Abstract
The evolution of blended instructional environments requires teachers to extend professional noticing to learning analytics data presentations. This research is situated in the context of dashboards generated by a clustering algorithm that analyzed the performance of over 300 high school physics students on a diagnostic questionnaire and clustered them into characteristic performance profiles, which the algorithm then projected on specific student cohorts. Teacher noticing was elicited using semi-structured interviews centered on three dashboards presenting students’ performance, and teachers were asked to characterize emerging problem-solving knowledge and suggest responses to identified issues. Analysis of interview data from a sample of 10 experienced physics teachers revealed that teacher noticing was sensitive to dashboard features. While teachers tended to focus on individual students during the characterization stage, they preferred to address groups or the whole class at the response stage and focused less on providing individual students with personalized support.
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Article Type: Research Article
EURASIA J Math Sci Tech Ed, Volume 22, Issue 4, April 2026, Article No: em2813
https://doi.org/10.29333/ejmste/18256
Publication date: 01 Apr 2026
Online publication date: 27 Mar 2026
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