Facilitating self-regulated learning with personalized scaffolds on student’s own regulation activities

Joep van der Graaf, Inge Molenaar, Lyn Lim, Yizhou Fan, Katharina Engelmann, Dragan Gašević, Maria Bannert

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

Abstract

The focus of education is increasingly set on students’ ability to regulate their own learning within technology-enhanced learning environments. Scaffolds have been used to foster self-regulated learning, but scaffolds often are standardized and do not do not adapt to the individual learning process. Learning analytics and machine learning offer an approach to better understand SRL-processes during learning. Yet, current approaches lack validity or require extensive analysis after the learning process. The FLORA project aims to investigate how to advance support given to students by i) improving unobtrusive data collection and machine learning techniques to gain better measurement and understanding of SRL-processes and ii) using these new insights to facilitate student’s SRL by providing personalized scaffolds. We will reach this goal by investigating and improving trace data in exploratory studies (exploratory study1 and study 2) and using the insight gained from these studies to develop and test personalized scaffolds based on individual learning processes in laboratory (experimental study 3 and study 4) and a subsequent field study (field study 5). At the moment study 2 is ongoing. The setup consists of a learning environment presented on a computer with a screen-based eye-tracker. Other data sources are log files and audio of students’ think aloud. The analysis will focus on detecting sequences that are indicative of micro-level self-regulated learning processes and aligning them between the different data sources.

Original languageEnglish
Title of host publicationProceedings of CrossMMLA in practice
Subtitle of host publicationCollecting, annotating and analyzing multimodal data across spaces co-located with 10th International Learning and Analytics Conference (LAK 2020)
EditorsMichail Giannakos, Daniel Spikol , Inge Molenaar , Daniele Di Mitri, Kshitij Sharma, Xavier Ochoa, Hammad Hammad
Place of PublicationAachen Germany
PublisherCEUR-WS
Pages46-48
Number of pages3
Volume2610
Publication statusPublished - 24 Mar 2020
EventCrossMMLA in practice: collecting, annotating and
analyzing multimodal data across spaces co-located with International Learning and Analysis Conference 2020
- Virtual, Online
Duration: 24 Mar 202024 Mar 2020
http://ceur-ws.org/Vol-2610/ (Proceedings)
http://crossmmla.org (Website)

Publication series

NameCEUR Workshop Proceedings
PublisherCEUR W/S
Volume2610
ISSN (Electronic)1613-0073

Conference

ConferenceCrossMMLA in practice: collecting, annotating and
analyzing multimodal data across spaces co-located with International Learning and Analysis Conference 2020
Abbreviated titleCrossMMLA 2020
CityVirtual, Online
Period24/03/2024/03/20
Internet address

Keywords

  • Adaptive systems
  • Instructional scaffolds
  • Learning analytics
  • Machine learning
  • Personalized learning
  • Self-regulated-learning

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