Self-regulated Learning Framework Research Articles

Motivational GenAI chatbot for promoting SRL in problem solving: Data-driven design principles

Self-regulated learning (SRL) is crucial in today‟s educational landscape, championing students' autonomy over their learning journey. Yet, SRL, especially in problem-solving, poses significant difficulties for students. This study innovatively bridges this gap with a set of design principles for a generative AI chatbot specifically shaped to promote students' SRL skills during problem-solving tasks. Leveraging insights from feedback collected from both educators and students involved in a blended SRL program, this research shines a light on the range of difficulties involves in SRL within problem solving context. It pinpoints three main difficulties: Inconsistencies in assessment and perception that create a gap between student self-evaluations and educator assessments; Cognitive strategies difficulties that hinder skill application; and beyond cognitive difficulties such as motivation and task self-management obstacles toward independent learning. Responding to these difficulties, the design principles of the proposed AI chatbot are focused on providing structured guidance, which includes explicit instruction in cognitive and motivational skills, offering personalized feedback based on the specific difficulties, and facilitating the assessment of these skills during task performance. This approach not only advances the theoretical framework of SRL but also translates these concepts into tangible support mechanisms, equipping educators with an innovative tool to enhance SRL. The chatbot stands as a testament to the power of generative AI in transforming educational practices, paving the way for learners to become more self-reliant and adept in navigating the complexities of the digital learning era.

Implementing a proposed framework for enhancing critical thinking skills in synthesizing AI-generated texts

Since the development of open and low-cost generative artificial intelligence (GenAI), the higher education community has witnessed high use of AI-generated texts in scholarly research and academic assignments, attracting ongoing debate about whether such practices constitute cheating. While scholars argue that integrating GenAI tools can enhance productivity, critics raise concerns about the negative effect of such integration on critical thinking (CrT). This study therefore proposed a framework for enhancing students’ CrT skills in synthesizing AI-generated information. The proposed framework is underpinned by various theoretical foundations, encompassing five interconnected step-wise phases (familiarizing, conceptualizing, inquiring, evaluating, and synthesizing). The study was conducted under two separate experiments. The first experiment (Study 1) validated the effectiveness of the proposed framework, providing CrT training to 179 postgraduate students. In the second study (n = 125), additional experiments were undertaken to confirm the effectiveness of the framework in different contexts. An experimental procedure involving pretest and posttest design was implemented wherein participants were randomly allocated to one of three groups: experimental group 1 (exposed to our framework), experimental group 2 (exposed to an alternative self-regulated learning framework), and a control group (exposed to a non-structured framework). Results from Study 1 revealed that the framework enhances students’ CrT skills to synthesize AI-generated texts. However, these CrT skills manifested through various rigorous training aimed at reinforcing learning. While the proposed framework holds considerable value in cultivating CrT skills, significant differences arise across various personality traits. In Study 2, the framework proved to be effective in different contexts. However, it did not make a difference, particularly in its capacity to enhance students’ self-regulated learning compared to other frameworks. We discussed the implications of the findings and recommended it to educators seeking to prepare students for the challenges of the AI-driven knowledge economy.

Analysis of China’s English Curriculum Standards for Compulsory Education from the Perspectives of Self-regulated Learning

This paper analyzes the English Curriculum Standards for Compulsory Education (2022 Edition) developed and implemented by the Ministry of Education in China. We used Self-regulated Learning as a perspective in analyzing the curriculum standard and identified several elements of Self-regulated Learning emphasized in the document. This paper further elaborates how an inclusion of self-regulation is aimed at enhancing the teaching and learning of English as a foreign language in China. It presents the stages and sub-processes of self-regulated learning, in analyzing the curriculum standard. In this qualitative text analysis, the findings have been reported on thematic basis using the self-regulated learning framework and the elements that facilitate self-regulation. The findings show that English education under the guidance of the Standards attaches high importance towards stimulating learners' autonomy and cultivating their self-regulated learning ability. This finding has an important implication for the future teaching and learning of English as a foreign language in China as such a policy focus on the self regulation can lead towards an improved engagement of the English learners in schools.

Judgments of relevance in preschoolers: a study of training and transfer of self-cueing strategies.

When facing a task, children must analyze it precisely to fully identify what its goal is. This is particularly difficult for young children, who mainly rely on environmental cues to get there. Research suggests that training children to look for the most relevant perceptual cues is promising. Furthermore, as transferring skills to a new task is difficult, the question of whether young children are able to transfer such training remains open. The aim of this study was to test the extent to which two strategies of goal self-cueing-labeling and pointing-can help 4-year-old children to identify the relevant cues to clearly identify the goal of the task. The effects of explicit strategy training were tested in a near transfer task. Ninety-nine typically developing 4 year olds took part in the study. They were divided into three groups: two were trained collectively in one of the two strategies and the third group as a control group with no strategy training. All children performed a cued card-sorting task four times: Pre-test, Collective training, Post-test, and Transfer with new cards. Results confirmed the beneficial effect of strategy training on goal identification, particularly after training (Post-test). In the transfer phase, all three groups performed equally well. This study contributes to our understanding of how young children seek information when they look for the most relevant cues for identifying the goal of a task, and the benefits they may derive in a transfer task. It seems that the use of visual cues and self-cueing strategies helps preschoolers to clearly identify the goal of a task. Results are discussed in the light of the self-regulated learning framework. Some possible classroom applications are suggested.

Brief report: noise reduction in preschool from a self-regulated learning perspective-implementation of a game-based voice regulation training program.

An 8-week voice regulation training program (VRTP) incorporating everyday activities was implemented in an experimental preschool classroom (EG; n = 34), which was compared with a control preschool classroom (CG; n = 31). The VRTP includes songs, games, and conversations aiming to raise children's awareness of noise levels and teach voice modulation skills. Grounded in the theoretical framework of self-regulated learning, the study's objectives were to evaluate the impact of the VRTP on noise levels, children's self-regulation, and pre-literacy skills. Noise levels were assessed weekly using an electronic noise meter before and during the program. The EG preschoolers demonstrated modest but significant improvements over their pre-VRTP levels of voice modulation, behavioral and emotional self-regulated learning, and pre-literacy skills, in contrast with the CG children. The findings provide evidence that young children's self-regulation may be enhanced in preschool, challenging the field of developmental-educational psychology to consider self-regulated learning during early childhood.

The Relationship Between Self-Regulated Learning Competency and Clinical Reasoning Tendency in Medical Students.

Self-regulated learning (SRL) is essential to professional learning and practice across disciplines. However, the literature provides limited insights into how medical educators could leverage the SRL framework to support trainees' strategic processes in clinical reasoning activities. In this study, we investigated the relationship between SRL competency and clinical reasoning tendency as 64 medical students diagnosed a virtual patient in a computer-simulated environment. We further examined whether students with different profiles of SRL competency and clinical reasoning tendency differed in their behavioral patterns and performance. The results suggested that SRL competency positively predicted clinical reasoning tendency. Enhancing medical students' SRL competency, especially their self-reflection skills, could increase the tendency toward relying on an analytic approach to clinical reasoning. Moreover, we identified two groups of students (i.e., analytic SRL learners, and non-analytic, low SRL learners) using K-means clustering analysis. The two groups of students differed in their behavioral patterns in clinical reasoning, as revealed by lag sequential analysis. Furthermore, analytic SRL learners ordered more relevant lab tests than non-analytic low SRL learners in clinical reasoning. This study has methodological and practical implications.

Exploring inclusive teaching practices to enhance effective learning of students with visual impairments in a secondary school in Saudi Arabia using a self-regulated learning framework

This study’s goal was to investigate the extent to which general classroom teachers were implementing practices to promote the inclusion of students with visual impairment (VI) using self-regulated learning (SRL) as a framework for the study. SRL can help students, including struggling students or students with special needs, acquire the skills to take control of their learning situations as well as feel more included in the classroom. Observations and self-report questionnaires designed from SRL practices that foster inclusion were used to collect the data. Overall, the teachers provided limited opportunities at a depth that promoted inclusion or supported the SRL development of students with VI. Teacher training in SRL and student readiness to meet higher expectations will support inclusion within an SRL framework.

Educational Design Principles of Using AI Chatbot That Supports Self-Regulated Learning in Education: Goal Setting, Feedback, and Personalization

The invention of ChatGPT and generative AI technologies presents educators with significant challenges, as concerns arise regarding students potentially exploiting these tools unethically, misrepresenting their work, or gaining academic merits without active participation in the learning process. To effectively navigate this shift, it is crucial to embrace AI as a contemporary educational trend and establish pedagogical principles for properly utilizing emerging technologies like ChatGPT to promote self-regulation. Rather than suppressing AI-driven tools, educators should foster collaborations among stakeholders, including educators, instructional designers, AI researchers, and developers. This paper proposes three key pedagogical principles for integrating AI chatbots in classrooms, informed by Zimmerman’s Self-Regulated Learning (SRL) framework and Judgment of Learning (JOL). We argue that the current conceptualization of AI chatbots in education is inadequate, so we advocate for the incorporation of goal setting (prompting), self-assessment and feedback, and personalization as three essential educational principles. First, we propose that teaching prompting is important for developing students’ SRL. Second, configuring reverse prompting in the AI chatbot’s capability will help to guide students’ SRL and monitoring for understanding. Third, developing a data-driven mechanism that enables an AI chatbot to provide learning analytics helps learners to reflect on learning and develop SRL strategies. By bringing in Zimmerman’s SRL framework with JOL, we aim to provide educators with guidelines for implementing AI in teaching and learning contexts, with a focus on promoting students’ self-regulation in higher education through AI-assisted pedagogy and instructional design.

Chatbot-based learning of logical fallacies in EFL writing: perceived effectiveness in improving target knowledge and learner motivation

ABSTRACT Chatbots have been increasingly applied for EFL education and demonstrated overall usefulness in improving knowledge and motivation, while this technology has yet to be used for learning logical fallacies (i.e. errors in reasoning) in EFL writing. However, knowledge of logical fallacies is essential, with which learners can avoid fallacies in EFL writing and have enhanced writing quality. To fill in the gap, this study investigated the perceived effectiveness of chatbots in developing knowledge of logical fallacy in EFL writing and enhancing learner motivation. Features of this learning method were also explored based on the comparison against website-based learning. Two groups of 15 Chinese EFL learners engaged in five-week autonomous, out-of-class, out-of-class learning of logical fallacies in EFL writing using a chatbot or a website. Semi-structured interviews, pre-post tests of fallacy knowledge and pre-post motivation questionnaires were conducted. The results showed that the chatbot was perceived as slightly less effective than the website in developing target knowledge but more effective in improving motivation. Compared to the website, chatbots were advantageous in high-quality human-computer interactions, study plan making, and high accessibility. Based on the research results, we discussed how this technology might influence fallacy learning based on the self-regulated learning framework.

Strategies to support self-regulated learning in integrated, student-centered curricula

Purpose With undergraduate medical education shifting to an integrated, student-centered approach, self-regulated learning (SRL) skills are critical for student success. Educational research holds that learning strategy effectiveness is context dependent. Our study aims to explore what strategies medical students use to support SRL when engaged in the specific context of an integrated, student-centered curriculum. Approach This study took place in two medical schools with integrated, student-centered curricula. Semi-structured interviews were conducted with first-year medical students from both schools, asking them to reflect on the learning strategies they used throughout their first year of medical school. Interview data was analyzed first deductively using the SRL framework and then inductively to understand the specific strategies being used. Findings Students engaged in strategies to support SRL in ways that were unique to the integrated, student-centered context. We found that medical students developed strategies to plan for integration and building connections across material during all three phases of self-regulated learning. Insights By identifying specific tasks and behaviors students utilized during their first year of medical school, this study provides a roadmap that students and educators can use to help students become self-regulated learners.

Ideal L2 self, enjoyment, and strategy use in L2 integrated writing: A self-regulatory learning perspective

This study investigated the relationship between students' ideal L2 writing selves, enjoyment, writing strategy use, and their final L2 integrated writing (IW) performance under the self-regulated learning framework. Our data were collected from 239 Chinese university EFL learners through questionnaires and an English IW task. The results indicated that students who were motivated by the ideal L2 writing self and enjoyed L2 writing tended to use more writing strategies to facilitate their writing. Essentially, from ideal L2 writing self to L2 IW, a serial mediation effect existed of enjoyment-strategy use. But we did not find the direct effects of ideal L2 writing self and enjoyment on L2 IW performance. Pedagogically, the findings of this study implied that the teaching and learning of integrated writing in L2 should foster students’ ability to regulate their motivational, emotional, and cognitive resources, ultimately being self-regulated L2 writers.

Emotions in self-regulated learning: A critical literature review and meta-analysis.

Emotion has been recognized as an important component in the framework of self-regulated learning (SRL) over the past decade. Researchers explore emotions and SRL at two levels. Emotions are studied as traits or states, whereas SRL is deemed functioning at two levels: Person and Task × Person. However, limited research exists on the complex relationships between emotions and SRL at the two levels. Theoretical inquiries and empirical evidence about the role of emotions in SRL remain somewhat fragmented. This review aims to illustrate the role of both trait and state emotions in SRL at Person and Task × Person levels. Moreover, we conducted a meta-analysis to synthesize 23 empirical studies that were published between 2009 and 2020 to seek evidence about the role of emotions in SRL. An integrated theoretical framework of emotions in SRL is proposed based on the review and the meta-analysis. We propose several research directions that deserve future investigation, including collecting multimodal multichannel data to capture emotions and SRL. This paper lays a solid foundation for developing a comprehensive understanding of the role of emotions in SRL and asking important questions for future investigation.

Examining Classroom Contexts in Support of Culturally Diverse Learners’ Engagement: An Integration of Self-Regulated Learning and Culturally Responsive Pedagogical Practices.

Research shows that culturally diverse students are often disengaged in multicultural classrooms. To address this challenge, literatures on self-regulated learning (SRL) and culturally responsive teaching (CRT) both document practices that foster engagement, although from different perspectives. This study examined how classroom teachers at schools that enrol students from diverse cultural communities on the West Coast of Canada built on a Culturally Responsive Self-Regulated Learning Framework to design complex tasks that integrated SRL pedagogical practices (SLPPs) and culturally-responsive pedagogical practices (CRPPs) to support student engagement. Two elementary school teachers and their 43 students (i.e., grades 4 and 5) participated in this study. We used a multiple, parallel case study design that embedded mixed methods approaches to examine how the teachers integrated SRLPPs and CRPPs into complex tasks; how culturally diverse students engaged in each teacher’s task; and how students’ experiences of engagement were related to their teachers' practices. We generated evidence through video-taped classroom observations, records of classroom practices, students’ work samples, a student self-report, and teacher interviews. Overall findings showed: (1) that teachers were able to build on the CR-SRL framework to guide their design of an CR-SRL complex task; (2) benefits to students’ engagement when those practices were present; and (3) dynamic learner-context interactions in that student engagement was situated in features of the complex task that were present on a given day. We close by highlighting implications of these findings, limitations, and future directions.
 

Teaching self-regulation through role modeling in K-12

For K-12 teachers to develop effective teaching skills, integration of role modeling strategies into teaching and learning process as a dimension of selfregulated learning is of the foremost value. Role modeling strategy training through a hybrid professional development model bears the potential to serve as a facilitating component in promoting K-12 teachers’ instructional competence. Conducted within the self-regulated learning framework, this study suggested findings of a teacher professional development training aimed at role modeling strategy implementation at K-12 level. Pursuing a mixed-method model, the current research was performed with 16 teachers who were trained and supervised to integrate role-modeling strategies into their teaching context. In this study, the data sources were role-modeling- integrated lesson plans, trainers’ feedback on these lesson plans, and online student products. The data collection methods included lesson plan evaluation through a role-modeling rubric in a quantitative fashion, whereas content analysis of trainer feedback on lesson plans, latterly revised lesson plans and online student products composed the qualitative aspect. Results revealed that this professional development training achieved significantly positive changes in teachers’ role modeling strategy implementation skills, particularly in terms of teachers’ role as agents in students’ self-regulated learning skills, promotion of student-centered learning and overall improvement in students’ self-regulated learning skills. Further, the integration of education technology tools into lessons was observed to have a positive impact on enhancing students’ self-regulated learning skills. This study could offer major contributions to designing teacher professional development training for researchers, practitioners, and teacher trainers, particularly in role modeling dimension of self-regulated learning.

TEAMwork! An Innovative Hospital Medicine Teaching Program to Enhance Learners' Educational Experience.

Workplace-based learning and assessment are dependent on frequent observations, feedback, teaching, and evaluations. Yet high physician workload and multiple learners limit these efforts. A local needs assessment demonstrated insufficient direct observations, small-group teaching, and high-quality evaluations for learners. In response, the authors developed and implemented Teaching Excellence Among Medical providers (TEAM), a teaching program to enhance the educational experience of learners in pediatric hospital medicine (PHM). The TEAM program, grounded in a self-regulated learning framework, launched in 2019. The University of Colorado Department of Pediatrics provided salary support. TEAM hospitalists completed professional development for teaching and feedback skills. During their TEAM shifts, they observed, provided feedback, completed evaluations, and led teaching sessions for learners on PHM rotations. The program's impact was evaluated by using an electronic work tracking form and surveying learners, attending hospitalists, and TEAM hospitalists, leading to iterative program improvements. Over an 18-month period, TEAM hospitalists completed 1,573 direct observations, 265 teaching sessions attended by 1,921 learners, and more than 497 learner evaluations. Survey results showed that 95% of learners indicated TEAM was "very effective" or "somewhat effective" in helping achieve individualized learning goals, and a similar percentage "strongly agreed" or "agreed" that TEAM hospitalists were effective educators. Eighty-four percent of attending hospitalist survey respondents reported TEAM contributed "very much" or "somewhat" to learner education. Attending hospitalists particularly appreciated TEAM's ability to focus on learners identified as struggling, while TEAM hospitalists rated observation of rounds and small-group teaching sessions as the most valuable TEAM activities. The TEAM program employed concepts of self-regulated learning to enhance education in PHM through direct observation, feedback, written evaluations, and teaching efforts. This program can mitigate many challenges facing attending physicians and can serve as a model for other institutions. Next steps include evaluation of higher-level learning outcomes.

Improving online instruction with self-regulated learning: A case study of kinematics graph interpretation

The objective of this study was to determine if students’ skills interpreting kinematics graphs could be improved in an online instruction environment by using the Self-Regulated Learning (SRL) framework. The gain in skills was measured by comparing pre-test and post-test results of the objective Test for Understanding Graphs – Kinematics (TUG-K) for N=19 students. The average score of the post-test was 62% better than the average score of the pre-test. The difference between the two average scores was statistically significant (p < 0.05). In addition, an opinion survey of the students – conducted before and after the SRL intervention – showed a statistically significant (p < 0.05) improvement of 15% regarding the students’ own sense of self-efficacy, and their capacity for delayed gratification (self-control). These two intra-personal skills, in addition to metacognition, are addressed and developed by the SRL framework. SRL also develops self-instruction skills which opens further possibilities of using class time to provide feedback and to thoroughly assess student knowledge with oral exams. These initial results provide a positive outlook that the development of SRL skills contributes to improvements in online instruction.

The role of motivation and vocabulary learning strategies in L2 vocabulary knowledge: A structural equation modeling analysis

This study explores the complex relationships between language learning motivation, vocabulary learning strategies, and two components of second language vocabulary knowledge (i.e., vocabulary size and depth), within the framework of self-regulated learning. Responses to questionnaires were gathered from 185 secondary-level Korean adolescent learners of English as a foreign language, regarding their motivation and vocabulary learning strategy use; additionally, the results of their vocabulary size and depth tests were collected. We adopted structural equation modeling for analysis, with vocabulary learning strategies consisting of memory, cognitive, and metacognitive categories, and vocabulary knowledge consisting of vocabulary size and depth. The results showed that motivation directly predicted vocabulary learning strategies and vocabulary knowledge, and indirectly predicted vocabulary knowledge via vocabulary learning strategies. When further classified, intrinsic motivation was found to have a stronger influence on the use of vocabulary learning strategies and vocabulary knowledge than extrinsic motivation. We discuss the implications of increasing learners’ motivation and repertoire of strategies for improving vocabulary size and depth.

Self-Regulated Learning and Scientific Research Using Artificial Intelligence for Higher Education Systems

Self-regulated learning (SRL) is a very significant ability for students in the learning process, and SRL strategies are used to assist students in learning efficiently in higher education. Many SRL environments face challenges and barriers, including less immediate feedback and guidance, a high level of self-personalization, environment, and resources from teachers or learner designers. Hence, in this paper, the artificial intelligence framework for self-regulated learning (AIF-SRL) overcame the SRL environment's challenges in higher education. Self-regulated students are active participants in their learning and may select from a strategic portfolio and monitor their progress towards the objective. The proposed artificial intelligence-based solution has been used to monitor student behavior through students' feedback and responses. The experimental results show that the proposed AIF-SRL method improves the student's self-evaluation, self-regulation behavior, self-efficacy, learning gain, and self-satisfaction compared to other existing methods.

A study of the impacts of motivational regulation and self-regulated second-language writing strategies on college students' proximal and distal writing enjoyment and anxiety.

Motivational regulation is crucial to explaining autonomous self-regulated learning, yet has received relatively little empirical attention. This study therefore examined how 230 college students’ motivational-regulation strategies affected their proximal and distal second-language writing-achievement emotions (i.e., enjoyment and anxiety), and sought evidence of interactive effects of such strategies and self-regulated learning strategies on each of these two types of emotions. All the studied types of motivational-regulation strategy were found to directly predict both proximal and distal writing enjoyment, under a “the more the happier” principle, but only a performance-oriented motivational regulation strategy predicted proximal or distal writing anxiety. A social-behavior learning strategy was found to counteract the high proximal anxiety caused by heavy use of the performance self-talk motivational regulation strategy; and motivational-regulation predictors also emerged as stable predictors of both proximal and distal writing well-being. These findings are expected to be both theoretically valuable to the study of motivational regulation under the self-regulated learning framework, and of practical value to educators, learners, and curriculum designers.

Do learners with higher readiness feel less anxious when studying online at home?

In response to the COVID-19 outbreak in many parts of the world, online education has become a more viable option. Some studies have assessed undergraduate students’ readiness for online learning, while others examined students’ anxiety about online learning at home. The relationship between readiness and anxiety about online learning is, however, not well explored. This paper has two purposes: (1) to develop a new and valid instrument—the Home-based Online Learning Readiness Questionnaire (HOLRQ)—to measure students’ readiness to study online at home based on a theoretical framework of self-regulated learning. As a replacement for the previous readiness scale, this new instrument adds a section on learning strategies and updates and develops new items. (2) to investigate the relationship between readiness and anxiety in online learning. In order to explore those issues, 527 undergraduate students in China were surveyed in this study. The results indicated that HOLRQ was validated in the following six domains: motivation, self-efficacy, information technology skills, resource management, learning strategies and help-seeking. Chinese undergraduate students were more prepared in resource management, motivation, and help seeking, but less prepared in learning strategies, information technology skills, and self-efficacy. However, the regression analysis showed that readiness did not predict online learning anxiety. It means even highly prepared self-regulated learners may experience anxiety when learning online from home. The findings provide insights for instructors and administrators to determine how students really feel about learning from home with online education.