality of an essay). The research design was a pre-posttest with three conditions (no, generalized (same for all), or personalized scaffolds). A 45-minute reading and writing task was used, during which SRL processes were recorded in real-time. Findings revealed that different real-time SRL processes could be used to personalize scaffolds, meaning that we were able to personalize the content of scaffold based on students’ actual learning behavior. In addition, students in the personalized condition complied more with the scaffolds than students in the other conditions. This compliance with the scaffolds was generally associated with better learning outcomes. To conclude, our approach showed how design decisions could be evaluated and provided insight into the personalization of scaffolds.

Relations between undergraduates’ self-regulated learning skill mastery during digital training and biology performance

Undergraduate STEM lecture courses enroll hundreds who must master declarative, conceptual, and applied learning objectives. To support them, instructors have turned to active learning designs that require students to engage in self-regulated learning (SRL). Undergraduates struggle with SRL, and universities provide courses, workshops, and digital training to scaffold SRL skill development and enactment. We examined two theory-aligned designs of digital skill trainings that scaffold SRL and how students’ demonstration of metacognitive knowledge of learning skills predicted exam performance in biology courses where training took place. In Study 1, students’ (n = 49) responses to training activities were scored for quality and summed by training topic and level of understanding. Behavioral and environmental regulation knowledge predicted midterm and final exam grades; knowledge of SRL processes did not. Declarative and conceptual levels of skill-mastery predicted exam performance; application-level knowledge did not. When modeled by topic at each level of understanding, declarative knowledge of behavioral and environmental regulation and conceptual knowledge of cognitive strategies predicted final exam performance. In Study 2 (n = 62), knowledge demonstrated during a redesigned video-based multimedia version of behavioral and environmental regulation again predicted biology exam performance. Across studies, performance on training activities designed in alignment with skill-training models predicted course performances and predictions were sustained in a redesign prioritizing learning efficiency. Training learners’ SRL skills –and specifically cognitive strategies and environmental regulation– benefited their later biology course performances across studies, which demonstrate the value of providing brief, digital activities to develop learning skills. Ongoing refinement to materials designed to develop metacognitive processing and learners’ ability to apply skills in new contexts can increase benefits.

Probing Scaffolding Self-Regulated Learning Responses, Resources Contribution and Target Achievements of University Students in Statistics Course

Scaffolding self-regulated learning is an emerging research agenda in higher education. However, scaffolding self-regulated learning in a relatively complex subject such as statistics is still understudied. The present study addresses this matter by observing university students’ engagement in scaffolding self-regulated learning in a statistics course focusing on their scaffolding responses, resources contribution and target achievements. It was an exploratory case study with the participation of 26 private university students who are enrolled in the fifth semester as their third-year studies. The results indicate that university students are aware of statistics learning goals but overtargeting achievements, as they were less likely to achieve their decided targets. Students prefer to write a self-reflection than take notes. The different duration of watching videos and reading modules does not vary in quiz performance. The current findings add a novel understanding that self-regulated statistics learning requires advanced scaffolds to promote higher outcomes because of its characteristics as a complex and abstract subject.

The nexus between emotional intelligence, learning engagement, motivation, and achievement in team-based mobile language learning

The extant literature on the integration of mobile instant messaging (MIM) into language pedagogy has documented its potential to leverage engagement and learning outcomes. Nevertheless, how individual differences in regard to emotional, sociocognitive, and motivational bearings contribute to language learning gains is hardly documented. This study is devoted to addressing this lacuna through an analysis of structural equation modeling (SEM) which explores the association between trait-emotional intelligence (TEI), online learning engagement (OLE), EFL learning motivation, and their grades in English for Academic Purposes (EAP) programs. Three-hundred-seventy-one freshmen in an Indonesian university were involved in EAP programs framed in team-based mobile language learning (TBML), mediated by Telegram as a complementary learning app. The students responded to an online survey eliciting their post-lesson perception of the abovementioned variables. Students’ course grades complemented the data collection. The results of our SEM analysis acknowledged the linear correlation from TEI, OLE, to EFL motivation as the drives to students’ learning achievement. This has also been supported by a robust correlation between the variables. The findings of the present study implicate the pedagogical potentials of TBML to scaffold self-regulated learning and satisfactory language learning gains, provided that students’ emotional intelligence, engagement, and motivation are intensively potentiated across different learning phases. Research limitations and recommendations are also explored.

Scaffolding self-regulated learning from causal-relations texts: Diagramming and self-assessment to improve metacomprehension accuracy?

The accuracy of students’ relative comprehension judgments when reading texts is typically rather low. This has been ascribed to students grounding their comprehension judgments on cues that are not diagnostic of their actual comprehension level. Asking students to complete causal diagrams—a diagramming scaffold—before judging comprehension has proved effective in providing them with more diagnostic cues and thereby fostered metacomprehension accuracy and self-regulated learning. However, there is still room for improvement. We investigated experimentally whether adding the instruction to students to self-assess their causal diagrams: (1) would lead to more accurate judgments than comprehension judgments, (2) would boost their utilization of diagnostic diagram cues by increasing the saliency of those cues, and (3) would enhance metacomprehension accuracy. Participants (N = 427 secondary students in The Netherlands) were randomly assigned to one of three conditions, namely (1) only diagram completion, (2) diagram completion plus diagram self-assessment, or a (3) filler task after reading (control). Self-assessments were more accurate than comprehension judgments, while both correlated strongly. However, no significant differences were found between diagramming conditions concerning diagram cue utilization and metacomprehension accuracy. Apparently, students self-assess their diagrams even without instruction to do so. Nonetheless, the effect of the diagramming scaffold for improving relative metacomprehension accuracy was replicated and extended to absolute metacomprehension accuracy.

Scaffolding self-regulated learning in student-generated questioning using mobile phones

Scaffolding self-regulated learning in student-generated questioning using mobile phones

Scaffolding Self-Regulated Learning for English as an Additional Language Literacy Learners

Scaffolding Self-Regulated Learning for English as an Additional Language Literacy Learners

Tools Designed to Support Self-Regulated Learning in Online Learning Environments: A Systematic Review

Self-regulated learning (SRL) is a crucial higher-order skill required by learners of the 21st century, who will need to become lifelong learners to adapt to the continually changing environments. Literature provides examples of tools for scaffolding SRL in online environments. In this article, we provide the state-of-the-art concerning tools that support SRL in terms of theoretical models underpinning development, supported SRL processes, tool functionalities, used data and visualizations. We reviewed 42 articles published between 2008 and 2020, including information from 25 tools designed to support SRL. Our findings indicate that: 1) many of the studies do not explicitly specify the SRL theoretical model used to guide the design process of the tool; 2) goal setting, monitoring, and self-evaluation are the most prevalent SRL processes supported through functionalities, such as content navigation, user input forms, collaboration features, and recommendations; 3) the relationship between tool functionalities and SRL processes are rarely described; and 4) few tools assess the impact on learners’ SRL process and learning performance. Finally, we highlight some lessons learned that might contribute to implementing future tools that support learners’ SRL processes.

Lessons Learned and Future Directions of MetaTutor: Leveraging Multichannel Data to Scaffold Self-Regulated Learning With an Intelligent Tutoring System.

Self-regulated learning (SRL) is critical for learning across tasks, domains, and contexts. Despite its importance, research shows that not all learners are equally skilled at accurately and dynamically monitoring and regulating their self-regulatory processes. Therefore, learning technologies, such as intelligent tutoring systems (ITSs), have been designed to measure and foster SRL. This paper presents an overview of over 10 years of research on SRL with MetaTutor, a hypermedia-based ITS designed to scaffold college students’ SRL while they learn about the human circulatory system. MetaTutor’s architecture and instructional features are designed based on models of SRL, empirical evidence on human and computerized tutoring principles of multimedia learning, Artificial Intelligence (AI) in educational systems for metacognition and SRL, and research on SRL from our team and that of other researchers. We present MetaTutor followed by a synthesis of key research findings on the effectiveness of various versions of the system (e.g., adaptive scaffolding vs. no scaffolding of self-regulatory behavior) on learning outcomes. First, we focus on findings from self-reports, learning outcomes, and multimodal data (e.g., log files, eye tracking, facial expressions of emotion, screen recordings) and their contributions to our understanding of SRL with an ITS. Second, we elaborate on the role of embedded pedagogical agents (PAs) as external regulators designed to scaffold learners’ cognitive and metacognitive SRL strategy use. Third, we highlight and elaborate on the contributions of multimodal data in measuring and understanding the role of cognitive, affective, metacognitive, and motivational (CAMM) processes. Additionally, we unpack some of the challenges these data pose for designing real-time instructional interventions that scaffold SRL. Fourth, we present existing theoretical, methodological, and analytical challenges and briefly discuss lessons learned and open challenges.

Examining the relationships among self-regulated learning, homework timeliness, and course achievement: a context of female students learning quantitative topics

Homework completion is associated with learning achievement, but students’ challenges revolve around meeting deadlines and preventing procrastination. Promoting students’ self-regulated learning (SRL) can overcome these challenges. We explored the role of SRL (forethought and learning strategies) on the timeliness of homework submissions performed by undergraduate female students in quantitative courses. Data were collected from a survey and Learning Management System log. A structural equation modeling analysis indicated that the forethought components had a direct effect on the learning strategies. The use of learning strategies significantly influenced homework timeliness, which then significantly affected course achievement. Discussion regarding mixed results and strategies for scaffolding SRL through homework assignments are included. A suggestion on guiding students to execute suitable learning strategies for mastering quantitative topics is presented.

Waves: Scaffolding Self-regulated Learning to Teach Science in a Whole-Body Educational Game

This study employed mixed methods to investigate the efficacy of scaffolding self-regulated learning prompts within a whole-body educational game, Waves. This game was designed to teach middle school–aged children basic concepts of waves by moving their bodies to mimic the motions of waves, physically experiencing different velocities and wavelengths. Textual prompts intended to scaffold self-regulated learning behaviors respond to learner actions (or non-actions) within Waves. The adult facilitator reinforced the in-game prompts by speaking them aloud. This study is framed around the research questions: (1) How does a whole-body educational game effectively teach players about STEM concepts? (2) How can self-regulated learning be effectively scaffolded in a whole-body educational game? and (3) Are self-regulated learning scaffolds utilized the same way by all players? A quantitative pre-post assessment of learning and self-regulation skills was further elucidated by a case study analyzing the recorded discourse of two partners while participating in the larger study.

Scaffolding self-regulated learning through student-generated quizzes

Self-regulated learning assumes learners are active agents who can establish and make progress toward learning goals. Classroom activities can facilitate the emergence of self-regulated learning. O...

Self-Regulated Learning in Learning Environments With Pedagogical Agents That Interact in Natural Language

This article discusses the occurrence and measurement of self-regulated learning (SRL) both in human tutoring and in computer tutors with agents that hold conversations with students in natural language and help them learn at deeper levels. One challenge in building these computer tutors is to accommodate, encourage, and scaffold SRL because these skills are not adequately developed for most students. Automated measures of SRL are needed to track progress in meeting this challenge. A direct approach is to train students on fundamentals of metacognition and SRL, which is the approach taken by iSTART, MetaTutor, and other agent environments. An indirect approach to promoting SRL, taken by AutoTutor, is to track the student's knowledge and SRL based on the student's language and to respond intelligently with discourse moves to promote SRL. This fine-grained adaptivity considers the student's cognitive states, the discourse interaction, and the student's emotional states in a recent AutoTutor version.

A Perspective on State-of-the-art Research on Self-regulated Learning

Theory and research on self-regulated learning (SRL) are growingrapidly but the field is still within grasp. A search of PsycINFO (2005June 25 08:30) using the phrase ‘‘self-regulated learning’’ yielded 463hits when the search examined all text. There were 207 hits when thesearch focused just on titles. Using the Google Scholar search engine,the exact phrase ‘‘self-regulated learning’’ yielded about 471 hits (2005June 25 08:31; Google does not report an exact number of hits). Thepapers in this special issue focus on a particular sector of this field,scaffolding self-regulated learning and metacognition in either face-to-face tutoring sessions or computer-based learning environments. Wasthis a wise choice?In 1976, when there was a single article (Mlott et al. 1976) in theliterature that used the phrase ‘‘self-regulated learning’’ (PsycINFO,2005 June 25 08:32 searched using dates 1900 to 1976), Wood et al.(1976) introduced the term scaffolding. By this, they meant ‘‘a formof assistance that enables the child or novice to solve a problem, car-ry out a task, or achieve a goal that would be beyond his or herunassisted efforts’’(p. 90). A nascent question was born: Do learnersneed scaffolding to self-regulate learning? The answer is: Yes and no.When there were 103 publications on self-regulated learning in1995 (PsycINFO, 2005 June 25 08:36 searched using dates 1900 to1995), I argued self-regulating learning is ubiquitous (Winne, 1995a,1995b). This is logically entailed on adopting any of several currentand widespread stances, two among them being particularly promi-nent: Learners are agents. Learners construct knowledge. Whetherscaffolding is available or not, these paradigmatic stances necessitatethat learners can and do self-regulate learning. Empirically, it isimpossible to prove every learner is constantly engaged in SRL be-cause data to validate this claim can not be collected for each learnerat every instant whenever they learn. Studies show, however, thatSRL is common across learners and tasks (e.g., see Boekaerts

Scaffolding Self-regulated Learning and Metacognition – Implications for the Design of Computer-based Scaffolds

Scaffolding Self-regulated Learning and Metacognition – Implications for the Design of Computer-based Scaffolds