Causal Models for Learning Technology

Parenting Strategy for Enhancing Children’s Self-Regulated Learning

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.

From the perspective of lifelong learning, promoting adult learners' self-regulated learning by using information technology effectively is crucial important to the creation of the learning society. It is necessary to conduct in-depth study on the factors affecting the self-regulated learning of adult when using web-based learning space. This study deeply integrated technology acceptance theory and self-regulated learning theory, on the basis of which constructed an influencing factors model of self-regulated learning of adult in web-based learning space. This study selected 670 adult learners who participated in public welfare courses as the research objects, the structural equation model(SEM) analysis method is used to test and modify the model. The results show that openness to experience, risk propensity, technical support availability and external equipment accessibility can affect the self-regulated learning of adult when using web-based learning space, finally, the study put forward relevant strategies to promote the self-regulated learning of adult learners when using web-based learning space.

IntroductionIn the 21st century, self-regulated learning (SRL) plays a vital role in the cultivation of high-quality talent. Grounded in self-regulated learning theory and attachment theory, this study aims to systematically examine the relationship between adolescents’ SRL and academic achievement, considering the roles of academic emotions and teacher–student relationships (TSR). Additionally, the study investigates whether a recursive pathway exists from academic emotions back to SRL.MethodsThe study draws on nationwide survey data from China (N = 88,149 students) and employs structural equation modeling (SEM) to analyze the relationships among SRL, academic emotions, TSR, and academic achievement. Academic emotions were included as a mediating variable, and TSR as a moderating variable.ResultsThe results indicate that: (1) adolescents’ levels of SRL are significantly and positively associated with their academic achievement; (2) three types of academic emotions (e.g., positive high-arousal emotions) significantly mediate the relationship between SRL and academic achievement; and (3) TSR moderate both the first stage of the mediation pathway (“SRL → academic emotions → academic achievement”) and the direct effect of SRL on academic achievement.DiscussionOverall, the findings support the cognition–emotion cyclical model, suggesting that students can enhance SRL through positive academic emotions, while appropriate SRL strategies can in turn foster positive academic emotions, forming a virtuous cycle that strengthens students’ autonomy in the learning process. This study extends Pintrich’s SRL model by empirically validating a recursive loop between SRL and academic emotions. Furthermore, it highlights a “dual empowerment” path of emotional regulation and metacognitive monitoring through optimized TSR, offering vital policy insights for fostering high-order autonomous learning in the AI era.

Why do large-scale field experiments in education often have muted effects? Drawing on system dynamics and selfregulated learning theory, we sought answer this question by simulating the behavior of self-regulated (discrepancyreducing) learners over time affected by different types of educational interventions. We analyze three types of interventions: changing students’ learning rates (learning strategies), intercepts (prior knowledge or teaching effectiveness), and norms of study (achievement goals). We uncover situations where educational interventions can affect achievement in the short run, but typical cross-sectional analyses do not find a measurable effect in the long run. Results indicate that highly motivated, self-regulated learners may resist external interventions, particularly those targeting learning strategies or prior knowledge. In contrast, interventions show the greatest effect on achievement when students are under time constraints and struggling to achieve their desired performance. Ultimately, self-regulated learners may be the hardest to help, a phenomenon we call the “self-regulated learning paradox.”

New statistical methods allow discovery of causal models purely from observational data in some circumstances. Educational research that does not easily lend itself to experimental investigation can benefit from such discovery, particularly when the process of inquiry potentially affects measurement. Whether controlled or authentic, educational inquiry is sprinkled with hidden variables that only change over the long term, making them challenging and expensive to investigate experimentally. Big data learning analytics offers methods and techniques to observe such changes over longer terms at various levels of granularity. Learning analytics also allows construction of candidate models that expound hidden variables as well as their relationships with other variables of interest in the research. This article discusses the core ideas of causality and modeling of causality in the context of educational research with big data analytics as the underlying data supply mechanism. It provides results from studies that illustrate the need for causal modeling and how learning analytics could enhance the accuracy of causal models.

Much research has been conducted on metacognition, self-regulation, and self-regulated learning, but the articles in this special issue make it clear that we still have many unanswered questions. Recommendations for research include providing clear definitions of processes, identifying relevant theories, ensuring that assessments clearly reflect processes, linking processes with academic outcomes, conducting more educational developmental research, and tying processes firmly with instructional methods. Collectively, these recommen-dations will enhance our understanding of metacognition, self-regulation, and self-regulated learning and will lead to solid implications for educational policy and practice.

Over the past 40 years, experts in the field of educational psychology have conducted extensive research on self-regulated learning (SRL) as a learning theory. The theory of SRL refers to a learner’s ability to understand and control their learning environment through goal setting, self-monitoring, self-instruction, and self-reinforcement in order to succeed in their studies (Schraw et al., 2006). Teachers who understand the application of self-regulated learning strategies may help their students gain important insight for transferring knowledge, skills, and abilities from one field to another. Furthermore, it helps the students prepare for lifelong learning and become autonomous learners. In her recent book Self-regulated Learning and Second Language Writing: Fostering Strategic Language Learners, Lin Sophie Teng explains the principles of using SRL theory in language learning, specifically through the application of SRL to students in a second language (L2) writing class. The book offers valuable knowledge for teachers to understand how to foster integrated strategies of SRL and language learning strategies in all of their students to succeed in language learning. [First paragraph]

The importance of self-regulation of learning became evident during the Covid-19 pandemic and the accompanying school closures. Using data from N = 254 German teachers, we analyze how teachers promoted self-regulation of learning in distance education and reasons why they did not promote it. Additionally, we examine which teacher and class variables predict the promotion of self-regulation of learning in distance education and whether teachers’ technology competency moderates these relationships. Further, we look into whether these relationships differ during the first lockdown in spring 2020, for which teachers were not able to prepare for ahead of time, and the beginning of the new school year 2020/2021. Qualitative analyses indicate that teachers focused on promoting metacognitive strategies and used technology to engage students. Reasons why teachers did not promote self-regulation of learning are mostly a lack of resources and misconceptions about students’ competences and needs. Regression analysis show that teachers’ self-efficacy to promote self-regulation of learning and students’ grade predict teachers’ promotion of self-regulation of learning in distance education. Moderation analyses reveal interactions between teachers’ technology competency and class size as well as grade—but only for the period after the summer holidays in 2020/2021. This paper uncovers areas of improvement for teacher education, such as their misconceptions, self-efficacy beliefs, and their technology competency—not only for homeschooling during a pandemic, but also for future learning opportunities in the 21st century that will contain the need for more self-regulation of learning due to the increasing use of technology and digital learning.

Over-reliance on generative artificial intelligence (AI) for writing tasks can have negative effects on engineering education. Despite growing concerns over generative AI’s impact on authentic writing skills, limited research has critically examined alternatives that integrate between self-regulated learning (SRL) theories with knowledge visualization tools to foster monitoring and evaluation processes in engineering education. This exploratory study addresses this gap by exploring how machine learning-based text analytics can scaffold SRL, extending prior frameworks on information processing. Thirty participants were recruited from two sections of an engineering technology course. As a course task, participants wrote essays using the knowledge visualization system over a semester. SRL skills were measured through a survey, and final course grades served as a measurement of learning performance (LP). First, there were no noticeable relationships between students’ SRL, LP, and writing performance (WP). Second, regression analysis showed that SRL and course grades do not significantly predict WP. Third, engineering students’ WP significantly increased over time. Lastly, there were no differences in WP changes over time between high and low SRL or LP groups. However, there is a main effect of LP on WP, and an interaction effect of LP and time was observed in the evaluating component.

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.

Self-Regulated Learning and Socio-Cognitive Theory

This introductory paper to the special issue of High Ability Studies aims to provide a “guide for the perplexed” relating to self-regulated learning (SRL) theory, research, and applications. We begin by defining SRL and its key cyclical stages and criterial attributes. We move on to discuss a number of motivational and meta-motivational constructs supporting SRL. We then briefly present a number of issues related to teaching and promoting SRL. Finally, we review research shedding light on SRL in gifted, high ability, and high achieving students.

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.

With the advancement of generative artificial intelligence (AI), foreign language (FL) education has stepped into a transformative new era. Despite the widespread usage of generative AI, understanding the role of self-regulation in micro-level language skills within generative AI-enhanced learning environments is inadequate. To address this gap, drawn on self-regulated learning (SRL) theory, this study explores how mobile self-regulated learning (MSRL) affects FL learners’ generative AI awareness (optimism and dependence) and morphological skills (discrimination, interpretation, and recognition) among 614 English FL learners. The results of path analysis based on partial least squares reveal that MSRL is positively associated with both dimensions of generative AI awareness and negatively associated with the three morphological skills. The mediation analysis results reveal that the dependence dimension of generative AI awareness significantly mediates the relationship between MSRL and morphological skills (interpretation and discrimination). Contrarily, optimism does not play a mediating role. This study extends SRL theory into micro-level language skills development in AI contexts and provides pedagogical implications for designing language instruction and learning platforms in the digital era.