More Web Proxy on the site http://driver.im/

research-article

Personalized visualizations to promote young learners' SRL: the learning path app

Authors:

Ryan S. BakerAuthors Info & Claims

LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge

Pages 330 - 339

https://doi.org/10.1145/3375462.3375465

Published: 23 March 2020 Publication History

Abstract

This paper describes the design and evaluation of personalized visualizations to support young learners' Self-Regulated Learning (SRL) in Adaptive Learning Technologies (ALTs). Our learning path app combines three Personalized Visualizations (PV) that are designed as an external reference to support learners' internal regulation process. The personalized visualizations are based on three pillars: grounding in SRL theory, the usage of trace data and the provision of clear actionable recommendations for learners to improve regulation. This quasi-experimental pre-posttest study finds that learners in the personalized visualization condition improved the regulation of their practice behavior, as indicated by higher accuracy and less complex moment-by-moment learning curves compared to learners in the control group. Learners in the PV condition showed better transfer on learning. Finally, students in the personalized visualizations condition were more likely to under-estimate instead of over-estimate their performance. Overall, these findings indicates that the personalized visualizations improved regulation of practice behavior, transfer of learning and changed the bias in relative monitoring accuracy.

References

[1]

Aghababyan, A., Lewkow, N. and Baker, R. 2017. Exploring the asymmetry of metacognition. Proceedings of the Seventh International Learning Analytics & Knowledge Conference (2017), 115--119.

Digital Library

[2]

Aleven, V., Roll, I., McLaren, B.M. and Koedinger, K.R. 2016. Help Helps, but only so Much: Research on Help Seeking with Intelligent Tutoring Systems. International Journal of Artificial Intelligence in Education. 26, 1 (2016), 205--223.

[3]

Arroyo, I., Ferguson, K., Johns, J., Dragon, T., Mehranian, H., Fisher, D., Barto, A., Mahadevan, S. and Woolf, B. 2007. Repairing disengagement with non invasive interventions. International Conference on Artificial Intelligence in Education. August 2016 (2007), 195--202.

Digital Library

[4]

Azevedo, R. 2015. Defining and measuring engagement and learning in science: Conceptual, theoretical, methodological, and analytical issues. Educational Psychologist. 50, 1 (2015), 84--94.

[5]

Azevedo, R., Johnson, A., Chauncey, A. and Burkett, C. 2010. Self-regulated learning with MetaTutor: Advancing the science of learning with MetaCognitive tools. New science of learning. Springer. 225--247.

[6]

Azevedo, R., Martin, S.A., Taub, M., Mudrick, N. V, Millar, G.C. and Grafsgaard, J.F. 2016. Are Pedagogical Agents' External Regulation Effective in Fostering Learning with Intelligent Tutoring Systems? Intelligent Tutoring Systems (Cham, 2016), 197--207.

Digital Library

[7]

Azevedo, R., Moos, D.C., Greene, J.A., Winters, F.I. and Cromley, J.G. 2008. Why is externally-facilitated regulated learning more effective than self-regulated learning with hypermedia? Educational Technology Research and Development. 56, 1 (2008), 45--72.

[8]

Baker, R.S., Hershkovitz, A., Rossi, L.M., Goldstein, A.B. and Gowda, S.M. 2013. Predicting Robust Learning With the Visual Form of the Moment-by-Moment Learning Curve. Journal of the Learning Sciences. 22, 4 (2013), 639--666.

[9]

Baker, R.S.J.D., Goldstein, A.B. and Heffernan, N.T. 2011. Detecting learning moment-by-moment. International Journal of Artificial Intelligence in Education. 21, 1--2 (2011), 5--25.

Digital Library

[10]

Bannert, M., Hildebrand, M. and Mengelkamp, C. 2009. Effects of a metacognitive support device in learning environments. Computers in Human Behavior. 25, 4 (2009), 829--835.

Digital Library

[11]

Bannert, M., Molenaar, I., Azevedo, R., Järvelä, S. and Gašević, D. Relevance of Learning Analytics to Measure and Support Students ' Learning in Adaptive Educational Technologies.

Digital Library

[12]

Bannert, M. and Reimann, P. 2012. Supporting self-regulated hypermedia learning through prompts. Instructional Science. 40, 1 (2012), 193--211.

[13]

Bodily, R., Jivet, I., Kay, J., Davis, D., Verbert, K., Aleven, V. and Xhakaj, F. 2018. Open Learner Models and Learning Analytics Dashboards: A Systematic Review. Proceedings of the 8th International Conference on Learning Analytics and Knowledge (2018).

Digital Library

[14]

Bodily, R. and Verbert, K. 2017. Review of research on student-facing learning analytics dashboards and educational recommender systems. IEEE Transactions on Learning Technologies. (2017).

Digital Library

[15]

Bull, S. and Kay, J. 2013. Open Learner Models as Drivers for Metacognitive Processes.

[16]

Butler, D.L. and Winne, P.H. 1995. Feedback and Self-Regulated Learning: A Theoretical Synthesis. Review of Educational Research. (1995).

[17]

Elo, A. 1978. The rating of chessplayers, past and present. (1978).

[18]

Foster, N.L., Was, C.A., Dunlosky, J. and Isaacson, R.M. 2017. Even after thirteen class exams, students are still overconfident: the role of memory for past exam performance in student predictions. Metacognition and Learning. 12, 1 (2017), 1--19.

[19]

Hattie, J. and Timberley, H. 2007. The power of feedback. Medical Education. 44, 1 (2007), 16--17.

[20]

Jivet, I., Scheffel, M., Specht, M. and Drachsler, H. 2018. License to evaluate: preparing learning analytics dashboards for educational practice. Proceedings of the 8th International Conference on Learning Analytics and Knowledge - LAK '18. (2018), 31--40.

Digital Library

[21]

Klinkenberg, S., Straatemeier, M. and Van Der Maas, H.L.J. 2011. Computer adaptive practice of Maths ability using a new item response model for on the fly ability and difficulty estimation. Computers and Education. 57, 2 (2011), 1813--1824.

Digital Library

[22]

van Loon, M.H., de Bruin, A.B.H., van Gog, T. and van Merriënboer, J.J.G. 2013. The effect of delayed-JOLs and sentence generation on children's monitoring accuracy and regulation of idiom study. Metacognition and Learning. 8, 2 (2013), 173--191.

[23]

Molenaar, Inge; Horvers, A.; Dijkstra, R. 2020. Young Learners' Regulation of Practice Behavior in Adaptive Learning Technologies. Frontiers in Psychology.

[24]

Molenaar, I., Horvers, A. and Baker, R.S. 2019. What can moment-by-moment learning curves tell about students ' self- regulated learning? Learning and Instruction. July 2018 (2019), 101206.

[25]

Molenaar, I., Horvers, A. and Baker, R.S.J.D. 2019. Towards Hybrid Human-System Regulation: Understanding Children' SRL Support Needs in Blended Classrooms. In proceedings of the 9th International learning analytics & knowledge conference (2019).

[26]

Molenaar, I., Horvers, A., Dijkstra, R. and Baker, R. 2019. Designing Dashboards to support learners' Self-Regulated Learning. In companion proceedings of the 9th International learning analytics & knowledge conference (2019), 1--12.

[27]

Molenaar, I. and Järvelä, S. 2014. Sequential and temporal characteristics of self and socially regulated learning. Metacognition and Learning. (2014).

[28]

Molenaar, I. and Knoop-van Campen, C. 2018. How Teachers Make Dashboard Information Actionable. IEEE Transactions on Learning Technologies. 1382, c (2018), 1--9.

Digital Library

[29]

Molenaar, I., Roda, C., Van Boxtel, C. and Sleegers, P. 2012. Dynamic scaffolding of socially regulated learning in a computer-based learning environment. Computers and Education. 59, 2 (2012), 515--523.

Digital Library

[30]

Roll, I., Aleven, V., McLaren, B.M. and Koedinger, K.R. 2011. Improving students' help-seeking skills using metacognitive feedback in an intelligent tutoring system. Learning and instruction. 21, 2 (2011), 267--280.

[31]

Roll, I. and Winne, P. 2015. Understanding, evaluating, and supporting self-regulated learning using learning analytics. Journal of Learning Analytics. (2015).

[32]

Schwendimann, B.A., Rodríguez-Triana, M.J., Vozniuk, A., Prieto, L.P., Boroujeni, M.S., Holzer, A., Gillet, D. and Dillenbourg, P. 2016. Understanding learning at a glance. (2016), 532--533.

Digital Library

[33]

Winne, P. and Hadwin, A. 1998. Studying as self-regulated learning. Metacognition in educational theory and practice.

[34]

Winne, P.H. 2010. Improving measurements of self-regulated learning. Educational Psychologist. 45, 4 (2010), 267--276.

[35]

Winne, P.H. and Baker, R.S.J. d. 2013. The Potentials of Educational Data Mining for Researching Metacognition, Motivation and Self-Regulated Learning. JEDM - Journal of Educational Data Mining. 5, 1 (2013), 1--8.

[36]

Winne, P.H. and Hadwin, A.F. 2013. nStudy: Tracing and supporting self-regulated learning in the Internet. International handbook of metacognition and learning technologies. Springer. 293--308.

[37]

Winne, P.H. and Hadwin, A.F. 2017. Studying as self-regulated learning. January 1998 (2017).

[38]

Zimmerman, B.J. 2000. Attaining Self-Regulation: A Social Cognitive Perspective. Handbook of Self-Regulation. (Jan. 2000), 13--39.

Cited By

Bhowmick MGoswami SChakraborty RBhowmick PNandy SAfrose NKewatkar S(2024)Artificial Intelligence in Skill DevelopmentHuman Resource Strategies in the Era of Artificial Intelligence10.4018/979-8-3693-6412-3.ch006(135-166)Online publication date: 4-Oct-2024
https://doi.org/10.4018/979-8-3693-6412-3.ch006
Horvers AKooi RKnoop-van Campen CDijkstra RBaars MMolenaar I(2024)How does co-regulation with Adaptive Learning Technologies affect primary school students' goal-setting, regulation of practice behavior and learning outcomes?Frontiers in Education10.3389/feduc.2024.14354839Online publication date: 17-Dec-2024
https://doi.org/10.3389/feduc.2024.1435483
Knoop‐van Campen Cvan der Graaf JHorvers AKooi RDijkstra RMolenaar I(2024)Enacting control with student dashboards: The role of motivationJournal of Computer Assisted Learning10.1111/jcal.1293640:3(1137-1153)Online publication date: 14-Jan-2024
https://doi.org/10.1111/jcal.12936
Show More Cited By

Index Terms

Personalized visualizations to promote young learners' SRL: the learning path app
1. Applied computing
  1. Education
    1. E-learning
2. Human-centered computing
  1. Visualization
    1. Visualization design and evaluation methods

Recommendations

Towards Hybrid Human-System Regulation: Understanding Children' SRL Support Needs in Blended Classrooms
LAK19: Proceedings of the 9th International Conference on Learning Analytics & Knowledge

This paper proposes a new approach to translate learner data into self-regulated learning support. Learning phases in blended classrooms place unique requirements on students' self-regulated learning (SRL). Learning path graphs merge moment-by-moment ...
Personalization in OELEs: Developing a Data-Driven Framework to Model and Scaffold SRL Processes
Artificial Intelligence in Education
Abstract
This research focuses on developing a data-driven framework for modeling and scaffolding learners’ self-regulated learning (SRL) processes in open-ended learning environments (OELE). The aim of this work is to offer a personalized and productive ...
Supporting learners' self-regulated learning in Massive Open Online Courses
Abstract
In MOOCs, learners are typically presented with great autonomy over their learning process. Therefore, learners should engage in self-regulated learning (SRL) in order to successfully study in a MOOC. Learners however often struggle to self-...
Highlights
- Learners struggle to regulate their learning in massive open online courses (MOOCs).
- A self-regulated learning (SRL) intervention was implemented in three MOOCs.
- Learners' SRL was measured with trace data variables.
- ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge

March 2020

679 pages

ISBN:9781450377126

DOI:10.1145/3375462

General Chairs:
Christoph Rensing
Technische Universität Darmstadt, Germany
,
Hendrik Drachsler
DIPF Leibniz Institute for Research and Information in Education & University of Frankfurt, Germany
,
Program Chairs:
Vitomir Kovanović
University of South Australia, Australia
,
Niels Pinkwart
Humboldt-Universität zu Berlin, Germany
,
Maren Scheffel
Open Universiteit, Netherlands
,
Katrien Verbert
KU Leuven, Belgium

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 March 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Conference

LAK '20

LAK '20: 10th International Conference on Learning Analytics and Knowledge

March 23 - 27, 2020

Frankfurt, Germany

Acceptance Rates

LAK '20 Paper Acceptance Rate 80 of 261 submissions, 31%;

Overall Acceptance Rate 236 of 782 submissions, 30%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

31
Total Citations
View Citations
887
Total Downloads

Downloads (Last 12 months)125
Downloads (Last 6 weeks)15

Reflects downloads up to 22 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Bhowmick MGoswami SChakraborty RBhowmick PNandy SAfrose NKewatkar S(2024)Artificial Intelligence in Skill DevelopmentHuman Resource Strategies in the Era of Artificial Intelligence10.4018/979-8-3693-6412-3.ch006(135-166)Online publication date: 4-Oct-2024
https://doi.org/10.4018/979-8-3693-6412-3.ch006
Horvers AKooi RKnoop-van Campen CDijkstra RBaars MMolenaar I(2024)How does co-regulation with Adaptive Learning Technologies affect primary school students' goal-setting, regulation of practice behavior and learning outcomes?Frontiers in Education10.3389/feduc.2024.14354839Online publication date: 17-Dec-2024
https://doi.org/10.3389/feduc.2024.1435483
Knoop‐van Campen Cvan der Graaf JHorvers AKooi RDijkstra RMolenaar I(2024)Enacting control with student dashboards: The role of motivationJournal of Computer Assisted Learning10.1111/jcal.1293640:3(1137-1153)Online publication date: 14-Jan-2024
https://doi.org/10.1111/jcal.12936
Teich KLoock VRummel N(2024)Meeting the challenges of continuing education online courses: Can we promote self‐regulated learning strategies with adaptive support?British Journal of Educational Technology10.1111/bjet.13453Online publication date: 12-Mar-2024
https://doi.org/10.1111/bjet.13453
Sergent TDaniel MBouchet FCarron T(2024)How Can Self-Evaluation and Self-Efficacy Skills of Young Learners be Scaffolded in a Web Application?IEEE Transactions on Learning Technologies10.1109/TLT.2024.336012117(1184-1197)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TLT.2024.3360121
Tepgec MHeil JIfenthaler D(2024)Feedback literacy matters: unlocking the potential of learning analytics-based feedbackAssessment & Evaluation in Higher Education10.1080/02602938.2024.2367587(1-17)Online publication date: 27-Jun-2024
https://doi.org/10.1080/02602938.2024.2367587
Zheng L(2024)Intelligent Language Acquisition Model for Online Student Interaction with Educators Using 6G-Cyber Enhanced Wireless NetworkWireless Personal Communications10.1007/s11277-024-11197-xOnline publication date: 17-Jun-2024
https://doi.org/10.1007/s11277-024-11197-x
Xu XZhao WLi YQiao LTao JLiu F(2024)The impact of visualizations with learning paths on college students’ online self-regulated learningEducation and Information Technologies10.1007/s10639-024-12933-3Online publication date: 6-Aug-2024
https://doi.org/10.1007/s10639-024-12933-3
Wang SSun Z(2024)Roles of artificial intelligence experience, information redundancy, and familiarity in shaping active learning: Insights from intelligent personal assistantsEducation and Information Technologies10.1007/s10639-024-12895-6Online publication date: 26-Jul-2024
https://doi.org/10.1007/s10639-024-12895-6
Mejeh MSarbach LHascher T(2024)Effects of adaptive feedback through a digital tool – a mixed-methods study on the course of self-regulated learningEducation and Information Technologies10.1007/s10639-024-12510-829:14(1-43)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s10639-024-12510-8
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents