Explaining predictive models to learning specialists using personas
Abstract
This paper describes a method we have developed to convert statistical predictive models into visual narratives which explain student classifications. Building off of the work done within the user experience community, we apply the concept of personas to predictive models. These personas provide familiar and memorable descriptions of the learners identified by data mining activities, and bridge the gap between the data scientist and the learning specialist.
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Personas can be helpful in translating complex information into actionable steps.
“In addition to using Personas as a user representation for the development team, Personas can also be helpful in translating complex information into actionable steps (Brooks and Greer, 2014).”
From Using Personas in the development of eHealth interventions for chronic pain: A scoping review and narrative synthesis (2023) · cites this paper as Brooks and Greer, 2014
Cited by
Sorted by citations
Use Cases for Design Personas: A Systematic Review and New Frontiers
Salminen, J., Wenyun Guan, K., Jung, S.-G., & Jansen, B. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517589 · Cited by 96 · see citing works
Bartels, S. L., Taygar, A. S., Johnsson, S. I., Petersson, S., Flink, I., Boersma, K., McCracken, L. M., & Wicksell, R. K. (2023) · Internet Interventions · doi:10.1016/j.invent.2023.100619 · Cited by 46 · see citing works
Including Learning Analytics in the Loop of Self-Paced Online Course Learning Design
Yan, H., Lin, F., & Kinshuk (2021) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-020-00225-z · Cited by 25 · see citing works
- Authors
- Brooks, C., & Greer, J.
- Affiliations
- University of Saskatchewan (CA)
- Venue
- Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
- Publisher
- ACM
- Published
- 2014-03-24
- Pages
- 26-30
- OpenAlex
- W2085670292
- Cited by
- 32
- References
- 0
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 1 · 2023: 4 · 2022: 6 · 2021: 9 · 2020: 3 · 2019: 1 · 2018: 1 · 2017: 2 · 2015: 2
- FWCI
- 0.85
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Learning Analytics and Knowledge (LAK) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Human-Computer Interaction › Persona Design and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Keywords
- personas, visual narrative, student classification, learning analytics, data mining, user experience design
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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