Self-regulated learning and learning analytics in online learning environments

Viberg, Olga; Khalil, Mohammad; Baars, Martine

doi:10.1145/3375462.3375483

Cited by 128 publications

(124 citation statements)

References 45 publications

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“…The aim of this workshop paper is to discuss our proposal of a research area which we call Mobile Multimodal Learning Analytics Methodology (MOLAM) to trace, interpret and support students' development of self-regulated learning (SRL) strategies, skills and knowledge. While the focus of the learning analytics research varies (Khalil & Ebner, 2016;Viberg et al, 2018), increasing research attempts have recently targeted the area of self-regulated learning (SRL; Viberg, Khalil & Baars, 2020).…”

Section: Background and Methods Proposalmentioning

confidence: 99%

Mobile Multimodal Learning Analytics Method to Foster Student Self-Regulated Learning

Khalil

2020

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The aim of this workshop paper is to propose Mobile Multimodal LearningAnalytics Methodology (MOLAM). The methodology is suggested to be developed through the lenses of multidisciplinary and multichannel data research approaches, based on the theoretical foundations of Self-Regulated Learning (SRL). MOLAM is theory supported, driven by learning analytics, learner-centered focused, and mobile technology utilized. We argue that MOLAM will have a potential to support learners, teachers and researchers in their understanding and their further fostering of student SRL in formal and informal learning environments.

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Section: Background and Methods Proposalmentioning

confidence: 99%

Mobile Multimodal Learning Analytics Method to Foster Student Self-Regulated Learning

Khalil

2020

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“…To support L2 learners to develop their ability to regulate their own learning across formal and informal learning environments, LA is critical (Viberg, Khalil, et al, 2020). LA focuses on the improvement of learning outcomes as well as learner support (e.g., for the development of SRL) and teaching (Ferguson & Clow, 2017).…”

Section: La L2 Learning and Srlmentioning

confidence: 99%

“…Scholars have shown that different LA approaches can be used to measure and support student SRL (Noroozi et al, 2019;Pardo et al, 2017;Tabuenca et al, 2015). However, the results of a recent review of empirical research on SRL and LA in online learning settings highlighted that very limited support has been offered to foster SRL through LA (Viberg, Khalil, et al, 2020).…”

Section: La L2 Learning and Srlmentioning

confidence: 99%

“…Scholars have also argued that, by applying LA, learning designers should be able to better meet L2 learners' needs, predict their behaviours and learning outcomes, and provide them with personalised and adaptive learning activities (Lan et al, 2017). Furthermore, researchers have stated that through data visualisations (e.g., student/teacher-facing dashboards of learning activities) L2 learners, educators, and researchers will be better equipped with timely decision-making information to support more successful L2 learning (Viberg, Khalil, et al, 2020).…”

Section: La L2 Learning and Srlmentioning

confidence: 99%

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Mobile-assisted language learning through learning analytics for self-regulated learning (MALLAS): A conceptual framework

Viberg

Wasson

Kukulska‐Hulme

2020

AJET

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Many adult second and foreign language learners have insufficient opportunities to engage in language learning. However, their successful acquisition of a target language is critical for various reasons, including their fast integration in a host country and their smooth adaptation to new work or educational settings. This suggests that they need additional support to succeed in their second language acquisition. We argue that such support would benefit from recent advances in the fields of mobile-assisted language learning, self-regulated language learning, and learning analytics. In particular, this paper offers a conceptual framework, mobile-assisted language learning through learning analytics for self-regulated learning (MALLAS), to help learning designers support second language learners through the use of learning analytics to enable self-regulated learning. Although the MALLAS framework is presented here as an analytical tool that can be used to operationalise the support of mobile-assisted language learning in a specific exemplary learning context, it would be of interest to researchers who wish to better understand and support self-regulated language learning in mobile contexts. Implications for practice and policy: MALLAS is a conceptual framework that captures the dimensions of self-regulated language learning and learning analytics that are required to support mobile-assisted language learning. Designers of mobile-assisted language learning solutions using MALLAS will have a solution with sound theoretically underpinned solution. Learning designers can use MALLAS as a guide to direct their design choices regarding the development of mobile-assisted language learning apps and services.

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“…Neste cenário, tem-se observado a importância de prover mecanismos tecnológicos integrados aos ambientes de aprendizagem para fomentar o processo IX Congresso Brasileiro de Informática na Educação (CBIE 2020) Anais do XXXI Simpósio Brasileiro de Informática na Educação (SBIE 2020) de aprendizagem autorregulada [Viberg et al 2020].…”

Section: Introductionunclassified

Uma Análise dos Recursos Tecnológicos Utilizados na Estimulação da Aprendizagem Autorregulada em Ambientes Educacionais na Última Década

Lima¹,

Araújo²,

Dorça³

2020

Anais Do XXXI Simpósio Brasileiro De Informática Na Educação (SBIE 2020)

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Os Ambientes Virtuais de Aprendizagem têm se tornado cada vez mais inteligentes e providos de recursos individualizados para propiciar um processo de aprendizagem mais engajador e efetivo. Em especial, tecnologias que, além de oferecer suporte, também fomente e estimule a aprendizagem autorregulada são desejáveis, já que esta competência traz inúmeros benefícios. Dessa forma, este trabalho apresenta uma Revisão Sistemática da Literatura que busca traçar um panorama de tais tecnologias, considerando trabalhos publicadas entre 2011 e 2020. Os resultados mostraram que técnicas de visualização da informação, recursos de aprendizagem interativos, recomendação de conteúdo e estratégias de feedback são utilizadas em todas as fases do processo de autorregulação, principalmente no ensino superior.

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Self-regulated learning and learning analytics in online learning environments

Cited by 128 publications

References 45 publications

Mobile Multimodal Learning Analytics Method to Foster Student Self-Regulated Learning

Mobile Multimodal Learning Analytics Method to Foster Student Self-Regulated Learning

Mobile-assisted language learning through learning analytics for self-regulated learning (MALLAS): A conceptual framework

Uma Análise dos Recursos Tecnológicos Utilizados na Estimulação da Aprendizagem Autorregulada em Ambientes Educacionais na Última Década

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