Noticing data-scale misconceptions in a zoom-mediated mathematics classroom situation: A study of pre-service teachers’ statistical thinking
DOI:
https://doi.org/10.33830/ijdmde.v3i2.12886Keywords:
Mathematics classroom situation, Pre-service teachers, Statistical thinking, The data measurement scale situation, Synchronous online learningAbstract
This study examines the statistical thinking of pre-service mathematics teachers as they engaged in a Zoom-mediated Mathematics Classroom Situation (MCS) built around a data measurement scale problem, with particular attention to how the synchronous online format shaped their noticing of two persistent misconceptions: mismatching chart type to a variable’s measurement scale, and conflating the mode with its frequency count rather than the category itself. Twenty-nine sixth-semester pre-service teachers at an Indonesian university worked through three MCS stages entirely online, individual response, small-group discussion in Zoom breakout rooms, and whole-group discussion, with all sessions recorded and analyzed using NVivo’s Coding Comparison and Cluster Analysis features. The findings show that only 3.45% of participants selected the correct chart together with a scale-based justification, and none combined the mode with a fully rational justification, even after small-group and whole-group discussion. Rather than being resolved through peer interaction, both misconceptions persisted across all three MCS stages, suggesting that the breakout-room format, while enabling discussion without physical relocation, did not by itself prompt participants to notice or correct their peers’ scale-reasoning errors. These results indicate that statistics methods courses delivered online need to build in explicit prompts for justifying chart and central-tendency choices by data scale, rather than assuming that synchronous peer discussion alone will surface these gaps. The study contributes a new MCS example combining chart-selection and central-tendency misconceptions within a single ordinal-scale dataset, and offers concrete recommendations for structuring noticing-focused tasks in distance-delivered mathematics teacher education.
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