Regulating Collaborative Learning in SQL-Tutor
Pages 341 - 344
Abstract
In recent years, there has been a surge in the use of intelligent computer-supported collaborative learning (CSCL) tools to improve student learning. The aim of this project is to provide adaptive support for student collaboration. My work will focus on designing effective interventions to enhance peer-to-peer collaboration in the context of an Intelligent Tutoring System (ITS), as well as to promote socially-shared regulation of learning (SSRL). In online collaborative learning environments, it can be challenging to engage students. A lack of genuine interaction, social identity, background of students and user empowerment may have negative effects on the learning process. This research will focus on virtualization of the online collaboration, such that the system would help students collaborate as well as support self-regulation and group regulation to achieve higher learning gains.
References
[1]
Vanlehn, K., The behavior of tutoring systems. International journal of artificial intelligence in education, 2006. 16(3): p. 227- 265.
[2]
Woolf, B.P., Building intelligent interactive tutors: Studentcentered strategies for revolutionizing e-learning. 2010: Morgan Kaufmann.
[3]
Sottilare, R.A., et al., Design Recommendations for Intelligent Tutoring Systems: Volume 2-Instructional Management. Vol. 2. 2014: US Army Research Laboratory.
[4]
Tchounikine, P., N. Rummel, and B.M. McLaren, Computer supported collaborative learning and intelligent tutoring systems, in Advances in intelligent tutoring systems. 2010, Springer. p. 447--463.
[5]
Magnisalis, I., S. Demetriadis, and A. Karakostas, Adaptive and intelligent systems for collaborative learning support: A review of the field. IEEE transactions on Learning Technologies, 2011. 4(1): p. 5--20.
[6]
Lehtinen, E., et al., Computer supported collaborative learning: A review. The JHGI Giesbers reports on education, 1999. 10: p. 1999.
[7]
Kaptelinin, V., B.A. Nardi, and C. Macaulay, Methods & tools: The activity checklist: a tool for representing the "space" of context. interactions, 1999. 6(4): p. 27--39.
[8]
Baghaei, N., A. Mitrovic, and W. Irwin, Supporting collaborative learning and problem-solving in a constraintbased CSCL environment for UML class diagrams. International Journal of Computer-Supported Collaborative Learning, 2007. 2(2--3): p. 159--190.
[9]
Floryan, M., et al. Collaboration and content recognition features in an inquiry tutor. in International Conference on Intelligent Tutoring Systems. 2010. Springer.
[10]
Soller, A., Supporting social interaction in an intelligent collaborative learning system. International Journal of Artificial Intelligence in Education (IJAIED), 2001. 12: p. 40--62.
[11]
Kobbe, L., et al., Specifying computer-supported collaboration scripts. International Journal of Computer-Supported Collaborative Learning, 2007. 2(2): p. 211--224.
[12]
Olsen, J.K., et al., Using an Intelligent Tutoring System to Support Collaborative as well as Individual Learning, in Intelligent Tutoring Systems: 12th International Conference, ITS 2014, Honolulu, HI, USA, June 5--9, 2014. Proceedings, S. Trausan-Matu, et al., Editors. 2014, Springer International Publishing: Cham. p. 134--143.
[13]
Johnson, D.W. and R.T. Johnson, Cooperative learning and achievement. 1990.
[14]
Kollar, I., F. Fischer, and F.W. Hesse, Collaboration scripts--a conceptual analysis. Educational Psychology Review, 2006. 18(2): p. 159--185.
[15]
Mitrovic, A. Experiences in implementing constraint-based modeling in SQL-Tutor. in Intelligent Tutoring Systems. 1998. Springer.
[16]
Mitrovic, A. and S. Ohlsson, Evaluation of a constraint-based tutor for a database language. 1999.
[17]
Hadwin, A.F., S. Järvelä, and M. Miller, Self-regulated, coregulated, and socially shared regulation of learning. Handbook of self-regulation of learning and performance, 2011. 30: p. 65- 84.
[18]
Zimmerman, B.J., Attaining self-regulation: A social cognitive perspective, in Handbook of self-regulation. 2000, Elsevier. p. 13--39.
[19]
Azevedo, R. and J.G. Cromley, Does training on self-regulated learning facilitate students' learning with hypermedia? Journal of educational psychology, 2004. 96(3): p. 523.
[20]
Winne, P.H. and A.F. Hadwin, nStudy: Tracing and supporting self-regulated learning in the Internet, in International handbook of metacognition and learning technologies. 2013, Springer. p. 293--308.
[21]
Miller, M. and A. Hadwin, Scripting and awareness tools for regulating collaborative learning: Changing the landscape of support in CSCL. Computers in Human Behavior, 2015. 52: p. 573--588.
[22]
Inaba, A. and R. Mizoguchi. Learners' roles and predictable educational benefits in collaborative learning. in International Conference on Intelligent Tutoring Systems. 2004. Springer.
Recommendations
Scripting and awareness tools for regulating collaborative learning
Self-, co-, and shared regulation play a key role in successful collaboration.CSCL scripts and group awareness tools offer potential to support regulation.Scripts and group awareness tools used in our design based research are introduced.Scripts and ...
Online collaborative learning in dyads
Research on collaborative learning traditionally assumes a certain degree of symmetry between the learning partners in terms of both their learning-relevant traits and their individual learning outcomes. However, if one collaborative partner is clearly ...
Comments
Information & Contributors
Information
Published In
July 2018
393 pages
ISBN:9781450355896
DOI:10.1145/3209219
- General Chairs:
- Tanja Mitrovic,
- Jie Zhang,
- Program Chairs:
- Li Chen,
- David Chin
Copyright © 2018 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]
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 03 July 2018
Check for updates
Qualifiers
- Research-article
Conference
UMAP '18: 26th Conference on User Modeling, Adaptation and Personalization
July 8 - 11, 2018
Singapore, Singapore
Acceptance Rates
UMAP '18 Paper Acceptance Rate 26 of 93 submissions, 28%;
Overall Acceptance Rate 162 of 633 submissions, 26%
Upcoming Conference
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 145Total Downloads
- Downloads (Last 12 months)11
- Downloads (Last 6 weeks)1
Reflects downloads up to 30 Aug 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in