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Perusall’s Machine Learning Towards Self-regulated Learning

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Innovative Technologies and Learning (ICITL 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13117))

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Abstract

This current work presents exploratory research related to Perusall activity. One of the objectives of this study was to analyze the Perusalll's features, with emphasis on peer work, which can increase individual motivation facilitating self-regulation learning. Perusall is a social web tool that uses a machine learning algorithm, which assesses the quality of annotations and students’ engagement. This tool was integrated with the LMS of Universidade Aberta (Portugal) and it was used as a pilot project in a Curricular Unit, from the 2nd year of the Education undergraduate program. We designed a collaborative activity inspired by Inquiry-based Learning and peer-instruction, to be performed on Perusall. 115 students, from 2 classes, were involved. To assess students’ work, their engagement and motivation (basis for self-regulation) we analyzed Perusall´s reports and scoring based on 6 different components. We also asked students to report positive and negative aspects related to their experience with Perusall. Our findings confirm that collaborative reading tools can help students to get more involved in self-learning, as well machine learning can help instructors work, namely monitoring and assessment tasks.

Financed national funds through FCT – Fundação para a Ciência e a Tecnologia, I.P., under the projects UIDB/04372/2020 e UIDP/04372/2020.

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Author information

Authors and Affiliations

  1. Laboratory of Distance Education and eLearning, Universidade Aberta, Lisbon, Portugal

    Manuela Francisco

  2. Center for Studies in Education and Innovation, Politécnico de Leira, Leiria, Portugal

    Manuela Francisco

  3. Universidade Aberta, Lisbon, Portugal

    Cristina Amado

Authors
  1. Manuela Francisco
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  2. Cristina Amado
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Correspondence to Manuela Francisco .

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Editors and Affiliations

  1. National Cheng Kung University, Tainan, Taiwan

    Yueh-Min Huang

  2. National Cheng Kung University, Tainan, Taiwan

    Chin-Feng Lai

  3. University of Trás-os-Montes and Alto Douro, Villa Real, Portugal

    Tânia Rocha

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Francisco, M., Amado, C. (2021). Perusall’s Machine Learning Towards Self-regulated Learning. In: Huang, YM., Lai, CF., Rocha, T. (eds) Innovative Technologies and Learning. ICITL 2021. Lecture Notes in Computer Science(), vol 13117. Springer, Cham. https://doi.org/10.1007/978-3-030-91540-7_6

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  • DOI: https://doi.org/10.1007/978-3-030-91540-7_6

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-91540-7

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