A conceptual framework and a professional development model for supporting teachers’ “triple SRL–SRT processes” and promoting students’ academic outcomes

Kramarski, Bracha; Heaysman, Orna

doi:10.1080/00461520.2021.1985502

Cited by 42 publications

(26 citation statements)

References 75 publications

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“…Sixth, while we acknowledge that cognition, metacognition, and emotions are important for SRL, more attention needs to be paid to the role of motivation (as states that also fluctuate during task performance, perhaps at different time epochs, and that can be deeply intertwined to the concept of flow ( Csikszentmihalyi et al, 2018 ). Seventh, training teachers to learn and use SRL in their classrooms is key in fostering their students’ SRL ( Kramarski, 2018 ; Callan and Shim, 2019 ; Dignath and Veenman, 2020 ; Kramarski and Heaysman, 2021 ) and must remain a major thrust of research and education in our field. Lastly, AI-based immersive virtual environments hold great promise to enhance students’ SRL, especially with the use of AI, NLP, computer vision, and machine learning and nanomaterials (e.g., sensors) that can significantly advance and address conceptual, theoretical, methodological, analytical issues and have a major education impact on students of all ages.…”

Section: Lessons Learned and Open Challengesmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Lessons Learned and Open Challengesmentioning

confidence: 99%

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

et al. 2022

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“…Through the enrichment of learning environments with multimedia content (Becker et al, 2020), ML provides a varied learning experience (Park et al, 2018), which improves learners' academic outcomes (Chen et al, 2020;Mitra and Gupta, 2020) and increases their motivation, learning efficacy (Blau, 2019), and learning involvement (Hatun Ataş and Delialioğlu, 2018). Several theoretical frameworks of ML usage have been proposed (Okai-Ugbaje, 2021), among them, "Mobagogy, "which emphasizes the pedagogical benefits of ML implementation in education (Schuck et al, 2013), as well as social collaborative learning (Danish and Hmelo-Silver, 2020), Self-regulated learning (Kramarski and Heaysman, 2021), and self-determination theory (Yang et al, 2019).…”

Section: Literature Review Mobile Learning In Teacher Educationmentioning

confidence: 99%

Mobile-learning adoption in teacher education amidst COVID-19: Identifying two critical stages by exploring teachers’ emotions

2022

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Mobile learning (ML) is extremely relevant to distance teaching. Although much is known about ML usage in teacher education, less is known about crucial points in teachers’ ML adoption process under constraints such as the COVID-19 pandemic. The aim of this exploratory case study was to gain insight into the ML adoption process, including its critical points, by examining teachers’ emotion-related language. This study investigated the emotional response of 32 inservice teachers to Mobile Learning (ML) adoption while attending ML training during the COVID-19 pandemic. The data were collected using semi-structured interviews (10), focus groups (3), and participants’ reflections (96) at five time points. The data underwent multilevel analysis (content and linguistic analyses), revealing two critical stages during the ML adoption process and indicating several factors that may affect the quality of emotional response, thereby promoting or impeding this process. The study highlights the critical sages and their related features that must be addressed to promote optimal ML adoption in teacher education in both routine and emergency conditions.

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“…Prior research has shown that teachers' emotions are not only related to their own wellbeing and health (Chang, 2013;Taxer and Gross, 2018), but that they also have a direct link to children's motivation and emotions (Becker et al, 2014;van Doorn et al, 2014). Furthermore, based on self-regulated learning (SRL) theory, Kramarski and Heaysman (2021) state that to support children's regulation skills, it is essential to pay attention to educators' own regulation skills. This is because deliberate teaching, modeling, and activation of students' self-regulation requires both knowledge and skills in self-regulation (Peeters et al, 2014;Karlen et al, 2020;Kramarski and Heaysman, 2021).…”

Section: Being Aware: Building Theoretical Understandingmentioning

confidence: 99%

“…Furthermore, based on self-regulated learning (SRL) theory, Kramarski and Heaysman (2021) state that to support children's regulation skills, it is essential to pay attention to educators' own regulation skills. This is because deliberate teaching, modeling, and activation of students' self-regulation requires both knowledge and skills in self-regulation (Peeters et al, 2014;Karlen et al, 2020;Kramarski and Heaysman, 2021). In terms of emotion regulation, a similar statement is supported by research that has linked early childhood educators' own emotion regulation skills to their abilities to provide support for children's socio-emotional development (Ciucci et al, 2015;Braun et al, 2020).…”

Section: Being Aware: Building Theoretical Understandingmentioning

confidence: 99%

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Enhancing Early Childhood Educators’ Skills in Co-regulating Children’s Emotions: A Collaborative Learning Program

et al. 2022

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The aim of this article is to introduce a research-based work-integrated collaborative learning program that focuses on early childhood education and care (ECEC) professionals’ skills in co-regulation of emotions. The collaborative learning program draws on the theoretical framework that acknowledges the situated and socially shared nature of regulated learning and emotion regulation as well as years of research highlighting the importance of versatile and sensitive adults in supporting children’s learning of regulation skills during their early years. The program aims to improve professionals’ shared awareness of children’s emotion regulation development and abilities to identify and develop practices that support children in learning these skills, so that professionals can provide conscious and consistent co-regulation of emotions for children in everyday interactions. The design of the program has been developed by considering the aspects of effective collaborative and professional learning. This paper focuses on describing the theoretical grounding and implementation of a 32-week long collaborative learning program for ECEC professionals in Northern Finland (N = 450). Also, the development of a video-stimulated questionnaire (VSQ) for assessment of professionals’ learning during the program will be described. VSQ measures professionals’ abilities to identify and interpret everyday ECEC interactions from the point of view of (co-)regulation of emotions. Developing research-based collaborative programs that increase systematic support for children to learn regulation skills is essential, as these skills affect children’s lives well into adulthood. They set a basis for children’s learning and social skills and general wellbeing.

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A conceptual framework and a professional development model for supporting teachers’ “triple SRL–SRT processes” and promoting students’ academic outcomes

Cited by 42 publications

References 75 publications

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

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

Mobile-learning adoption in teacher education amidst COVID-19: Identifying two critical stages by exploring teachers’ emotions

Enhancing Early Childhood Educators’ Skills in Co-regulating Children’s Emotions: A Collaborative Learning Program

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