Predicting regulatory activities for socially shared regulation to optimize collaborative learning

Järvelä, Sanna; Nguyen, Andy; Vuorenmaa, Eija; Malmberg, Jonna; Järvenoja, Hanna

doi:10.1016/j.chb.2023.107737

Cited by 26 publications

(7 citation statements)

References 51 publications

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“…We have been working on developing an empirically testable model of trigger events as a conceptual framework for advancing research about regulation within complex individual and collaborative learning situations. Building on Winne andHadwin's (1998) SRL andHadwin et al's (2018) SSRL theories and supported by recent empirical research, we have empirically identified specific types of events (which we call triggers) that invite regulatory responses in collaborative learning (Haataja et al, 2018;Järvelä et al, 2022Järvelä et al, , 2023Sobocinski et al, 2020;Vuorenmaa et al, 2022). This trigger-based framework provides a theoretical direction for advancing research about regulation during complex learning in both individual and collaborative learning contexts which guides empirical identification where regulation happens or not.…”

Section: Trigger Concept Framework For Ssrlmentioning

confidence: 71%

Human and artificial intelligence collaboration for socially shared regulation in learning

Järvelä

Nguyen

Hadwin

2023

Brit J Educational Tech

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Artificial intelligence (AI) has generated a plethora of new opportunities, potential and challenges for understanding and supporting learning. In this paper, we position human and AI collaboration for socially shared regulation (SSRL) in learning. Particularly, this paper reflects on the intersection of human and AI collaboration in SSRL research, which presents an exciting prospect for advancing our understanding and support of learning regulation. Our aim is to operationalize this human‐AI collaboration by introducing a novel trigger concept and a hybrid human‐AI shared regulation in learning (HASRL) model. Through empirical examples that present AI affordances for SSRL research, we demonstrate how humans and AI can synergistically work together to improve learning regulation. We argue that the integration of human and AI strengths via hybrid intelligence is critical to unlocking a new era in learning sciences research. Our proposed frameworks present an opportunity for empirical evidence and innovative designs that articulate the potential for human‐AI collaboration in facilitating effective SSRL in teaching and learning. Practitioner notesWhat is already known about this topic For collaborative learning to succeed, socially shared regulation has been acknowledged as a key factor. Artificial intelligence (AI) is a powerful and potentially disruptive technology that can reveal new insights to support learning. It is questionable whether traditional theories of how people learn are useful in the age of AI. What this paper adds Introduces a trigger concept and a hybrid Human‐AI Shared Regulation in Learning (HASRL) model to offer insights into how the human‐AI collaboration could occur to operationalize SSRL research. Demonstrates the potential use of AI to advance research and practice on socially shared regulation of learning. Provides clear suggestions for future human‐AI collaboration in learning and teaching aiming at enhancing human learning and regulatory skills. Implications for practice and/or policy Educational technology developers could utilize our proposed framework to better align technological and theoretical aspects for their design of adaptive support that can facilitate students' socially shared regulation of learning. Researchers and practitioners could benefit from methodological development incorporating human‐AI collaboration for capturing, processing and analysing multimodal data to examine and support learning regulation.

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Section: Trigger Concept Framework For Ssrlmentioning

confidence: 71%

Human and artificial intelligence collaboration for socially shared regulation in learning

Järvelä

Nguyen

Hadwin

2023

Brit J Educational Tech

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“…If we break down the social impact of studies based on their social source, the 10 research papers that had the greatest impact on Twitter (tweets or retweets) were “Emotional AI and EdTech: serving the public good?” ( McStay, 2020 ) with 86 records, “Deploying a robotic positive psychology coach to improve college students’ psychological well-being” with 22 records ( Jeong et al, 2023 ), “Sentiment analysis for formative assessment in higher education: a systematic literature review” with 14 records ( Grimalt-Álvaro and Usart, 2023 ), “Towards AI-powered personalization in MOOC learning” with 14 records ( Yu et al, 2017 ), “Predicting regulatory activities for socially shared regulation to optimize collaborative learning” with 13 records ( Järvelä et al, 2023 ), “Engagement detection in online learning: a review” with 12 records ( Dewan et al, 2019 ), “Artificial intelligence in early childhood education: A scoping review” with 12 records ( Su and Yang, 2022 ), “Humanoid Robots as Teachers and a Proposed Code of Practice” with 9 records ( Newton and Newton, 2019 ), “Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study” with 8 records ( Kastrati et al, 2021 ), and “Deep Learning-Based Cost-Effective and Responsive Robot for Autism Treatment” with 8 records ( Singh et al, 2023 ).…”

Section: Resultsmentioning

confidence: 99%

“…The analysis conducted also shows that there is significant variability in the lag in months for scientific research to make an impact on social media. According to the analysis of the 10 most scientifically impactful documents, we find research whose first social impact occurred in the same month of publication ( Yu et al, 2017 ; Newton and Newton, 2019 ; Sharma et al, 2019 ; Järvelä et al, 2023 ), while in others, the first social impact occurred more than a year after publication ( Jaques et al, 2014 ; Graesser, 2016 ; Kim et al, 2018 ). Notably, the case of the research “Predicting affect from gaze data during interaction with an intelligent tutoring system” ( Jaques et al, 2014 ), whose first social impact was 7 years and 1 month after its publication, with a 25th percentile of 91 months, a 75th percentile of 98 months, a median of 95.5 months, and a last record of social impact 8 years and 11 months after its publication.…”

Section: Discussionmentioning

confidence: 99%

Analysis of social metrics on scientific production in the field of emotion-aware education through artificial intelligence

Roda-Segarra,

Mengual-Andrés,

Payà Rico

2024

Front. Artif. Intell.

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Research in the field of Artificial Intelligence applied to emotions in the educational context has experienced significant growth in recent years. However, despite the field’s profound implications for the educational community, the social impact of this scientific production on digital social media remains unclear. To address this question, the present research has been proposed, aiming to analyze the social impact of scientific production on the use of Artificial Intelligence for emotions in the educational context. For this purpose, a sample of 243 scientific publications indexed in Scopus and Web of Science has been selected, from which a second sample of 6,094 social impact records has been extracted from Altmetric, Crossref, and PlumX databases. A dual analysis has been conducted using specially designed software: on one hand, the scientific sample has been analyzed from a bibliometric perspective, and on the other hand, the social impact records have been studied. Comparative analysis based on the two dimensions, scientific and social, has focused on the evolution of scientific production with its corresponding social impact, sources, impact, and content analysis. The results indicate that scientific publications have had a high social impact (with an average of 25.08 social impact records per publication), with a significant increase in research interest starting from 2019, likely driven by the emotional implications of measures taken to curb the COVID-19 pandemic. Furthermore, a lack of alignment has been identified between articles with the highest scientific impact and those with the highest social impact, as well as a lack of alignment in the most commonly used terms from both scientific and social perspectives, a significant variability in the lag in months for scientific research to make an impact on social media, and the fact that the social impact of the research did not emerge from the interest of Twitter users unaffiliated with the research, but rather from the authors, publishers, or scientific institutions. The proposed comparative methodology can be applied to any field of study, making it a useful tool given that current trends in accreditation agencies propose the analysis of the repercussion of scientific research in social media.

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“…There has been a growing body of research dedicated to the use of multimodal and multichannel data to triangulate data from the learning process (eg, Azevedo et al., 2022; Molenaar et al., 2023; Olsen et al., 2020). Coupled with advancements in the field of learning analytics to better measure and support students' learning, even at a larger scale (Raković et al., 2023), as well as the implementation of and investigation into artificial intelligence approaches (Dijkstra et al., 2023; Järvelä et al., 2023), there is much potential for the further improvement of personalized scaffold support. However, in order to leverage technological solutions for adaptive support, the personalized scaffolding model used in the current study offers the basis on which other tech‐powered innovations may build upon.…”

Section: Discussionmentioning

confidence: 99%

How do students learn with real‐time personalized scaffolds?

Lim,

Bannert,

van der Graaf

et al. 2023

Brit J Educational Tech

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Scaffolds 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.

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Predicting regulatory activities for socially shared regulation to optimize collaborative learning

Cited by 26 publications

References 51 publications

Human and artificial intelligence collaboration for socially shared regulation in learning

Human and artificial intelligence collaboration for socially shared regulation in learning

Analysis of social metrics on scientific production in the field of emotion-aware education through artificial intelligence

How do students learn with real‐time personalized scaffolds?

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