Using learning analytics in iterative design of a digital modeling tool
Pages 602 - 603
Abstract
Iterative design is a powerful method for developing digital classroom tools and curricula. We explore how infusing learning analytics into this process has influenced our development of EcoSurvey, a digital modeling tool for mapping the organisms and interactions in the local ecosystem. We have found that analytic techniques can help us discover areas in which students struggle to engage with scientific modeling, and we can iteratively use learning analytics to demonstrate the impact of design changes.
References
[1]
T. D.-B. R. Collective. Design-based research: An emerging paradigm for educational inquiry. Educational Researcher, 32(1):5--8, 2003.
[2]
M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The WEKA data mining software. ACM SIGKDD Explorations, 11(1):10--18, 2009.
[3]
J. B. Homer. Why We Iterate: Scientific Modeling in Theory and Practice. System Dynamics Review, 12(1):1--19, 1996.
[4]
National Research Council. A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas. The National Academies Press, Washington, DC, 2012.
[5]
D. Quigley, J. Ostwald, and T. Sumner. Scientific Modeling: Using learning analytics to examine student practices and classroom variation. in Proceedings of the Seventh International Conference on Learning Analytics & Knowledge, pages 1--10, in press.
[6]
C. V. Schwarz, B. J. Reiser, E. A. Davis, L. Kenyon, A. Achér, D. Fortus, Y. Shwartz, B. Hug, and J. Krajcik. Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners. Journal of Research in Science Teaching, 46(6):632--654, 2009.
[7]
S. Severance, W. R. Penuel, T. Sumner, and H. Leary. Organizing for Teacher Agency in Curricular Co-Design. Journal of the Learning Sciences, 25(4):531--564, 2016.
[8]
Z. Xing, J. Pei, and E. Keogh. A brief survey on sequence classification. ACM SIGKDD Explorations Newsletter, 12(1):40, 2010.
Index Terms
- Using learning analytics in iterative design of a digital modeling tool
Recommendations
Scientific modeling: using learning analytics to examine student practices and classroom variation
LAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge ConferenceModeling has a strong focus in current science learning frameworks as a critical skill for students to learn. However, understanding students' scientific models and their modeling practices at scale is a difficult task that has not been taken up by the ...
The learning analytics of computational scientific modeling with self-explanation for subgoals and demonstration scaffolding
AbstractThe emphasis of using computing tools in scientific practice has called for new forms of scientific modeling. Therefore, researchers are paying increasing attention to computational scientific modeling in which students use the computational ...
Highlights- Computational scientific modeling translates science concepts into executable model.
- Students receiving the self-explanation prompts demonstrated better model quality.
- Frequent modeling actions were not required for the self-...
Using A/B Testing as a Pedagogical Tool for Iterative Design in HCI Classrooms
EduCHI '23: Proceedings of the 5th Annual Symposium on HCI EducationThis paper explores the use of A/B testing as a pedagogical tool for iterative design in HCI classrooms and outlines a vision for experiment-inspired design. The traditional focus on the statistical aspects of A/B testing education has meant that the ...
Comments
Information & Contributors
Information
Published In
March 2017
631 pages
ISBN:9781450348706
DOI:10.1145/3027385
- General Chairs:
- Alyssa Wise,
- Phillip H. Winne,
- Grace Lynch,
- Program Chairs:
- Xavier Ochoa,
- Inge Molenaar,
- Shane Dawson,
- Publications Chair:
- Marek Hatala
Copyright © 2017 Owner/Author.
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 March 2017
Check for updates
Author Tags
Qualifiers
- Poster
Funding Sources
Conference
LAK '17
LAK '17: 7th International Learning Analytics and Knowledge Conference
March 13 - 17, 2017
British Columbia, Vancouver, Canada
Acceptance Rates
LAK '17 Paper Acceptance Rate 36 of 114 submissions, 32%;
Overall Acceptance Rate 236 of 782 submissions, 30%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 232Total Downloads
- Downloads (Last 12 months)41
- Downloads (Last 6 weeks)11
Reflects downloads up to 08 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in